[
  {
    "slug": "a1qa-aesthetic-equipment-online-shop",
    "vendorSlug": "a1qa",
    "title": "Assisting a leading aesthetic medicine company in ensuring high quality of an online shop",
    "sourceUrl": "https://www.a1qa.com/portfolio/comprehensive-qa-support-for-an-aesthetic-medicine-company/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1564",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a longstanding leader providing aesthetic medicine solutions and equipment to 2,500+ clinics across the Middle East",
      "segment": "specialty",
      "region": "mena",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": [
        "2,500+ clinics across the Middle East",
        "Serving the entire Arab region"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Salesforce",
        "namedWhere": "named in the project overview (\"an online shop based on Salesforce to process orders and product warranties as well as monitor the return of goods\")"
      },
      {
        "system": "integrations",
        "platformName": "Microsoft Dynamics GP",
        "namedWhere": "named in the scope list (\"A desktop app integrated with Microsoft Dynamics GP (handling customers’ invoices, business accounting)\")"
      }
    ],
    "problem": {
      "paraphrase": "The company sells aesthetic medicine equipment to clinics through an online shop on Salesforce that handles orders, warranties and returns, plus a desktop accounting app linked to Microsoft Dynamics GP. It had no settled QA process, the software had complex logic, and it needed to pass an audit to keep to industry standards. It asked a1qa to build a test strategy and adjust its QA workflows.",
      "quote": "the company did not have well-established QA processes"
    },
    "approach": {
      "testingTypes": [
        "functional"
      ],
      "mainScope": "functional",
      "teamShape": "2 QA experts engaged, including a QA manager",
      "duration": "3 months of cooperation",
      "tooling": [
        "Atlassian Jira",
        "TestRail (integrated with Atlassian Jira)"
      ]
    },
    "metrics": [
      {
        "metric": "clinics supplied",
        "value": "2,500+",
        "unit": "clinics",
        "kind": "other",
        "wording": "The client is a longstanding leader providing aesthetic medicine solutions and equipment to 2,500+ clinics across the Middle East.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1565"
      },
      {
        "metric": "test case writing speed with shared steps",
        "value": "three times",
        "unit": "x",
        "kind": "other",
        "wording": "To minimize the time spent on developing repetitive test cases, they implemented a shared steps method which proved to be three times faster than writing each test case from scratch.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1566"
      },
      {
        "metric": "months of cooperation (stat block)",
        "value": "3",
        "unit": "months",
        "kind": "other",
        "numeric": 3,
        "wording": "3 months of cooperation",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1567"
      },
      {
        "metric": "QA experts engaged (stat block)",
        "value": "2",
        "unit": "QA experts",
        "kind": "other",
        "numeric": 2,
        "wording": "2 QA experts engaged",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1568"
      },
      {
        "metric": "software components tested (stat block)",
        "value": "3",
        "unit": "software components",
        "kind": "other",
        "numeric": 3,
        "wording": "3 software components tested",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1569"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "cost",
      "what was out of scope",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D7",
      "questionForVendor": "Did the client pass the audit it mentioned, and what QA documentation and process did your team hand over at the end?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "a1qa-ecommerce-automation-rescue",
    "vendorSlug": "a1qa",
    "title": "eCommerce · Flaky tests cut from 18% to 4.6%",
    "sourceUrl": "https://www.a1qa.com/portfolio/digital-commerce-automation-rescue-for-stable-ci/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1055",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "A European online commerce platform",
      "segment": "ecommerce-d2c",
      "region": "europe",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": [
        "about 1,100 automated tests"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "namedWhere": "named in the client description (\"A European online commerce platform\") and the scope list (\"search, cart and checkout, order status and customer notifications\"); platform not named"
      }
    ],
    "problem": {
      "paraphrase": "The client had about 1,100 automated tests that its engineers had stopped trusting. In the critical subset 18% of tests were flaky, and 24% of reported failures were not reproducible product defects. Reruns and maintenance took about 45 engineering hours a week, and a regression decision took about 2 working days. The client wanted to know which tests to keep, modernize or replace.",
      "quote": "engineers no longer trusted the suite"
    },
    "approach": {
      "testingTypes": [
        "automation"
      ],
      "mainScope": "automation",
      "teamShape": "3 automation engineers, 1 senior quality engineering architect, and part-time DevOps support",
      "duration": "12 weeks for assessment and the bounded rescue pilot",
      "tooling": [
        "Selenium",
        "Java",
        "TestNG",
        "REST Assured",
        "Jenkins",
        "Docker",
        "Allure",
        "Git"
      ],
      "engagementModel": "project"
    },
    "metrics": [
      {
        "metric": "flaky test rate (page title)",
        "value": "18% to 4.6%",
        "unit": "%",
        "kind": "other",
        "wording": "Flaky tests cut from 18% to 4.6%",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1056"
      },
      {
        "metric": "flaky test rate before the engagement",
        "value": "18",
        "unit": "%",
        "kind": "other",
        "numeric": 18,
        "wording": "The critical subset produced an 18% flaky test rate.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1057"
      },
      {
        "metric": "failures that were not product defects before the engagement",
        "value": "24",
        "unit": "%",
        "kind": "defects",
        "numeric": 24,
        "wording": "24% of reported failures did not represent a reproducible product defect.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1058"
      },
      {
        "metric": "weekly maintenance and rerun effort before the engagement",
        "value": "about 45",
        "unit": "engineering hours per week",
        "kind": "other",
        "wording": "Maintenance and rerun work consumed about 45 engineering hours each week.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1059"
      },
      {
        "metric": "CI runs within budget without rerun before the engagement",
        "value": "52",
        "unit": "%",
        "kind": "other",
        "numeric": 52,
        "wording": "Only 52% of CI runs completed within the agreed 90-minute budget and without rerun.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1060"
      },
      {
        "metric": "regression decision time before the engagement",
        "value": "about 2",
        "unit": "working days",
        "kind": "other",
        "wording": "A regression decision for the selected scope took about 2 working days, limiting the product area to 2 planned releases per month.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1061"
      },
      {
        "metric": "planned releases per month before the engagement",
        "value": "2",
        "unit": "releases per month",
        "kind": "other",
        "numeric": 2,
        "wording": "A regression decision for the selected scope took about 2 working days, limiting the product area to 2 planned releases per month.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1061"
      },
      {
        "metric": "tests stabilised",
        "value": "170",
        "unit": "tests",
        "kind": "test-count",
        "numeric": 170,
        "wording": "After stabilizing 170 tests whose business intent and architecture remained useful,",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1062"
      },
      {
        "metric": "tests replaced",
        "value": "150",
        "unit": "tests",
        "kind": "test-count",
        "numeric": 150,
        "wording": "Next, the team replaced 150 tests that could not be repaired economically.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1063"
      },
      {
        "metric": "tests retired",
        "value": "one hundred",
        "unit": "tests",
        "kind": "test-count",
        "wording": "Ultimately, one hundred obsolete or duplicate tests were retired from the pilot scope.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1064"
      },
      {
        "metric": "weeks for assessment and pilot (stat block)",
        "value": "12",
        "unit": "weeks",
        "kind": "other",
        "numeric": 12,
        "wording": "12 weeks for assessment and the bounded rescue pilot",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1065"
      },
      {
        "metric": "QA experts assigned (stat block)",
        "value": "5",
        "unit": "QA experts",
        "kind": "other",
        "numeric": 5,
        "wording": "5 QA experts assigned to the project",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1066"
      },
      {
        "metric": "tests in the inherited suite (stat block)",
        "value": "1,100",
        "unit": "tests",
        "kind": "test-count",
        "numeric": 1100,
        "wording": "1,100 tests in the inherited suite",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1067"
      },
      {
        "metric": "tests in the audited critical subset (stat block)",
        "value": "420",
        "unit": "tests",
        "kind": "test-count",
        "numeric": 420,
        "wording": "420 tests in the audited critical subset",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1068"
      },
      {
        "metric": "accepted automated tests in the stabilised suite (stat block)",
        "value": "320",
        "unit": "tests",
        "kind": "test-count",
        "numeric": 320,
        "wording": "320 accepted automated tests in the stabilized critical suite",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1069"
      },
      {
        "metric": "flaky test rate reduction (stat block)",
        "value": "13.4",
        "unit": "%",
        "kind": "other",
        "numeric": 13.4,
        "wording": "13.4% reduction in flaky test rate across the agreed validation window",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1070"
      },
      {
        "metric": "false-failure rate decrease (stat block)",
        "value": "17",
        "unit": "%",
        "kind": "defects",
        "numeric": 17,
        "wording": "17% decrease in false-failure rate for the pilot scope",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1071"
      },
      {
        "metric": "working days saved per new accepted scenario (stat block)",
        "value": "2.7",
        "unit": "working days",
        "kind": "time-reduction-abs",
        "numeric": 2.7,
        "wording": "2.7 working days saved for a new scenario to become an accepted test",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1072"
      },
      {
        "metric": "CI runs within budget without rerun increase (stat block)",
        "value": "39",
        "unit": "%",
        "kind": "other",
        "numeric": 39,
        "wording": "39% increase in CI runs completed within the 90-minute budget and without rerun",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1073"
      },
      {
        "metric": "hours saved per regression decision (stat block)",
        "value": "17",
        "unit": "hours",
        "kind": "time-reduction-abs",
        "numeric": 17,
        "wording": "17 hours saved per critical scope regression decision",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1074"
      },
      {
        "metric": "additional planned releases per month (stat block)",
        "value": "1",
        "unit": "releases per month",
        "kind": "other",
        "numeric": 1,
        "wording": "1 additional planned release per month after the pilot scope became reliable",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1075"
      },
      {
        "metric": "engineering hours freed per week (stat block)",
        "value": "29",
        "unit": "engineering hours per week",
        "kind": "time-reduction-abs",
        "numeric": 29,
        "wording": "29 engineering hours freed per week for suite maintenance and reruns",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1076"
      },
      {
        "metric": "engineering hours returned to new work per week (stat block)",
        "value": "29",
        "unit": "engineering hours per week",
        "kind": "other",
        "numeric": 29,
        "wording": "29 engineering hours per week returned to new automated scenarios and exploratory work",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1077"
      },
      {
        "metric": "regression and triage cost (stat block)",
        "value": "32",
        "unit": "%",
        "kind": "cost",
        "numeric": 32,
        "wording": "32% lower regression and triage cost after ongoing maintenance was included",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1078"
      }
    ],
    "notSaid": [
      "duration over which the metric held",
      "cost",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Peak-season readiness in 90 days",
      "dimension": "D4",
      "questionForVendor": "How long was the agreed validation window behind the flaky-test rate, and has that rate held on the accepted tests since the pilot?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "a1qa-fashion-brand-website",
    "vendorSlug": "a1qa",
    "title": "a1qa delivers QA support for a global fashion brand’s website",
    "sourceUrl": "https://www.a1qa.com/portfolio/a1qa-delivers-qa-support-for-a-global-fashion-brand-website/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1079",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a high-street fashion brand headquartered in London",
      "segment": "fashion-apparel",
      "region": "europe",
      "country": "United Kingdom",
      "channels": [
        "stores",
        "ecommerce",
        "mobile"
      ],
      "sizeIndicators": [
        "300+ brick-and-mortar stores across the UK, Ireland, and internationally",
        "6 dedicated online stores"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "namedWhere": "named in the Challenges section (\"transferring their eCommerce platform from monolithic to microservices-based architecture\"); platform not named"
      },
      {
        "system": "mobile-commerce",
        "namedWhere": "named in the Solution section (\"native mobile apps (Android, iOS) in the regression testing scope\")"
      },
      {
        "system": "payments",
        "namedWhere": "named in the Solution section (\"The modules of payment and discounts\")"
      },
      {
        "system": "integrations",
        "namedWhere": "named in the Solution section (microservices testing of how the services interact)"
      }
    ],
    "problem": {
      "paraphrase": "The client kept adding features to its online stores while moving its ecommerce platform from a monolith to microservices. Some tasks were too large to finish within one sprint under its definition of done. To release new features faster, it looked for extra nearshore QA specialists with functional testing and test automation experience.",
      "quote": "some tasks were too large to complete them within a sprint"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "automation",
        "mobile",
        "api"
      ],
      "mainScope": "functional",
      "teamShape": "4 QA specialists engaged; They assisted 2 client delivery teams",
      "duration": "2+ years of project duration",
      "tooling": [
        "Java",
        "Cucumber",
        "JavaScript",
        "WebdriverIO",
        "Atlassian Jira/Confluence",
        "DbVisualizer",
        "BrowserStack",
        "Kobiton",
        "Qubit"
      ],
      "engagementModel": "staff-augmentation"
    },
    "metrics": [
      {
        "metric": "brick-and-mortar stores",
        "value": "300+",
        "unit": "stores",
        "kind": "other",
        "wording": "The client is a high-street fashion brand headquartered in London that runs 300+ brick-and-mortar stores across the UK, Ireland, and internationally.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1080"
      },
      {
        "metric": "dedicated online stores",
        "value": "6",
        "unit": "online stores",
        "kind": "other",
        "numeric": 6,
        "wording": "They also have 6 dedicated online stores.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1081"
      },
      {
        "metric": "client delivery teams assisted",
        "value": "2",
        "unit": "teams",
        "kind": "other",
        "numeric": 2,
        "wording": "They assisted 2 client delivery teams",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1082"
      },
      {
        "metric": "project duration (stat block)",
        "value": "2+",
        "unit": "years",
        "kind": "other",
        "wording": "2+ years of project duration",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1083"
      },
      {
        "metric": "QA specialists engaged (stat block)",
        "value": "4",
        "unit": "QA specialists",
        "kind": "other",
        "numeric": 4,
        "wording": "4 QA specialists engaged",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1084"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Ecommerce platform migration",
      "dimension": "D2",
      "questionForVendor": "By how much did QA costs fall, against what baseline, and over which part of the move to microservices?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "a1qa-flash-sales-fashion-retailer",
    "vendorSlug": "a1qa",
    "title": "Improved quality of the solutions for a flash sales retailer in the USA",
    "sourceUrl": "https://www.a1qa.com/portfolio/improved-quality-of-the-solutions-for-a-flash-sales-retailer-in-the-usa/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1085",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "One of the largest and most popular in the USA retailers, which sells high-fashion goods and offers scheduled flash sales",
      "segment": "fashion-apparel",
      "region": "north-america",
      "country": "United States",
      "channels": [
        "ecommerce",
        "mobile"
      ],
      "sizeIndicators": [
        "One of the largest and most popular in the USA retailers",
        "25 M members",
        "2 eCommerce websites",
        "4 mobile apps (2 for Android and 2 for iOS)"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "namedWhere": "named in the client description (\"2 eCommerce websites that allow users to discover private sale boutiques\", \"A newly acquired website\", \"A CMS that allows managing all products on offer\"); platform not named"
      },
      {
        "system": "mobile-commerce",
        "namedWhere": "named in the client description (\"4 mobile apps (2 for Android and 2 for iOS) to make sales accessible from any devices\")"
      }
    ],
    "problem": {
      "paraphrase": "The page has no separate challenge section. It says a US retailer of high-fashion goods runs scheduled flash sales. The retailer asked a1qa to improve the quality of a set of applications. These were two ecommerce websites for private sale boutiques, four Android and iOS apps, a newly acquired website, and a CMS for products and fashion articles.",
      "quote": "to enhance the quality of a range of IT applications"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "api",
        "mobile"
      ],
      "mainScope": "functional",
      "teamShape": "a dedicated team; Today seven experts continue to fulfill necessary testing activities",
      "duration": "5+ years of the project duration",
      "tooling": [
        "Airflow",
        "Braintree",
        "Snowflake",
        "Android SDK",
        "DDMS",
        "Xcode",
        "VPN (GlobalProtect)",
        "SQL Developer",
        "Crashlytics",
        "ActiveBatch",
        "Google Analytics",
        "Airship",
        "Databricks",
        "AWS Services",
        "VMware",
        "VMware Horizon Clients",
        "VirtualBox",
        "Charles Web Debugging Proxy",
        "Optimizely",
        "Evergage",
        "Jenkins",
        "Postman",
        "GitHub",
        "Jira",
        "TestRail"
      ],
      "engagementModel": "dedicated-team"
    },
    "metrics": [
      {
        "metric": "ecommerce websites in scope",
        "value": "2",
        "unit": "websites",
        "kind": "other",
        "numeric": 2,
        "wording": "2 eCommerce websites that allow users to discover private sale boutiques.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1086"
      },
      {
        "metric": "website members",
        "value": "25 M",
        "unit": "members",
        "kind": "other",
        "wording": "The members of websites bring fashion products in limited quantity evoking the sense of urgency amongst 25 M members to make quick buying decisions before the product is gone for good.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1087"
      },
      {
        "metric": "mobile apps in scope",
        "value": "4",
        "unit": "mobile apps",
        "kind": "other",
        "numeric": 4,
        "wording": "4 mobile apps (2 for Android and 2 for iOS) to make sales accessible from any devices and engage more buyers.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1088"
      },
      {
        "metric": "experts on the project today",
        "value": "seven",
        "unit": "experts",
        "kind": "other",
        "wording": "Today seven experts continue to fulfill necessary testing activities and ensure the quality of the solutions.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1089"
      },
      {
        "metric": "project duration (stat block)",
        "value": "5+",
        "unit": "years",
        "kind": "other",
        "wording": "5+ years of the project duration",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1090"
      },
      {
        "metric": "project effort (stat block)",
        "value": "300+",
        "unit": "man-months",
        "kind": "other",
        "wording": "300+ man-months in project efforts",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1091"
      },
      {
        "metric": "QA professionals assigned (stat block)",
        "value": "7",
        "unit": "QA professionals",
        "kind": "other",
        "numeric": 7,
        "wording": "7 QA professionals assigned to the project",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1092"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "what was out of scope",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D1",
      "questionForVendor": "Which sales figure was tracked to support the claim that testing boosted digital sales, on which sites and apps, and over what period?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "a1qa-grocery-ai-regression",
    "vendorSlug": "a1qa",
    "title": "Retail · AI-powered test automation cut release decision time by 50%",
    "sourceUrl": "https://www.a1qa.com/portfolio/retail-ai-regression-testing-for-decreased-release-time/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1093",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a large grocery and retail operator",
      "segment": "grocery",
      "channels": [
        "ecommerce",
        "mobile"
      ],
      "sizeIndicators": [
        "a large grocery and retail operator",
        "sells across several channels",
        "a regression suite of about 5,000 tests across several regions and roles"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "namedWhere": "named in the Client section (\"runs the whole customer experience on one eCommerce platform\"); platform not named"
      },
      {
        "system": "mobile-commerce",
        "namedWhere": "named in the Cloud execution section and stat block (\"across web and mobile, covering the delivery and checkout flows\")"
      }
    ],
    "problem": {
      "paraphrase": "The client's internal QA team kept a regression suite of about 5,000 tests across several regions and user roles. Each release waited about two working weeks for a go or no-go decision. Every new test made the next regression cycle longer. The client asked a1qa to shorten release decision time, starting with one role and region slice before scaling.",
      "quote": "each new test only made the next regression cycle slower"
    },
    "approach": {
      "testingTypes": [
        "automation",
        "api",
        "mobile",
        "functional"
      ],
      "mainScope": "automation",
      "teamShape": "a QA lead, two automation engineers, a test designer, and a DevOps engineer for cloud execution",
      "duration": "~7 weeks to the first measured result (Discovery and Pilot)",
      "tooling": [
        "Azure DevOps",
        "Java",
        "Playwright (Chromium)",
        "REST Assured",
        "TestNG",
        "Claude (LLM for test design, test-data generation, and failure classification)",
        "Self-healing locator engine",
        "Flaky test detection",
        "AWS",
        "Jenkins",
        "TestRail",
        "Jira"
      ],
      "engagementModel": "project"
    },
    "metrics": [
      {
        "metric": "release decision time cut (page title)",
        "value": "50",
        "unit": "%",
        "kind": "time-reduction-pct",
        "numeric": 50,
        "wording": "AI-powered test automation cut release decision time by 50%",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1094"
      },
      {
        "metric": "release decision time before the engagement",
        "value": "about two weeks",
        "unit": "weeks",
        "kind": "other",
        "wording": "making a release decision took about two weeks,",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1095"
      },
      {
        "metric": "release decision time on the pilot slice",
        "value": "about two working weeks to under one",
        "unit": "working weeks",
        "kind": "time-reduction-abs",
        "wording": "The pilot slice cut release decision time from about two working weeks to under one.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1096"
      },
      {
        "metric": "weeks to first proven result",
        "value": "~7",
        "unit": "weeks",
        "kind": "other",
        "wording": "~7 weeks to the first proven result (Discovery and Pilot)",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1097"
      },
      {
        "metric": "tests in regression scope",
        "value": "~5,000",
        "unit": "tests",
        "kind": "test-count",
        "wording": "~5,000 tests in regression scope",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1098"
      },
      {
        "metric": "release-cycle hotspots targeted",
        "value": "9",
        "unit": "hotspots",
        "kind": "other",
        "numeric": 9,
        "wording": "9 release-cycle hotspots targeted",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1099"
      },
      {
        "metric": "areas covered",
        "value": "4",
        "unit": "areas",
        "kind": "other",
        "numeric": 4,
        "wording": "4 areas covered (web, mobile, delivery, checkout)",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1100"
      },
      {
        "metric": "faster acceptance (stat block)",
        "value": "2×",
        "unit": "x",
        "kind": "other",
        "numeric": 2,
        "wording": "2× faster acceptance (5 business days or fewer)",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1101"
      },
      {
        "metric": "setup time on converted tests",
        "value": "30+",
        "unit": "%",
        "kind": "time-reduction-pct",
        "wording": "30%+ less setup time on converted tests",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1102"
      },
      {
        "metric": "build-linked release decisions",
        "value": "100",
        "unit": "%",
        "kind": "other",
        "numeric": 100,
        "wording": "100% build-linked release decision",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1103"
      },
      {
        "metric": "expected speed-up on pilot scope (QA Director quote)",
        "value": "2×",
        "unit": "x",
        "kind": "other",
        "numeric": 2,
        "wording": "Discovery gave us the confidence that 2× faster is realistically achievable on the pilot scope",
        "claimedBy": "client",
        "verifiable": "no",
        "evidenceId": "ev-1104"
      }
    ],
    "notSaid": [
      "measurement method",
      "duration over which the metric held",
      "cost",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Peak-season readiness in 90 days",
      "dimension": "D4",
      "questionForVendor": "Which scope do the title figure and the stat block figure each cover, and how was release decision time measured?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "a1qa-gulf-qa-vendor-transition",
    "vendorSlug": "a1qa",
    "title": "Retail and automotive · 2x testing capacity achieved after a 3-week vendor transition",
    "sourceUrl": "https://www.a1qa.com/portfolio/retail-automotive-qa-vendor-transition-2x-testing-throughput/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1105",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a large Gulf retail and automotive group",
      "segment": "specialty",
      "region": "mena",
      "channels": [],
      "sizeIndicators": [
        "a large Gulf retail and automotive group running a broad multi-product portfolio",
        "a wide range of product streams"
      ]
    },
    "systems": [],
    "problem": {
      "paraphrase": "The group had spread testing across separate arrangements over a multi-product estate. Testing volumes were rising and it wanted more QA output at the current cost level. Any handover had to cover many product streams in parallel, not one after another, and the existing release suites had to keep the same coverage from the first day of ownership.",
      "quote": "lift QA output while maintaining costs at the current level"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "automation"
      ],
      "mainScope": "functional",
      "teamShape": "a single accountable team to own testing across the portfolio; team size kept unchanged",
      "duration": "Full ownership across the portfolio was taken over in three weeks",
      "tooling": [
        "Java",
        "Playwright",
        "Jenkins",
        "TestRail",
        "Jira",
        "AWS"
      ],
      "engagementModel": "managed-testing"
    },
    "metrics": [
      {
        "metric": "testing capacity (page title)",
        "value": "2x",
        "unit": "x",
        "kind": "other",
        "numeric": 2,
        "wording": "2x testing capacity achieved after a 3-week vendor transition",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1106"
      },
      {
        "metric": "vendor transition length (page title)",
        "value": "3",
        "unit": "weeks",
        "kind": "other",
        "numeric": 3,
        "wording": "2x testing capacity achieved after a 3-week vendor transition",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1106"
      },
      {
        "metric": "testing throughput with the same team (Results)",
        "value": "2x",
        "unit": "x",
        "kind": "other",
        "numeric": 2,
        "wording": "The same team delivered 2x greater testing throughput while maintaining coverage levels.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1107"
      },
      {
        "metric": "portfolio-wide QA transition time (Results)",
        "value": "three weeks",
        "unit": "weeks",
        "kind": "other",
        "wording": "The portfolio-wide QA transition was completed across multiple product streams in just three weeks.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1108"
      },
      {
        "metric": "weeks to ownership transfer (stat block)",
        "value": "3",
        "unit": "weeks",
        "kind": "other",
        "numeric": 3,
        "wording": "3 weeks to complete portfolio-wide QA ownership transfer",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1109"
      },
      {
        "metric": "accountable teams (stat block)",
        "value": "1",
        "unit": "team",
        "kind": "other",
        "numeric": 1,
        "wording": "1 accountable team owning testing across the multi-product estate",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1110"
      },
      {
        "metric": "testing throughput (stat block)",
        "value": "2x",
        "unit": "x",
        "kind": "other",
        "numeric": 2,
        "wording": "2x testing throughput achieved with the same team size",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1111"
      },
      {
        "metric": "portfolio takeover time (Head of QA quote)",
        "value": "three weeks",
        "unit": "weeks",
        "kind": "other",
        "wording": "They took over the full portfolio in three weeks and kept our release coverage of the existing suites steady while doing twice the work with the same team.",
        "claimedBy": "client",
        "verifiable": "no",
        "evidenceId": "ev-1112"
      },
      {
        "metric": "work done with the same team (Head of QA quote)",
        "value": "twice",
        "unit": "x",
        "kind": "other",
        "wording": "They took over the full portfolio in three weeks and kept our release coverage of the existing suites steady while doing twice the work with the same team.",
        "claimedBy": "client",
        "verifiable": "no",
        "evidenceId": "ev-1112"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D7",
      "questionForVendor": "Which retail systems and product streams were in the portfolio you took over, and what baseline was the throughput gain measured against?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "a1qa-home-appliances-sap-commerce",
    "vendorSlug": "a1qa",
    "title": "Manufacturing · $120K saved annually with optimized QA toolset and test automation",
    "sourceUrl": "https://www.a1qa.com/portfolio/a1qa-provides-extensive-qa-support-to-a-large-us-based-manufacturer-of-home-appliances/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1545",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "one of the leading US-based manufacturers of home appliances",
      "segment": "specialty",
      "region": "north-america",
      "country": "United States",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": [
        "operating globally and possessing several trademarks offering an extensive blend of products",
        "Seven brands in different locales.",
        "B2B and B2C dimensions"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "SAP Commerce Cloud (SAP Hybris Commerce Platform)",
        "namedWhere": "named in the Challenges section (\"migration to SAP Hybris Commerce Platform\") and the functional testing section (\"SAP Commerce Cloud customizable features\")"
      },
      {
        "system": "payments",
        "platformName": "Paymetric, PayPal, Affirm",
        "namedWhere": "named in the functional testing section (\"Each payment platform (Paymetric, PayPal, Affirm)\")"
      },
      {
        "system": "integrations",
        "namedWhere": "named in the services list (\"Integration testing\") and the Configured Accertify section"
      }
    ],
    "problem": {
      "paraphrase": "To cut hosting costs the client was moving to SAP Hybris, but standard features did not fit its needs and the promotion module needed heavy customization. Its in-house Black Friday test plan did not cover all user behavior in peak periods. Several subprojects tested in their own ways, which left testing out of sync, and US state tax rules had to be checked.",
      "quote": "their Black Friday testing strategy wasn’t fully defined"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "automation",
        "performance"
      ],
      "mainScope": "automation",
      "teamShape": "33 QA experts involved; teams ramped up or down upon client's request",
      "duration": "4+ years of the project duration",
      "tooling": [
        "VPN GlobalProtect",
        "Citrix Workspace",
        "WebPageTest",
        "Apache JMeter",
        "Dynatrace",
        "Postman",
        "SAP ECC",
        "SAP Java Connector",
        "Splunk",
        "PuTTY",
        "Selenium",
        "Cucumber",
        "G Suite"
      ]
    },
    "metrics": [
      {
        "metric": "annual saving (case heading)",
        "value": "$120K",
        "unit": "USD per year",
        "kind": "cost",
        "wording": "$120K saved annually with optimized QA toolset and test automation",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1546"
      },
      {
        "metric": "operational cost reduction (Results)",
        "value": "120K",
        "unit": "per year",
        "kind": "cost",
        "wording": "Reduced operational costs by 120K per year due to the selection of a more economical testing tool.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1547"
      },
      {
        "metric": "brands in different locales",
        "value": "Seven",
        "unit": "brands",
        "kind": "other",
        "wording": "Seven brands in different locales.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1548"
      },
      {
        "metric": "brands serving commercial purposes",
        "value": "five",
        "unit": "brands",
        "kind": "other",
        "wording": "Amid them, five brands serve commercial purposes, although, the availability of some functions depends on the country.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1549"
      },
      {
        "metric": "release cadence",
        "value": "once every 2-3 weeks",
        "unit": "weeks",
        "kind": "other",
        "wording": "The client delivers new functionality once every 2-3 weeks.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1550"
      },
      {
        "metric": "automated scripts",
        "value": "around 5,000",
        "unit": "automated scripts",
        "kind": "test-count",
        "wording": "The number of automated scripts skyrocketed and reached around 5,000.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1551"
      },
      {
        "metric": "manual testing days saved in two months",
        "value": "50",
        "unit": "business days",
        "kind": "time-reduction-abs",
        "numeric": 50,
        "wording": "Test automation introduction was effective: in just two months, it contributed to saving 50 business days of the manual testing team consisting of 4 people.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1552"
      },
      {
        "metric": "release verification testing time cut (test automation section)",
        "value": "90",
        "unit": "%",
        "kind": "time-reduction-pct",
        "numeric": 90,
        "wording": "All in all, test automation by a1qa curtailed the time required for release verification testing by 90%.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1553"
      },
      {
        "metric": "release verification testing time cut (Results)",
        "value": "90",
        "unit": "%",
        "kind": "time-reduction-pct",
        "numeric": 90,
        "wording": "Accelerated time to benefit due to test automation best practices, which contributed to cutting the release verification testing time by 90%.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1554"
      },
      {
        "metric": "manual testing days saved overall (Results)",
        "value": "hundreds",
        "unit": "business days",
        "kind": "time-reduction-abs",
        "wording": "Faster time to benefit and reduced operational cost due to test automation implementation, which saved hundreds of business days of manual testing teams.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1555"
      },
      {
        "metric": "performance test scripts developed",
        "value": "around 200",
        "unit": "performance test scripts",
        "kind": "test-count",
        "wording": "The team developed around 200 performance test scripts.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1556"
      },
      {
        "metric": "load test length",
        "value": "8",
        "unit": "hours",
        "kind": "other",
        "numeric": 8,
        "wording": "The team carried out 8-hour load tests, and the number of potential users was increased by 30%.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1557"
      },
      {
        "metric": "increase in number of potential users",
        "value": "30",
        "unit": "%",
        "kind": "other",
        "numeric": 30,
        "wording": "The team carried out 8-hour load tests, and the number of potential users was increased by 30%.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1557"
      },
      {
        "metric": "download time decrease (Results)",
        "value": "30",
        "unit": "%",
        "kind": "time-reduction-pct",
        "numeric": 30,
        "wording": "Improved solutions’ performance and stability owing to building the test approach from the ground up, decreasing the download time by 30%, and ensuring unhindered operation of all websites during Black Friday.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1558"
      },
      {
        "metric": "project duration (stat block)",
        "value": "4+",
        "unit": "years",
        "kind": "other",
        "wording": "4+ years of the project duration",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1559"
      },
      {
        "metric": "QA experts involved (stat block)",
        "value": "33",
        "unit": "QA experts",
        "kind": "other",
        "numeric": 33,
        "wording": "33 QA experts involved",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1560"
      },
      {
        "metric": "automated test cases prepared (stat block)",
        "value": "5,000+",
        "unit": "automated test cases",
        "kind": "test-count",
        "wording": "5,000+ automated tests cases prepared",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1561"
      },
      {
        "metric": "loading speed increase (stat block)",
        "value": "30",
        "unit": "%",
        "kind": "other",
        "numeric": 30,
        "wording": "30% increase in loading speed of all eCommerce solutions",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1562"
      },
      {
        "metric": "performance scripts written (stat block)",
        "value": "200+",
        "unit": "performance scripts",
        "kind": "test-count",
        "wording": "200+ performance scripts written",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1563"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Peak-season readiness in 90 days",
      "dimension": "D3",
      "questionForVendor": "What peak concurrent user load did the Black Friday tests reach, and what baseline do the download time and loading speed gains use?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "a1qa-kitchen-retailer-wms",
    "vendorSlug": "a1qa",
    "title": "Quality assurance of kitchen retailer’s software, WMS, and CRM systems",
    "sourceUrl": "https://www.a1qa.com/portfolio/quality-assurance-of-kitchen-retailers-software-wms-and-crm-systems/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1519",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a British manufacturer and kitchen retailer",
      "segment": "specialty",
      "region": "europe",
      "country": "United Kingdom",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": [
        "a fast-growing business"
      ]
    },
    "systems": [
      {
        "system": "wms",
        "namedWhere": "named in the title, the client description and the Challenges section (\"the system was responsible for accounting goods in stock and managing users’ orders\")"
      },
      {
        "system": "ecommerce-platform",
        "namedWhere": "named in the client description (\"Users can design kitchens applying a 2D/3D computer-aided solution and buy them through the client’s product\"); platform not named"
      }
    ],
    "problem": {
      "paraphrase": "The client, a kitchen maker and retailer, had a demanding delivery roadmap and strict release deadlines. It wanted QA engineers who could learn its products quickly and adapt to change. The WMS, which tracks goods in stock and manages customer orders, needed close attention, and the client was concerned that testing it called for specialist knowledge.",
      "quote": "the system was highly sophisticated and required specific expertise"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "automation"
      ],
      "mainScope": "functional",
      "teamShape": "started with one engineer gradually increased to a team of 20 people working on different client’s products, in cooperation with the in-house team",
      "duration": "2+ years of project duration",
      "tooling": [
        "Jira",
        "Postman",
        "Workbench",
        "Sentry",
        "Emissary",
        "Xray",
        "MySQL",
        "Rundeck",
        "Jenkins",
        "barcode reader"
      ],
      "engagementModel": "dedicated-team"
    },
    "metrics": [
      {
        "metric": "automated tests run",
        "value": "more than 1,000",
        "unit": "automated tests",
        "kind": "test-count",
        "wording": "Now more than 1,000 automated tests are run against 12 environments.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1520"
      },
      {
        "metric": "environments the automated tests run against",
        "value": "12",
        "unit": "environments",
        "kind": "other",
        "numeric": 12,
        "wording": "Now more than 1,000 automated tests are run against 12 environments.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1520"
      },
      {
        "metric": "team growth",
        "value": "one engineer to a team of 20 people",
        "unit": "people",
        "kind": "other",
        "wording": "The a1qa team involvement that started with one engineer gradually increased to a team of 20 people working on different client’s products.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1521"
      },
      {
        "metric": "subprojects involved",
        "value": "11",
        "unit": "subprojects",
        "kind": "other",
        "numeric": 11,
        "wording": "The partnership with the client continues up to now, and the a1qa engineers are involved in 11 subprojects assuring the quality of the overall software product in cooperation with the in-house team.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1522"
      },
      {
        "metric": "project duration (stat block)",
        "value": "2+",
        "unit": "years",
        "kind": "other",
        "wording": "2+ years of project duration",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1523"
      },
      {
        "metric": "QA engineers on the project (stat block)",
        "value": "20",
        "unit": "QA engineers",
        "kind": "other",
        "numeric": 20,
        "wording": "20 QA engineers working on the project",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1524"
      },
      {
        "metric": "subprojects (stat block)",
        "value": "11",
        "unit": "subprojects",
        "kind": "other",
        "numeric": 11,
        "wording": "11 subprojects the team is involved in",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1525"
      },
      {
        "metric": "automated tests written (stat block)",
        "value": "1000",
        "unit": "automated tests",
        "kind": "test-count",
        "numeric": 1000,
        "wording": "1000 automated tests written",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1526"
      },
      {
        "metric": "environments set up (stat block)",
        "value": "12",
        "unit": "environments",
        "kind": "other",
        "numeric": 12,
        "wording": "12 environments set up",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1527"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "cost",
      "what was out of scope",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "OMS or WMS replacement",
      "dimension": "D2",
      "questionForVendor": "Which automated tests cover the WMS stock and order flows, as opposed to the design module and the CRM?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "a1qa-magento-ee-migration",
    "vendorSlug": "a1qa",
    "title": "Full-cycle testing of Magento e-commerce solution",
    "sourceUrl": "https://www.a1qa.com/portfolio/full-cycle-testing-of-magento-e-commerce-solution/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1528",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "the owner of a large US-based e-commerce solution specializing in selling natural foods, pet care goods, household cleaners, and other organic products",
      "segment": "ecommerce-d2c",
      "region": "north-america",
      "country": "United States",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": [
        "a large US-based e-commerce solution",
        "the customer’s business was growing fast"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Magento (Community Edition to Enterprise Edition)",
        "namedWhere": "named in the title and the Challenges section (\"migration from Magento Community Edition to Magento Enterprise Edition\")"
      },
      {
        "system": "data-migration",
        "namedWhere": "named in the Solution section (\"All the information about the online orders was migrating to a new database\")"
      },
      {
        "system": "payments",
        "namedWhere": "named in the functional testing details (\"placing orders, payments, bonuses\"; \"The most important issues were connected with the payment option and prices display\")"
      }
    ],
    "problem": {
      "paraphrase": "The retailer's business was growing fast and it needed to move from Magento Community Edition to Magento Enterprise Edition, add an adaptive design and improve existing features. It had no QA process. The development team had missed its deadline and the functional requirements were poorly written, so little time was left for testing and stabilization before release.",
      "quote": "The customer had no QA processes in place"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "data-migration",
        "performance",
        "mobile"
      ],
      "mainScope": "functional",
      "teamShape": "Initially, the team was made up of 2 engineers and was further scaled up to 4 specialists.",
      "duration": "5 months of the project duration",
      "tooling": [
        "Zoho BugTracker",
        "MySQL"
      ]
    },
    "metrics": [
      {
        "metric": "team size growth",
        "value": "2 engineers to 4 specialists",
        "unit": "engineers",
        "kind": "other",
        "wording": "Initially, the team was made up of 2 engineers and was further scaled up to 4 specialists.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1529"
      },
      {
        "metric": "share of defects of major severity and higher (text)",
        "value": "over 50",
        "unit": "%",
        "kind": "defects",
        "wording": "Over 50% were of the major severity and higher and could have resulted in negative user experience after the release.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1530"
      },
      {
        "metric": "mobile devices tested (text)",
        "value": "four",
        "unit": "mobile devices",
        "kind": "other",
        "wording": "To check the quality of the new design, it was decided to test the solution against four mobile devices (iPhone, iPad, Samsung Galaxy, Samsung Galaxy Tab).",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1531"
      },
      {
        "metric": "project duration (stat block)",
        "value": "5",
        "unit": "months",
        "kind": "other",
        "numeric": 5,
        "wording": "5 months of the project duration",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1532"
      },
      {
        "metric": "full-time QA engineers (stat block)",
        "value": "4",
        "unit": "QA engineers",
        "kind": "other",
        "numeric": 4,
        "wording": "4 full-time QA engineers involved in the project",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1533"
      },
      {
        "metric": "mobile devices used for testing (stat block)",
        "value": "4",
        "unit": "mobile devices",
        "kind": "other",
        "numeric": 4,
        "wording": "4 mobile devices used for testing",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1534"
      },
      {
        "metric": "share of defects of major severity and higher (stat block)",
        "value": "50",
        "unit": "%",
        "kind": "defects",
        "numeric": 50,
        "wording": "50% of the defects found were of major severity and higher",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1535"
      },
      {
        "metric": "delivering on time (stat block)",
        "value": "100",
        "unit": "%",
        "kind": "other",
        "numeric": 100,
        "wording": "100% delivering on time",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1536"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "cost",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Ecommerce platform migration",
      "dimension": "D2",
      "questionForVendor": "How did you confirm that all online orders moved to the new database without loss, and what concurrent user limit did load testing find?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "a1qa-uk-online-fashion-magento",
    "vendorSlug": "a1qa",
    "title": "Complex testing of e-commerce website",
    "sourceUrl": "https://www.a1qa.com/portfolio/complex-testing-of-e-commerce-website/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1537",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "One of the biggest online sellers of clothes, shoes, and home goods in the UK",
      "segment": "fashion-apparel",
      "region": "europe",
      "country": "United Kingdom",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": [
        "One of the biggest online sellers of clothes, shoes, and home goods in the UK",
        "over 500 famous brands in fashion, beauty, and home goods",
        "new big sales every day"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Magento",
        "namedWhere": "named in the Solution section (\"The website was managed by the Magento e-commerce platform.\")"
      },
      {
        "system": "payments",
        "platformName": "PayPal, Braintree",
        "namedWhere": "named in the Challenges section (\"new payment methods\") and the project specifics list (\"PayPal, Braintree\")"
      },
      {
        "system": "integrations",
        "namedWhere": "named in the project specifics list (\"SEKO Warehouse\", \"integration with other online sellers\")"
      }
    ],
    "problem": {
      "paraphrase": "The retailer wanted to deliver change requests coming from its marketing and customer support staff while it expanded the website. New features such as additional payment methods and delivery options were being added, and the page links rising sales to that new functionality.",
      "quote": "implement business requests from Marketing and Customer Support departments employees"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "performance",
        "automation",
        "mobile"
      ],
      "mainScope": "functional",
      "teamShape": "distributed team",
      "duration": "21 months of project duration",
      "tooling": [
        "Php",
        "MySQL",
        "Google Chrome",
        "Apple Safari",
        "Java",
        "Selenium",
        "Mozilla Firefox",
        "MS Internet Explorer"
      ],
      "engagementModel": "dedicated-team"
    },
    "metrics": [
      {
        "metric": "brands presented on the website",
        "value": "over 500",
        "unit": "brands",
        "kind": "other",
        "wording": "The website under test presented over 500 famous brands in fashion, beauty, and home goods.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1538"
      },
      {
        "metric": "daily sale discounts",
        "value": "up to 70",
        "unit": "%",
        "kind": "other",
        "wording": "It launched new big sales every day including discounts up to 70%.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1539"
      },
      {
        "metric": "mobile devices tested",
        "value": "13",
        "unit": "mobile devices",
        "kind": "other",
        "numeric": 13,
        "wording": "The website was tested on 13 mobile devices and various operating systems.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1540"
      },
      {
        "metric": "project effort (stat block)",
        "value": "35,5",
        "unit": "months",
        "kind": "other",
        "wording": "35,5 months of project efforts",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1541"
      },
      {
        "metric": "project duration (stat block)",
        "value": "21",
        "unit": "months",
        "kind": "other",
        "numeric": 21,
        "wording": "21 months of project duration",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1542"
      },
      {
        "metric": "major and critical defects found (stat block)",
        "value": "145",
        "unit": "defects",
        "kind": "defects",
        "numeric": 145,
        "wording": "145 Major and Critical defects found",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1543",
        "outlier": true
      },
      {
        "metric": "test survey checks written and run (stat block)",
        "value": "3,067",
        "unit": "checks",
        "kind": "test-count",
        "numeric": 3067,
        "wording": "3,067 test survey checks wrote and run",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1544",
        "outlier": true
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Payments and PCI scope",
      "dimension": "D2",
      "questionForVendor": "Which payment methods did the functional testing cover, and did the scope include any PCI DSS or card data checks?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "aspire-systems-aptos-pos-automation",
    "vendorSlug": "aspire-systems",
    "title": "Leveraged Aspire's POS Automation Framework for a US based Retailer",
    "sourceUrl": "https://www.aspiresys.com/casestudies/leveraged-aspire-systems-pos-automation-framework-usa-retailer.pdf",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1113",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a leading American specialty retailer of casual apparel, footwear, and accessories for young men and women",
      "segment": "fashion-apparel",
      "region": "north-america",
      "country": "United States",
      "channels": [
        "stores"
      ],
      "sizeIndicators": [
        "229 total stores, including a few pop-up stores, across 33 states",
        "More than 100 years into the business"
      ]
    },
    "systems": [
      {
        "system": "pos",
        "platformName": "APTOS",
        "namedWhere": "named in The Need section and Aspire's Solution section"
      },
      {
        "system": "loyalty",
        "namedWhere": "named in Aspire's Solution section (Loyalty modules as POS components)"
      },
      {
        "system": "integrations",
        "namedWhere": "named in the Challenge list and The Need section (third party integrations)"
      }
    ],
    "problem": {
      "paraphrase": "The retailer sells several brands and had no test framework able to cover them across multiple platforms. The APTOS point of sale in each store had to work with every third-party integration, and checking its scalability and reliability depended on large data sets.",
      "quote": "Unable to support multiple brands due to lack of robust framework"
    },
    "approach": {
      "testingTypes": [
        "automation",
        "functional"
      ],
      "mainScope": "automation",
      "tooling": [
        "POS DCqaf (Aspire's POS Framework)",
        "TestComplete",
        "VBScript",
        "SVN",
        "Jira",
        "Jenkins",
        "TestLink",
        "Extent Reports",
        "Robotic Arm"
      ]
    },
    "metrics": [
      {
        "metric": "defect leakage",
        "value": "0",
        "unit": "% defect leakage",
        "kind": "defects",
        "numeric": 0,
        "wording": "Maintained a 0% defect leakage",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1114"
      },
      {
        "metric": "regression routines reduced",
        "value": "90",
        "unit": "%",
        "kind": "time-reduction-pct",
        "numeric": 90,
        "wording": "Rapid automation lessened the regression routines by 90%",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1115"
      },
      {
        "metric": "cost saved against manual testing",
        "value": "around 80",
        "unit": "%",
        "kind": "cost",
        "numeric": 80,
        "wording": "Saved around 80% of the cost that could have incurred if done manually",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1116"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "team size on both sides",
      "date of the work",
      "what was out of scope",
      "whether the client is still a client"
    ],
    "readingNotes": {
      "scenario": "POS rollout across stores",
      "dimension": "D4",
      "questionForVendor": "Which regression routines were cut, and what manual cost estimate was the reported saving measured against?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "aspire-systems-composable-commerce-qe",
    "vendorSlug": "aspire-systems",
    "title": "Transforming a premier retailer’s e-commerce architecture with composable commerce",
    "sourceUrl": "https://www.aspiresys.com/casestudies/Transforming_a_premier_retailers_e-commerce_architecture_with_composable_commerce.pdf",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1117",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a prominent department store chain with 290+ stores across southern US states",
      "segment": "enterprise-omnichannel",
      "region": "north-america",
      "country": "United States",
      "channels": [
        "stores",
        "ecommerce"
      ],
      "sizeIndicators": [
        "290+ stores across southern US states",
        "over 13 decades",
        "over 150+ new brands",
        "millions of customers",
        "over one million active users"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Salesforce Commerce Cloud (SFCC), commercetools, Vercel Next.js",
        "namedWhere": "named in the Business Challenges section and Our Strategy section"
      }
    ],
    "problem": {
      "paraphrase": "The department store chain was moving core ecommerce features from Salesforce Commerce Cloud to a headless commercetools setup in phases. It was also rebuilding its front end on a different web framework. The live site serves over a million users and could not be disrupted. It needed migrated features checked for function and performance, an automation framework spanning old and new stacks, and a short release timeline at controlled cost.",
      "quote": "without disrupting the live production environment"
    },
    "approach": {
      "testingTypes": [
        "automation",
        "functional",
        "performance",
        "api"
      ],
      "mainScope": "automation",
      "teamShape": "dedicated QA and QE team of 19 specialized resources in a hybrid model (4 onsite, 15 offshore)",
      "tooling": [
        "Selenium WebDriver/Grid",
        "Maven",
        "TestNG",
        "LambdaTest",
        "Kane AI",
        "Jenkins",
        "Git",
        "TestLink",
        "Postman",
        "Jira",
        "GCP, Fastly and PerimeterX (cloud infrastructure used to simulate high-concurrency traffic)"
      ],
      "engagementModel": "dedicated-team"
    },
    "metrics": [
      {
        "metric": "production releases validated and supported",
        "value": "over 200+",
        "unit": "production releases",
        "kind": "other",
        "wording": "Successfully validated and supported over 200+ production releases with zero critical downtime",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1118"
      },
      {
        "metric": "critical downtime",
        "value": "zero",
        "unit": "critical downtime",
        "kind": "other",
        "numeric": 0,
        "wording": "Successfully validated and supported over 200+ production releases with zero critical downtime",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1118"
      },
      {
        "metric": "defects found before production",
        "value": "9,000+",
        "unit": "defects",
        "kind": "defects",
        "wording": "Identified, logged, and mitigated 9,000+ software defects prior to production deployment using Jira for seamless lifecycle management",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1119"
      },
      {
        "metric": "automated test scripts in library",
        "value": "750+",
        "unit": "automated test scripts",
        "kind": "test-count",
        "wording": "Built a permanent library of 750+ automated test scripts that drastically reduced regression testing cycles and accelerated time-to-market for new feature rollouts",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1120"
      },
      {
        "metric": "total operational savings over the engagement",
        "value": "USD ~7 million",
        "unit": "USD",
        "kind": "cost",
        "wording": "Generated USD ~7 million in total operational savings over the course of the engagement",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1121"
      },
      {
        "metric": "functional test cases in TestLink",
        "value": "over 1,450",
        "unit": "functional test cases",
        "kind": "test-count",
        "wording": "Engineered a repository of over 1,450 functional test cases within TestLink, backed by rigorous API testing via Postman to validate decoupled microservices.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1122"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "date of the work",
      "what was out of scope",
      "whether the client is still a client"
    ],
    "readingNotes": {
      "scenario": "Ecommerce platform migration",
      "dimension": "D4",
      "questionForVendor": "Which of the reported releases and defects belong to the commercetools migration rather than to earlier work for the same client?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "aspire-systems-dcqaf-plus-size-sfcc",
    "vendorSlug": "aspire-systems",
    "title": "Future-proof test automation solution using AI-led DCqaf framework for American plus-size fashion retailer",
    "sourceUrl": "https://www.aspiresys.com/casestudies/future-proof-test-automation-solution-using-ai-led-dcqaf-framework-or-american-plus-size-fashion-retailer.pdf",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1123",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a leading American plus-size apparel and home goods company based in New York City",
      "segment": "fashion-apparel",
      "region": "north-america",
      "country": "United States",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": [
        "For more than 100 years",
        "a huge customer base in the country"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Salesforce Commerce Cloud",
        "namedWhere": "named in The Result section (DCqaf components reused in Salesforce Commerce Cloud); BM site named in Aspire's Solution"
      }
    ],
    "problem": {
      "paraphrase": "The apparel retailer sells online and by catalog and needed its site to run across 13 browsers and several device types. Its linear test automation did not scale, daily data repetition caused repeated data loss, and continuous integration with its build server and issue tracker was hard to set up. It wanted a framework that could grow with a complex application while keeping infrastructure spending low.",
      "quote": "Linear automation testing was not scalable"
    },
    "approach": {
      "testingTypes": [
        "automation",
        "functional",
        "mobile"
      ],
      "mainScope": "automation",
      "tooling": [
        "DCqaf",
        "Auto POM",
        "AI-led self-healing scripts",
        "ML-led dashboard",
        "BM automation framework",
        "Jira",
        "TestRail",
        "Sauce Labs",
        "Selenium",
        "Jenkins",
        "Java"
      ]
    },
    "metrics": [
      {
        "metric": "test duration",
        "value": "50 person days to 18 person days",
        "unit": "person days",
        "kind": "time-reduction-abs",
        "wording": "Test duration was brought down from 50 person days to 18 person days",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1124"
      },
      {
        "metric": "authoring time and effort reduced with Auto POM",
        "value": "80",
        "unit": "%",
        "kind": "time-reduction-pct",
        "numeric": 80,
        "wording": "Time and effort reduction by 80% with accelerated authoring using Auto POM.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1125"
      },
      {
        "metric": "resources required",
        "value": "8 to 4",
        "unit": "resources",
        "kind": "other",
        "wording": "The number of resources required for the process halved, from 8 to 4",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1126"
      },
      {
        "metric": "bugs found early during scripting",
        "value": "25",
        "unit": "%",
        "kind": "defects",
        "numeric": 25,
        "wording": "25% of the bugs were identified at an early stage as scripting",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1127"
      },
      {
        "metric": "maintenance cost reduction",
        "value": "35",
        "unit": "%",
        "kind": "cost",
        "numeric": 35,
        "wording": "Maintenance costs were cut by 35% with the implementation of AI-powered self-healing scripts and machine-learning algorithms.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1128"
      },
      {
        "metric": "projected cost savings for one year",
        "value": "$150,000",
        "unit": "USD",
        "kind": "cost",
        "numeric": 150000,
        "wording": "Projected cost savings of $150,000 for 1 year with 24 sprint cycles for 1 site",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1129"
      },
      {
        "metric": "automation effort saved with DCqaf",
        "value": "30",
        "unit": "%",
        "kind": "time-reduction-pct",
        "numeric": 30,
        "wording": "Using DCqaf automation framework, 30% of the automation efforts were saved",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1130"
      },
      {
        "metric": "browser combinations in regression run",
        "value": "13",
        "unit": "browser combinations",
        "kind": "other",
        "numeric": 13,
        "wording": "Entire automation regression suite was executed for all brands and 13 browser combinations before code freeze stage without any bugs",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1131"
      },
      {
        "metric": "platforms covered by a single script",
        "value": "3",
        "unit": "platforms",
        "kind": "other",
        "numeric": 3,
        "wording": "A single test script worked on all 3 platforms - desktop, mobile & tablet",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1132"
      }
    ],
    "notSaid": [
      "measurement method",
      "duration over which the metric held",
      "date of the work",
      "what was out of scope",
      "whether the client is still a client"
    ],
    "readingNotes": {
      "scenario": "Ecommerce platform migration",
      "dimension": "D4",
      "questionForVendor": "Is the reported dollar saving a projection or a measured result, and which cost base was it calculated from?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "aspire-systems-jeweler-security-vapt",
    "vendorSlug": "aspire-systems",
    "title": "Aspire’s Robust Security Testing Fortifies India’s Leading Jeweler’s Digital Platforms",
    "sourceUrl": "https://www.aspiresys.com/casestudies/aspires-robust-security-testing-fortifies-indias-leading-jewelers-digital-platforms.pdf",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1138",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "one of India’s most celebrated and trusted jewelry brands",
      "segment": "specialty",
      "region": "india-apac",
      "country": "India",
      "channels": [
        "stores",
        "ecommerce",
        "mobile"
      ],
      "sizeIndicators": [
        "a legacy spanning over six decades",
        "a strong presence across South India and flagship stores across the globe",
        "an annual revenue of $500 million",
        "more than 10,000 personnel"
      ]
    },
    "systems": [
      {
        "system": "payments",
        "namedWhere": "named in The Need section (payment information) and The Challenges section (payment gateways)"
      },
      {
        "system": "mobile-commerce",
        "namedWhere": "named in The Need section (web and mobile apps) and the Technology Snapshot (Platform: Web & Mobile)"
      }
    ],
    "problem": {
      "paraphrase": "The jewelry retailer's web and mobile apps handle payments and customer data, and it had no clear view of their security posture. Payment gateways and APIs might expose card and personal details, the apps were open to DDoS, phishing and malware, and a breach could halt sales. It wanted weaknesses found and fixed, payment data protected and the apps brought in line with security standards.",
      "quote": "no clear visibility into the application’s security posture"
    },
    "approach": {
      "testingTypes": [
        "security"
      ],
      "mainScope": "security",
      "tooling": [
        "Burp Suite Professional",
        "Burp Add-ons",
        "Kali Linux Tool Sets",
        "Custom Scripts"
      ],
      "engagementModel": "project"
    },
    "metrics": [
      {
        "metric": "security vulnerabilities identified and remediated",
        "value": "Over 15",
        "unit": "security vulnerabilities",
        "kind": "defects",
        "wording": "Over 15 security vulnerabilities were identified and successfully remediated, significantly reducing the risk of cyberattacks and data leakage.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1139"
      },
      {
        "metric": "application uptime",
        "value": "99.9",
        "unit": "% uptime",
        "kind": "other",
        "numeric": 99.9,
        "wording": "By securing the application against DDoS attacks and API vulnerabilities, the solution contributed to a 99.9% uptime, ensuring a seamless customer experience and uninterrupted sales.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1140"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "date of the work",
      "whether the client is still a client"
    ],
    "readingNotes": {
      "scenario": "Payments and PCI scope",
      "dimension": "D8",
      "questionForVendor": "Which of the reported vulnerabilities sat in the payment flow, and was PCI DSS scope part of the test plan?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "aspire-systems-pos-oms-bos-e2e",
    "vendorSlug": "aspire-systems",
    "title": "American Retailer’s POS, OMS and BOS systems made error-free with Aspire’s E2E testing with 100% coverage",
    "sourceUrl": "https://www.aspiresys.com/casestudies/american-retailers-pos-oms-and-bos-systems-made-error-free-with-aspires-e2e-testing-with-100-percentage-coverage.pdf",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1141",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a globally recognized American retail chain, well-known for selling home décor, furniture, textiles, accessories, and gifts",
      "segment": "specialty",
      "region": "north-america",
      "country": "United States",
      "channels": [
        "stores",
        "ecommerce"
      ],
      "sizeIndicators": [
        "operate in 35 states",
        "around 360 stores"
      ]
    },
    "systems": [
      {
        "system": "pos",
        "platformName": "Veras POS",
        "namedWhere": "named in The Need section; platform named in the Technology Snapshot"
      },
      {
        "system": "oms",
        "platformName": "IBM Sterling OMS",
        "namedWhere": "named in The Need section; platform named in Aspire's Solution and the Technology Snapshot"
      },
      {
        "system": "ecommerce-platform",
        "platformName": "Open Commerce Platform (OCP)",
        "namedWhere": "named in Aspire's Solution and the Technology Snapshot"
      },
      {
        "system": "payments",
        "platformName": "Cybersource, PayPal",
        "namedWhere": "named in the Snapshot of our solution table"
      },
      {
        "system": "integrations",
        "namedWhere": "named in The Need section (integration with third parties) and the Snapshot of our solution table (POS and OMS integration for BOPIS and STS orders)"
      }
    ],
    "problem": {
      "paraphrase": "The retailer runs a POS and an order management system in its stores, tied to a back office server, and wanted end-to-end testing to validate and modernize them. It needed separate regression suites, more than 800 test scenarios written within a fixed timeframe, validation of feature-heavy work items, and checks on third-party integrations.",
      "quote": "time-efficient, inexpensive and effort-intensive"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "api"
      ],
      "mainScope": "functional",
      "teamShape": "three resources during peak periods; offshore and onshore teams",
      "tooling": [
        "DameWare",
        "Postman",
        "IBM HTTP API tester",
        "Jira"
      ]
    },
    "metrics": [
      {
        "metric": "POS test cases developed",
        "value": "375",
        "unit": "test cases",
        "kind": "test-count",
        "numeric": 375,
        "wording": "Developed 375 POS test cases and 550 OMS test cases",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1142"
      },
      {
        "metric": "OMS test cases developed",
        "value": "550",
        "unit": "test cases",
        "kind": "test-count",
        "numeric": 550,
        "wording": "Developed 375 POS test cases and 550 OMS test cases",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1142"
      },
      {
        "metric": "integration and E2E test cases created",
        "value": "300",
        "unit": "test cases",
        "kind": "test-count",
        "numeric": 300,
        "wording": "Created 300 integration and End-to-End (E2E) test cases covering order creation, order fulfilment, sales posting and various other omnichannel scenarios",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1143"
      },
      {
        "metric": "test scenarios required",
        "value": "800+",
        "unit": "test scenarios",
        "kind": "test-count",
        "wording": "Development of 800+ test scenarios within a given timeframe",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1144"
      },
      {
        "metric": "POS defects reported",
        "value": "120",
        "unit": "defects",
        "kind": "defects",
        "numeric": 120,
        "wording": "120 POS defects and 50 OMS defects were identified and reported",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1145",
        "outlier": false
      },
      {
        "metric": "OMS defects reported",
        "value": "50",
        "unit": "defects",
        "kind": "defects",
        "numeric": 50,
        "wording": "120 POS defects and 50 OMS defects were identified and reported",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1145"
      },
      {
        "metric": "test coverage across POS, BOS and OMS",
        "value": "100",
        "unit": "%",
        "kind": "coverage-pct",
        "numeric": 100,
        "wording": "100% test coverage was achieved, encompassing POS, BOS, and OMS systems.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1146"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "cost",
      "what was out of scope",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "POS rollout across stores",
      "dimension": "D2",
      "questionForVendor": "How was the coverage claim measured across POS, BOS and OMS, and against which list of requirements?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "aspire-systems-sfcc-pos-pricing-e2e",
    "vendorSlug": "aspire-systems",
    "title": "Aspire’s End-to-End Testing Strategy Reduces 96% Testing Efforts for a Top American Retailer",
    "sourceUrl": "https://www.aspiresys.com/casestudies/aspire-end-to-end-testing-strategy-reduces-96-percent-testing-efforts-for-a-top-american-retailer.pdf",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1147",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a leading American department store chain",
      "segment": "enterprise-omnichannel",
      "region": "north-america",
      "country": "United States",
      "channels": [
        "stores",
        "ecommerce",
        "mobile"
      ],
      "sizeIndicators": [
        "stores in 16 Southern states",
        "Founded in 1888"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Salesforce Commerce Cloud",
        "namedWhere": "named in the Challenges section and the Solutions section"
      },
      {
        "system": "pos",
        "platformName": "Xstore POS",
        "namedWhere": "named in the Key Challenges During Transition list and the POS and Pricing Systems section"
      },
      {
        "system": "data-migration",
        "namedWhere": "named in the Key Challenges During Transition list (customer and transaction data migration from the existing POS)"
      },
      {
        "system": "payments",
        "namedWhere": "named in the Key Challenges During Transition list (payment service integration testing) and the Solution in Detail tenders list"
      },
      {
        "system": "integrations",
        "platformName": "Oracle RPM (pricing)",
        "namedWhere": "named in the Key Challenges During Transition list and the list of integration systems for the new pricing systems"
      },
      {
        "system": "mobile-commerce",
        "namedWhere": "named in the Solutions section (mobile app automation for iOS and Android)"
      },
      {
        "system": "wms",
        "namedWhere": "named in the Products supported panel only"
      }
    ],
    "problem": {
      "paraphrase": "The department store chain was moving its online store to Salesforce Commerce Cloud in staged releases while replacing its legacy POS and pricing systems. It needed full test coverage across configurable features, integrations, changing product data and several browsers. It also needed checks on data migration and sync between old and new POS, stacked promotions and coupons, payment layers, and pricing links to store and ecommerce systems.",
      "quote": "the customer could not compromise on test coverage"
    },
    "approach": {
      "testingTypes": [
        "automation",
        "functional",
        "mobile",
        "data-migration"
      ],
      "mainScope": "automation",
      "teamShape": "Team size 30; onsite and offshore models",
      "tooling": [
        "DCqaf (Digital Commerce Quality Automation Framework)",
        "Selenium",
        "Appium",
        "Sauce Labs",
        "BrowserStack",
        "Java",
        "TestNG",
        "JUnit",
        "Maven",
        "SVN",
        "Git",
        "Jira",
        "Jenkins",
        "Eclipse",
        "NetBeans",
        "TestLink"
      ]
    },
    "metrics": [
      {
        "metric": "manual effort reduced by automation",
        "value": "96",
        "unit": "%",
        "kind": "time-reduction-pct",
        "numeric": 96,
        "wording": "Automation reduced 96% of the overall manual efforts and improved test efficiency while also optimizing the time spent on business critical Salesforce Commerce Cloud Configuration",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1148",
        "outlier": true
      },
      {
        "metric": "defect leakage",
        "value": "zero",
        "unit": "% defect leakage",
        "kind": "defects",
        "numeric": 0,
        "wording": "A zero percent defect leakage was maintained throughout the engagement",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1149"
      },
      {
        "metric": "POS regression test cases prepared",
        "value": "around 750",
        "unit": "test cases",
        "kind": "test-count",
        "numeric": 750,
        "wording": "Prepared around 750 Test cases as part of Regression suite for POS Testing and reported around 1000+ defects during the Implementation phase.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1150"
      },
      {
        "metric": "defects reported during implementation",
        "value": "around 1000+",
        "unit": "defects",
        "kind": "defects",
        "wording": "Prepared around 750 Test cases as part of Regression suite for POS Testing and reported around 1000+ defects during the Implementation phase.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1150"
      },
      {
        "metric": "stores in the new POS pilot",
        "value": "4",
        "unit": "stores",
        "kind": "other",
        "numeric": 4,
        "wording": "Currently, the New POS is piloted to 4 stores and it is running through without any key issues.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1151"
      },
      {
        "metric": "desktop web test cases automated",
        "value": "1120",
        "unit": "automated test cases",
        "kind": "test-count",
        "numeric": 1120,
        "wording": "Web Automation Testing Status: Desktop | Total Test Cases 1300 | Automated 1120 | Automation % Coverage 82 | Manual Effort (Hours) 320 | Automation Effort (hours) 10-12",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1152",
        "outlier": false
      },
      {
        "metric": "desktop web automation coverage",
        "value": "82",
        "unit": "%",
        "kind": "coverage-pct",
        "numeric": 82,
        "wording": "Web Automation Testing Status: Desktop | Total Test Cases 1300 | Automated 1120 | Automation % Coverage 82 | Manual Effort (Hours) 320 | Automation Effort (hours) 10-12",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1152"
      },
      {
        "metric": "desktop manual effort hours versus automation effort hours",
        "value": "320 to 10-12",
        "unit": "hours",
        "kind": "time-reduction-abs",
        "wording": "Web Automation Testing Status: Desktop | Total Test Cases 1300 | Automated 1120 | Automation % Coverage 82 | Manual Effort (Hours) 320 | Automation Effort (hours) 10-12",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1152"
      },
      {
        "metric": "mobile web test cases automated",
        "value": "1020",
        "unit": "automated test cases",
        "kind": "test-count",
        "numeric": 1020,
        "wording": "Web Automation Testing Status: Mobile | Total Test Cases 1200 | Automated 1020 | Automation % Coverage 86 | Manual Effort (Hours) 290 | Automation Effort (hours) 9-11",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1153"
      },
      {
        "metric": "mobile web automation coverage",
        "value": "86",
        "unit": "%",
        "kind": "coverage-pct",
        "numeric": 86,
        "wording": "Web Automation Testing Status: Mobile | Total Test Cases 1200 | Automated 1020 | Automation % Coverage 86 | Manual Effort (Hours) 290 | Automation Effort (hours) 9-11",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1153"
      },
      {
        "metric": "mobile manual effort hours versus automation effort hours",
        "value": "290 to 9-11",
        "unit": "hours",
        "kind": "time-reduction-abs",
        "wording": "Web Automation Testing Status: Mobile | Total Test Cases 1200 | Automated 1020 | Automation % Coverage 86 | Manual Effort (Hours) 290 | Automation Effort (hours) 9-11",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1153"
      },
      {
        "metric": "month 5 cost savings in dollars",
        "value": "$21,400",
        "unit": "USD",
        "kind": "cost",
        "numeric": 21400,
        "wording": "Total Savings - Month on Month: Month 5 | No. of Resources 3 | Total Cost of Development/Maintenance of Automation Scripting in $ $10200 | Automation % Coverage 100% | Cost Savings in $ $21,400 | Cost Savings in % 69.09%",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1154"
      },
      {
        "metric": "month 5 cost savings in percent",
        "value": "69.09",
        "unit": "%",
        "kind": "cost",
        "numeric": 69.09,
        "wording": "Total Savings - Month on Month: Month 5 | No. of Resources 3 | Total Cost of Development/Maintenance of Automation Scripting in $ $10200 | Automation % Coverage 100% | Cost Savings in $ $21,400 | Cost Savings in % 69.09%",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1154"
      },
      {
        "metric": "month 5 automation coverage",
        "value": "100",
        "unit": "%",
        "kind": "coverage-pct",
        "numeric": 100,
        "wording": "Total Savings - Month on Month: Month 5 | No. of Resources 3 | Total Cost of Development/Maintenance of Automation Scripting in $ $10200 | Automation % Coverage 100% | Cost Savings in $ $21,400 | Cost Savings in % 69.09%",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1154"
      }
    ],
    "notSaid": [
      "measurement method",
      "duration over which the metric held",
      "date of the work",
      "what was out of scope",
      "whether the client is still a client"
    ],
    "readingNotes": {
      "scenario": "Ecommerce platform migration",
      "dimension": "D4",
      "questionForVendor": "How was the reduction in manual effort calculated, and does it cover the web suite, the POS suite or both?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "aspire-systems-sfcc-sfom-integration",
    "vendorSlug": "aspire-systems",
    "title": "Leading American E-commerce app achieved 25+ production releases and zero defects with Aspire’s End-to-End Integration testing",
    "sourceUrl": "https://www.aspiresys.com/casestudies/integration-testing-for-leading-american-retailer-e-commerce-app.pdf",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1155",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "an American retail store chain offering fashionable jewelry and accessories at affordable prices for young women, teens, tweens, and kids",
      "segment": "specialty",
      "region": "north-america",
      "country": "United States",
      "channels": [
        "stores",
        "ecommerce"
      ],
      "sizeIndicators": [
        "3469 stores in 27 countries",
        "1547 are in USA and Canda",
        "1141 in Europe",
        "381 franchised or licensed stores in other countries"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Salesforce Commerce Cloud (SFCC)",
        "namedWhere": "named in The Needs section and the Technology Snapshot"
      },
      {
        "system": "oms",
        "platformName": "SFOM",
        "namedWhere": "named in The Needs and The Challenges sections"
      },
      {
        "system": "wms",
        "platformName": "WMS/Store/Proship",
        "namedWhere": "named in The Needs and The Challenges sections; platform string in the Technology Snapshot"
      },
      {
        "system": "integrations",
        "platformName": "MuleSoft Anypoint, Avalara",
        "namedWhere": "named in The Needs section (Avalara validations) and The Challenges section (Anypoint platform job runs)"
      },
      {
        "system": "payments",
        "namedWhere": "named in Aspire's Solution section (payment options in SFOM test combinations and the payment OMS module)"
      }
    ],
    "problem": {
      "paraphrase": "The jewelry and accessories chain needed integration testing of all its software modules within a fixed timeframe, covering SFCC, Salesforce Order Management, its WMS, a store picker app and Avalara tax checks. Regression had to finish quickly for a global business, and more than 300 end-to-end cases were needed. MuleSoft job runs and real-time WMS and store scenarios had to be synced across distant time zones.",
      "quote": "Integration testing of modules under strict deadlines"
    },
    "approach": {
      "testingTypes": [
        "functional"
      ],
      "mainScope": "functional",
      "tooling": [
        "Jira"
      ]
    },
    "metrics": [
      {
        "metric": "defect leakage in production",
        "value": "Zero",
        "unit": "defect leakage in production",
        "kind": "defects",
        "numeric": 0,
        "wording": "Zero-defect leakage in production",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1156"
      },
      {
        "metric": "test coverage",
        "value": "100",
        "unit": "%",
        "kind": "coverage-pct",
        "numeric": 100,
        "wording": "100% test coverage",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1157"
      },
      {
        "metric": "test cases prepared",
        "value": "500+",
        "unit": "test cases",
        "kind": "test-count",
        "wording": "500+ Test cases prepared and 300+ defects reported",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1158"
      },
      {
        "metric": "defects reported",
        "value": "300+",
        "unit": "defects",
        "kind": "defects",
        "wording": "500+ Test cases prepared and 300+ defects reported",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1158"
      },
      {
        "metric": "SFOM end-to-end test cases developed",
        "value": "300",
        "unit": "test cases",
        "kind": "test-count",
        "numeric": 300,
        "wording": "Development of 300 SFOM E2E Test Cases by considering all possible combinations of regions, order types, products, checkout options, payment options, promotions, and shipping types",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1159"
      },
      {
        "metric": "production releases supported",
        "value": "More than 25",
        "unit": "production releases",
        "kind": "other",
        "wording": "More than 25 production releases supported benefiting 20 million production users across all products",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1160"
      },
      {
        "metric": "production users across all products",
        "value": "20 million",
        "unit": "production users",
        "kind": "other",
        "wording": "More than 25 production releases supported benefiting 20 million production users across all products",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1160"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "team size on both sides",
      "cost",
      "date of the work",
      "whether the client is still a client"
    ],
    "readingNotes": {
      "scenario": "OMS or WMS replacement",
      "dimension": "D2",
      "questionForVendor": "What did the production defect leakage claim cover, and across which releases was it measured?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "aspire-systems-sfcc-sfra-migration",
    "vendorSlug": "aspire-systems",
    "title": "Leading American retailer saved USD 210K and reduces 80% testing efforts during SFCC to SFRA migration",
    "sourceUrl": "https://www.aspiresys.com/casestudies/leading-american-retailer-saved-usd210k-and-reduces-80percent-testing-efforts-during-sfcc-to-sfra-migration.pdf",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1161",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a leading American retailer operating for 100+ years",
      "segment": "fashion-apparel",
      "region": "north-america",
      "country": "United States",
      "channels": [
        "stores",
        "ecommerce"
      ],
      "sizeIndicators": [
        "240 odd stores are spread across 33 states, which include a few pop-ups",
        "operating for 100+ years"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Salesforce Commerce Cloud (SFCC/Demandware), SFRA",
        "namedWhere": "named in The Needs section and the Technology Snapshot"
      },
      {
        "system": "oms",
        "platformName": "APTOS EOM",
        "namedWhere": "named as EOM in Aspire's Solution section; APTOS-EOM in the Technology Snapshot"
      },
      {
        "system": "pos",
        "namedWhere": "named in the Solution panel and Aspire's Solution section (800+ Point of Sale test cases)"
      },
      {
        "system": "wms",
        "namedWhere": "named in Aspire's Solution section only"
      },
      {
        "system": "integrations",
        "namedWhere": "named in The Needs section (third-party integration) and the Technology Snapshot (many integration systems)"
      }
    ],
    "problem": {
      "paraphrase": "The apparel retailer was moving its storefront from SFCC to the SFRA architecture with third-party integrations intact, and wanted a more mature, scalable test automation setup with separate smoke and regression suites. Validating feature-heavy work items together with the client's own QA team was hard, and more than 900 test scenarios had to be written within a fixed timeframe.",
      "quote": "Implement a more mature and scalable test automation solution"
    },
    "approach": {
      "testingTypes": [
        "automation",
        "functional"
      ],
      "mainScope": "automation",
      "teamShape": "8 resources during peak period, supporting both SFCC and SFRA sites; shared the onshore QA load",
      "tooling": [
        "an unnamed test automation framework the vendor describes as using AI",
        "Selenium",
        "Java",
        "TestNG",
        "Maven",
        "Jenkins",
        "Eclipse",
        "TestLink",
        "Extent Reports",
        "Git",
        "SVN",
        "Jira"
      ]
    },
    "metrics": [
      {
        "metric": "savings from faster regression cycles",
        "value": "USD 210,000",
        "unit": "USD",
        "kind": "cost",
        "numeric": 210000,
        "wording": "USD 210,000 saved by speeding up regression cycles with 80% rapid build releases",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1162",
        "outlier": true
      },
      {
        "metric": "rapid build releases",
        "value": "80",
        "unit": "% rapid build releases",
        "kind": "other",
        "numeric": 80,
        "wording": "USD 210,000 saved by speeding up regression cycles with 80% rapid build releases",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1162"
      },
      {
        "metric": "testing cost saved",
        "value": "88",
        "unit": "%",
        "kind": "cost",
        "numeric": 88,
        "wording": "Saved 88% testing cost and reduced 80% testing efforts",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1163"
      },
      {
        "metric": "testing effort reduced",
        "value": "80",
        "unit": "%",
        "kind": "time-reduction-pct",
        "numeric": 80,
        "wording": "Saved 88% testing cost and reduced 80% testing efforts",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1163"
      },
      {
        "metric": "automation test coverage of storefront E2E and functional suites",
        "value": "70",
        "unit": "%",
        "kind": "coverage-pct",
        "numeric": 70,
        "wording": "Achieved 70% of test coverage with respect to Storefront E2E and Functional test cases automation suites",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1164"
      },
      {
        "metric": "SFCC production release interval",
        "value": "every 2-4 weeks",
        "unit": "release interval",
        "kind": "other",
        "wording": "Supported production releases for SFCC every 2-4 weeks and for SFRA by next month",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1165"
      },
      {
        "metric": "defects reported for SFRA",
        "value": "More than 500",
        "unit": "defects",
        "kind": "defects",
        "wording": "More than 500 defects reported for SFRA",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1166"
      },
      {
        "metric": "onshore QA load shared",
        "value": "60",
        "unit": "% of onshore QA load",
        "kind": "other",
        "numeric": 60,
        "wording": "Completed regression testing ahead of planned timeframe by sharing 60% of onshore QA load",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1167"
      },
      {
        "metric": "SFCC end-to-end test cases",
        "value": "125",
        "unit": "test cases",
        "kind": "test-count",
        "numeric": 125,
        "wording": "Prepared 125 End-to-End test cases and 300+ functional test cases for SFCC",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1168"
      },
      {
        "metric": "SFCC functional test cases",
        "value": "300+",
        "unit": "test cases",
        "kind": "test-count",
        "wording": "Prepared 125 End-to-End test cases and 300+ functional test cases for SFCC",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1168"
      },
      {
        "metric": "SFRA end-to-end test cases",
        "value": "100",
        "unit": "test cases",
        "kind": "test-count",
        "numeric": 100,
        "wording": "Prepared 100 End-to-End test cases and 110+ functional test cases for SFRA",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1169"
      },
      {
        "metric": "SFRA functional test cases",
        "value": "110+",
        "unit": "test cases",
        "kind": "test-count",
        "wording": "Prepared 100 End-to-End test cases and 110+ functional test cases for SFRA",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1169"
      },
      {
        "metric": "integration and end-to-end test cases",
        "value": "120+",
        "unit": "test cases",
        "kind": "test-count",
        "wording": "Creation of 120+ Integration and End-to-End test cases covering order creation, order fulfilment, sales posting and other various omnichannel scenarios",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1170"
      },
      {
        "metric": "SFRA manual test cases",
        "value": "700+",
        "unit": "test cases",
        "kind": "test-count",
        "wording": "Created 700+ SFRA manual test cases",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1171"
      },
      {
        "metric": "Point of Sale test cases",
        "value": "800+",
        "unit": "test cases",
        "kind": "test-count",
        "wording": "Development of 800+ Point of Sale test cases",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1172"
      },
      {
        "metric": "test scenarios required",
        "value": "900+",
        "unit": "test scenarios",
        "kind": "test-count",
        "wording": "Developing 900+ test scenarios within a given time frame was tedious",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1173"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "date of the work",
      "what was out of scope",
      "whether the client is still a client"
    ],
    "readingNotes": {
      "scenario": "Ecommerce platform migration",
      "dimension": "D4",
      "questionForVendor": "What baseline were the reported cost saving figures measured against, and over what period?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "bugraptors-multi-brand-magento-drupal",
    "vendorSlug": "bugraptors",
    "title": "The QA Strategy Behind a Flawless Multi-Brand eCommerce Implementation",
    "sourceUrl": "https://www.bugraptors.com/case-study/multi-brand-ecommerce-qa-strategy",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-0449",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a premier franchise partner for over 70 of the world's most beloved brands",
      "segment": "enterprise-omnichannel",
      "region": "mena",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": [
        "for over 70 of the world's most beloved brands",
        "to millions of devoted customers",
        "across the high-value Kuwait, KSA, and UAE markets"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Magento",
        "namedWhere": "named in Key Results (\"between the Magento and Drupal ecosystems\") and the Our Tools list"
      },
      {
        "system": "integrations",
        "platformName": "Magento and Drupal",
        "namedWhere": "named in Key Results (integration defects between the Magento and Drupal ecosystems)"
      },
      {
        "system": "data-migration",
        "platformName": "Amazon S3 to AEM DAM",
        "namedWhere": "named in Key Results (\"asset migration from Amazon S3 to AEM DAM\")"
      },
      {
        "system": "loyalty",
        "platformName": "H&M Hello Member",
        "namedWhere": "named in the Before vs After table (\"Loyalty Program Rollout\")"
      }
    ],
    "problem": {
      "paraphrase": "The client, a franchise operator for many international brands, was rebuilding its multi-brand ecommerce setup. The page reports a loyalty program launch stalled by technical faults and frequent P1 and P2 production failures traced to backend misconfiguration. QA teams depended on developers for environment setup and third-party configuration. The page also lists security of an Azure-hosted multi-tenant system and load with hundreds of users as concerns.",
      "quote": "frequent, high-impact production failures rooted in backend misconfigurations"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "data-migration",
        "security",
        "performance"
      ],
      "mainScope": "functional",
      "teamShape": "QA experts embedded in agile sprints; a dedicated QA owner assigned to each brand launch",
      "tooling": [
        "Confluence"
      ],
      "engagementModel": "dedicated-team"
    },
    "metrics": [
      {
        "metric": "integration defects between Magento and Drupal",
        "value": "60",
        "unit": "%",
        "kind": "defects",
        "numeric": 60,
        "wording": "Slashed integration defects by an incredible 60% through rigorous, preventative testing between the Magento and Drupal ecosystems.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1733"
      },
      {
        "metric": "data integrity in S3 to AEM DAM asset migration",
        "value": "100",
        "unit": "%",
        "kind": "other",
        "numeric": 100,
        "wording": "Guaranteed 100% data integrity during a high-stakes asset migration from Amazon S3 to AEM DAM",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1734"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Ecommerce platform migration",
      "dimension": "D2",
      "questionForVendor": "How were integration defects between Magento and Drupal counted before and after, and how was data integrity checked on the asset migration?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "bugraptors-trade-in-qa-transformation",
    "vendorSlug": "bugraptors",
    "title": "End-to-End QA Transformation for a Premier US Retail Trade-In Platform",
    "sourceUrl": "https://www.bugraptors.com/case-study/retail-trade-in-platform-qa-transformation",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-0442",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a prominent US-based retail brand specializing in smartphone and electronics trade-in and lifecycle management",
      "segment": "specialty",
      "region": "north-america",
      "country": "United States",
      "channels": [
        "stores",
        "ecommerce",
        "mobile"
      ],
      "sizeIndicators": [
        "serving both B2B and B2C channels."
      ]
    },
    "systems": [
      {
        "system": "pos",
        "namedWhere": "named in the client overview (\"integration across Web, Mobile, POS (Point of Sale), and Backend systems\")"
      },
      {
        "system": "mobile-commerce",
        "namedWhere": "named in the client overview (Web, Mobile, POS and Backend) and the Moboraptors (Mobile) framework in the results"
      },
      {
        "system": "integrations",
        "namedWhere": "named in the client overview (inventory synchronization, integration across Web, Mobile, POS and Backend) and the Before/After table (\"3rd party dependency\")"
      }
    ],
    "problem": {
      "paraphrase": "The client runs a device trade-in service covering diagnostics, valuation, promotional credits, inventory sync and customer notifications across web, mobile, point of sale and backend systems. Its test automation was scattered and undocumented, and scripts used hard-coded data. P1 incidents recurred each release and fixes broke other features. Regression took more than a week, and the system had never been stress tested before peak events.",
      "quote": "Automation scripts relied on hard-coded data"
    },
    "approach": {
      "testingTypes": [
        "automation",
        "functional",
        "performance",
        "security",
        "mobile"
      ],
      "mainScope": "automation",
      "tooling": [
        "Raptorvista",
        "Moboraptors",
        "Docker (Test Containers)",
        "Jenkins",
        "PostgreSQL",
        "TestRail",
        "Slack",
        "JMeter",
        "Blazemeter"
      ]
    },
    "metrics": [
      {
        "metric": "P1/P2 defects in production",
        "value": "Zero",
        "unit": "P1/P2 production defects",
        "kind": "defects",
        "numeric": 0,
        "wording": "Achieved and maintained Zero P1/P2 defects in production from the start of the engagement to the present day.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1726"
      },
      {
        "metric": "automation coverage",
        "value": "20% to 70%",
        "unit": "%",
        "kind": "coverage-pct",
        "wording": "Scaled automation from a fragmented 20% to a comprehensive 70%, automating the majority of P1 use cases.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-0446"
      },
      {
        "metric": "on-time delivery rate",
        "value": "around 75% to 98%",
        "unit": "%",
        "kind": "other",
        "wording": "Improved on-time delivery rates from around 75% to 98%, resolving hotfix chaos.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1727"
      },
      {
        "metric": "regression execution time",
        "value": "over 10 days to 6 days",
        "unit": "days",
        "kind": "time-reduction-abs",
        "wording": "Reduced regression execution time from over 10 days to just 6 days.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1728"
      },
      {
        "metric": "direct effort savings from suite optimisation",
        "value": "15",
        "unit": "%",
        "kind": "other",
        "numeric": 15,
        "wording": "Optimized test suites by identifying and removing redundant cases, resulting in 15% direct effort savings.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1729"
      },
      {
        "metric": "manual testing effort reduced by frameworks",
        "value": "100x hours",
        "unit": "hours",
        "kind": "other",
        "wording": "Engineered Raptorvista (Web) and Moboraptors (Mobile), commercial-grade automation frameworks that reduced manual testing effort by 100x hours.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1730"
      },
      {
        "metric": "release frequency",
        "value": "Monthly to Bi-weekly",
        "kind": "other",
        "wording": "Release Frequency | Monthly | Bi-weekly",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1731"
      },
      {
        "metric": "critical defects (client quote)",
        "value": "zero for five years",
        "kind": "defects",
        "wording": "Bugraptors didn't just automate our tests; they engineered confidence, delivering zero critical defects for five years running while accelerating our release velocity.",
        "claimedBy": "client",
        "verifiable": "no",
        "evidenceId": "ev-1732"
      }
    ],
    "notSaid": [
      "measurement method",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "POS rollout across stores",
      "dimension": "D4",
      "questionForVendor": "Which POS flows does the automation coverage figure include, and how are high-priority production defects counted?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "cybage-japan-marketplace-automation",
    "vendorSlug": "cybage",
    "title": "Implemented a Multi-Layer Test Automation Solution for a Leading Japanese Electronic Commerce and Online Retailing Company",
    "sourceUrl": "https://www.cybage.com/success-story/retail/implemented-a-multi-layer-test-automation-solution-for-a-leading-japanese-electronic-commerce-and-online-retailing-company",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1300",
    "publishedOn": "2024-03-20",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a Leading Japanese Electronic Commerce and Online Retailing Company",
      "segment": "marketplace",
      "region": "india-apac",
      "country": "Japan",
      "channels": [
        "ecommerce",
        "marketplace"
      ],
      "sizeIndicators": [
        "serves 1.5 billion members across the world",
        "Has 70+ businesses spanning e-commerce, travel, banking, marketing, and media"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "namedWhere": "named in the title (\"Electronic Commerce and Online Retailing Company\") and the Business Impact section (\"per marketplace per cycle\"); platform not named"
      }
    ],
    "problem": {
      "paraphrase": "The client is a Japanese ecommerce and online retail group with many businesses. It wanted to lower the cost of fixing defects and release faster. It also wanted QA run as a separate function with attention to the customer experience, including for elderly users.",
      "quote": "Executing independent QA activities focusing on customer experience."
    },
    "approach": {
      "testingTypes": [
        "automation",
        "api",
        "functional"
      ],
      "mainScope": "automation",
      "tooling": []
    },
    "metrics": [
      {
        "metric": "UI regression test time",
        "value": "5 hrs. from 72 hrs.",
        "unit": "hours",
        "kind": "time-reduction-abs",
        "wording": "With automation, UI regression test time reduced to 5 hrs. from 72 hrs.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1662"
      },
      {
        "metric": "testing effort saved",
        "value": "70+",
        "unit": "man days per marketplace per cycle",
        "kind": "other",
        "wording": "Saving 70+ man days for testing per marketplace per cycle.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1663"
      },
      {
        "metric": "API tests executed daily",
        "value": "over 14000",
        "unit": "API tests executed daily",
        "kind": "test-count",
        "wording": "Seamless API test automation (over 14000 tests executed daily).",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1664"
      },
      {
        "metric": "release interval",
        "value": "3 weeks from 2 months",
        "unit": "release interval",
        "kind": "other",
        "wording": "More frequent releases (3 weeks from 2 months) with enhanced test coverage.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1665"
      }
    ],
    "notSaid": [
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Peak-season readiness in 90 days",
      "dimension": "D4",
      "questionForVendor": "Which frameworks run the UI and API suites, and how was the cut in UI regression time measured?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "deviqa-art-gallery-sales-platform",
    "vendorSlug": "deviqa",
    "title": "QA for an art management and sales platform",
    "sourceUrl": "https://deviqa.com/case-studies/qa-for-an-art-management-and-sales-platform/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-0921",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "A New York art gallery",
      "segment": "specialty",
      "region": "north-america",
      "country": "United States",
      "channels": [],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "oms",
        "namedWhere": "named in the client description (\"From managing orders and private sales to public exhibitions\")"
      }
    ],
    "problem": {
      "paraphrase": "The gallery runs a digital platform for managing, presenting and selling artworks, books and art objects, including orders and private sales. QA ran apart from the development and release flow, and testing happened mostly at the UAT stage. User stories had no structure, bug reports lacked context and many test cases were out of date. DeviQA was asked to tie QA into the release cycle, cut testing time and improve issue tracking.",
      "quote": "The QA process ran in isolation from the development and release flow"
    },
    "approach": {
      "testingTypes": [
        "functional"
      ],
      "mainScope": "functional",
      "teamShape": "1 manual QA engineer",
      "duration": "Since 2023",
      "tooling": [
        "Jira",
        "Confluence",
        "GitHub",
        "Slack",
        "1Password",
        "Mailtrap",
        "Google Docs",
        "Postman",
        "Microsoft D365"
      ],
      "engagementModel": "dedicated-team"
    },
    "metrics": [
      {
        "metric": "release velocity",
        "value": "30",
        "unit": "%",
        "kind": "other",
        "numeric": 30,
        "wording": "Release velocity increased by 30%",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-0922"
      },
      {
        "metric": "critical production bugs",
        "value": "25",
        "unit": "%",
        "kind": "defects",
        "numeric": 25,
        "wording": "Critical production bugs decreased by 25%",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-0923"
      },
      {
        "metric": "overall testing time",
        "value": "40",
        "unit": "%",
        "kind": "time-reduction-pct",
        "numeric": 40,
        "wording": "Overall testing time decreased by 40%",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-0924"
      },
      {
        "metric": "early-stage defect detection",
        "value": "60",
        "unit": "%",
        "kind": "defects",
        "numeric": 60,
        "wording": "Early-stage defect detection rose by 60%",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-0925"
      },
      {
        "metric": "lost or unclear tickets",
        "value": "95",
        "unit": "%",
        "kind": "other",
        "numeric": 95,
        "wording": "Lost/unclear tickets decreased by 95%",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-0926"
      },
      {
        "metric": "task processing speed",
        "value": "20",
        "unit": "%",
        "kind": "other",
        "numeric": 20,
        "wording": "Task processing speed increased by 20%",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-0927"
      },
      {
        "metric": "bug reports with screenshots or videos",
        "value": "100",
        "unit": "%",
        "kind": "other",
        "numeric": 100,
        "wording": "100% of reports include screenshots or videos",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-0928"
      },
      {
        "metric": "ticket reopen rate",
        "value": "50",
        "unit": "%",
        "kind": "other",
        "numeric": 50,
        "wording": "Ticket reopen rate decreased by 50%",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-0929"
      },
      {
        "metric": "defect reproduction time",
        "value": "30",
        "unit": "%",
        "kind": "time-reduction-pct",
        "numeric": 30,
        "wording": "Defect reproduction time decreased by 30%",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-0930"
      },
      {
        "metric": "regression risk",
        "value": "70",
        "unit": "%",
        "kind": "other",
        "numeric": 70,
        "wording": "Regression risk decreased by 70%",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-0931"
      },
      {
        "metric": "test execution time",
        "value": "35",
        "unit": "%",
        "kind": "time-reduction-pct",
        "numeric": 35,
        "wording": "Test execution time decreased by 35%",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-0932"
      },
      {
        "metric": "test cases created",
        "value": "515",
        "unit": "test cases",
        "kind": "test-count",
        "numeric": 515,
        "wording": "515 Test cases created",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-0933"
      },
      {
        "metric": "test coverage",
        "value": "90",
        "unit": "%",
        "kind": "coverage-pct",
        "numeric": 90,
        "wording": "90% Test coverage",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-0934"
      },
      {
        "metric": "bugs logged",
        "value": "1k+",
        "unit": "bugs",
        "kind": "defects",
        "wording": "1k+ Number of bugs logged",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-0935"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "cost",
      "what was out of scope"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D1",
      "questionForVendor": "How were the percentage changes measured, over what period, and what were the starting values?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "deviqa-chargeafter-pos-financing",
    "vendorSlug": "deviqa",
    "title": "QA services for point-of-sale financing platform",
    "sourceUrl": "https://deviqa.com/case-studies/chargeafter/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-0907",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": true,
      "name": "ChargeAfter",
      "segment": "enterprise-omnichannel",
      "channels": [
        "ecommerce",
        "stores"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "payments",
        "namedWhere": "named in the title and the About project section (\"point-of-sale financing technology for eCommerce and physical retail\", financing options \"during online and in-store checkout\")"
      }
    ],
    "problem": {
      "paraphrase": "ChargeAfter connects consumers, merchants and lenders so shoppers can compare financing offers at online and in-store checkout. Before DeviQA joined, a regression run took two weeks and API testing was manual. There was no UI or API automation, no automated smoke or pull-request tests and no dedicated test environment. There were also no per-environment pipelines and no standard test case design. DeviQA was asked to build UI and API automation from scratch.",
      "quote": "No tests ran automatically after developers submitted pull requests"
    },
    "approach": {
      "testingTypes": [
        "automation",
        "api",
        "functional"
      ],
      "mainScope": "automation",
      "teamShape": "4 full-stack QA engineers",
      "duration": "Since 2021",
      "tooling": [
        "Azure DevOps",
        "Confluence",
        "Slack Teams",
        "Postman",
        "TypeScript",
        "Cypress",
        "Playwright",
        "Allure",
        "BrowserStack",
        "Mixpanel",
        "MongoDB",
        "Google Cloud",
        "SFTP"
      ],
      "engagementModel": "dedicated-team"
    },
    "metrics": [
      {
        "metric": "regression run time",
        "value": "2 weeks to 6 hours",
        "kind": "time-reduction-abs",
        "wording": "Regression testing reduced from 2 weeks to 6 hours",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0908"
      },
      {
        "metric": "regression run speed (stat block)",
        "value": "5x",
        "unit": "x",
        "kind": "other",
        "numeric": 5,
        "wording": "5x Faster regression testing run",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0909"
      },
      {
        "metric": "automated API tests created",
        "value": "1,000+",
        "unit": "automated API tests",
        "kind": "test-count",
        "wording": "1,000+ automated API tests created",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0910"
      },
      {
        "metric": "automated UI and API tests created",
        "value": "4,000+",
        "unit": "automated UI and API tests",
        "kind": "test-count",
        "wording": "4,000+ automated UI and API tests created",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0911"
      },
      {
        "metric": "smoke run duration",
        "value": "15-20",
        "unit": "minutes",
        "kind": "other",
        "wording": "Automated smoke tests run every 2 hours across two frameworks and complete in 15-20 minutes",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0912"
      },
      {
        "metric": "smoke run frequency",
        "value": "every 2",
        "unit": "hours",
        "kind": "other",
        "wording": "Automated smoke tests run every 2 hours across two frameworks and complete in 15-20 minutes",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0912"
      },
      {
        "metric": "pull-request API suite duration",
        "value": "approximately 10",
        "unit": "minutes",
        "kind": "other",
        "wording": "A targeted API suite runs after each pull request and completes in approximately 10 minutes",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0913"
      },
      {
        "metric": "application test coverage",
        "value": "90",
        "unit": "%",
        "kind": "coverage-pct",
        "numeric": 90,
        "wording": "90% Application test coverage",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0914"
      },
      {
        "metric": "automation coverage",
        "value": "90",
        "unit": "%",
        "kind": "coverage-pct",
        "numeric": 90,
        "wording": "Automation coverage reached 90%, while regression testing decreased from 2 weeks to 6 hours.",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0915"
      },
      {
        "metric": "parallel threads",
        "value": "12",
        "unit": "parallel threads",
        "kind": "other",
        "numeric": 12,
        "wording": "Tests executed across 12 parallel threads",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0916"
      },
      {
        "metric": "supported browsers",
        "value": "2",
        "unit": "browsers",
        "kind": "other",
        "numeric": 2,
        "wording": "2 Supported browsers",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0917"
      },
      {
        "metric": "browser versions covered",
        "value": "three latest Chrome versions and two Safari versions",
        "kind": "other",
        "wording": "Test suites cover the three latest Chrome versions and two Safari versions",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0918"
      },
      {
        "metric": "test suite integrations",
        "value": "Five",
        "unit": "test suite integrations",
        "kind": "other",
        "numeric": 5,
        "wording": "Five test suite integrations implemented and maintained",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0919"
      },
      {
        "metric": "reported blocker, critical, major or minor bugs",
        "value": "50",
        "unit": "%",
        "kind": "defects",
        "numeric": 50,
        "wording": "50% Reported blocker/critical/ major/minor bugs",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0920"
      }
    ],
    "notSaid": [
      "measurement method",
      "duration over which the metric held",
      "cost",
      "what was out of scope"
    ],
    "readingNotes": {
      "scenario": "Payments and PCI scope",
      "dimension": "D4",
      "questionForVendor": "Did the automated suites cover lender offer and checkout flows end to end, and was any PCI DSS scope part of the work?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "deviqa-soapbox-wms-qa",
    "vendorSlug": "deviqa",
    "title": "QA services for warehouse management software",
    "sourceUrl": "https://deviqa.com/case-studies/soapbox/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-0891",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": true,
      "name": "Soapbox",
      "segment": "ecommerce-d2c",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "wms",
        "namedWhere": "named in the title and the About project section (\"e-commerce fulfillment and warehouse management platform\")"
      },
      {
        "system": "oms",
        "namedWhere": "named in the About project section (\"order processing\")"
      }
    ],
    "problem": {
      "paraphrase": "Before the engagement Soapbox ran tests by hand, test coverage stood at 40%, a regression run took about 1.5 weeks and stories took about two weeks to move forward. The page describes the QA process as poor and the Android app code as carrying many major and critical issues. Soapbox asked DeviQA to structure test documentation, widen coverage and cut testing time on the web platform and the Android app prototype.",
      "quote": "A huge amount of major/critical issues in the code base of the mobile app"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "automation",
        "mobile"
      ],
      "mainScope": "functional",
      "teamShape": "3 manual QA engineers, 1 automation QA engineer, 1 mobile developer",
      "duration": "Since 2021",
      "tooling": [
        "Figma",
        "GitLab",
        "GitLab issues",
        "Sentry",
        "Datadog",
        "Android Studio",
        "Cypress",
        "MS Visual Studio Code",
        "Google Docs",
        "Google Sheets",
        "React Native",
        "React Native Reanimated",
        "React Navigation",
        "App Center",
        "Swagger"
      ],
      "engagementModel": "dedicated-team"
    },
    "metrics": [
      {
        "metric": "test coverage before DeviQA",
        "value": "40",
        "unit": "%",
        "kind": "coverage-pct",
        "numeric": 40,
        "wording": "40% test coverage",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0892"
      },
      {
        "metric": "software covered with test cases",
        "value": ">90",
        "unit": "%",
        "kind": "coverage-pct",
        "wording": ">90% of the software is covered with detailed and relevant test cases",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0893"
      },
      {
        "metric": "test cases and automated scripts created",
        "value": "2,500+",
        "unit": "test cases and automated scripts",
        "kind": "test-count",
        "wording": "DeviQA created 2,500+ test cases and automated scripts, achieving 90% coverage of the Soapbox web platform.",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0894"
      },
      {
        "metric": "web platform coverage",
        "value": "90",
        "unit": "%",
        "kind": "coverage-pct",
        "numeric": 90,
        "wording": "DeviQA created 2,500+ test cases and automated scripts, achieving 90% coverage of the Soapbox web platform.",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0894"
      },
      {
        "metric": "test cases created (stat block)",
        "value": "2.5k+",
        "unit": "test cases",
        "kind": "test-count",
        "wording": "2.5k+ Test cases created",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0895"
      },
      {
        "metric": "automated scripts in the first 6 months",
        "value": ">400",
        "unit": "automated scripts",
        "kind": "test-count",
        "wording": ">400 automated scripts were created in the first 6 months",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0896"
      },
      {
        "metric": "regression run time",
        "value": "1.5 weeks to three days",
        "kind": "time-reduction-abs",
        "wording": "Regression testing decreased from 1.5 weeks to three days.",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0897"
      },
      {
        "metric": "regression testing speed (stat block)",
        "value": "X3",
        "unit": "x",
        "kind": "other",
        "numeric": 3,
        "wording": "X3 Faster regression testing",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0898"
      },
      {
        "metric": "time to move stories forward before DeviQA",
        "value": "~2",
        "unit": "weeks",
        "kind": "other",
        "wording": "~2 weeks to move stories forward",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0899"
      },
      {
        "metric": "task handling speed",
        "value": "2x",
        "unit": "x",
        "kind": "other",
        "numeric": 2,
        "wording": "2x faster task handling",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0900"
      },
      {
        "metric": "share of code refactored",
        "value": "60",
        "unit": "%",
        "kind": "other",
        "numeric": 60,
        "wording": "60% of the code was refactored to improve application stability",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0901"
      },
      {
        "metric": "mobile app files refactored",
        "value": "200+",
        "unit": "files",
        "kind": "other",
        "wording": "200+ Refactored files of the mobile app",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0902"
      },
      {
        "metric": "successful releases",
        "value": "95+",
        "unit": "releases",
        "kind": "other",
        "wording": "95+ successful releases",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0903"
      },
      {
        "metric": "web platform releases supported",
        "value": "80+",
        "unit": "releases",
        "kind": "other",
        "wording": "DeviQA supported 80+ releases of the Soapbox web platform and 15+ releases of its Android application.",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0904"
      },
      {
        "metric": "Android app releases supported",
        "value": "15+",
        "unit": "releases",
        "kind": "other",
        "wording": "DeviQA supported 80+ releases of the Soapbox web platform and 15+ releases of its Android application.",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0904"
      },
      {
        "metric": "critical bugs reported",
        "value": "550+",
        "unit": "bugs",
        "kind": "defects",
        "wording": "550+ Critical bugs reported",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0905"
      },
      {
        "metric": "defects reported",
        "value": "1,500+",
        "unit": "defects",
        "kind": "defects",
        "wording": "The team reported 1,500+ defects, with 60% classified as blocker, critical, or major.",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0906"
      },
      {
        "metric": "share of defects blocker, critical or major",
        "value": "60",
        "unit": "%",
        "kind": "defects",
        "numeric": 60,
        "wording": "The team reported 1,500+ defects, with 60% classified as blocker, critical, or major.",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0906"
      }
    ],
    "notSaid": [
      "measurement method",
      "duration over which the metric held",
      "cost",
      "what was out of scope"
    ],
    "readingNotes": {
      "scenario": "OMS or WMS replacement",
      "dimension": "D2",
      "questionForVendor": "Which warehouse and order flows did the web coverage figure include, and how was coverage counted?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "gspann-apparel-product-data-sfcc",
    "vendorSlug": "gspann",
    "title": "Product Data Accuracy Across 23 Countries with QE at Scale",
    "sourceUrl": "https://www.gspann.com/insights/case-study/product-data-accuracy-across-23-countries-with-qe-at-scale",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-0985",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "A multinational apparel retailer.",
      "segment": "fashion-apparel",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": [
        "Online commerce represented 43% of total sales",
        "launched hundreds of products every week across 23 countries"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Salesforce Commerce Cloud (SFCC)",
        "namedWhere": "named in the Our Solution section"
      },
      {
        "system": "oms",
        "platformName": "IBM Sterling",
        "namedWhere": "named only in the Technologies Used list"
      },
      {
        "system": "integrations",
        "platformName": "PIM, content management, digital merchandising, localization and campaign tools",
        "namedWhere": "named in the Challenge section"
      }
    ],
    "problem": {
      "paraphrase": "The retailer launched hundreds of products a week across 23 countries. The vendor states it had difficulty keeping product attributes, prices and campaign setups correct on its ecommerce site. Product information, content, merchandising, localization and campaign systems all fed the product pages, which added to the data errors, and region-specific variations had to be checked.",
      "quote": "maintaining product data quality across their extensive e-commerce platform"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "automation",
        "localization"
      ],
      "mainScope": "functional",
      "teamShape": "a dedicated team of specialists across quality assurance (QA), product content, and campaign operations",
      "tooling": [
        "Playwright (TypeScript)",
        "Jira / Confluence",
        "Postman API",
        "Smartsheet",
        "Scene7",
        "StoryStream",
        "Figma",
        "Instagram"
      ],
      "engagementModel": "dedicated-team"
    },
    "metrics": [
      {
        "metric": "accuracy in PDP vs Excel data comparisons",
        "value": "100",
        "unit": "%",
        "kind": "other",
        "numeric": 100,
        "wording": "100% accuracy in PDP vs Excel data comparisons",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1580"
      },
      {
        "metric": "pricing errors during high-volume sale events",
        "value": "Zero",
        "unit": "pricing errors",
        "kind": "defects",
        "numeric": 0,
        "wording": "Zero pricing errors during high-volume sale events",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1581"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Peak-season readiness in 90 days",
      "dimension": "D1",
      "questionForVendor": "How many product pages and promotions were checked to support the accuracy and pricing error claims, and during which sale events?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "gspann-apparel-sauce-visual-regression",
    "vendorSlug": "gspann",
    "title": "Catch Cross-Platform UI Issues Early with Sauce Visual Testing",
    "sourceUrl": "https://www.gspann.com/insights/case-study/remove-visual-inconsistencies-with-sauce-visual-regression-testing",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-0990",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "A multinational apparel retailer.",
      "segment": "fashion-apparel",
      "channels": [
        "stores",
        "ecommerce",
        "marketplace"
      ],
      "sizeIndicators": [
        "hundreds of physical stores",
        "a dozen wildly popular e-commerce websites",
        "several online marketplaces across the globe, especially the APAC and EU markets"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "namedWhere": "named in the Challenge section (a dozen e-commerce websites, platform not named)"
      }
    ],
    "problem": {
      "paraphrase": "The retailer runs about a dozen ecommerce sites that change content constantly, and the sites drifted apart in look and feel. Staff compared pages by eye, which took a lot of time and still missed differences, confusing shoppers. The retailer wanted faster review, checks soon after each deployment, coverage of every page on every site, and comparisons across more browsers and operating systems.",
      "quote": "a tedious and near-impossible process of visually comparing every page"
    },
    "approach": {
      "testingTypes": [
        "automation"
      ],
      "mainScope": "automation",
      "tooling": [
        "WebdriverIO",
        "Travis CI",
        "Sauce Visual (screener.io)",
        "Percy (BrowserStack), evaluated in proof of concept",
        "wdio-image-comparison-service, evaluated in proof of concept"
      ]
    },
    "metrics": [],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D4",
      "questionForVendor": "Which pages, sites and browser and operating system combinations does the daily visual run cover, and what did it catch before release?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "gspann-apparel-wcag-accessibility",
    "vendorSlug": "gspann",
    "title": "Making E-Commerce Inclusive: Accessibility Compliance Boosts UX and Builds Trust",
    "sourceUrl": "https://www.gspann.com/insights/case-study/making-e-commerce-inclusive-accessibility-compliance-boosts-ux-and-builds-trust",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-0987",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "A multinational apparel retailer.",
      "segment": "fashion-apparel",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "namedWhere": "named in the Challenge section (e-commerce platform, not named by product)"
      }
    ],
    "problem": {
      "paraphrase": "The clothing retailer's ecommerce site did not meet accessibility standards, so shoppers with disabilities had trouble finding products, moving around the site and checking out. The retailer wanted to reach WCAG levels A and AA, fix its inefficient process for handling accessibility violations, and lower its legal and reputational risk.",
      "quote": "Attain compliance with Web Content Accessibility Guidelines (WCAG) levels A and AA"
    },
    "approach": {
      "testingTypes": [
        "accessibility"
      ],
      "mainScope": "accessibility",
      "teamShape": "a dedicated accessibility team",
      "tooling": [
        "Lighthouse",
        "Node.js",
        "jQuery",
        "JavaScript",
        "HTML",
        "NVDA",
        "JAWS (Windows)",
        "Talkback (Android)",
        "VoiceOver (iOS/Mac)"
      ]
    },
    "metrics": [
      {
        "metric": "Lighthouse accessibility score across all pages",
        "value": "90 and above",
        "unit": "Lighthouse accessibility score",
        "kind": "other",
        "wording": "Measurable Progress: The Lighthouse accessibility score increased to 90 and above across all pages, providing a tangible success metric.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1582"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D8",
      "questionForVendor": "What was the automated accessibility score before the work, and did the third-party audit firm retest the site against WCAG A and AA?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "gspann-athletic-jenkins-pipeline",
    "vendorSlug": "gspann",
    "title": "Launch Faster by Elevating Code Quality with Jenkins CI/CD",
    "sourceUrl": "https://www.gspann.com/insights/case-study/achieve-faster-time-to-market-by-improving-code-quality-with-jenkins-ci-cd-pipeline",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-0991",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "An athletic apparel retailer.",
      "segment": "fashion-apparel",
      "channels": [],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "integrations",
        "platformName": "downstream inventory systems",
        "namedWhere": "named in the Challenge and Our Solution sections"
      }
    ],
    "problem": {
      "paraphrase": "The retailer ran its automation suites through its previous CI service, which needed the automation team to start each run by hand. Delivery dates therefore depended on that team. Test data was also a problem: inventory gaps in downstream systems made most frontend automation tests fail, and hard-coded data added manual work.",
      "quote": "almost 90% of frontend automation tests failed"
    },
    "approach": {
      "testingTypes": [
        "automation"
      ],
      "mainScope": "automation",
      "tooling": [
        "Jenkins",
        "Jenkins Shared Services",
        "TestNG",
        "Selenium",
        "TestRail",
        "GitLab",
        "Travis CI (previous CI tool)"
      ]
    },
    "metrics": [
      {
        "metric": "drop in the test failure rate",
        "value": "83",
        "unit": "%",
        "kind": "defects",
        "numeric": 83,
        "wording": "83% drop in the test failure rate",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1583"
      },
      {
        "metric": "share of scripts failing due to test data issues",
        "value": "90% to 7%",
        "unit": "%",
        "kind": "defects",
        "wording": "With the new system, only 7% of the scripts fail due to test data issues, which is an incredible improvement against the previous failure rate of 90%.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1584"
      }
    ],
    "notSaid": [
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D4",
      "questionForVendor": "Over how many runs and what period was the script failure rate measured, and does it count all failures or only test data failures?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "gspann-d365-sterling-oms-automation",
    "vendorSlug": "gspann",
    "title": "Dynamics 365 Test Automation Cuts Release Time by 90%",
    "sourceUrl": "https://www.gspann.com/insights/case-study/dynamics-365-test-automation-cuts-release-time-by-90",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-0982",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "A leading US-based retail company that sells high-quality home furnishings and kitchenware.",
      "segment": "specialty",
      "region": "north-america",
      "country": "US",
      "channels": [],
      "sizeIndicators": [
        "billion-dollar retailer",
        "multiple premium brands",
        "omnichannel specialty retailer"
      ]
    },
    "systems": [
      {
        "system": "oms",
        "platformName": "IBM Sterling OMS",
        "namedWhere": "named in the Challenge section"
      },
      {
        "system": "wms",
        "platformName": "Warehouse Management for Open Systems (WMOS)",
        "namedWhere": "named in the Challenge section"
      },
      {
        "system": "integrations",
        "platformName": "Microsoft Dynamics 365, Oracle, Microsoft Azure",
        "namedWhere": "named in the Challenge section"
      }
    ],
    "problem": {
      "paraphrase": "Releases of the retailer's Microsoft Dynamics 365 workflows were held up by manual testing. Dynamics 365 is tightly linked to IBM Sterling OMS, WMOS, Oracle and Azure, so each release needed end-to-end checks that took 2 to 3 weeks. Each premium brand needed its own repeated cycle. Thin documentation tied testing to a few specialists, and manual errors caused rework and delayed releases.",
      "quote": "every release required extensive end-to-end testing"
    },
    "approach": {
      "testingTypes": [
        "automation",
        "api",
        "functional"
      ],
      "mainScope": "automation",
      "tooling": [
        "Java",
        "Selenium 4",
        "Cucumber 7",
        "JUnit 5",
        "RestAssured 5",
        "Allure Reporting",
        "Report Portal",
        "Azure Portal"
      ]
    },
    "metrics": [
      {
        "metric": "release cycle time reduction",
        "value": "90",
        "unit": "%",
        "kind": "time-reduction-pct",
        "numeric": 90,
        "wording": "90% reduction in release cycle time from 2-3 weeks to 1-2 days",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1571"
      },
      {
        "metric": "monthly savings from reduced manual testing effort",
        "value": "$7,000-$10,000",
        "unit": "USD per month",
        "kind": "cost",
        "wording": "$7,000-$10,000 monthly savings through reduced manual testing effort",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1572"
      },
      {
        "metric": "regression testing duration",
        "value": "5-7 days to just hours",
        "unit": "days",
        "kind": "time-reduction-abs",
        "wording": "Regression testing compressed from 5-7 days to just hours",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1573"
      },
      {
        "metric": "manual testing hours saved per month",
        "value": "140-150",
        "unit": "hours per month",
        "kind": "time-reduction-abs",
        "wording": "140-150 hours per month saved in manual testing activities",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1574"
      },
      {
        "metric": "annual savings from reduced error correction and rework",
        "value": "$10,000+",
        "unit": "USD per year",
        "kind": "cost",
        "wording": "$10,000+ annual savings from reduced error correction and rework",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1575"
      },
      {
        "metric": "potential annual revenue increase",
        "value": "5-7",
        "unit": "%",
        "kind": "other",
        "wording": "5-7% potential annual revenue increase through faster time-to-market",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1576"
      },
      {
        "metric": "automated test pass rate",
        "value": "90%+",
        "unit": "%",
        "kind": "other",
        "wording": "90%+ automated test pass rate, ensuring consistent quality delivery",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1577"
      },
      {
        "metric": "business-critical scenarios automated",
        "value": "100+",
        "unit": "scenarios",
        "kind": "test-count",
        "wording": "Automated 100+ business-critical scenarios across integrated enterprise systems",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1578"
      }
    ],
    "notSaid": [
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "OMS or WMS replacement",
      "dimension": "D4",
      "questionForVendor": "How were the monthly hours and dollar savings measured, and which automated scenarios run against Sterling OMS and WMOS rather than Dynamics alone?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "gspann-home-furnishings-ui-api-regression",
    "vendorSlug": "gspann",
    "title": "UI/APIs Automated Regression Testing Improves App Performance",
    "sourceUrl": "https://www.gspann.com/insights/case-study/automated-regression-testing-for-ui-and-apis-improves-application-performance-and-scalability",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-0984",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "A leading US-based retail company that sells high-quality home furnishings and kitchenware.",
      "segment": "specialty",
      "region": "north-america",
      "country": "US",
      "channels": [],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "integrations",
        "namedWhere": "named in the Our Solution section (messaging queue automation and message validation)"
      }
    ],
    "problem": {
      "paraphrase": "The retailer's UI and API test suite ran slowly. It had to be rewritten to run in another environment or to cover different user roles and feature switches. It also duplicated page objects and helper libraries across tasks. Its test reports were hard to extend, and the retailer wanted a reporting portal with visualization.",
      "quote": "a tremendous waste of time and resources"
    },
    "approach": {
      "testingTypes": [
        "automation",
        "api",
        "functional"
      ],
      "mainScope": "automation",
      "tooling": [
        "Spring Boot",
        "Java",
        "Selenium",
        "Selenium Grid",
        "Cucumber BDD",
        "TestNG",
        "REST Assured",
        "Kafka",
        "Jdbc template"
      ]
    },
    "metrics": [
      {
        "metric": "reporting transparency",
        "value": "100",
        "unit": "%",
        "kind": "other",
        "numeric": 100,
        "wording": "Reporting is 100% transparent and can verify test execution progress without generating a status request.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1579"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "team size on both sides",
      "cost",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D4",
      "questionForVendor": "How much did suite run time fall on the new execution grid, and how many UI, API and messaging scenarios does it hold?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "gspann-mao-oms-migration",
    "vendorSlug": "gspann",
    "title": "Quality Engineering Streamlines Order Management System Migration",
    "sourceUrl": "https://www.gspann.com/insights/case-study/omnichannel-quality-engineering-makes-order-management-system-transition-a-breeze",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-0977",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "A multi-brand specialty retailer.",
      "segment": "specialty",
      "channels": [
        "stores",
        "ecommerce"
      ],
      "sizeIndicators": [
        "global multi-channel retailer",
        "multi-brand specialty retailer"
      ]
    },
    "systems": [
      {
        "system": "oms",
        "platformName": "Manhattan Active Omni",
        "namedWhere": "named in the summary and the Challenge section"
      },
      {
        "system": "payments",
        "namedWhere": "named in the Our Solution section (New payment gateway testing)"
      },
      {
        "system": "loyalty",
        "namedWhere": "named in the Our Solution section (loyalty program and gift cards)"
      },
      {
        "system": "integrations",
        "platformName": "Retail Management Systems (RMS); S4 (SAP validation for Finance)",
        "namedWhere": "named in the Our Solution section"
      }
    ],
    "problem": {
      "paraphrase": "The retailer chose Manhattan Active Omni to replace an order management system it found complex and inadequate. Neither the retailer nor its main technology partner had any quality engineering setup. GSPANN was asked to build automated end-to-end testing for orders placed online, in store and through partners, including carts that mix home delivery and in-store pickup. It was also asked to add new test management tools.",
      "quote": "no quality engineering infrastructure in place"
    },
    "approach": {
      "testingTypes": [
        "automation",
        "functional"
      ],
      "mainScope": "automation",
      "tooling": [
        "GCP",
        "Firebase Analytics",
        "Firebase Crashlytics",
        "Swift",
        "PostgreSQL",
        "Objective-C",
        "Java",
        "Oracle",
        "Hibernate"
      ]
    },
    "metrics": [
      {
        "metric": "time for GSPANN engineers to get up to speed on MAO testing",
        "value": "less than one month",
        "unit": "month",
        "kind": "other",
        "wording": "Despite the lack of information from the technology partner, our engineers, starting entirely from scratch, could get up-to-speed on MAO testing in less than one month.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1570"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "team size on both sides",
      "cost",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "OMS or WMS replacement",
      "dimension": "D2",
      "questionForVendor": "Which test automation framework did you build for the Manhattan Active Omni order flows, given the case lists no test tool?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "gspann-outdoor-retailer-qa-setup",
    "vendorSlug": "gspann",
    "title": "QE Best Practices Boost Confidence for Global Retailer",
    "sourceUrl": "https://www.gspann.com/insights/case-study/qe-best-practices-accelerate-customer-confidence-and-growth-for-a-global-sportswear-retailer",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-0989",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "The client is an online specialty retailer in the business of selling clothing and outdoor recreation gear.",
      "segment": "specialty",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": []
    },
    "systems": [],
    "problem": {
      "paraphrase": "The online retailer had no organized QA. No bugs were logged and there was no change history for its legacy application. Developers ran unit tests on their own and could push code to production without testing. Each release got one round of uncoordinated user testing. It needed a test strategy, end-to-end and regression testing, test management and an automated deployment process.",
      "quote": "Not a single bug had been logged"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "automation"
      ],
      "mainScope": "automation",
      "tooling": [
        "Nightwatch.js",
        "Sauce Labs",
        "Jenkins",
        "Faker",
        "Confluence",
        "Jira",
        "Github"
      ]
    },
    "metrics": [],
    "notSaid": [
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D4",
      "questionForVendor": "How many automated tests and test cases did you leave in the repository, and how did release time and production bug counts change?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "gspann-womens-fashion-mobile-automation",
    "vendorSlug": "gspann",
    "title": "Speed Up Releases with QA Automation for Mobile Apps",
    "sourceUrl": "https://www.gspann.com/insights/case-study/shorten-release-cycles-using-qa-automation-for-multiplatform-mobile-apps",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-0992",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "The company is a fast-growing US-based brand that produces fashion apparel for women.",
      "segment": "fashion-apparel",
      "region": "north-america",
      "country": "US",
      "channels": [
        "mobile"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "mobile-commerce",
        "platformName": "new mobile app (iOS and Android)",
        "namedWhere": "named in the Challenge section"
      }
    ],
    "problem": {
      "paraphrase": "The brand was building a new mobile app while its web tests were only partly automated and its mobile tests were manual. More than 500 test cases had to run on browsers and on iOS and Android, builds arrived faster after the brand cut its project timeline, and buying test devices was costly.",
      "quote": "a manual mobile testing component that consumed costly internal resource bandwidth"
    },
    "approach": {
      "testingTypes": [
        "automation",
        "mobile",
        "api",
        "functional"
      ],
      "mainScope": "automation",
      "tooling": [
        "Appium",
        "Fillo",
        "HttpClient",
        "Java",
        "JavaFaker",
        "Jenkins",
        "Sauce Labs",
        "TestRail"
      ]
    },
    "metrics": [
      {
        "metric": "test cases to automate",
        "value": "over 500",
        "unit": "test cases",
        "kind": "test-count",
        "wording": "They needed to automate over 500 test cases altogether, requiring a dedicated 43 hours for four quality engineers to undertake a manual execution process.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1585"
      },
      {
        "metric": "manual execution effort before automation",
        "value": "43 hours for four quality engineers",
        "unit": "hours",
        "kind": "other",
        "wording": "They needed to automate over 500 test cases altogether, requiring a dedicated 43 hours for four quality engineers to undertake a manual execution process.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1585"
      },
      {
        "metric": "client's cut to the project completion timeline (context stated in the Challenge section)",
        "value": "three months",
        "unit": "months",
        "kind": "other",
        "wording": "The company reduced the overall project completion timeline for rolling out new apps and features by three months, providing additional incentive to quickly implement an automated testing solution.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1586"
      },
      {
        "metric": "total cost of ownership reduction from one integrated framework",
        "value": "50",
        "unit": "%",
        "kind": "cost",
        "numeric": 50,
        "wording": "It reduced the company’s total cost of ownership by 50% since they don’t need to maintain two frameworks anymore.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1587"
      },
      {
        "metric": "test runtime for back-end API validation",
        "value": "two or more days to less than two hours",
        "unit": "hours",
        "kind": "time-reduction-abs",
        "wording": "Earlier, the mobile app team required two or more days of manual testing to validate the back-end APIs accessed by the mobile apps. The new system executes entire test suites on multiple platforms in less than two hours.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1588"
      }
    ],
    "notSaid": [
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D4",
      "questionForVendor": "How many test cases were automated by the end, and how was the cut in total cost of ownership calculated?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "gspann-womens-fashion-mobile-cicd",
    "vendorSlug": "gspann",
    "title": "Accelerate Mobile Releases with Sauce Labs in CI/CD Pipeline",
    "sourceUrl": "https://www.gspann.com/insights/case-study/accelerate-your-mobile-app-releases-by-integrating-sauce-labs-with-ci-cd-pipeline",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-0993",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "The company is a fast-growing US-based brand that produces fashion apparel for women.",
      "segment": "fashion-apparel",
      "region": "north-america",
      "country": "US",
      "channels": [
        "mobile"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "mobile-commerce",
        "platformName": "enterprise mobile app (Android and iOS builds)",
        "namedWhere": "named in the Challenge section"
      }
    ],
    "problem": {
      "paraphrase": "Each new build of the brand's mobile app had to be checked in and tested by hand on every device in its support policy. Each build was also released to several environments at once, which delayed releases. Anyone with code repository access could see sensitive data such as keys. The brand wanted build check-in to trigger automated certification on all supported devices.",
      "quote": "it ran into a significant bottleneck that delayed new releases"
    },
    "approach": {
      "testingTypes": [
        "automation",
        "mobile",
        "security"
      ],
      "mainScope": "automation",
      "tooling": [
        "Jenkins",
        "Jenkins Plugins",
        "Google Firebase",
        "TestFlight",
        "Sauce Labs",
        "Mac Server",
        "SonarSource/SonarCloud",
        "Atlassian Bitbucket"
      ]
    },
    "metrics": [
      {
        "metric": "build cycle including back-end API validation",
        "value": "two or more days to under three hours",
        "unit": "hours",
        "kind": "time-reduction-abs",
        "wording": "Earlier, the mobile app team required two or more days of manual processing to validate the back-end APIs accessed by the mobile apps. The new system executes the entire build cycle in under three hours, including running test suites on multiple devices.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1589"
      }
    ],
    "notSaid": [
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D4",
      "questionForVendor": "Is this the same engagement as your mobile automation case for this brand, and on how many devices does each build certify?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "impactqa-bamilo-oms-wms",
    "vendorSlug": "impactqa",
    "title": "Enhanced User Experience by 60% using Automated Solutions for Bamilo(powered by Rocket Internet)",
    "sourceUrl": "https://www.impactqa.com/case-studies/rocket-internet/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-0887",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": true,
      "name": "Bamilo",
      "segment": "ecommerce-d2c",
      "region": "mena",
      "channels": [
        "ecommerce",
        "mobile"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "oms",
        "namedWhere": "named in the first paragraph of the body text (web application and mobile app OMS and WMS systems)"
      },
      {
        "system": "wms",
        "namedWhere": "named in the first paragraph of the body text (web application and mobile app OMS and WMS systems)"
      },
      {
        "system": "mobile-commerce",
        "namedWhere": "named in the first paragraph of the body text (mobile app) and in the localization sentence (the app and web portal)"
      }
    ],
    "problem": {
      "paraphrase": "The page has no problem statement. It says ImpactQA tested Bamilo's web application and mobile app together with its OMS and WMS. The work followed the business requirement and functional specification documents the client supplied. ImpactQA also tested localization of the app and web portal for end users in the Middle East.",
      "quote": "to test web application and mobile app OMS and WMS systems"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "automation",
        "localization",
        "mobile"
      ],
      "mainScope": "functional",
      "tooling": []
    },
    "metrics": [
      {
        "metric": "user experience",
        "value": "60",
        "unit": "%",
        "kind": "other",
        "numeric": 60,
        "wording": "Enhanced User Experience by 60% using Automated Solutions for Bamilo(powered by Rocket Internet)",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0887"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "OMS or WMS replacement",
      "dimension": "D2",
      "questionForVendor": "How was the user experience gain measured, and which OMS and WMS flows did the automated tests cover?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "impactqa-nykaa-ecommerce-qa",
    "vendorSlug": "impactqa",
    "title": "India's Leading Skincare and Cosmetics eCommerce Store Increased their Application Performance Efficiency by 40%",
    "sourceUrl": "https://www.impactqa.com/case-studies/nyka/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-0884",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": true,
      "name": "Nykaa",
      "segment": "specialty",
      "region": "india-apac",
      "country": "India",
      "channels": [
        "ecommerce",
        "mobile"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "namedWhere": "named in the body text (shopping cart functionality); no platform product named"
      },
      {
        "system": "mobile-commerce",
        "namedWhere": "named in the body text (Nykaa's web and mobile application)"
      },
      {
        "system": "payments",
        "namedWhere": "named in the body text (payment gateways)"
      },
      {
        "system": "oms",
        "namedWhere": "named in the body text (OMS for pre and post order handling, order tracking, cancellation and returns)"
      }
    ],
    "problem": {
      "paraphrase": "The page has no problem statement. It says ImpactQA provided quality assurance and testing to Nykaa, an Indian skincare and cosmetics ecommerce store. The scope covered user interface testing of the web and mobile applications, the shopping cart and payment gateways. It also covered order management from order placement through notifications, tracking, cancellation and returns.",
      "quote": "end-to-end quality assurance and testing solutions to Nykaa"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "mobile"
      ],
      "mainScope": "functional",
      "teamShape": "dedicated QA team",
      "tooling": [],
      "engagementModel": "dedicated-team"
    },
    "metrics": [
      {
        "metric": "application performance efficiency",
        "value": "40",
        "unit": "%",
        "kind": "other",
        "numeric": 40,
        "wording": "India's Leading Skincare and Cosmetics eCommerce Store Increased their Application Performance Efficiency by 40%",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0884"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Payments and PCI scope",
      "dimension": "D2",
      "questionForVendor": "What does the application performance efficiency figure measure, and was performance or load testing part of the work at all?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "noesis-global-retailer-managed-testing",
    "vendorSlug": "noesis",
    "title": "Excellence in Enterprise Testing in the Retail Sector",
    "sourceUrl": "https://noesis.co.nl/success-stories/enterprise-testing-excellence-for-a-retail-giant",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1634",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a global retail group operating in over +90 countries",
      "segment": "enterprise-omnichannel",
      "region": "europe",
      "channels": [
        "stores"
      ],
      "sizeIndicators": [
        "operating in over +90 countries",
        "a diverse portfolio of retail brands across sectors such as food, electronics, and fashion"
      ]
    },
    "systems": [
      {
        "system": "pos",
        "namedWhere": "named in the Solution section of the vendor PDF (POS Lab Test Factory for POS testing of multiple releases) and in the services list on the page (POS lab setup)"
      }
    ],
    "problem": {
      "paraphrase": "The client wanted its testing turned into one enterprise-wide model that used fewer resources and cost less. The vendor PDF lists the challenges. The testing model had to be reworked to cover the application landscape and to support newer engineering and testing methods. Performance had to be monitored as user adoption grew. Regression and performance testing had to run across 500+ applications, including ERP-based ones.",
      "quote": "Streamline the testing process into an enterprise-wide, resource-efficient, and cost-effective model."
    },
    "approach": {
      "testingTypes": [
        "functional",
        "automation",
        "performance",
        "security"
      ],
      "mainScope": "functional",
      "teamShape": "Dedicated Test Centre providing testing services in a managed model",
      "tooling": [
        "Microsoft (tool re-evaluations, customisations and integrations)",
        "IBM (tool re-evaluations, customisations and integrations)"
      ],
      "engagementModel": "managed-testing"
    },
    "metrics": [
      {
        "metric": "critical incidents in production systems",
        "value": "90",
        "unit": "%",
        "kind": "defects",
        "numeric": 90,
        "wording": "90% reduction in critical incidents in production systems",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1635"
      },
      {
        "metric": "effort from test automation",
        "value": "40",
        "unit": "%",
        "kind": "time-reduction-pct",
        "numeric": 40,
        "wording": "40% effort reduction from test automation",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1636"
      },
      {
        "metric": "regression test cycle duration for critical business applications",
        "value": "3 weeks",
        "unit": "weeks",
        "kind": "time-reduction-abs",
        "numeric": 3,
        "wording": "Overall regression test cycle duration reduced to 3 weeks for critical business applications.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1637"
      },
      {
        "metric": "applications in testing scope",
        "value": "500+",
        "unit": "applications",
        "kind": "other",
        "wording": "How Noesis helped a multinational store to expand through testing across 500+ applications",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1638"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "POS rollout across stores",
      "dimension": "D2",
      "questionForVendor": "What was the critical incident count before the managed service started, and over which period was the reduction measured?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "noesis-portuguese-retailer-ai-regression",
    "vendorSlug": "noesis",
    "title": "Optimized Time-to-Market with AI Based Automated Functional Testing",
    "sourceUrl": "https://www.noesis.pt/en/success-stories/use-case-optimized-time-to-market-with-ai-based-automated-functional-testing",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1639",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a major Portuguese Retailer",
      "segment": "enterprise-omnichannel",
      "region": "europe",
      "country": "Portugal",
      "channels": [
        "stores"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "pos",
        "namedWhere": "named in The Challenge section of the vendor PDF (the store area (POS)) and in the Solution section"
      },
      {
        "system": "integrations",
        "platformName": "Kafka",
        "namedWhere": "named in The Challenge section of the vendor PDF (the integration area (Kafka)) and in the Solution section"
      }
    ],
    "problem": {
      "paraphrase": "The retailer wanted shorter implementation times, and its regression battery took roughly 5 days to run across the Kafka integration layer and the store POS. Noesis set out to cut that execution time, remove the effort of maintaining the automated tests, and validate core business processes such as stock management and promotional actions.",
      "quote": "reduce the execution time of the regression battery"
    },
    "approach": {
      "testingTypes": [
        "automation",
        "functional",
        "api"
      ],
      "mainScope": "automation",
      "tooling": []
    },
    "metrics": [
      {
        "metric": "regression battery execution time before the work",
        "value": "roughly 5 days",
        "unit": "days",
        "kind": "other",
        "wording": "Working with this leading Retailer, we faced the challenge to reduce the execution time of the regression battery (that took roughly 5 days to execute) in both the integration area (Kafka) and in the store area (POS).",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1640"
      },
      {
        "metric": "regression cycle time saving (listed under Goals)",
        "value": "approximately 70",
        "unit": "%",
        "kind": "time-reduction-pct",
        "wording": "Time-to-Market Optimization: saving up approximately 70% of cycle time during regression testing.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1641"
      },
      {
        "metric": "FTE capacity released per sprint",
        "value": "0.5",
        "unit": "FTE per sprint",
        "kind": "other",
        "numeric": 0.5,
        "wording": "0.5 FTE capacity released in each sprint",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1642"
      },
      {
        "metric": "automated test cases",
        "value": "400+",
        "unit": "tests",
        "kind": "test-count",
        "wording": "400+ Automated test cases within 1 IA mechanism",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1643"
      },
      {
        "metric": "end-to-end flows executed daily",
        "value": "600+",
        "unit": "E2E flows per day",
        "kind": "test-count",
        "wording": "600+ E2E flows executed daily",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1644"
      },
      {
        "metric": "flows validated by the daily regression battery",
        "value": "+60",
        "unit": "flows",
        "kind": "test-count",
        "wording": "The daily execution of the regression battery, with validation of +60 flows, frees up 0.5 FTE capacity in each sprint, available to perform other tasks.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1645"
      }
    ],
    "notSaid": [
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "POS rollout across stores",
      "dimension": "D4",
      "questionForVendor": "Was the cycle time saving reached or does it remain a goal, and how many flows does the daily battery validate?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "noesis-sap-retail-bdd-robot",
    "vendorSlug": "noesis",
    "title": "SAP Automation Testing with BBD over robot framework",
    "sourceUrl": "https://www.noesis.pt/en/success-stories/sap-automation-testing-with-bbd-over-robot-framework",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1646",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "One of our clients, a key player in the retail sector",
      "segment": "enterprise-omnichannel",
      "channels": [],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "data-migration",
        "platformName": "SAP S/4HANA Retail",
        "namedWhere": "named in The Challenge section of the vendor PDF (ERP system migration to SAP S/4HANA Retail)"
      },
      {
        "system": "integrations",
        "namedWhere": "named in the Solution section of the vendor PDF (API tests for processes triggered by external systems and satellite systems)"
      }
    ],
    "problem": {
      "paraphrase": "The retailer was migrating its ERP system to SAP S/4HANA Retail as part of a wider technology change, with several initiatives running in parallel under an Agile model. It needed a simple, structured language shared by business and technical teams, and a much faster testing process to meet its delivery dates.",
      "quote": "migrating its ERP system to SAP S/4HANA Retail"
    },
    "approach": {
      "testingTypes": [
        "automation",
        "functional",
        "api"
      ],
      "mainScope": "automation",
      "teamShape": "SAP QM Team integrated into an Agile Squad model focused on retail processes",
      "tooling": [
        "Robot Framework",
        "Robot Framework Selenium library",
        "GitLab"
      ]
    },
    "metrics": [
      {
        "metric": "Goal stated before the work: automated SAP tests",
        "value": "+500",
        "unit": "tests",
        "kind": "test-count",
        "wording": "+500 automated SAP tests with BDD over Robot Framework",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1647"
      },
      {
        "metric": "Goal stated before the work: regression time or effort per SAP release",
        "value": "+40",
        "unit": "%",
        "kind": "time-reduction-pct",
        "wording": "+40% less regression time/effort for each SAP release",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1648"
      },
      {
        "metric": "Goal stated before the work: SAP retail basis configurations under automated regression",
        "value": "+450",
        "unit": "configurations",
        "kind": "test-count",
        "wording": "+450 SAP retail basis configurations tested via automated regression in GitLab Pipeline",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1649"
      },
      {
        "metric": "Goal stated before the work: SAP retail business flows with APIs under automated regression",
        "value": "+100",
        "unit": "business flows",
        "kind": "test-count",
        "wording": "+100 SAP retail business flows with APIs tested via automated regression in GitLab Pipelines",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1650"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "OMS or WMS replacement",
      "dimension": "D4",
      "questionForVendor": "Which SAP Retail business processes were automated in the end, and were the regression savings stated as goals ever measured after the work?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qa-madness-magento-bookstore-qa-strategy",
    "vendorSlug": "qa-madness",
    "title": "A Full-Scale QA Strategy for Online Bookstore",
    "sourceUrl": "https://www.qamadness.com/case_studies/a-full-scale-qa-strategy-for-online-bookstore/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1182",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "an online bookstore",
      "segment": "specialty",
      "region": "north-america",
      "country": "United States",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Magento",
        "namedWhere": "named in the Overview and Challenge sections"
      },
      {
        "system": "payments",
        "namedWhere": "named in the Solution section (payment and order confirmation flows in the cart validation sentence)"
      }
    ],
    "problem": {
      "paraphrase": "The named client is a US design and development studio that builds Magento stores. An online bookstore on Magento was the first of its projects to be tested by a third party, and the studio wanted outside QA expertise. The vendor joined at the start of the project and was asked to set out a QA strategy and test every module before release.",
      "quote": "their first project tested by a third-party"
    },
    "approach": {
      "testingTypes": [
        "functional"
      ],
      "mainScope": "functional",
      "tooling": [],
      "engagementModel": "project"
    },
    "metrics": [
      {
        "metric": "software issues important for navigation eliminated",
        "value": "nearly 95",
        "unit": "%",
        "kind": "defects",
        "wording": "Our strategy eliminated nearly 95% of software issues important for end-user website navigation.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1183"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "team size on both sides",
      "cost",
      "date of the work",
      "whether the client is still a client"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D1",
      "questionForVendor": "Against which list of navigation issues was the reported share counted, and over what period were those issues found?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qa-madness-magento-fashion-store-functional",
    "vendorSlug": "qa-madness",
    "title": "Functional Testing for Magento Store",
    "sourceUrl": "https://www.qamadness.com/case_studies/functional-testing-for-magento-store/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1184",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "A clothing & jewelry web store",
      "segment": "fashion-apparel",
      "region": "europe",
      "country": "United Kingdom",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": [
        "nearly 250,000 monthly visits"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Magento",
        "namedWhere": "named in the title, the project details (Platform: Magento) and the Solution section"
      }
    ],
    "problem": {
      "paraphrase": "A business requirement was missed during development and testing and only showed up after release. One product had several color and size options, a second had one color and several sizes. The client's SEO specialist could not merge the color attributes so that the first product offered all its colors. The vendor was asked to find the root cause of this behavior on the live Magento store.",
      "quote": "failed to unite the color attributes of both items"
    },
    "approach": {
      "testingTypes": [
        "functional"
      ],
      "mainScope": "functional",
      "teamShape": "2 QA engineers from QA Madness; the client has an internal development team",
      "tooling": [],
      "engagementModel": "project"
    },
    "metrics": [],
    "notSaid": [
      "cost",
      "date of the work",
      "whether the client is still a client",
      "what was out of scope"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D1",
      "questionForVendor": "After the color attribute defect was found, were regression checks for product attribute inheritance added, and who keeps running them?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qa-madness-magento-jewelry-test-set",
    "vendorSlug": "qa-madness",
    "title": "Functional Test Set for Jewelry Store",
    "sourceUrl": "https://www.qamadness.com/case_studies/functional-test-set-for-jewelry-store/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1185",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "A Magento-running store that specializes in diamond jewelry crafting",
      "segment": "specialty",
      "region": "mena",
      "country": "Israel",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Magento",
        "namedWhere": "named in the Overview section (\"A Magento-running store\")"
      }
    ],
    "problem": {
      "paraphrase": "Product requirements for the jewelry store kept changing during development. The client's programmers had to keep reviewing priorities and the release date was often pushed back. The vendor was asked to give the QA process a structure and shorten time to market.",
      "quote": "the release date was often postponed"
    },
    "approach": {
      "testingTypes": [
        "functional"
      ],
      "mainScope": "functional",
      "tooling": [],
      "engagementModel": "project"
    },
    "metrics": [],
    "notSaid": [
      "baseline before the engagement",
      "team size on both sides",
      "cost",
      "date of the work",
      "whether the client is still a client"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D1",
      "questionForVendor": "What was QA turnaround before and after the work, and how was it measured?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qa-madness-naduvi-shopify-qa-setup",
    "vendorSlug": "qa-madness",
    "title": "QA Process Setup and Full-Testing for an Online Interior Outlet Built on Shopify",
    "sourceUrl": "https://www.qamadness.com/case_studies/qa-process-setup-and-full-testing-for-an-online-interior-outlet-built-on-shopify/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1186",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": true,
      "name": "NADUVI",
      "segment": "specialty",
      "region": "europe",
      "country": "Netherlands",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": [
        "the largest online interior outlet in the Netherlands",
        "founded in 2019"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Shopify",
        "namedWhere": "named in the title (Built on Shopify)"
      }
    ],
    "problem": {
      "paraphrase": "NADUVI, an online interior outlet in the Netherlands built on Shopify, had no QA team and was looking for one. It needed specialists who could set up both manual and automated testing for its web store.",
      "quote": "NADUVI didn't have a QA team, and they were looking for one."
    },
    "approach": {
      "testingTypes": [
        "functional",
        "automation"
      ],
      "mainScope": "functional",
      "teamShape": "One QA specialist covering manual and automated testing at the start, then two part-time specialists with separate manual and automation scopes; at the time of writing one QA engineer works part-time, four days a week",
      "tooling": [
        "Slack"
      ],
      "engagementModel": "dedicated-team"
    },
    "metrics": [],
    "notSaid": [
      "cost",
      "date of the work",
      "what was out of scope",
      "baseline before the engagement"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D7",
      "questionForVendor": "What happened to the automated tests prepared for NADUVI, and why does the page say they are not used now?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qa-madness-uk-magento-two-stores",
    "vendorSlug": "qa-madness",
    "title": "QA Strategy for Multiple Web Stores",
    "sourceUrl": "https://www.qamadness.com/case_studies/qa-strategy-for-web-stores/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1187",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "The UK merchant owner of two e-commerce websites",
      "segment": "ecommerce-d2c",
      "region": "europe",
      "country": "United Kingdom",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": [
        "two e-commerce websites running on Magento 1 and Magento 2 platforms"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Magento 1 and Magento 2",
        "namedWhere": "named in the Overview section"
      }
    ],
    "problem": {
      "paraphrase": "A UK merchant ran two web stores, one on Magento 1 and one on Magento 2. It had its own development team but no in-house QA, and wanted a QA team to check the quality of both stores. The vendor was asked to plan and run testing across both sites.",
      "quote": "it lacked in-house web quality assurance"
    },
    "approach": {
      "testingTypes": [
        "functional"
      ],
      "mainScope": "functional",
      "duration": "Our cooperation started in 2017 and had been ongoing for the next two years.",
      "tooling": []
    },
    "metrics": [
      {
        "metric": "test cases planned for both stores",
        "value": "nearly 800",
        "unit": "test cases",
        "kind": "test-count",
        "wording": "First, we compiled testing documentation and checklists with nearly 800 test cases planned for both stores.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1188"
      },
      {
        "metric": "bugs detected over the engagement",
        "value": "nearly 1050",
        "unit": "bugs",
        "kind": "defects",
        "wording": "Over the 2-year cooperation, QA Madness team detected nearly 1050 bugs within both e-commerce platforms.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1189"
      },
      {
        "metric": "conversion rate increase",
        "value": "4.7",
        "unit": "%",
        "kind": "conversion",
        "numeric": 4.7,
        "wording": "Website smooth performance led to a conversion rate increase by 4.7%",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1190"
      },
      {
        "metric": "user session time increase",
        "value": "nearly 20",
        "unit": "seconds",
        "kind": "other",
        "wording": "Our QA strategy helped to increase user session time in nearly 20 seconds.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1191"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D1",
      "questionForVendor": "How was the conversion increase measured, from what baseline, and how was it tied to testing rather than other site changes?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qable-flipkart-ar-3d-view",
    "vendorSlug": "qable",
    "title": "AR/VR and 3D view QA for India's largest e-commerce platform",
    "sourceUrl": "https://qable.io/casestudy/flipkart",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1721",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": true,
      "name": "Flipkart",
      "segment": "marketplace",
      "region": "india-apac",
      "country": "India",
      "channels": [
        "marketplace",
        "ecommerce",
        "mobile"
      ],
      "sizeIndicators": [
        "India's largest e-commerce marketplace"
      ]
    },
    "systems": [
      {
        "system": "mobile-commerce",
        "platformName": "Flipkart app (Android and iOS), AR/VR and 3D product view",
        "namedWhere": "named in the About section and Services item 01 (\"all AR/VR interactions within the Flipkart app\"); web 3D view on Chrome and Firefox named in the Before/After table"
      }
    ],
    "problem": {
      "paraphrase": "Flipkart added an AR and 3D product view to its app so shoppers can place appliances, electronics and beauty products in their own rooms before buying. The feature raised new test problems: surface detection, 3D rendering quality, a wide spread of Android hardware, and consistent behavior under different lighting. The page says there was no existing process for testing AR features.",
      "quote": "No established process for testing AR/VR features"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "mobile"
      ],
      "mainScope": "mobile",
      "teamShape": "Dedicated QA engineers",
      "tooling": [
        "ARCore",
        "ARKit",
        "BrowserStack",
        "Chrome DevTools",
        "3D View SDK"
      ],
      "engagementModel": "dedicated-team"
    },
    "metrics": [
      {
        "metric": "real devices validated",
        "value": "10+",
        "unit": "real devices",
        "kind": "other",
        "wording": "10+ Real devices validated",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-1722"
      },
      {
        "metric": "AR frameworks covered",
        "value": "2",
        "unit": "AR frameworks",
        "kind": "other",
        "numeric": 2,
        "wording": "2 AR frameworks covered",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-1723"
      },
      {
        "metric": "product categories tested",
        "value": "100",
        "unit": "%",
        "kind": "coverage-pct",
        "numeric": 100,
        "wording": "100% Product categories tested",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-1724"
      },
      {
        "metric": "bug patterns identified",
        "value": "5+",
        "unit": "bug patterns",
        "kind": "defects",
        "wording": "5+ Bug patterns identified",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-1725"
      }
    ],
    "notSaid": [
      "measurement method",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D1",
      "questionForVendor": "Which devices made up the test matrix, and what recurring AR bug patterns did you find and fix?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qable-torrid-backend-db-governance",
    "vendorSlug": "qable",
    "title": "Enterprise QA for large-scale fashion e-commerce platform",
    "sourceUrl": "https://qable.io/casestudy/torrid",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1718",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": true,
      "name": "Torrid",
      "segment": "fashion-apparel",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": [
        "a large-scale retail platform",
        "serving millions of customers across web and digital channels."
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "namedWhere": "named in the Platforms field (\"Web (E-Commerce), Backend Systems\") and Services item 01 (\"Torrid's e-commerce infrastructure\"); platform product not named"
      }
    ],
    "problem": {
      "paraphrase": "Torrid, a fashion brand selling through a large e-commerce platform, needed data integrity, stable releases and a consistent storefront as the platform grew. The page names backend database validation, test coverage across environments and structured QA governance as the gaps it wanted outside help with.",
      "quote": "Backend database validation, cross-environment test coverage, and structured QA governance"
    },
    "approach": {
      "testingTypes": [
        "functional"
      ],
      "mainScope": "functional",
      "teamShape": "Dedicated QA engineers",
      "tooling": [
        "Jira",
        "Microsoft Teams",
        "Excel"
      ],
      "engagementModel": "dedicated-team"
    },
    "metrics": [
      {
        "metric": "story-level test traceability",
        "value": "100",
        "unit": "%",
        "kind": "other",
        "numeric": 100,
        "wording": "100% Story-level test traceability",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-1719"
      },
      {
        "metric": "missed release deadlines",
        "value": "Zero",
        "unit": "missed release deadlines",
        "kind": "other",
        "numeric": 0,
        "wording": "Zero Missed release deadlines",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-1720"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D7",
      "questionForVendor": "How many QA engineers were embedded in Torrid's team, over how many sprints does the missed-deadlines claim run, and which database was validated?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qarea-angelinos-braintree-subscriptions",
    "vendorSlug": "qarea",
    "title": "Development services for Angelinos Coffee & Tea Online Store",
    "sourceUrl": "https://qarea.com/projects/angelinos-coffee-tea-online-store",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1690",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": true,
      "name": "Angelinos Coffee & Tea",
      "segment": "specialty",
      "region": "north-america",
      "country": "United States",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Drupal",
        "namedWhere": "named in the Solutions section and the Technologies list (Drupal 7)"
      },
      {
        "system": "payments",
        "platformName": "Braintree",
        "namedWhere": "named in the Challenge section (old payment system could not change the future delivery date for activated subscriptions) and the Solutions section (\"the new version of Braintree\")"
      }
    ],
    "problem": {
      "paraphrase": "The online coffee and tea store, released in 2015 and updated many times, needed updates and new features. Its back end lacked user operations such as invitations and gifts for invited users. The old payment system could not change the next delivery date on active subscriptions, so it had to be replaced, and the store wanted a newsletter subscription system. QA support was required at every stage.",
      "quote": "we had to provide QA support at every stage of project development."
    },
    "approach": {
      "testingTypes": [
        "functional",
        "automation",
        "security"
      ],
      "mainScope": "functional",
      "teamShape": "1 PM, 4 developers, 2 QA engineers",
      "duration": "More than 1 year",
      "tooling": [
        "Drupal 7",
        "PHP",
        "jQuery",
        "MySQL"
      ],
      "engagementModel": "dedicated-team"
    },
    "metrics": [],
    "notSaid": [
      "baseline before the engagement",
      "cost",
      "date of the work",
      "what was out of scope"
    ],
    "readingNotes": {
      "scenario": "Payments and PCI scope",
      "dimension": "D2",
      "questionForVendor": "What did the security testing cover around the new payment system and subscription billing, and was any PCI DSS scope defined?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qarea-fashion-distributor-store",
    "vendorSlug": "qarea",
    "title": "Quality Assurance Services For an eCommerce Store",
    "sourceUrl": "https://qarea.com/projects/quality-assurance-services-for-an-ecommerce-store",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1666",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a Ukrainian eCommerce store that is the official distributor of several well-known fashion brands",
      "segment": "fashion-apparel",
      "region": "europe",
      "country": "Ukraine",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "namedWhere": "named in the Challenge section (the store's code moved to a new environment; customer journey from product selection to order finalization); no platform named"
      },
      {
        "system": "payments",
        "namedWhere": "named in the Challenge section (payments to be tested across different user scenarios); no provider named"
      }
    ],
    "problem": {
      "paraphrase": "Shortly before the vendor joined, the store moved its code to a new environment. The vendor was asked to confirm the site still worked as before. The requested checks covered UI and user experience, the customer journey from product choice to order completion, payments in different user scenarios, and behavior on web and mobile. The client also reported SEO issues and asked for an SEO audit.",
      "quote": "the client transferred the code to a new environment"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "mobile"
      ],
      "mainScope": "functional",
      "teamShape": "1 QA Engineer with SEO competencies (part-time), 3 to 4 Manual QA Engineers at different stages, 1 QA Lead (part-time)",
      "duration": "two months",
      "tooling": [
        "Chrome DevTools",
        "Jira",
        "Telegram"
      ],
      "engagementModel": "project"
    },
    "metrics": [],
    "notSaid": [
      "cost",
      "date of the work",
      "whether the client is still a client",
      "what was out of scope"
    ],
    "readingNotes": {
      "scenario": "Ecommerce platform migration",
      "dimension": "D1",
      "questionForVendor": "How many defects did the main QA round and the regression rounds find after the move, and how many blocked the launch?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qarea-meal-planning-app-store",
    "vendorSlug": "qarea",
    "title": "Meal Planning App Development And Testing Services",
    "sourceUrl": "https://qarea.com/projects/meal-planning-application",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1691",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "The client",
      "segment": "grocery",
      "region": "north-america",
      "country": "United States",
      "channels": [
        "mobile",
        "ecommerce"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "mobile-commerce",
        "namedWhere": "named in the Challenge section (\"Shopping for the necessary ingredients within the app\")"
      },
      {
        "system": "ecommerce-platform",
        "platformName": "PrestaShop",
        "namedWhere": "named in the Solutions section (\"we implemented the PrestaShop eCommerce solution for reliable order processing\")"
      }
    ],
    "problem": {
      "paraphrase": "The client wanted a mobile social platform for food lovers and to reach the market fast. Users photograph and share meals, get feedback from dietitians, plan meals, identify the ingredients they need and shop for them inside the app. Administrators stock a web store with products and orders are processed through an ecommerce back end. The vendor built and tested the app.",
      "quote": "Shopping for the necessary ingredients within the app"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "mobile",
        "security",
        "performance"
      ],
      "mainScope": "mobile",
      "teamShape": "1 designer, 1 PM, 4 developers, 1 testing and QA specialist",
      "duration": "6 months",
      "tooling": [
        "Golang",
        "React.js",
        "React Native",
        "GitLab",
        "Jenkins",
        "WordPress"
      ]
    },
    "metrics": [],
    "notSaid": [
      "date of the work",
      "whether the client is still a client",
      "what was out of scope"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D2",
      "questionForVendor": "Which parts of the ingredient shopping and order flow did the stress and security tests cover?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qarea-memorabilia-testrail-automation",
    "vendorSlug": "qarea",
    "title": "Manual and Automated QA for a Memorabilia Manufacturer",
    "sourceUrl": "https://qarea.com/projects/manual-and-automated-qa-for-a-memorabilia-manufacturer",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1668",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a renowned US-based manufacturer of memorabilia for school and college students",
      "segment": "specialty",
      "region": "north-america",
      "country": "United States",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": [
        "a company with a 40-year history"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "namedWhere": "named in the opening section (the client \"recently ventured into the eCommerce industry\"); no platform named"
      }
    ],
    "problem": {
      "paraphrase": "The client, a memorabilia maker that had recently moved into ecommerce, had relied on manual QA engineers for some time. Its test cases had piled up over several years in spreadsheets. The vendor was asked to validate them, analyze user scenarios, pick a set for automation, and take over smoke and regression testing.",
      "quote": "the client had been working with a team of manual QA engineers"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "automation"
      ],
      "mainScope": "automation",
      "teamShape": "QA Team Lead, one to two Automation QAs (on different stages of the project), 1 Manual QA",
      "duration": "joined \"over seven months ago\" and will continue \"for at least another five months\"",
      "tooling": [
        "Selenium",
        "Java",
        "Maven",
        "GitLab",
        "TestRail",
        "Confluence",
        "Skype",
        "MS Teams"
      ]
    },
    "metrics": [
      {
        "metric": "test cases moved to one format and store",
        "value": "10,000+",
        "unit": "test cases",
        "kind": "test-count",
        "wording": "Moved 10,000+ test cases to a single format and storage location",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1669"
      },
      {
        "metric": "test cases selected for automation",
        "value": "700+",
        "unit": "test cases",
        "kind": "test-count",
        "wording": "Together with the client, selected 700+ cases that needed to be automated",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1670"
      }
    ],
    "notSaid": [
      "cost",
      "date of the work",
      "what was out of scope"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D4",
      "questionForVendor": "How many of the cases selected for automation were automated and running in the regression suite when the page was written?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qarea-natural-products-mobile-app",
    "vendorSlug": "qarea",
    "title": "Quality assurance and testing services for Natural Products Retailer",
    "sourceUrl": "https://qarea.com/projects/global-online-natural-products-retailer",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1667",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "A US-based online retailer",
      "segment": "ecommerce-d2c",
      "region": "north-america",
      "country": "United States",
      "channels": [
        "ecommerce",
        "mobile"
      ],
      "sizeIndicators": [
        "over 30k natural supplements and personal care products",
        "in more than 150 countries"
      ]
    },
    "systems": [
      {
        "system": "mobile-commerce",
        "namedWhere": "named in the Challenge section (a new mobile application planned for web, iOS and Android)"
      },
      {
        "system": "payments",
        "namedWhere": "named in the Challenge section (the app had to work across \"languages, as well as shipping and payment methods\"); no provider named"
      }
    ],
    "problem": {
      "paraphrase": "The retailer wanted a dedicated QA team to test its new mobile application, planned for web, iOS and Android. The app had to cope with fast growth and work across platforms, languages, and shipping and payment methods.",
      "quote": "a dedicated team for quality assurance and testing"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "mobile"
      ],
      "mainScope": "mobile",
      "teamShape": "Testing & QA, 2 people, working with the client's in-house developers",
      "duration": "page header: 3 years, in progress; Results text: started six months ago",
      "tooling": [
        "Charles Proxy",
        "Jira"
      ],
      "engagementModel": "dedicated-team"
    },
    "metrics": [],
    "notSaid": [
      "cost",
      "date of the work",
      "what was out of scope"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D2",
      "questionForVendor": "Which engagement duration on the page is current, and which payment and shipping methods were in the test scope?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qarea-print-on-demand-multilayer",
    "vendorSlug": "qarea",
    "title": "Multilayer Testing Services for Global Print-on-Demand E-commerce Platform",
    "sourceUrl": "https://qarea.com/projects/global-print-on-demand-service",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1671",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a leading US-based print-on-demand company",
      "segment": "ecommerce-d2c",
      "region": "north-america",
      "country": "United States",
      "channels": [
        "ecommerce",
        "mobile"
      ],
      "sizeIndicators": [
        "over 30 million orders per year",
        "over 10 million users globally",
        "more than 26 million annual orders",
        "Over 1,000 physical products with over 1 million design and layout options"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "namedWhere": "named in the About section (\"an e-commerce platform that handles over 30 million orders per year\"); checkout named in the Impact section; no platform named"
      },
      {
        "system": "mobile-commerce",
        "namedWhere": "named in the Challenge section (multi-platform support) and the Solutions section (iOS applications, mobile web, iOS and Android devices)"
      },
      {
        "system": "integrations",
        "namedWhere": "named in the Challenge section (acquisitions and integrations as a testing layer)"
      }
    ],
    "problem": {
      "paraphrase": "The client runs a print-on-demand ecommerce platform for companies and individual buyers, with many products and design options. The vendor lists eight layers of test complexity. They are product range, fast growth, several platforms, high transaction volume, AI personalization, a cloud migration, acquisitions with their integrations, and the need to grow the QA team.",
      "quote": "Testing an e-commerce platform that handles over 30 million orders per year"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "automation",
        "mobile",
        "api",
        "accessibility",
        "performance"
      ],
      "mainScope": "automation",
      "teamShape": "Team Lead: 1; Testing & QA: 20+; led by a Project Manager; team expanded from 3 to 20+ members",
      "duration": "several years",
      "tooling": [
        "Appium",
        "TestNG",
        "XCUITest",
        "Swift",
        "Pytest",
        "JUnit5",
        "Selenium",
        "Jenkins",
        "TestRail",
        "BrowserStack",
        "LambdaTest",
        "Firebase",
        "Fabric",
        "Postman",
        "XCode"
      ],
      "engagementModel": "dedicated-team"
    },
    "metrics": [
      {
        "metric": "iOS tests engineered",
        "value": "80",
        "unit": "tests",
        "kind": "test-count",
        "numeric": 80,
        "wording": "Engineered 80 tests for iOS applications",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1672"
      },
      {
        "metric": "automated backend tests",
        "value": "100+",
        "unit": "tests",
        "kind": "test-count",
        "wording": "with 100+ automated backend tests",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1673"
      },
      {
        "metric": "mobile web e2e tests",
        "value": "180",
        "unit": "tests",
        "kind": "test-count",
        "numeric": 180,
        "wording": "and achieved 180 e2e test coverage.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1674"
      },
      {
        "metric": "devices in test",
        "value": "over 40",
        "unit": "devices",
        "kind": "other",
        "wording": "Used over 40 iOS and Android devices, including both flagship and older models.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1675"
      },
      {
        "metric": "AI-enhanced version rollout",
        "value": "100",
        "unit": "% of users",
        "kind": "other",
        "numeric": 100,
        "wording": "Ensured successful rollout of the AI-enhanced version to 100% of users.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1676"
      },
      {
        "metric": "end-to-end iOS tests",
        "value": "400",
        "unit": "tests",
        "kind": "test-count",
        "numeric": 400,
        "wording": "Achieved 400 end-to-end iOS test coverage",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1677"
      },
      {
        "metric": "build failures reduction",
        "value": "15",
        "unit": "%",
        "kind": "other",
        "numeric": 15,
        "wording": "Reduced build failures by 15%.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1678"
      },
      {
        "metric": "testing cost decrease",
        "value": "20",
        "unit": "%",
        "kind": "cost",
        "numeric": 20,
        "wording": "decreased testing costs by 20%.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1679"
      },
      {
        "metric": "test cases covered by automation",
        "value": "80",
        "unit": "% of test cases",
        "kind": "coverage-pct",
        "numeric": 80,
        "wording": "Introduced automated testing protocols covering 80% of test cases",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1680"
      },
      {
        "metric": "manual QA effort reduction",
        "value": "25",
        "unit": "%",
        "kind": "time-reduction-pct",
        "numeric": 25,
        "wording": "reduced manual QA efforts by 25%.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1681"
      },
      {
        "metric": "major feature releases approved",
        "value": "40+",
        "unit": "releases",
        "kind": "other",
        "wording": "Launched and approved 40+ major feature releases",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1682"
      },
      {
        "metric": "user engagement increase",
        "value": "15",
        "unit": "%",
        "kind": "other",
        "numeric": 15,
        "wording": "resulting in a 15% increase in user engagement.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1683"
      },
      {
        "metric": "checkout slowdown flagged",
        "value": "almost 15%",
        "unit": "%",
        "kind": "other",
        "wording": "When a developer introduced code that slowed down the checkout process by almost 15%, our system flagged it within minutes.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1684"
      },
      {
        "metric": "holiday traffic jump handled",
        "value": "47",
        "unit": "%",
        "kind": "other",
        "numeric": 47,
        "wording": "During the holidays, when traffic jumped 47%, the platform handled an avalanche of orders without any serious glitches.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1685"
      },
      {
        "metric": "test coverage change",
        "value": "doubled",
        "kind": "other",
        "wording": "we doubled our test coverage",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1686"
      },
      {
        "metric": "test creation time reduction",
        "value": "60",
        "unit": "%",
        "kind": "time-reduction-pct",
        "numeric": 60,
        "wording": "reducing the time required for test creation by 60%.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1687"
      },
      {
        "metric": "customer satisfaction score increase",
        "value": "over 25%",
        "unit": "%",
        "kind": "satisfaction",
        "wording": "The smooth experience we created across mobile and web led to an over 25% boost in customer satisfaction scores.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1688"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "cost",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Peak-season readiness in 90 days",
      "dimension": "D4",
      "questionForVendor": "What user or order volumes did your load scenarios simulate before the holiday traffic jump, and how were the cost and effort reductions measured?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qarea-shower-door-configurator",
    "vendorSlug": "qarea",
    "title": "Web App Development For A Shower Door Solutions Provider",
    "sourceUrl": "https://qarea.com/projects/web-app-development-for-a-shower-door-solutions-provider",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1689",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a major US-based eCommerce company dealing with modern shower solutions",
      "segment": "specialty",
      "region": "north-america",
      "country": "United States",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": [
        "one of the leaders in their field",
        "the client's enterprise consists of several smaller entities"
      ]
    },
    "systems": [
      {
        "system": "integrations",
        "namedWhere": "named in the Challenge section (\"Integrate the client's existing CRM into the new solution.\")"
      }
    ],
    "problem": {
      "paraphrase": "The client already had an ecommerce store and an older solution built on technologies no longer supported. It wanted to stop pulling product specifications from spreadsheets, speed up size and cost calculations, show products visually, scale, and connect its CRM. The vendor built a new web app with two product configurators, first for the client's sales managers, then for third-party dealers, then for end customers, with QA engineers on the team.",
      "quote": "Get rid of the old unproductive way of retrieving specifications from spreadsheets"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "performance"
      ],
      "mainScope": "functional",
      "teamShape": "1 Project Manager, 1 Solution Architect, 2 Front-End Developers, 1 Back-End Developer, 2 UI/UX Designers, 3 QA Engineers",
      "duration": "\"This particular project has been continuing for 2+ years\" (ongoing)",
      "tooling": [
        "PHP",
        "Laravel",
        "Angular",
        "Gulp",
        "MySQL",
        "Git",
        "Jenkins"
      ],
      "engagementModel": "dedicated-team"
    },
    "metrics": [],
    "notSaid": [
      "baseline before the engagement",
      "cost",
      "date of the work",
      "what was out of scope"
    ],
    "readingNotes": {
      "scenario": "Ecommerce platform migration",
      "dimension": "D1",
      "questionForVendor": "In this full-cycle build, what did the QA engineers own, and what did the performance and load tests measure on the configurators?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qatestlab-allure-glasses-store",
    "vendorSlug": "qatestlab",
    "title": "Allure: men's and women's glasses online store",
    "sourceUrl": "https://qatestlab.com/resources/case-studies/e-commerce/allure/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1192",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": true,
      "name": "Allure",
      "segment": "specialty",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": [
        "The store has a large variety of sunglasses and optical glasses of different brands."
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "WordPress",
        "namedWhere": "named in the body text (\"WordPress store\")"
      }
    ],
    "problem": {
      "paraphrase": "The page lists the agency Loginaut as the customer and the Allure glasses store, built on WordPress, as the site under test. The store's functions and design had to be checked in several browsers and on Android, iOS and Windows Phone devices, and pages had to match templates the customer supplied.",
      "quote": "Particular attention was paid to testing of the cart and adaptive page-proofs."
    },
    "approach": {
      "testingTypes": [
        "functional",
        "mobile"
      ],
      "mainScope": "functional",
      "teamShape": "1 test engineer",
      "duration": "95 hours",
      "tooling": [
        "Jira",
        "VMware Workstation"
      ]
    },
    "metrics": [],
    "notSaid": [
      "baseline before the engagement",
      "cost",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D1",
      "questionForVendor": "How many bugs did the testing hours produce in the customer's tracker, and how many of them were in the cart?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qatestlab-bath-body-store-magento",
    "vendorSlug": "qatestlab",
    "title": "Bath and Body Products Online Store",
    "sourceUrl": "https://qatestlab.com/resources/case-studies/e-commerce/bath-and-body-products-online-store/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1193",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "Magento-based online store of exclusive bath and body products created out of the finest natural ingredients.",
      "segment": "specialty",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Magento",
        "namedWhere": "named in the Project Overview section"
      }
    ],
    "problem": {
      "paraphrase": "The page lists the agency ECommerce Partners as the customer and an unnamed bath and body products store on Magento, part of the E-commerce Partners platform, as the site under test. The client needed functional, cross-browser and regression testing to check newly developed functions, and wanted results with suggestions for improvement."
    },
    "approach": {
      "testingTypes": [
        "functional"
      ],
      "mainScope": "functional",
      "teamShape": "1 test engineer",
      "duration": "50 man-hours",
      "tooling": [
        "Jira"
      ]
    },
    "metrics": [],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "cost",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D1",
      "questionForVendor": "How many bugs did the testing find, and which of the suggested improvements did the store put in place?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qatestlab-brand-apparel-members-shop",
    "vendorSlug": "qatestlab",
    "title": "Brand Apparel Shop",
    "sourceUrl": "https://qatestlab.com/resources/case-studies/e-commerce/brand-apparel-shop/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1241",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "The project was the Internet store based on a Magento platform for exclusive members only.",
      "segment": "fashion-apparel",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Magento",
        "namedWhere": "named in the first paragraph of the body text"
      }
    ],
    "problem": {
      "paraphrase": "The page lists the agency ECommerce Partners as the customer and an unnamed members-only Magento store, selling brands at daily discounts, as the site under test. The main purpose was functional testing of the developed functions at each development stage, plus cross-browser testing so that every page rendered and worked the same in several browsers.",
      "quote": "The system was tested on each of the development stages."
    },
    "approach": {
      "testingTypes": [
        "functional"
      ],
      "mainScope": "functional",
      "teamShape": "1 QA leader, 1 test engineer",
      "duration": "Over 320 man-hours",
      "tooling": [
        "Confluence",
        "Mantis"
      ]
    },
    "metrics": [],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "cost",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D1",
      "questionForVendor": "How many bugs were reported at each development stage, and were the test plan and test cases handed over for the client's own use?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qatestlab-camperdown-magento-rms",
    "vendorSlug": "qatestlab",
    "title": "Australian Online Liquor Store Camperdown Cellars",
    "sourceUrl": "https://qatestlab.com/resources/case-studies/e-commerce/australian-online-liquor-store-camperdown-cellars/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1194",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": true,
      "name": "Camperdown Cellars",
      "segment": "specialty",
      "region": "india-apac",
      "country": "Australia",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Magento",
        "namedWhere": "named in the body text and in the Benefits section"
      },
      {
        "system": "integrations",
        "platformName": "Microsoft Dynamics RMS",
        "namedWhere": "named in the body text (\"Magento platform in Microsoft Dynamics RMS system integration\")"
      }
    ],
    "problem": {
      "paraphrase": "The online liquor store also offers party equipment rental, ice delivery, snacks and drink planning. It asked for manual functional testing, cross-browser testing of the design and regression testing. It also asked for testing of its Magento store integrated with Microsoft Dynamics RMS. The testers worked alongside the client's external development team.",
      "quote": "testing of the Magento platform in Microsoft Dynamics RMS system integration"
    },
    "approach": {
      "testingTypes": [
        "functional"
      ],
      "mainScope": "functional",
      "teamShape": "2 testing engineers",
      "duration": "160 man-hours",
      "tooling": [
        "Jira",
        "TargetProcess",
        "HotGloo",
        "Photoshop"
      ]
    },
    "metrics": [
      {
        "metric": "fixed bugs",
        "value": "Over 150",
        "unit": "bugs",
        "kind": "defects",
        "wording": "Over 150 fixed bugs.",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-1195"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "cost",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D2",
      "questionForVendor": "Which Magento to Dynamics RMS data flows did the integration test plan cover, and how many of the fixed bugs sat in that integration?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qatestlab-cherrybrook-load-testing",
    "vendorSlug": "qatestlab",
    "title": "Cherrybrook: Premium Pet Supplies Shop",
    "sourceUrl": "https://qatestlab.com/resources/case-studies/e-commerce/cherrybrook-premium-pet-supplies-shop/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1196",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": true,
      "name": "Cherrybrook",
      "segment": "specialty",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": [
        "Cherrybrook offers top-quality products for your pets."
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "namedWhere": "the online shop is described in the body text; no platform is named"
      }
    ],
    "problem": {
      "paraphrase": "The page lists the agency ECommerce Partners as the customer and the Cherrybrook pet supplies online shop as the system under test. The aim was to confirm that the site could take heavy expected traffic and keep running without serious errors or breakdowns.",
      "quote": "ensuring that the system is able to withstand heavy potential loads"
    },
    "approach": {
      "testingTypes": [
        "performance"
      ],
      "mainScope": "performance",
      "teamShape": "1 Load testing engineer",
      "duration": "80 hours",
      "tooling": [
        "Mercury LoadRunner"
      ]
    },
    "metrics": [
      {
        "metric": "concurrent users in load test",
        "value": "500",
        "unit": "users",
        "kind": "load-users",
        "numeric": 500,
        "wording": "QATestLab specialists have performed load testing for 500 concurrent users.",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-1197"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Peak-season readiness in 90 days",
      "dimension": "D3",
      "questionForVendor": "What response times and error rates did the load test record, and what changed on the site after your recommendations?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qatestlab-chocolate-shop-accessibility",
    "vendorSlug": "qatestlab",
    "title": "Ensuring The Inclusivity of Exclusive Chocolate Shop Within Tight Deadlines",
    "sourceUrl": "https://qatestlab.com/resources/case-studies/e-commerce/ensuring-the-inclusivity-of-exclusive-chocolate-shop-within-tight-deadlines/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1198",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "an exclusive chocolate shop",
      "segment": "specialty",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Shopify",
        "namedWhere": "named in the Project Overview section"
      }
    ],
    "problem": {
      "paraphrase": "The chocolate shop runs a Shopify store where shoppers find and customize products. The store had to work across many customer journeys and keep its animations and interactive elements accessible on popular devices. It had to be usable by people with disabilities and keep a customized gifting flow stable with seasonal stock logic. Accessibility compliance had to be reached before a fixed release date.",
      "quote": "Achieve accessibility compliance under time pressure to meet the scheduled website release date."
    },
    "approach": {
      "testingTypes": [
        "accessibility",
        "functional",
        "mobile",
        "performance"
      ],
      "mainScope": "accessibility",
      "tooling": [
        "Google Chrome",
        "Safari"
      ]
    },
    "metrics": [],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "team size on both sides",
      "cost",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D8",
      "questionForVendor": "Which blockers in the order and gifting flow did you report before release, and how was WCAG conformance checked?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qatestlab-clothing-shoe-chain-magento",
    "vendorSlug": "qatestlab",
    "title": "Retail Clothing and Shoe Store",
    "sourceUrl": "https://qatestlab.com/resources/case-studies/e-commerce/retail-clothing-and-shoe-store/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1199",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "Magento-based American retail clothing and shoe store chain offering unique affordable fashion.",
      "segment": "fashion-apparel",
      "region": "north-america",
      "channels": [
        "stores",
        "ecommerce"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Magento",
        "namedWhere": "named in the store description at the top of the body text"
      }
    ],
    "problem": {
      "paraphrase": "The page lists the agency ECommerce Partners as the customer and an unnamed American clothing and shoe store chain as the owner of the Magento website under test. The work checked that design and functions met product requirements and that the site behaved the same in popular browsers. It also checked that fixes added no new bugs and how the site held up under heavy load."
    },
    "approach": {
      "testingTypes": [
        "functional",
        "performance"
      ],
      "mainScope": "functional",
      "teamShape": "2 test engineers",
      "duration": "300 man-hours",
      "tooling": [
        "Jira"
      ]
    },
    "metrics": [],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "cost",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D1",
      "questionForVendor": "What load level and response times did the load test measure, and what does the improved website performance claim refer to in numbers?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qatestlab-israeli-clothes-mobile-site",
    "vendorSlug": "qatestlab",
    "title": "Brand Clothes Online Store (Mobile Version)",
    "sourceUrl": "https://qatestlab.com/resources/case-studies/e-commerce/brand-clothes-online-store-mobile-version/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1200",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "Mobile version of Israeli brand clothes online store (on Magento platform).",
      "segment": "fashion-apparel",
      "region": "mena",
      "country": "Israel",
      "channels": [
        "ecommerce",
        "mobile"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Magento",
        "namedWhere": "named in the store description at the top of the body text"
      },
      {
        "system": "mobile-commerce",
        "namedWhere": "named in the title and the store description (\"Mobile version\")"
      }
    ],
    "problem": {
      "paraphrase": "The page lists the agency ECommerce Partners as the customer and an unnamed Israeli clothing brand's Magento store as the site under test. Mobile shoppers have small screens and slow connections, and not every resource of the full site suits them. The task was to check that the mobile site displayed correctly on different screens and devices, with attention to graphics and animation that slow pages down.",
      "quote": "checking of correct website displaying on different screens and devices"
    },
    "approach": {
      "testingTypes": [
        "mobile",
        "functional"
      ],
      "mainScope": "mobile",
      "teamShape": "1 test engineer",
      "duration": "125 man-hours",
      "tooling": [
        "DeviceAnywhere",
        "iExplorer",
        "iPad Peek",
        "iTunes",
        "Jira"
      ]
    },
    "metrics": [],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "cost",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D2",
      "questionForVendor": "Which devices and screen sizes did the testing cover, and what measured page load times support the improved mobile performance claim?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qatestlab-perfume-store-accessibility",
    "vendorSlug": "qatestlab",
    "title": "How a Perfume Store Met Standards Without Breaking the Bank",
    "sourceUrl": "https://qatestlab.com/resources/case-studies/e-commerce/how-a-perfume-store-met-standards-without-breaking-the-bank/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1201",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a luxury perfume and aroma brand on Shopify",
      "segment": "specialty",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Shopify",
        "namedWhere": "named in the Project Overview section"
      }
    ],
    "problem": {
      "paraphrase": "The perfume and aroma brand's Shopify store had barriers for shoppers with disabilities and did not work well with common assistive technologies. The client wanted an accessibility evaluation against WCAG 2.1 on a fixed budget, because its resources were limited.",
      "quote": "Store lacked compatibility with common assistive technologies."
    },
    "approach": {
      "testingTypes": [
        "accessibility"
      ],
      "mainScope": "accessibility",
      "tooling": [
        "Chrome",
        "Firefox",
        "Safari",
        "Edge",
        "NVDA",
        "JAWS",
        "VoiceOver"
      ]
    },
    "metrics": [],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "team size on both sides",
      "cost",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D8",
      "questionForVendor": "How many issues did the audit log at each priority level, and how was WCAG conformance checked after the fixes?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qatestlab-sporting-goods-automation",
    "vendorSlug": "qatestlab",
    "title": "Online Stores of Sporting Goods",
    "sourceUrl": "https://qatestlab.com/resources/case-studies/e-commerce/online-stores-of-sporting-goods/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1202",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": true,
      "name": "JustBats.com, JustBallGloves.com",
      "segment": "specialty",
      "channels": [
        "ecommerce",
        "mobile"
      ],
      "sizeIndicators": [
        "The online stores that specialize on sale of a wide range of baseball bats, balls and gloves."
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "namedWhere": "the stores JustBats.com and JustBallGloves.com are named in the Benefits section; no platform is named"
      },
      {
        "system": "mobile-commerce",
        "namedWhere": "\"JustBats.com / mobile\" and \"JustBallGloves.com / mobile\" named in the Benefits section"
      }
    ],
    "problem": {
      "paraphrase": "The online stores selling baseball bats, balls and gloves were growing and needed automated tests of basic functions to run regular regression on the desktop and mobile versions. Each failure had to come with a report listing the steps that led to the error and a screenshot.",
      "quote": "covering desktop and mobile versions"
    },
    "approach": {
      "testingTypes": [
        "automation",
        "functional"
      ],
      "mainScope": "automation",
      "teamShape": "2 automated test engineers",
      "duration": "60 man-hours",
      "tooling": [
        "Capybara",
        "Cucumber",
        "Selenium Web Driver"
      ]
    },
    "metrics": [],
    "notSaid": [
      "baseline before the engagement",
      "cost",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D4",
      "questionForVendor": "How many of the customer's test cases were automated, and who maintained the automated suite after handover?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qualitest-apparel-self-service-salesforce",
    "vendorSlug": "qualitest",
    "title": "How QualityAI Helped an Apparel Retailer Increase Customer Satisfaction Ten-Fold",
    "sourceUrl": "https://www.quality-ai.com/insights/case-studies/qualityai-increases-customer-satisfaction-ten-fold-apparel-retailer",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1203",
    "publishedOn": "2026-06-09",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "one of the world’s largest and most admired fashion companies",
      "segment": "fashion-apparel",
      "channels": [
        "stores",
        "ecommerce"
      ],
      "sizeIndicators": [
        "connecting with consumers in over 40 countries",
        "Their 140-year history"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "namedWhere": "eCommerce websites named in the section 'Blending physical stores with eCommerce across multiple geographical locations'; chatbot on the eCommerce site named in the SOLUTION block; no platform named"
      },
      {
        "system": "integrations",
        "platformName": "Narvar",
        "namedWhere": "chatbot integration with Narvar named in the Key benefits section"
      }
    ],
    "problem": {
      "paraphrase": "Online customer inquiries were rising sharply, and the retailer wanted customers to find answers through self-service rather than an agent. It was adding a self-service site and knowledge base on Salesforce Experience Cloud and a Salesforce chatbot on its ecommerce site. Complex chatbot workflows and dashboard issues in Tableau CRM had to be tested.",
      "quote": "Handling a large rise in the number of online inquiries from customers"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "performance"
      ],
      "mainScope": "functional",
      "teamShape": "our cloud service functional test team",
      "tooling": [
        "Jira"
      ]
    },
    "metrics": [
      {
        "metric": "Increase in customer satisfaction",
        "value": "ten-fold",
        "kind": "satisfaction",
        "wording": "New workflows created enabled queries to be answered quickly and comprehensively, with a ten-fold increase in customer satisfaction.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1204"
      },
      {
        "metric": "New self-service Salesforce tools introduced",
        "value": "two",
        "unit": "self-service Salesforce tools",
        "kind": "other",
        "wording": "Furthermore, the project’s scope was to deliver enhancements to our Client’s existing customer service flows and to seamlessly support past, current, and future customer inquiries for assistance with queries about products, services, billing, loyalty points, and returns by introducing two new self-service Salesforce tools.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1205"
      },
      {
        "metric": "Test coverage for functionality",
        "value": "100",
        "unit": "%",
        "kind": "coverage-pct",
        "numeric": 100,
        "wording": "we created detailed test scenarios ensuring 100% test coverage for functionality while considering both positive and negative scenarios.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1206"
      },
      {
        "metric": "Functional and regression scenarios created",
        "value": "900+",
        "unit": "scenarios",
        "kind": "test-count",
        "wording": "A robust suite of 900+ functional and regression scenarios was created which captured 100% of our Client’s requirements.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1207"
      },
      {
        "metric": "Requirements captured by the scenario suite",
        "value": "100",
        "unit": "%",
        "kind": "coverage-pct",
        "numeric": 100,
        "wording": "A robust suite of 900+ functional and regression scenarios was created which captured 100% of our Client’s requirements.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1207"
      },
      {
        "metric": "Bugs raised during the project",
        "value": "Close to 200",
        "unit": "bugs",
        "kind": "defects",
        "wording": "Close to 200 bugs were raised during various stages in the project which ensured a 0% defect leakage in production.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1208"
      },
      {
        "metric": "Defect leakage in production",
        "value": "0",
        "unit": "%",
        "kind": "defects",
        "numeric": 0,
        "wording": "Close to 200 bugs were raised during various stages in the project which ensured a 0% defect leakage in production.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1208"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Ecommerce platform migration",
      "dimension": "D1",
      "questionForVendor": "What customer satisfaction score was measured before and after the self-service launch, and over what period?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qualitest-athletic-payments-crowd",
    "vendorSlug": "qualitest",
    "title": "How QualityAI Helped a Leading Athletic Retailer Improve Digital User Experience",
    "sourceUrl": "https://www.quality-ai.com/insights/case-studies/qualityai-improves-digital-user-experience-athletic-retailer",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1209",
    "publishedOn": "2026-06-22",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a Canadian multinational athletic apparel retailer of yoga wear and athletic wear, lifestyle apparel, accessories, and personal care products",
      "segment": "fashion-apparel",
      "region": "north-america",
      "country": "Canada",
      "channels": [
        "stores",
        "ecommerce"
      ],
      "sizeIndicators": [
        "The company has stores internationally and sells online.",
        "supporting millions of transactions"
      ]
    },
    "systems": [
      {
        "system": "payments",
        "platformName": "Apple Pay and locale-specific payment methods",
        "namedWhere": "payment methods named in the Client overview and SOLUTIONS sections; Apple Pay named in the Key Benefits list"
      },
      {
        "system": "ecommerce-platform",
        "namedWhere": "online sales named in the Client overview; websites named in the section 'Let's get physical: creating a positive online shopping experience'; no platform named"
      }
    ],
    "problem": {
      "paraphrase": "The retailer was expanding into new locations and customer groups and needed to know its online services would work with local technologies and payment methods. Its main concern was local currency handling at busy moments, especially during sales events.",
      "quote": "local currency transitions at pinch points, particularly during sales events"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "localization",
        "mobile"
      ],
      "mainScope": "localization",
      "duration": "testing completed 24 hours before each targeted sale window",
      "tooling": []
    },
    "metrics": [
      {
        "metric": "Geographical locations covered in one testing window",
        "value": "10",
        "unit": "geographical locations",
        "kind": "other",
        "numeric": 10,
        "wording": "We also took appropriate steps to ensure that processes were followed and could be undertaken in 10 geographical locations in a 24-hour window from end-to-end.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1210"
      },
      {
        "metric": "End-to-end testing window",
        "value": "24",
        "unit": "hours",
        "kind": "other",
        "numeric": 24,
        "wording": "We also took appropriate steps to ensure that processes were followed and could be undertaken in 10 geographical locations in a 24-hour window from end-to-end.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1210"
      },
      {
        "metric": "Lead time between test completion and sale window",
        "value": "24",
        "unit": "hours",
        "kind": "other",
        "numeric": 24,
        "wording": "The testing would complete 24 hours before the targeted sale window began allowing their team to fix any potential issues that arose during the testing process.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1211"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Payments and PCI scope",
      "dimension": "D2",
      "questionForVendor": "How many payment failures did the crowd testers find before each sale, and what figure supports the improved conversion rates claim?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qualitest-cosmetics-pos-testing",
    "vendorSlug": "qualitest",
    "title": "How QualityAI Improved POS Testing for a Leading Global Retailer",
    "sourceUrl": "https://www.quality-ai.com/insights/case-studies/qualityai-improves-pos-testing-global-retailer",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1212",
    "publishedOn": "2026-07-10",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a large commercial retail cosmetics company which markets through online, wholesale partners, a home shopping channel, and over 200 stores worldwide",
      "segment": "specialty",
      "channels": [
        "stores",
        "ecommerce"
      ],
      "sizeIndicators": [
        "over 200 stores worldwide"
      ]
    },
    "systems": [
      {
        "system": "pos",
        "namedWhere": "named in the Business Needs and Objectives section"
      },
      {
        "system": "oms",
        "namedWhere": "named in the Business Needs and Objectives section"
      },
      {
        "system": "ecommerce-platform",
        "namedWhere": "named in the Business Needs and Objectives section"
      }
    ],
    "problem": {
      "paraphrase": "The retailer was pushing a large volume of changes through its POS, OMS and e-commerce systems to stores and online channels. Its testing practice was immature, with some test cases and documents but no reporting metrics, and it needed help with functional and regression testing and with reproducing defects reported from stores.",
      "quote": "Testing a POS application with different settings and configurations is difficult."
    },
    "approach": {
      "testingTypes": [
        "functional"
      ],
      "mainScope": "functional",
      "teamShape": "13-person effort led by a Senior Test Specialist and composed of a dedicated on-shore team and a large off-shore team",
      "duration": "7-month",
      "tooling": [
        "Excel",
        "SharePoint",
        "RTS"
      ],
      "engagementModel": "managed-testing"
    },
    "metrics": [],
    "notSaid": [
      "measurement method",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "POS rollout across stores",
      "dimension": "D2",
      "questionForVendor": "How many store-reported defects did the POS lab reproduce, and which POS settings and configurations did the regression suite cover?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qualitest-diy-retailer-integrations",
    "vendorSlug": "qualitest",
    "title": "How Phased Integrations Supported Digital Transformation for a Leading DIY Retailer",
    "sourceUrl": "https://www.quality-ai.com/insights/case-studies/phased-integrations-support-digital-transformation-diy-retailer",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1213",
    "publishedOn": "2026-06-22",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "one of the largest and longest established DIY and home improvement retailers in the UK",
      "segment": "specialty",
      "region": "europe",
      "country": "United Kingdom",
      "channels": [
        "stores",
        "ecommerce"
      ],
      "sizeIndicators": [
        "one of the largest and longest established DIY and home improvement retailers in the UK",
        "Founded nearly half a century ago",
        "legacy POS estate across 150 stores"
      ]
    },
    "systems": [
      {
        "system": "pos",
        "namedWhere": "named in the section 'Test regime grows to cover complex enterprise integration program'"
      },
      {
        "system": "integrations",
        "namedWhere": "warehouse and delivery integrations named in the section 'Test regime grows to cover complex enterprise integration program'"
      }
    ],
    "problem": {
      "paraphrase": "The retailer was running a digital transformation that tied many downstream applications together, including warehouse and delivery integrations. It also replaced its legacy POS estate in 150 stores while the old system stayed in support. It sold through a website, stores, home delivery and Click & Collect, and needed a test program and strategy run by an outside partner.",
      "quote": "The Client needed a program and a strategy for a managed test delivery."
    },
    "approach": {
      "testingTypes": [
        "functional",
        "performance",
        "api"
      ],
      "mainScope": "functional",
      "duration": "strategic IT testing partner to the Client for over 10 years",
      "tooling": [],
      "engagementModel": "managed-testing"
    },
    "metrics": [
      {
        "metric": "Length of the testing partnership",
        "value": "over 10",
        "unit": "years",
        "kind": "other",
        "wording": "QualityAI has been strategic IT testing partner to the Client for over 10 years because the Client needed a full digital transformation.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1214"
      },
      {
        "metric": "Stores in the legacy POS estate in scope",
        "value": "150",
        "unit": "stores",
        "kind": "other",
        "numeric": 150,
        "wording": "Also in scope was the transformations of legacy POS estate across 150 stores.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1215"
      },
      {
        "metric": "Defects found during system integration or real-time testing",
        "value": "95",
        "unit": "%",
        "kind": "defects",
        "numeric": 95,
        "wording": "95% of defects are identified by QualityAI during system integration or real-time testing.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1216"
      },
      {
        "metric": "Defects logged by user acceptance testing",
        "value": "5",
        "unit": "%",
        "kind": "defects",
        "numeric": 5,
        "wording": "Only 5% of defects logged by user acceptance testing.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1217"
      },
      {
        "metric": "Defects in live environments",
        "value": "Zero",
        "unit": "defects",
        "kind": "defects",
        "wording": "Zero defects in live environments.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1218"
      },
      {
        "metric": "Test scope and effort reduced by risk-based testing",
        "value": "10-15",
        "unit": "%",
        "kind": "time-reduction-pct",
        "wording": "Risk-based testing approach reduced 10-15% test scope and efforts by extending test coverage for all critical scenarios and functionality.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1219"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "POS rollout across stores",
      "dimension": "D2",
      "questionForVendor": "Over what period and how many releases were the integration-stage defect and live defect figures measured?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qualitest-fashion-salesforce-commerce",
    "vendorSlug": "qualitest",
    "title": "Salesforce Assurance for a Global Fashion House",
    "sourceUrl": "https://www.quality-ai.com/insights/case-studies/salesforce-assurance-for-a-global-fashion-house",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1220",
    "publishedOn": "2026-10-29",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "A leading global fashion house",
      "segment": "fashion-apparel",
      "region": "north-america",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": [
        "six e-commerce brand sites"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Salesforce Commerce Cloud",
        "namedWhere": "named in the Challenge section"
      },
      {
        "system": "oms",
        "platformName": "Manhattan Active Omni",
        "namedWhere": "named in the Challenge section"
      },
      {
        "system": "loyalty",
        "namedWhere": "loyalty capabilities named in the Challenge section"
      },
      {
        "system": "integrations",
        "platformName": "SAP S/4HANA, MDM and MuleSoft",
        "namedWhere": "named in the Challenge section"
      }
    ],
    "problem": {
      "paraphrase": "The client was rolling out six e-commerce brand sites, marketing platforms and loyalty functions across North America on Salesforce Commerce Cloud, Service Cloud and Marketing Cloud. These connected to SAP S/4HANA, Manhattan Active Omni, MDM and MuleSoft, while business users had little spare time and the schedule was tight.",
      "quote": "with stretched business users and significant schedule pressure"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "automation"
      ],
      "mainScope": "functional",
      "tooling": []
    },
    "metrics": [
      {
        "metric": "E-commerce brand sites in the implementation",
        "value": "six",
        "unit": "e-commerce brand sites",
        "kind": "other",
        "wording": "A leading global fashion house was undertaking a complex implementation of six e-commerce brand sites, marketing platforms, and loyalty capabilities across North America, spanning Salesforce Commerce Cloud, Service Cloud, and Marketing Cloud.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1221"
      },
      {
        "metric": "Defect leakage to production in the first holiday period after go-live",
        "value": "zero",
        "unit": "defects",
        "kind": "defects",
        "wording": "The program achieved zero defect leakage in production during the first holiday period post-go-live.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1222"
      },
      {
        "metric": "Defects identified before go-live",
        "value": "More than 1,200",
        "unit": "defects",
        "kind": "defects",
        "wording": "More than 1,200 defects were identified before go-live, and 95% automation coverage for e-commerce flows reduced regression execution from weeks to hours.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1223"
      },
      {
        "metric": "Automation coverage for e-commerce flows",
        "value": "95",
        "unit": "%",
        "kind": "coverage-pct",
        "numeric": 95,
        "wording": "More than 1,200 defects were identified before go-live, and 95% automation coverage for e-commerce flows reduced regression execution from weeks to hours.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1223"
      },
      {
        "metric": "Regression execution time",
        "value": "weeks to hours",
        "kind": "time-reduction-abs",
        "wording": "More than 1,200 defects were identified before go-live, and 95% automation coverage for e-commerce flows reduced regression execution from weeks to hours.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1223"
      }
    ],
    "notSaid": [
      "measurement method",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Ecommerce platform migration",
      "dimension": "D2",
      "questionForVendor": "Which tests covered the Manhattan Active Omni and SAP integrations, and which framework runs the regression suite behind the coverage figure?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qualitest-footwear-holiday-readiness",
    "vendorSlug": "qualitest",
    "title": "How Holiday Readiness Testing Protected Peak Performance for a UK Footwear Brand",
    "sourceUrl": "https://www.quality-ai.com/insights/case-studies/holiday-readiness-testing-protects-peak-performance-footwear-brand",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1224",
    "publishedOn": "2026-06-22",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a household-name UK footwear and apparel brand founded more than 70 years ago",
      "segment": "fashion-apparel",
      "region": "europe",
      "country": "United Kingdom",
      "channels": [],
      "sizeIndicators": [
        "household-name UK footwear and apparel brand",
        "founded more than 70 years ago"
      ]
    },
    "systems": [
      {
        "system": "integrations",
        "platformName": "Azure integration services (APIM and Queues) with MS Dynamics 365",
        "namedWhere": "named in the opening section on the client's system changes and in the Results bottleneck list"
      }
    ],
    "problem": {
      "paraphrase": "Ahead of Black Friday and the holiday season, the client was moving to the latest MS Dynamics 365 release. That release carried performance fixes for outages in a prior year. The client had also recently added Azure integration services to its ERP environment. It wanted assurance that the changed system would hold peak trading volumes and leave capacity for future growth.",
      "quote": "this particular year our Client was in the middle of critical system changes"
    },
    "approach": {
      "testingTypes": [
        "performance",
        "automation"
      ],
      "mainScope": "performance",
      "tooling": [
        "AppInsights",
        "PRTG Network Monitor"
      ]
    },
    "metrics": [
      {
        "metric": "High-priority performance bugs identified and corrected",
        "value": "10",
        "unit": "bugs",
        "kind": "defects",
        "numeric": 10,
        "wording": "We identified and corrected 10 high-priority performance bugs.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1225"
      },
      {
        "metric": "Data persistence under load",
        "value": "100",
        "unit": "%",
        "kind": "other",
        "numeric": 100,
        "wording": "Our teams assured 100% data persistence (zero data loss) under load.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1226"
      },
      {
        "metric": "End-to-end response time across 5 channels",
        "value": "under 40",
        "unit": "minutes",
        "kind": "other",
        "wording": "under 40 minutes on 5 channels and about 12K sales orders/hour",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1227"
      },
      {
        "metric": "Sales order throughput tested",
        "value": "about 12K",
        "unit": "sales orders/hour",
        "kind": "load-orders",
        "wording": "under 40 minutes on 5 channels and about 12K sales orders/hour",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1227"
      },
      {
        "metric": "Future growth the reusable assets are sized for",
        "value": "50",
        "unit": "%",
        "kind": "other",
        "numeric": 50,
        "wording": "We produced automated reusable assets to enable the Client to meet future goals of an additional 50% growth.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1228"
      },
      {
        "metric": "Thread restriction found at peak load",
        "value": "16",
        "unit": "threads",
        "kind": "other",
        "numeric": 16,
        "wording": "Insufficient thread utilization (restriction to 16 threads) made the system incapable of handling sufficient concurrent calls and caused visible spikes in response times.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1229"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Peak-season readiness in 90 days",
      "dimension": "D3",
      "questionForVendor": "Which channels were tested, and did the client's live Black Friday traffic stay within the response times and order rate you tested?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qualitest-pos-provider-eggplant-automation",
    "vendorSlug": "qualitest",
    "title": "How QualityAI Prepared a PoS Solution Provider for Retail with Automation Testing",
    "sourceUrl": "https://www.quality-ai.com/insights/case-studies/qualityai-prepares-pos-solution-provider-retail-automation-testing",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1232",
    "publishedOn": "2026-06-22",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a global retail technology provider who offers a multi-platform Point of Sale (PoS) system",
      "segment": "enterprise-omnichannel",
      "channels": [
        "stores"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "pos",
        "namedWhere": "named in the title and the Client Overview section"
      }
    ],
    "problem": {
      "paraphrase": "The client sells a multi-platform POS system to retail, leisure and hospitality businesses. It wanted to start test automation with its chosen automation tool and needed advice to train its in-house team. It also wanted help to speed up the first round of script writing, convert manual regression cases, and set up execution and reporting.",
      "quote": "The client needed guidance and consultancy to educate their in-house team on automation"
    },
    "approach": {
      "testingTypes": [
        "automation",
        "functional"
      ],
      "mainScope": "automation",
      "teamShape": "a full-time automation consultant delivering automation assets daily, scripts peer reviewed by a solution delivery manager, and a Senior Automation Expert providing ePF tool training",
      "tooling": [
        "eggPlant Functional (ePF)",
        "Team Foundation Server (TFS)"
      ],
      "engagementModel": "managed-testing"
    },
    "metrics": [
      {
        "metric": "Share of manual regression test cases automated",
        "value": "40",
        "unit": "%",
        "kind": "coverage-pct",
        "numeric": 40,
        "wording": "By the end of the engagement, QualityAI automated 40% of test cases from the manual regression pack, and achieved 75% of functional scripts coverage.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1233"
      },
      {
        "metric": "Functional scripts coverage",
        "value": "75",
        "unit": "%",
        "kind": "coverage-pct",
        "numeric": 75,
        "wording": "By the end of the engagement, QualityAI automated 40% of test cases from the manual regression pack, and achieved 75% of functional scripts coverage.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1233"
      },
      {
        "metric": "Reusable ePF code suites delivered",
        "value": "two",
        "unit": "suites",
        "kind": "other",
        "wording": "QualityAI delivered a reusable code infrastructure comprised of two suites of ePF source code to accelerate the delivery and code maintenance.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1234"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "POS rollout across stores",
      "dimension": "D4",
      "questionForVendor": "How many test cases were in the manual regression pack, and how long did it take to automate the share you report?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qualitest-sports-fashion-parallel-automation",
    "vendorSlug": "qualitest",
    "title": "How Parallel Testing Improved Performance for a Sports Ecommerce Platform",
    "sourceUrl": "https://www.quality-ai.com/insights/case-studies/parallel-testing-improves-performance-sports-ecommerce-platform",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1235",
    "publishedOn": "2026-06-09",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a well-established and reputable British sports-fashion retail business with over 40 years of operation",
      "segment": "fashion-apparel",
      "region": "europe",
      "country": "United Kingdom",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": [
        "over 40 years of operation",
        "employs over 44,488 individuals"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "namedWhere": "web application for an existing eCommerce website named in a section of the case page; no platform named"
      }
    ],
    "problem": {
      "paraphrase": "The retailer was building its own web application for its existing ecommerce website and had many features to test in a short time. Manual testing alone could not keep pace, and test data and shared objects were hard to manage across the site's several fascias.",
      "quote": "It was difficult to accomplish this using manual testing since there were many facets"
    },
    "approach": {
      "testingTypes": [
        "automation",
        "functional"
      ],
      "mainScope": "automation",
      "teamShape": "automation test specialists",
      "tooling": [
        "Java",
        "Selenium",
        "QualiFrame Framework"
      ]
    },
    "metrics": [],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Ecommerce platform migration",
      "dimension": "D4",
      "questionForVendor": "By how much did parallel execution across browsers cut regression run time, and how many test cases does the suite hold?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qualitylogic-pos-protractor-playwright-migration",
    "vendorSlug": "qualitylogic",
    "title": "Case Study: Protractor to Playwright Migration",
    "sourceUrl": "https://www.qualitylogic.com/knowledge-center/case-study-protractor-to-playwright-migration/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1693",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "The client provides global retail management software, including point-of-sale and store operations technology to specialty retailers across numerous countries",
      "segment": "specialty",
      "channels": [
        "stores"
      ],
      "sizeIndicators": [
        "specialty retailers across numerous countries"
      ]
    },
    "systems": [
      {
        "system": "pos",
        "namedWhere": "named in The Client section (\"point-of-sale and store operations technology\")"
      }
    ],
    "problem": {
      "paraphrase": "The client sells retail management software, including point-of-sale and store operations products, to specialty retailers in many countries. Its end-to-end tests were written in a test framework that Google had deprecated. Hundreds of test cases and 18,000+ lines of test code would lose support as the underlying web framework moved on. Google advised rebuilding them in another framework, which by hand would take hundreds of hours.",
      "quote": "all of it at risk of going stale"
    },
    "approach": {
      "testingTypes": [
        "automation",
        "functional"
      ],
      "mainScope": "automation",
      "teamShape": "QualityLogic's engineering team built the conversion tool; a hands-on engineer resolved the remaining inaccuracies",
      "tooling": [
        "Protractor",
        "Playwright",
        "custom Protractor-to-Playwright conversion tool built by QualityLogic"
      ]
    },
    "metrics": [
      {
        "metric": "Conversion success rate of the tool",
        "value": "95-98%",
        "unit": "%",
        "kind": "other",
        "wording": "95-98% conversion efficiency on the test cases run through the tool (the remainder handled by hand)",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1694"
      },
      {
        "metric": "Conversion time against estimated manual effort",
        "value": "15 hours; an estimated 460 hours",
        "unit": "hours",
        "kind": "time-reduction-abs",
        "wording": "QualityLogic utilized this tool to convert the 18,000+ lines of code representing hundreds of tests from Protractor to Playwright in just 15 hours, work that would’ve taken a team of engineers an estimated 460 hours.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1695"
      },
      {
        "metric": "Protractor test code at risk",
        "value": "18,000+",
        "unit": "lines of test code",
        "kind": "other",
        "wording": "Our client had hundreds of test cases and 18,000+ lines of code in Protractor, all of it at risk of going stale as Angular moved forward without a supported test framework behind it.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1696"
      },
      {
        "metric": "Assumed manual throughput of one experienced automation engineer",
        "value": "200-300 lines of code per day, of 4-8 tests",
        "unit": "lines of code per day",
        "kind": "other",
        "wording": "This move also occurred before the prevalence of AI coding tools, meaning the typical throughput of an experienced test automation engineer was 200-300 lines of code per day, of 4-8 tests.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1697"
      }
    ],
    "notSaid": [
      "measurement method",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "POS rollout across stores",
      "dimension": "D4",
      "questionForVendor": "How was the conversion rate of the old tests measured, and how many converted tests ran green against the POS product after migration?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "qualitylogic-toy-company-staff-augmentation",
    "vendorSlug": "qualitylogic",
    "title": "Managed QA Services with Staff Augmentation",
    "sourceUrl": "https://www.qualitylogic.com/wp-content/uploads/2017/01/QL_CaseStudy_Staff-Augmentation_011617.pdf",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1692",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "an educational children's toy company",
      "segment": "ecommerce-d2c",
      "channels": [
        "ecommerce",
        "mobile"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "namedWhere": "named in The Client section (\"a desktop and mobile web storefront\" built and hosted by a third-party e-commerce development and hosting company)"
      },
      {
        "system": "mobile-commerce",
        "namedWhere": "named in The Issue section (\"customized native mobile apps\") and The Outcome section (\"testing of their mobile app development\")"
      },
      {
        "system": "data-migration",
        "namedWhere": "named in The Issue section (transfer from the hosted site) and The Outcome section (\"the migration process\")"
      },
      {
        "system": "integrations",
        "namedWhere": "named in The Outcome section (\"the many databases and third-party product management solutions\") and The Client section (an API and backend systems to track and manage inventory)"
      }
    ],
    "problem": {
      "paraphrase": "The client sells educational toys for children, first on eBay and then through a web store built and hosted by an outside firm. As it grew it moved development, hosting and QA in house and planned native mobile apps. Its new small QA team needed more staff for the move off the hosted site, and the client could not hire skilled, affordable QA engineers locally against large tech companies.",
      "quote": "The client found it difficult to hire qualified and affordable QA engineers"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "mobile",
        "data-migration"
      ],
      "mainScope": "functional",
      "teamShape": "one QualityLogic QA engineer on mobile app and web interface testing, later joined by an additional QA engineer on databases and third-party product management solutions",
      "tooling": [],
      "engagementModel": "staff-augmentation"
    },
    "metrics": [],
    "notSaid": [
      "baseline before the engagement",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Ecommerce platform migration",
      "dimension": "D1",
      "questionForVendor": "How long did the added QA engineers stay, and what did migration testing check across the client's databases and product management tools?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "sciencesoft-b2c-estore-erp-integration",
    "vendorSlug": "sciencesoft",
    "title": "Testing of a B2C Ecommerce Solution for a Multi-Industry Company",
    "sourceUrl": "https://www.scnsoft.com/ecommerce/case-studies/testing-of-a-b2c-e-store-for-a-multi-industry-company",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1376",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a multi-industry company running chains of gas stations, roadside diners, gardening centers, as well as retail and wholesale stores nationwide",
      "segment": "enterprise-omnichannel",
      "channels": [
        "stores",
        "ecommerce"
      ],
      "sizeIndicators": [
        "chains of gas stations, roadside diners, gardening centers, as well as retail and wholesale stores nationwide"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "namedWhere": "named in the Challenge section (new B2C e-store alongside the existing online B2B store); platform not named"
      },
      {
        "system": "integrations",
        "namedWhere": "named in the Challenge section (\"The new B2C e-store had to be integrated with the Customer’s ERP system\") and in the Solution section (integration testing of ERP request handling from both e-stores)"
      }
    ],
    "problem": {
      "paraphrase": "The client ran an ERP, a BI module and a B2B online store. To widen trade it added a B2C e-store for individual buyers, which had to work with the ERP while the B2B store kept running. During the project, a legal change that made electronic invoices obligatory from July 2016 added a new ERP block for creating, signing and storing invoices, which also had to be tested.",
      "quote": "the old B2B store remained fully functional"
    },
    "approach": {
      "testingTypes": [
        "functional"
      ],
      "mainScope": "functional",
      "tooling": [
        "Atlassian JIRA",
        "VMWare Workstation",
        "MS Analysis Services",
        "Mindjet Mind Manager",
        "SQL Server Management Studio",
        "SQL Server Reporting Services",
        ".NET",
        "C#",
        "Perforce"
      ]
    },
    "metrics": [],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D2",
      "questionForVendor": "Which order and checkout flows between the consumer e-store and the ERP did regression cover, and how many test cases did it hold?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "sciencesoft-loyalty-app-performance",
    "vendorSlug": "sciencesoft",
    "title": "Performance Testing of a Loyalty Program Application",
    "sourceUrl": "https://www.scnsoft.com/ecommerce/case-studies/performance-testing-of-a-loyalty-program-application",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1705",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a Finnish retailing conglomerate engaged in the food trade, home and specialty goods trade, building and home improvement trade, car and machinery trade",
      "segment": "enterprise-omnichannel",
      "region": "europe",
      "country": "Finland",
      "channels": [
        "stores"
      ],
      "sizeIndicators": [
        "more than 3,000 outlets",
        "over 40 cooperation partners",
        "the widest and the most diverse customer loyalty program in Finland"
      ]
    },
    "systems": [
      {
        "system": "loyalty",
        "namedWhere": "named in the title and the Challenge section (customer loyalty program application)"
      }
    ],
    "problem": {
      "paraphrase": "The client runs a loyalty program that gives members benefits across thousands of outlets and dozens of partners. Shortly before the program's application was due for release, the client needed a provider to test its performance. It wanted to know how many users the app could serve at once, whether it stayed stable under long load, where the bottlenecks were, and how it behaved under stress.",
      "quote": "right before the application’s release"
    },
    "approach": {
      "testingTypes": [
        "performance"
      ],
      "mainScope": "performance",
      "duration": "120h for the whole project including scripting, test runs, analysis and reporting",
      "tooling": [],
      "engagementModel": "project"
    },
    "metrics": [
      {
        "metric": "endurance load target",
        "value": "2000 users per hour for 24 hours in a row",
        "unit": "users per hour",
        "kind": "load-users",
        "wording": "Whether the application can handle 2000 users per hour for 24 hours in a row.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1378"
      },
      {
        "metric": "project effort",
        "value": "120h",
        "unit": "hours",
        "kind": "other",
        "numeric": 120,
        "wording": "ScienceSoft’s professionals managed to carry out testing in an extremely short period right before the application’s release: 120h for the whole project including scripting, test runs, analysis and reporting.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1706"
      },
      {
        "metric": "outlets in the loyalty program",
        "value": "more than 3,000",
        "unit": "outlets",
        "kind": "other",
        "wording": "The Customer owns the widest and the most diverse customer loyalty program in Finland that provides their clients with benefits from more than 3,000 outlets and over 40 cooperation partners.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1707"
      },
      {
        "metric": "cooperation partners in the loyalty program",
        "value": "over 40",
        "unit": "cooperation partners",
        "kind": "other",
        "wording": "The Customer owns the widest and the most diverse customer loyalty program in Finland that provides their clients with benefits from more than 3,000 outlets and over 40 cooperation partners.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1707"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Peak-season readiness in 90 days",
      "dimension": "D3",
      "questionForVendor": "What maximum number of simultaneous users did the app reach in the test, and which load profile produced the hourly user figure?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "sciencesoft-magento-crm-dwh-managed",
    "vendorSlug": "sciencesoft",
    "title": "Managed Testing of an Ecommerce Solution, CRM and Data Warehouse for US Multi-Industry Corporation",
    "sourceUrl": "https://www.scnsoft.com/ecommerce/case-studies/comprehensive-managed-testing-services-for-us-multi-industry-corporation",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1698",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a US-based multi-business corporation present in a variety of industries, including hospitality, retail (ecommerce and brick-and-mortar fashion stores), and wellness",
      "segment": "enterprise-omnichannel",
      "region": "north-america",
      "country": "United States",
      "channels": [
        "stores",
        "ecommerce"
      ],
      "sizeIndicators": [
        "multi-business corporation present in a variety of industries"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Magento",
        "namedWhere": "named in the Challenge section (Magento-based ecommerce platform) and in the technologies list (Magento 2)"
      },
      {
        "system": "integrations",
        "namedWhere": "named in the Solution section (\"the platform’s integrations with such external systems as CMS, a PIM tool, a website payment and a POS system, a warehouse management tool, CRM, and a tax calculator\")"
      },
      {
        "system": "pos",
        "namedWhere": "named in the Solution section as an integration of the ecommerce platform and as a source of customer data checked in CRM and DWH testing"
      },
      {
        "system": "payments",
        "namedWhere": "named in the Solution section (website payment integration; \"payment-transaction processes complied with PCI DSS\")"
      }
    ],
    "problem": {
      "paraphrase": "The client's Magento store, a Dynamics 365 CRM for web and mobile, and an on-premises data warehouse were all still being built and customized. The client wanted one managed testing provider with Magento, Dynamics and data warehouse experience to check function, security and usability, and to confirm that new changes did not break existing features."
    },
    "approach": {
      "testingTypes": [
        "functional",
        "performance",
        "security",
        "mobile"
      ],
      "mainScope": "functional",
      "teamShape": "six test engineers on the ecommerce platform, two test engineers on web and mobile CRM testing, one test engineer on the data warehouse; team size scaled up and down",
      "tooling": [
        "Magento Test Framework",
        "Apache JMeter",
        "Atlassian JIRA",
        "Atlassian Confluence"
      ],
      "engagementModel": "managed-testing"
    },
    "metrics": [
      {
        "metric": "test engineers on the ecommerce platform",
        "value": "six",
        "unit": "test engineers",
        "kind": "other",
        "numeric": 6,
        "wording": "A group of six test engineers got down to testing as soon as the development process of the e-commerce platform started.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1699"
      },
      {
        "metric": "test engineers on CRM testing",
        "value": "two",
        "unit": "test engineers",
        "kind": "other",
        "numeric": 2,
        "wording": "Thus, two ScienceSoft’s test engineers paid special attention to functionality, performance, usability testing and role-based access control validation during web and mobile CRM testing.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1700"
      },
      {
        "metric": "user stories and test cases",
        "value": "2,200",
        "unit": "user stories and test cases",
        "kind": "test-count",
        "numeric": 2200,
        "wording": "With a significant number of different user stories and test cases (2,200) to implement for retail, hospitality and wellness employees, functionality testing took the majority of the testing time.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1701",
        "outlier": true
      },
      {
        "metric": "access levels for brick-and-mortar stores",
        "value": "8",
        "unit": "access levels",
        "kind": "other",
        "numeric": 8,
        "wording": "To manage this process, the test team created a roles matrix: for brick-and-mortar stores, 8 access levels were assigned, for hospitality employees, 7 levels, and for wellness centers, 5.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1702"
      },
      {
        "metric": "access levels for hospitality employees",
        "value": "7",
        "unit": "access levels",
        "kind": "other",
        "numeric": 7,
        "wording": "To manage this process, the test team created a roles matrix: for brick-and-mortar stores, 8 access levels were assigned, for hospitality employees, 7 levels, and for wellness centers, 5.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1702"
      },
      {
        "metric": "access levels for wellness centers",
        "value": "5",
        "unit": "access levels",
        "kind": "other",
        "numeric": 5,
        "wording": "To manage this process, the test team created a roles matrix: for brick-and-mortar stores, 8 access levels were assigned, for hospitality employees, 7 levels, and for wellness centers, 5.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1702"
      },
      {
        "metric": "iOS devices in mobile testing",
        "value": "three",
        "unit": "iOS devices",
        "kind": "other",
        "numeric": 3,
        "wording": "The mobile app was tested on three iOS devices of different versions and two Android devices.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1703"
      },
      {
        "metric": "Android devices in mobile testing",
        "value": "two",
        "unit": "Android devices",
        "kind": "other",
        "numeric": 2,
        "wording": "The mobile app was tested on three iOS devices of different versions and two Android devices.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1703"
      },
      {
        "metric": "client records in DWH load check",
        "value": "5,000+",
        "unit": "real client records",
        "kind": "other",
        "wording": "The test team also checked the DWH load limits, as it had to cope with the large volume of data (5,000+ real client records) and the maximum number of users that access the DWH through the e-commerce platform and CRM simultaneously.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1704"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Payments and PCI scope",
      "dimension": "D2",
      "questionForVendor": "Which POS and payment integration scenarios did the team test end to end, and what did the PCI DSS check cover?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "sciencesoft-retail-chain-pentest",
    "vendorSlug": "sciencesoft",
    "title": "Web Applications Penetration Testing for a Multinational Retail Chain",
    "sourceUrl": "https://www.scnsoft.com/case-studies/penetration-testing-for-a-multinational-retail-chain",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1712",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a multinational retail chain that numbers 12,000+ stores operating across more than 30 countries in Europe, Asia, Africa and Latin America",
      "segment": "grocery",
      "channels": [
        "stores",
        "ecommerce",
        "mobile"
      ],
      "sizeIndicators": [
        "12,000+ stores operating across more than 30 countries",
        "over €100 billion in revenue",
        "hypermarkets, supermarkets, convenience stores, cash & carry stores"
      ]
    },
    "systems": [],
    "problem": {
      "paraphrase": "A regional branch of the retailer planned to launch several web applications for its customers. Because the applications would process customers' personal information, the retailer wanted a penetration testing provider to assess their security before release. The vendor proposed adding the website the applications interact with to the test scope.",
      "quote": "supposed to process customers' personal information"
    },
    "approach": {
      "testingTypes": [
        "security"
      ],
      "mainScope": "security",
      "tooling": [
        "w3af",
        "metasploit",
        "BurpSuite",
        "Qualys online scanner",
        "web-sniffer.net"
      ],
      "engagementModel": "project"
    },
    "metrics": [
      {
        "metric": "client store count",
        "value": "12,000+",
        "unit": "stores",
        "kind": "other",
        "wording": "The Customer is a multinational retail chain that numbers 12,000+ stores operating across more than 30 countries in Europe, Asia, Africa and Latin America.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1713"
      },
      {
        "metric": "client country count",
        "value": "more than 30",
        "unit": "countries",
        "kind": "other",
        "wording": "The Customer is a multinational retail chain that numbers 12,000+ stores operating across more than 30 countries in Europe, Asia, Africa and Latin America.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1713"
      },
      {
        "metric": "client revenue as of 2015",
        "value": "over €100 billion",
        "unit": "EUR revenue",
        "kind": "other",
        "wording": "As of 2015, the retailer reported over €100 billion in revenue.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1714"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Payments and PCI scope",
      "dimension": "D8",
      "questionForVendor": "How many vulnerabilities did the test find, at what severity, and did any tested application or the website handle card data?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "sciencesoft-shopify-herbal-store",
    "vendorSlug": "sciencesoft",
    "title": "Phytopharmaceutical Ecommerce Website Testing Covering 150 Test Cases",
    "sourceUrl": "https://www.scnsoft.com/ecommerce/case-studies/ecommerce-website-testing-for-a-phytopharmaceuticals-manufacturer",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1708",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a producer of phytopharmaceuticals based in Canada",
      "segment": "ecommerce-d2c",
      "region": "north-america",
      "country": "Canada",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "Shopify",
        "namedWhere": "named in the Challenge section (\"an ecommerce website based on Shopify\") and in the technologies list"
      },
      {
        "system": "payments",
        "namedWhere": "named in the Solution section testing scope (\"selecting a payment method and making a payment\")"
      }
    ],
    "problem": {
      "paraphrase": "The client built a Shopify store to sell its herbal products online and wanted it checked on desktop and mobile devices. Its products carry quantity limits per order, so the client needed proof that the ordering flow enforced those limits. The client had no complete, documented requirements for the site, so the vendor held meetings with the client to recover them before writing tests.",
      "quote": "quantity limitations per order"
    },
    "approach": {
      "testingTypes": [
        "functional"
      ],
      "mainScope": "functional",
      "teamShape": "a Test Lead who wrote the test plan and a test engineer who ran functional testing",
      "tooling": [
        "Confluence Jira"
      ]
    },
    "metrics": [
      {
        "metric": "smoke testing checklist",
        "value": "120+",
        "unit": "checks",
        "kind": "test-count",
        "wording": "To carry out smoke testing, a test engineer designed and executed a checklist comprising 120+ checks.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1709"
      },
      {
        "metric": "functional test cases designed and executed",
        "value": "150",
        "unit": "test cases",
        "kind": "test-count",
        "numeric": 150,
        "wording": "In accordance with the recovered requirements, the test engineer designed and executed 150 test cases (including a set of test cases verifying specifically the adherence to the set quantity limitations per order) to test the website functionality in detail.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1710"
      },
      {
        "metric": "defects found",
        "value": "71",
        "unit": "defects",
        "kind": "defects",
        "numeric": 71,
        "wording": "The test engineer discovered 71 defects and reported each to the Client via Confluence Jira.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1711"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D1",
      "questionForVendor": "How many of the reported defects affected the cart, checkout or payment steps, and how were they rated for severity?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "shift-asia-apparel-group-long-term-qa",
    "vendorSlug": "shift-asia",
    "title": "Elevating eCommerce Quality with Long-Term QA Partnership",
    "sourceUrl": "https://shiftasia.com/case-studies/elevating-ecommerce-quality-with-long-term-qa-partnership/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1785",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a global apparel retail group known for its innovative approach to fashion and technology",
      "segment": "fashion-apparel",
      "channels": [
        "stores",
        "ecommerce"
      ],
      "sizeIndicators": [
        "The company operates multiple well-known clothing brands",
        "a strong international presence through both physical stores and online platforms"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "namedWhere": "named in the closing section (\"their eCommerce platform\"); platform product not named"
      }
    ],
    "problem": {
      "paraphrase": "The client, an apparel retail group selling through stores and online, already used an offshore development vendor but had no QA team of its own. New features reached release without testing by a separate QA team, which the case links to slower release cycles and unstable site performance.",
      "quote": "they lacked a dedicated quality assurance (QA) team"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "automation"
      ],
      "mainScope": "functional",
      "teamShape": "dedicated testers provided based on project demand, scaling up during heavy release periods and down during quiet phases, with test management and PMO support",
      "duration": "Over the course of six years",
      "tooling": [
        "Slack",
        "Microsoft Teams"
      ],
      "engagementModel": "dedicated-team"
    },
    "metrics": [
      {
        "metric": "rollout projects completed",
        "value": "7",
        "unit": "rollout projects",
        "kind": "other",
        "numeric": 7,
        "wording": "7 rollout projects completed",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1786"
      },
      {
        "metric": "enhancement phases delivered",
        "value": "10",
        "unit": "enhancement phases",
        "kind": "other",
        "numeric": 10,
        "wording": "10 enhancement phases delivered with high QA standards",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1787"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D7",
      "questionForVendor": "Which tools run the regression automation, how did team size change over the engagement, and what share of regression is now automated?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "shift-asia-b2b-materials-marketplace",
    "vendorSlug": "shift-asia",
    "title": "SHIFT ASIA Rescues B2B Trading Platform from Quality Crisis",
    "sourceUrl": "https://shiftasia.com/case-studies/shift-asia-rescues-b2b-trading-platform-from-quality-crisis/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1792",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "The client provides an innovative business platform for industrial material purchasing",
      "segment": "marketplace",
      "channels": [
        "ecommerce",
        "marketplace"
      ],
      "sizeIndicators": [
        "a large-scale B2B trading platform in the manufacturing industry"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "namedWhere": "named in the scope description (\"member registration, login, listing, searching, purchasing, and payment\"); platform product not named"
      },
      {
        "system": "payments",
        "namedWhere": "named in the scope description (\"purchasing, and payment\")"
      }
    ],
    "problem": {
      "paraphrase": "The client runs a B2B platform where manufacturers buy industrial materials. Its release kept slipping because of quality problems, and critical defects turned up in acceptance testing just before the planned release date. It needed the system brought to releasable quality within two months to keep that date.",
      "quote": "critical defects were identified during the acceptance testing phase"
    },
    "approach": {
      "testingTypes": [
        "functional"
      ],
      "mainScope": "functional",
      "duration": "within a two-month testing and modification period",
      "tooling": []
    },
    "metrics": [
      {
        "metric": "defects identified and fixed",
        "value": "Hundreds",
        "unit": "defects",
        "kind": "defects",
        "wording": "Hundreds of defects, including critical ones, were identified and rectified",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1793"
      },
      {
        "metric": "release timing",
        "value": "within the targeted two-month period",
        "unit": "months",
        "kind": "other",
        "wording": "enabling its successful release within the targeted two-month period",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1794"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Peak-season readiness in 90 days",
      "dimension": "D1",
      "questionForVendor": "How many testers worked on the recovery, and how many of the defects found were critical and in the purchasing and payment flows?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "shift-asia-ecommerce-agency-qa-process",
    "vendorSlug": "shift-asia",
    "title": "Case Study: SHIFT ASIA's QA Solution to transform eCommerce quality processes for success",
    "sourceUrl": "https://shiftasia.com/case-studies/case-study-shift-asias-qa-solution-to-transform-ecommerce-quality-processes-for-success/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1778",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a development agency offering tailored eCommerce solutions for businesses in Japan and Vietnam",
      "segment": "ecommerce-d2c",
      "region": "india-apac",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "EC-Cube",
        "namedWhere": "named in the Tools section (\"E-commerce Platforms: EC-Cube, Shopify\")"
      },
      {
        "system": "ecommerce-platform",
        "platformName": "Shopify",
        "namedWhere": "named in the Tools section (\"E-commerce Platforms: EC-Cube, Shopify\")"
      }
    ],
    "problem": {
      "paraphrase": "The client builds and runs online stores for businesses. It had no defined quality process, defects surfaced late during acceptance, and its documentation was inconsistent. The result was delays, lost trust with its own customers and lost accounts.",
      "quote": "which led to the loss of important accounts"
    },
    "approach": {
      "testingTypes": [
        "functional"
      ],
      "mainScope": "functional",
      "tooling": [
        "Redmine",
        "CAT system"
      ]
    },
    "metrics": [
      {
        "metric": "identified defects",
        "value": "400",
        "unit": "%",
        "kind": "defects",
        "numeric": 400,
        "wording": "Increased Issue Detection: The number of identified defects rose by 400% through rigorous functional and ad-hoc testing.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1779",
        "outlier": true
      },
      {
        "metric": "testing timeline",
        "value": "from six weeks to three weeks",
        "unit": "weeks",
        "kind": "time-reduction-abs",
        "wording": "Testing timelines were reduced from six weeks to three weeks, cutting project delivery times in half.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1780"
      },
      {
        "metric": "project delivery times",
        "value": "in half",
        "unit": "project delivery times",
        "kind": "other",
        "wording": "Testing timelines were reduced from six weeks to three weeks, cutting project delivery times in half.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1780"
      }
    ],
    "notSaid": [
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D1",
      "questionForVendor": "How were identified defects counted before and after the work, and over what period was the reported rise measured?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "shift-asia-home-improvement-ecommerce",
    "vendorSlug": "shift-asia",
    "title": "Case Study: Enhancing System Reliability for a Major Japanese eCommerce Platform",
    "sourceUrl": "https://shiftasia.com/case-studies/case-study-enhancing-system-reliability-for-a-major-japanese-ecommerce-platform/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1781",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a prominent retailer in Japan specializing in home improvement solutions",
      "segment": "enterprise-omnichannel",
      "region": "india-apac",
      "country": "Japan",
      "channels": [
        "stores",
        "ecommerce",
        "mobile"
      ],
      "sizeIndicators": [
        "operating large-scale home centers",
        "an extensive online shopping platform"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "namedWhere": "named in the summary (\"their eCommerce platform and mobile application\"); platform product not named"
      },
      {
        "system": "mobile-commerce",
        "namedWhere": "named in the summary (\"their eCommerce platform and mobile application\")"
      },
      {
        "system": "oms",
        "namedWhere": "named in the Challenges section (\"ordering, cataloging, order management, payment processing, and delivery functions\")"
      },
      {
        "system": "payments",
        "namedWhere": "named in the Challenges section (\"payment processing\")"
      }
    ],
    "problem": {
      "paraphrase": "A Japanese home improvement retailer with home centers, an online shop and logistics services had recurring backend problems behind its ecommerce site and mobile app. Ordering, catalog, order management, payment and delivery functions were affected by integration and scalability issues, which hurt platform performance.",
      "quote": "backend complexities resulted in recurring issues"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "mobile"
      ],
      "mainScope": "functional",
      "teamShape": "a SHIFT ASIA project manager as the primary point of communication with the client's teams, plus testers who designed and executed test cases",
      "duration": "over a multi-year engagement",
      "tooling": [
        "JIRA",
        "Confluence",
        "Backlog"
      ]
    },
    "metrics": [
      {
        "metric": "testing period",
        "value": "from six to three weeks",
        "unit": "weeks",
        "kind": "time-reduction-abs",
        "wording": "SHIFT ASIA shortened the testing period from six to three weeks, greatly improving the development process's speed.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1782"
      }
    ],
    "notSaid": [
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D2",
      "questionForVendor": "Which order management and payment flows were in the manual test scope, and how was the shorter testing period measured?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "shift-asia-saas-ecommerce-qa-system",
    "vendorSlug": "shift-asia",
    "title": "Build a comprehensive QA testing system for a well-known Japanese e-commerce platform",
    "sourceUrl": "https://shiftasia.com/case-studies/build-comprehensive-qa-system-e-commerce-platform/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1789",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a leading Japanese SaaS-based eCommerce platform empowering businesses of all sizes",
      "segment": "ecommerce-d2c",
      "region": "india-apac",
      "country": "Japan",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": []
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "namedWhere": "named in the client description (\"SaaS-based eCommerce platform\"); platform product not named"
      },
      {
        "system": "loyalty",
        "namedWhere": "named in the test design scope (\"Admin, User Interface, API, and Coupons/Loyalty program\")"
      },
      {
        "system": "payments",
        "namedWhere": "named in the Challenges section (\"critical to customers’ business and involves payments\")"
      }
    ],
    "problem": {
      "paraphrase": "The client runs a Japanese SaaS ecommerce platform used by online stores. Its own engineers could not keep up with testing, and the platform handles payments. It wanted to outsource testing, add test automation and work with a flexible test team.",
      "quote": "the internal engineering team struggled to keep pace with testing"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "automation",
        "api"
      ],
      "mainScope": "automation",
      "teamShape": "Japanese engineers assigned to review and inspect specifications; a flexible test team was a stated client requirement",
      "tooling": [
        "CAT",
        "Jenkins",
        "Selenium",
        "Allure",
        "JUnit",
        "Postman",
        "MongoDB",
        "PostgreSQL",
        "Robo 3T",
        "pgAdmin"
      ],
      "engagementModel": "managed-testing"
    },
    "metrics": [],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Payments and PCI scope",
      "dimension": "D4",
      "questionForVendor": "Which coupon, loyalty and payment flows does the automated regression suite cover, and what share of test design runs automatically?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "shift-asia-supermarket-membership-portal",
    "vendorSlug": "shift-asia",
    "title": "From Paper to Portal: How a Japanese Supermarket Modernised Membership Registration",
    "sourceUrl": "https://shiftasia.com/case-studies/from-paper-to-portal-how-a-japanese-supermarket-modernised-membership-registration/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1790",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "A major supermarket chain operating across Japan, with a large network of stores",
      "segment": "grocery",
      "region": "india-apac",
      "country": "Japan",
      "channels": [
        "stores",
        "mobile"
      ],
      "sizeIndicators": [
        "operating across Japan, with a large network of stores",
        "a loyal customer base built over decades"
      ]
    },
    "systems": [
      {
        "system": "loyalty",
        "namedWhere": "named in the client description (\"an established membership rewards program that offers discounts, point accumulation, and exclusive benefits\") and the solution (\"multi-device digital membership registration portal\")"
      }
    ],
    "problem": {
      "paraphrase": "A Japanese supermarket chain signed up members for its rewards program only on paper forms in store. Many shoppers gave up partway through the form, there was no way to register online or by phone, and store staff had no digital tool to help customers register.",
      "quote": "Staff lacked a digital tool for assisted registration"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "mobile"
      ],
      "mainScope": "functional",
      "teamShape": "team peaked at 9 members (5 developers, 3 testers and 1 BrSE) and contracted to three members for maintenance; testing was manual and ran alongside development",
      "duration": "six-month initial build phase; more than two-year engagement",
      "tooling": [
        "Vue.js",
        "Nuxt",
        "AWS Lambda"
      ]
    },
    "metrics": [
      {
        "metric": "time in production since launch",
        "value": "over two years",
        "unit": "years in production",
        "kind": "other",
        "wording": "The application has remained in active production for over two years since launch",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1791"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "cost",
      "what was out of scope",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D2",
      "questionForVendor": "What did the testers cover beyond registration forms, such as the link to existing membership data, and were any tests automated?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "testfort-dreamline-configurator-load",
    "vendorSlug": "testfort",
    "title": "End-to-End QA for DreamLine's Custom Product Configurator",
    "sourceUrl": "https://testfort.com/cases/testing-services-for-dreamline-wizard",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1715",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": true,
      "name": "DreamLine",
      "segment": "ecommerce-d2c",
      "region": "north-america",
      "country": "United States",
      "channels": [
        "ecommerce"
      ],
      "sizeIndicators": [
        "a global manufacturer of premium shower solutions",
        "With a large catalog of customizable products"
      ]
    },
    "systems": [
      {
        "system": "ecommerce-platform",
        "platformName": "PrestaShop and WordPress",
        "namedWhere": "named in the Technologies list (\"AngularJS, WordPress, PrestaShop\")"
      },
      {
        "system": "integrations",
        "namedWhere": "named in the Challenge section (data moving \"between the configurator, backend APIs, and eCommerce functionality\")"
      }
    ],
    "problem": {
      "paraphrase": "DreamLine sells customizable shower doors, enclosures and bases through an online store with a multi-step product configurator, where each choice changes the next questions. QA had to confirm that data passed correctly between the configurator, the backend APIs and the store without breaking product selection or order processing, and had to find slow endpoints and infrastructure limits.",
      "quote": "without introducing errors into product selection or order processing"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "api",
        "performance",
        "security",
        "automation"
      ],
      "mainScope": "functional",
      "teamShape": "2 Manual QAs, 1 Automation QA, 1 QA Lead",
      "duration": "5+ years",
      "tooling": [
        "Charles Proxy",
        "JMeter",
        "SoapUI Pro",
        "Netdata",
        "Selenium WebDriver",
        "BrowserStack",
        "Jira"
      ]
    },
    "metrics": [
      {
        "metric": "API requests processed in one hour",
        "value": "31,240",
        "unit": "API requests in one hour",
        "kind": "other",
        "numeric": 31240,
        "wording": "31,240 API requests processed in one hour",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-0582"
      },
      {
        "metric": "slowest average endpoint response",
        "value": "2.708",
        "unit": "sec",
        "kind": "other",
        "numeric": 2.708,
        "wording": "2.708 sec slowest average endpoint response",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-1716"
      },
      {
        "metric": "CPU utilization under production load",
        "value": "40",
        "kind": "other",
        "numeric": 40,
        "wording": "40 CPU utilization under production load",
        "claimedBy": "vendor",
        "verifiable": "partial",
        "evidenceId": "ev-1717"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "Peak-season readiness in 90 days",
      "dimension": "D3",
      "questionForVendor": "How many parallel users did the load test simulate, and is the CPU figure a percentage of production server capacity?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "testfort-supplement-retailer-mobile-app",
    "vendorSlug": "testfort",
    "title": "QA for a Famous Supplement Platform",
    "sourceUrl": "https://testfort.com/cases/qa-for-a-famous-supplement-platform",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1236",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a leading US-based retailer of natural products, offering nutritional supplements, personal care items",
      "segment": "ecommerce-d2c",
      "region": "north-america",
      "country": "United States",
      "channels": [
        "ecommerce",
        "mobile"
      ],
      "sizeIndicators": [
        "over 30,000 products shipped to millions of customers across 150+ countries"
      ]
    },
    "systems": [
      {
        "system": "mobile-commerce",
        "platformName": "Android and iOS apps",
        "namedWhere": "named in the Client and Challenge sections (new mobile app across Android, iOS and web)"
      },
      {
        "system": "payments",
        "namedWhere": "named in the Challenge section (\"payment methods\") and the Before list (\"Bugs in shipping and payment processes\")"
      }
    ],
    "problem": {
      "paraphrase": "An online retailer of supplements and personal care products launched a new mobile app for a customer base spread across many countries. It wanted a dedicated QA team to keep the app and website working the same way across Android, iOS and web, across languages and payment methods, while the catalog and traffic grew. The page lists platform inconsistencies, performance problems and shipping and payment bugs as the starting point.",
      "quote": "Bugs in shipping and payment processes"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "mobile"
      ],
      "mainScope": "mobile",
      "teamShape": "5 QA Engineers, 1 QA Team Lead",
      "duration": "3 years (ongoing)",
      "tooling": [
        "Charles Proxy",
        "Jira",
        "Exploratory Testing Tours"
      ],
      "engagementModel": "dedicated-team"
    },
    "metrics": [
      {
        "metric": "bug reports addressed",
        "value": "2000+",
        "unit": "bug reports",
        "kind": "defects",
        "wording": "2000+ bug reports addressed",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1237"
      },
      {
        "metric": "tickets tested",
        "value": "1000+",
        "unit": "tickets",
        "kind": "other",
        "wording": "1000+ tickets tested",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1238"
      },
      {
        "metric": "app store rating",
        "value": "4.6 to 4.9",
        "unit": "app rating",
        "kind": "satisfaction",
        "wording": "App rating improved from 4.6 to 4.9",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1239"
      },
      {
        "metric": "performance-related complaints",
        "value": "40",
        "unit": "%",
        "kind": "satisfaction",
        "numeric": 40,
        "wording": "40% decrease in performance-related complaints",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1240"
      }
    ],
    "notSaid": [
      "measurement method",
      "duration over which the metric held",
      "cost",
      "what was out of scope",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D1",
      "questionForVendor": "How was the drop in performance complaints counted, over what period, and from what starting number?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "testingxperts-apparel-distributor-ai-automation",
    "vendorSlug": "testingxperts",
    "title": "Scaled Quality at Speed: Up to 50% QA Cycle Reduction with AI-Enabled Automation for a US Apparel Distributor",
    "sourceUrl": "https://www.testingxperts.com/case-study/qa-cycle-reduction-with-ai-enabled-automation-for-us-apparel-distributor/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1651",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a leading US-based apparel distributor serving a vast retail ecosystem with imprinted clothing and accessories",
      "segment": "fashion-apparel",
      "region": "north-america",
      "country": "USA",
      "channels": [],
      "sizeIndicators": [
        "Revenue: $1B+"
      ]
    },
    "systems": [],
    "problem": {
      "paraphrase": "The distributor relied heavily on manual regression testing across a large and growing set of applications, which lengthened QA cycles and delayed releases. It needed automation built into its CI/CD workflows that could scale across that application footprint, and it had to handle sensitive user and transaction data during testing in line with privacy rules.",
      "quote": "Heavy reliance on manual regression testing led to longer QA cycles"
    },
    "approach": {
      "testingTypes": [
        "automation",
        "functional"
      ],
      "mainScope": "automation",
      "tooling": [
        "Tx-HyperAutomate",
        "UiPath",
        "Azure DevOps"
      ]
    },
    "metrics": [
      {
        "metric": "QA cycle time",
        "value": "50",
        "unit": "%",
        "kind": "time-reduction-pct",
        "numeric": 50,
        "wording": "50% Faster QA Cycles",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1652"
      },
      {
        "metric": "manual testing effort",
        "value": "60",
        "unit": "%",
        "kind": "time-reduction-pct",
        "numeric": 60,
        "wording": "60% Reduction in Manual Testing Effort",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1653"
      },
      {
        "metric": "release quality",
        "value": "30",
        "unit": "%",
        "kind": "other",
        "numeric": 30,
        "wording": "30% Improvement in Release Quality",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1654"
      },
      {
        "metric": "applications covered by automated regression suites",
        "value": "175+",
        "unit": "applications",
        "kind": "other",
        "wording": "Implemented robust automated regression suites across 175+ applications, reducing manual effort and accelerating QA cycles by up to 50%.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1655"
      },
      {
        "metric": "compliance with data privacy standards in test data handling",
        "value": "100",
        "unit": "%",
        "kind": "other",
        "numeric": 100,
        "wording": "Leveraged UiPath to automate test data creation, masking, and orchestration, ensuring 100% compliance with data privacy standards.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1656"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "First outsourced QA team for a mid-size retailer",
      "dimension": "D4",
      "questionForVendor": "Which of the automated applications serve retail ordering or customer channels, and how were the reported percentage gains measured?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "testingxperts-apparel-distributor-code-migration",
    "vendorSlug": "testingxperts",
    "title": "A Leading US-Based Apparel Distributor Achieves 98% Code Coverage in Legacy-to-Modern Code Migration",
    "sourceUrl": "https://www.testingxperts.com/case-study/apparel-distributor-achieves-code-coverage-legacy-to-moderncode-migration/",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1657",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "a leading technology-enabled apparel and accessory distributor serving the US and Canadian wholesale markets",
      "segment": "fashion-apparel",
      "region": "north-america",
      "country": "USA",
      "channels": [],
      "sizeIndicators": [
        "Revenue: $1B+"
      ]
    },
    "systems": [
      {
        "system": "integrations",
        "namedWhere": "named in the Challenges and Solution sections (live dependencies, including databases and APIs, and inter-component flows)"
      }
    ],
    "problem": {
      "paraphrase": "The distributor was moving code from VB.NET to C# and needed proof that every function behaved the same in both versions. Non-UI files needed exhaustive unit tests. The tests had to run first against the original VB.NET code to set a reference. Real flows with live databases and APIs had to be checked as well as isolated units.",
      "quote": "Even minor discrepancies could disrupt order processing or fulfillment accuracy."
    },
    "approach": {
      "testingTypes": [
        "automation",
        "functional",
        "api",
        "data-migration"
      ],
      "mainScope": "automation",
      "tooling": [
        "xUnit"
      ]
    },
    "metrics": [
      {
        "metric": "line code coverage across migrated components",
        "value": "98%+",
        "unit": "%",
        "kind": "coverage-pct",
        "wording": "98%+ Line Code Coverage Achieved",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1658"
      },
      {
        "metric": "functional equivalence validated",
        "value": "100",
        "unit": "%",
        "kind": "other",
        "numeric": 100,
        "wording": "100% Functional Equivalence Validated",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1659"
      },
      {
        "metric": "manual regression testing time",
        "value": "30-40",
        "unit": "%",
        "kind": "time-reduction-pct",
        "wording": "30-40% Reduction in Manual Regression Testing Time",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1660"
      },
      {
        "metric": "effort for future enhancements and refactoring",
        "value": "20-30",
        "unit": "%",
        "kind": "other",
        "wording": "20-30% Less Effort for Future Enhancements & Refactoring",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1661"
      }
    ],
    "notSaid": [
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "OMS or WMS replacement",
      "dimension": "D4",
      "questionForVendor": "Does the line coverage figure apply to the non-UI files only or to the whole migrated codebase, and which order processing functions were migrated?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "zensar-apparel-chain-mao-azure",
    "vendorSlug": "zensar",
    "title": "Large American Apparel Chain Gains Greater Business Agility",
    "sourceUrl": "https://www.zensar.com/assets/files/1WMDZ5vrdfT0AN1XI4wOxF/Large_American_Apparel_Chain_Greater_Business_Agility.pdf",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1740",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "An American specialty retailer",
      "segment": "fashion-apparel",
      "region": "north-america",
      "channels": [
        "stores",
        "ecommerce"
      ],
      "sizeIndicators": [
        "annual revenue of $600M",
        "1,200+ associates"
      ]
    },
    "systems": [
      {
        "system": "oms",
        "platformName": "Manhattan Active Omni (MAO)",
        "namedWhere": "named in the Overview section (Zensar's brief)"
      },
      {
        "system": "integrations",
        "platformName": "Azure-based integration backbone replacing Tibco and OCI integrations",
        "namedWhere": "named in the Overview section (Zensar's brief) and the Solution section"
      },
      {
        "system": "wms",
        "namedWhere": "named in the Solution section (end-to-end QA of related systems)"
      },
      {
        "system": "payments",
        "namedWhere": "named in the Solution section (end-to-end QA of related systems)"
      },
      {
        "system": "loyalty",
        "namedWhere": "named in the Solution section (end-to-end QA of related systems)"
      }
    ],
    "problem": {
      "paraphrase": "The womenswear retailer had outgrown its homegrown order management stack and relied on many legacy integrations that carried business logic. It chose Manhattan Active Omni and a new Azure integration layer, and needed help to design the integrations, assess the existing ones in depth and avoid disrupting its enterprise architecture during the rollout.",
      "quote": "limitations of its homegrown order management stack"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "performance"
      ],
      "mainScope": "functional",
      "teamShape": "Zensar Industry Solutions and Enterprise Architecture team working with the client's team",
      "tooling": []
    },
    "metrics": [
      {
        "metric": "test cases in the implementation",
        "value": "over 3,000",
        "unit": "test cases",
        "kind": "test-count",
        "wording": "Leveraging our extensive omnichannel and enterprise integration expertise, we delivered a flawless solution implementation experience with over 3,000 test cases.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1741"
      },
      {
        "metric": "performance benchmark level",
        "value": "3X",
        "unit": "times the existing sales",
        "kind": "other",
        "numeric": 3,
        "wording": "Benchmark performance at 3X the existing sales and integration, UAT, and post go-live support levels.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1742"
      },
      {
        "metric": "integrations standardized",
        "value": "150+",
        "unit": "integrations",
        "kind": "other",
        "wording": "150+ integrations standardized across the enterprise.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1743"
      },
      {
        "metric": "omnichannel points of interaction impacted",
        "value": "75+",
        "unit": "points of interaction",
        "kind": "other",
        "wording": "75+ omnichannel points of interaction impacted.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1744"
      },
      {
        "metric": "business logic removed from integration",
        "value": "100",
        "unit": "%",
        "kind": "other",
        "numeric": 100,
        "wording": "100 percent removal of business logic from integration.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1745"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "OMS or WMS replacement",
      "dimension": "D2",
      "questionForVendor": "How did you run the performance benchmark across Manhattan Active Omni and the Azure integrations, and which order volumes did it use?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "zensar-brownfield-wmos-automation",
    "vendorSlug": "zensar",
    "title": "Driving Next-generation Fulfillment with Manhattan WMOS 2010 and Brownfield Warehouse Automation",
    "sourceUrl": "https://www.zensar.com/insights/case-study/consumer-services/it-services/driving-next-generation-fulfillment-with-manhattan",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1757",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "A leading retailer operating a large distribution network",
      "segment": "enterprise-omnichannel",
      "channels": [],
      "sizeIndicators": [
        "operating a large distribution network"
      ]
    },
    "systems": [
      {
        "system": "wms",
        "platformName": "Manhattan WMOS 2010",
        "namedWhere": "named in the title, Summary and Client overview sections"
      },
      {
        "system": "integrations",
        "platformName": "goods-to-person robotics, automated packing, sortation, conveyors, MHE and enterprise interfaces",
        "namedWhere": "named in the Summary and Solution sections"
      }
    ],
    "problem": {
      "paraphrase": "The retailer's existing warehouse ran on Manhattan WMOS 2010 and needed more throughput, better use of storage and less manual work to cope with rising volumes and peak days. New automation, including goods-to-person robots, packing, sortation and conveyors, had to be connected to the existing platform while the warehouse kept operating.",
      "quote": "without disrupting business continuity"
    },
    "approach": {
      "testingTypes": [
        "functional"
      ],
      "mainScope": "functional",
      "tooling": []
    },
    "metrics": [
      {
        "metric": "sales growth supported",
        "value": "65",
        "unit": "%",
        "kind": "other",
        "numeric": 65,
        "wording": "65% Sales growth supported",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1758"
      },
      {
        "metric": "peak-day fulfillment rate increase",
        "value": "+34",
        "unit": "%",
        "kind": "other",
        "numeric": 34,
        "wording": "+34% Peak-day fulfillment rate increase",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1759"
      },
      {
        "metric": "units per hour for goods-to-person picks",
        "value": "3X",
        "unit": "units per hour",
        "kind": "other",
        "numeric": 3,
        "wording": "3X Higher UPH for GTP picks",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1760"
      },
      {
        "metric": "labor productivity",
        "value": "+25",
        "unit": "%",
        "kind": "other",
        "numeric": 25,
        "wording": "+25% productivity Improved labor efficiency through increased warehouse automation",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1761"
      },
      {
        "metric": "storage capacity",
        "value": "+50",
        "unit": "%",
        "kind": "other",
        "numeric": 50,
        "wording": "+50% storage capacity Expanded storage utilization using goods-to-person automation",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1762"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "OMS or WMS replacement",
      "dimension": "D2",
      "questionForVendor": "How did you test the robot, sortation and conveyor integrations with Manhattan WMOS while the warehouse stayed live?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "zensar-department-store-mawm-dc",
    "vendorSlug": "zensar",
    "title": "Leading US Department Store Modernizes Distribution Center Operations with Manhattan Active® Warehouse Management and Integrated Automation",
    "sourceUrl": "https://www.zensar.com/insights/case-study/consumer-services/it-services/manhattan-active-wm-implementation-warehouse-automation",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1751",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "A leading North American department store retailer",
      "segment": "enterprise-omnichannel",
      "region": "north-america",
      "country": "United States",
      "channels": [],
      "sizeIndicators": [
        "One of the largest department stores in the US"
      ]
    },
    "systems": [
      {
        "system": "wms",
        "platformName": "Manhattan Active Warehouse Management",
        "namedWhere": "named in the title, Summary and Solution sections"
      },
      {
        "system": "integrations",
        "platformName": "goods-to-person robotics, pocket sorter, auto boxer, auto bagger and enterprise systems",
        "namedWhere": "named in the Summary and Solution sections (Automation and enterprise integration)"
      }
    ],
    "problem": {
      "paraphrase": "To reach 800,000 units a day at peak, the retailer was building a new automated distribution center of about 3 million sq. ft. It needed a warehouse management platform connected to goods-to-person robots, sorters and packing machines and to its enterprise applications, able to serve several brands as it grew.",
      "quote": "required a modern warehouse management platform"
    },
    "approach": {
      "testingTypes": [
        "functional"
      ],
      "mainScope": "functional",
      "tooling": []
    },
    "metrics": [
      {
        "metric": "average fulfilment time",
        "value": "<1 day",
        "unit": "days",
        "kind": "time-reduction-abs",
        "wording": "<1 day Average fulfilment from -1.5-2 days",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1752"
      },
      {
        "metric": "throughput uplift from warehouse automation",
        "value": "+60",
        "unit": "%",
        "kind": "other",
        "numeric": 60,
        "wording": "+60% Throughput uplift - warehouse automation impact",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1753"
      },
      {
        "metric": "order-picking accuracy",
        "value": "100",
        "unit": "%",
        "kind": "other",
        "numeric": 100,
        "wording": "100% Order-picking accuracy",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1754"
      },
      {
        "metric": "peak-season throughput the client needed",
        "value": "800,000",
        "unit": "units per day",
        "kind": "other",
        "numeric": 800000,
        "wording": "The client needed a throughput of 800,000 units per day during peak season and was therefore commissioning around $700M distribution center spanning around 3 million sq. ft., and needed a modern warehouse management platform.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1755"
      },
      {
        "metric": "distribution center investment",
        "value": "around $700 million",
        "unit": "USD",
        "kind": "other",
        "wording": "One of the largest department stores in the US commissioned around $700 million distribution center spanning around 3 million sq. ft. and required a modern warehouse management platform.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1756"
      },
      {
        "metric": "distribution center floor area",
        "value": "around 3 million",
        "unit": "sq. ft.",
        "kind": "other",
        "wording": "One of the largest department stores in the US commissioned around $700 million distribution center spanning around 3 million sq. ft. and required a modern warehouse management platform.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1756"
      }
    ],
    "notSaid": [
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "OMS or WMS replacement",
      "dimension": "D2",
      "questionForVendor": "Which test types made up the end-to-end QA for the warehouse platform and robotics integrations, and was peak daily volume tested before go-live?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "zensar-home-furnishings-mao-oms",
    "vendorSlug": "zensar",
    "title": "Retail Giant Gains a Business Edge by Modernizing Order Management",
    "sourceUrl": "https://www.zensar.com/assets/files/1mdKdoiTxLg45JOBUu0DZa/Retail_Giant_Gains_Business_Edge_Modernizing_Order_Management.pdf",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1735",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "An upscale American home-furnishings company",
      "segment": "specialty",
      "region": "north-america",
      "channels": [
        "stores",
        "ecommerce"
      ],
      "sizeIndicators": [
        "6,000 employees",
        "annual revenue of ~$3B",
        "72 galleries",
        "39 outlets"
      ]
    },
    "systems": [
      {
        "system": "oms",
        "platformName": "Manhattan Active Omni (MAO)",
        "namedWhere": "named in the Overview section (Zensar's brief)"
      },
      {
        "system": "integrations",
        "namedWhere": "named in the Solution section (Deployment and optimization: Integration)"
      }
    ],
    "problem": {
      "paraphrase": "The retailer wanted a new order management system and business flows for its UK and EU operations on Manhattan Active Omni, with customizations and automation. Its own technology team lacked experience with a greenfield rollout of the full omnichannel suite and the customization it needed. Zensar was brought in for functional validation, automation of repetitive test cases and performance benchmarking.",
      "quote": "did not have the requisite expertise"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "automation",
        "performance"
      ],
      "mainScope": "functional",
      "teamShape": "Zensar Industry Solutions team working with the client's team; Zensar also took part in user acceptance testing",
      "tooling": [
        "Sierra"
      ]
    },
    "metrics": [
      {
        "metric": "functional test cases executed",
        "value": "3k+",
        "unit": "test cases",
        "kind": "test-count",
        "wording": "Executed 3k+ functional test cases",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1504"
      },
      {
        "metric": "defects identified",
        "value": "2k+",
        "unit": "defects",
        "kind": "defects",
        "wording": "Identified 2k+ defects",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1736"
      },
      {
        "metric": "major defects that required design changes",
        "value": "15",
        "unit": "major defects",
        "kind": "defects",
        "numeric": 15,
        "wording": "Among a total of ~2,000 defects identified, we found 15 major defects that warranted design changes.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1737"
      },
      {
        "metric": "regression effort reduction",
        "value": "50",
        "unit": "%",
        "kind": "time-reduction-pct",
        "numeric": 50,
        "wording": "Reduced regression effort by 50 percent",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1738"
      },
      {
        "metric": "major go-live initiatives supported",
        "value": "four",
        "unit": "go-live initiatives",
        "kind": "other",
        "numeric": 4,
        "wording": "In all, we supported four major go-live initiatives.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1739"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "OMS or WMS replacement",
      "dimension": "D4",
      "questionForVendor": "What was the regression effort before test automation on the Manhattan Active Omni rollout, and how was the reported reduction measured?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "zensar-oracle-retail-mulesoft-upgrade",
    "vendorSlug": "zensar",
    "title": "Global Luxury Home Furnishings Retailer Modernizes its Merchandising Backbone with Oracle Retail and MuleSoft",
    "sourceUrl": "https://www.zensar.com/insights/case-study/consumer-services/it-services/oracle-retail-mulesoft-modernization-luxury-retailer",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1763",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "A global luxury home furnishings retailer",
      "segment": "specialty",
      "channels": [
        "stores",
        "ecommerce"
      ],
      "sizeIndicators": [
        "extensive network of design galleries, digital channels, hospitality offerings, and distribution operations across North America and Europe"
      ]
    },
    "systems": [
      {
        "system": "integrations",
        "platformName": "MuleSoft 4.9",
        "namedWhere": "named in the Summary, Zensar's Brief and Solution sections"
      }
    ],
    "problem": {
      "paraphrase": "The retailer's Oracle Retail merchandising platform at version 19 was close to the end of standard support, and accumulated technical debt caused performance problems and long nightly batches. Because the platform exchanges data with order management, warehouse, point-of-sale, finance and analytics systems, it had to be upgraded together with its MuleSoft integration layer without stopping trading or distribution center work.",
      "quote": "Seven versions of accumulated technical debt"
    },
    "approach": {
      "testingTypes": [
        "functional",
        "api"
      ],
      "mainScope": "functional",
      "tooling": []
    },
    "metrics": [
      {
        "metric": "batch cycle time",
        "value": "50",
        "unit": "%",
        "kind": "other",
        "numeric": 50,
        "wording": "50% Faster batch cycles supporting more efficient overnight merchandising operations",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1764"
      },
      {
        "metric": "application performance across merchandising processes",
        "value": "3x-5x",
        "unit": "times",
        "kind": "other",
        "wording": "3x-5x Application performance improvement across critical merchandising processes",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1765"
      },
      {
        "metric": "unplanned downtime reduction",
        "value": "80",
        "unit": "%",
        "kind": "other",
        "numeric": 80,
        "wording": "80% Reduction in unplanned downtime, improving platform availability and operational resilience",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1766"
      },
      {
        "metric": "Oracle Retail versions upgraded in one step",
        "value": "7",
        "unit": "versions",
        "kind": "other",
        "numeric": 7,
        "wording": "7 versions Upgraded directly from Oracle Retail version 19 to version 26 in one transformation",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1767"
      },
      {
        "metric": "platform patch update time",
        "value": "Under 4 hours",
        "unit": "hours",
        "kind": "time-reduction-abs",
        "wording": "Under 4 hours Reduced the time required for platform patch updates",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1768"
      },
      {
        "metric": "transaction processing",
        "value": "40",
        "unit": "%",
        "kind": "other",
        "numeric": 40,
        "wording": "40% faster Improved transaction processing across the retail technology ecosystem",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1769"
      },
      {
        "metric": "interfaces migrated",
        "value": "200",
        "unit": "interfaces",
        "kind": "other",
        "numeric": 200,
        "wording": "200 interfaces Migrated interfaces spanning more than 30 connected applications",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1770"
      },
      {
        "metric": "connected applications spanned by migrated interfaces",
        "value": "more than 30",
        "unit": "applications",
        "kind": "other",
        "wording": "200 interfaces Migrated interfaces spanning more than 30 connected applications",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1770"
      },
      {
        "metric": "accounting discrepancies in before-and-after validation",
        "value": "Zero",
        "unit": "discrepancies",
        "kind": "defects",
        "numeric": 0,
        "wording": "Zero discrepancies Completed before-and-after accounting validation without discrepancies",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1771"
      },
      {
        "metric": "time to steady-state production support",
        "value": "within six days",
        "unit": "days",
        "kind": "other",
        "wording": "Moved the program to steady-state production support within six days of go-live",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1772"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "OMS or WMS replacement",
      "dimension": "D2",
      "questionForVendor": "How were the migrated interfaces tested before cutover, and what did before-and-after validation of inventory and cross-application data cover beyond accounting?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "zensar-sporting-goods-wms-rollout",
    "vendorSlug": "zensar",
    "title": "Leading Global Sporting Goods Retailer Standardizes Warehouse Operations Across 15 Countries",
    "sourceUrl": "https://www.zensar.com/insights/case-study/consumer-services/it-services/leading-global-sporting-goods-retailer-standardizes-warehouse-operations",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1746",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "A leading global sporting goods retailer",
      "segment": "specialty",
      "channels": [],
      "sizeIndicators": [
        "distribution centers in 15 countries",
        "initial rollout spanning six major regional distribution centers"
      ]
    },
    "systems": [
      {
        "system": "wms",
        "platformName": "Manhattan WMS",
        "namedWhere": "named in the title, Summary and Challenges sections"
      },
      {
        "system": "integrations",
        "platformName": "MHE platforms, carrier networks and external warehouse systems",
        "namedWhere": "named in the Solution section (Connected warehouse ecosystem)"
      }
    ],
    "problem": {
      "paraphrase": "The retailer chose Manhattan WMS to standardize warehouse operations, starting with six regional distribution centers and aiming at 15 countries. Several go-lives ran in parallel, so it needed a repeatable rollout. It also needed a data-driven testing approach that showed defects and release readiness end to end and cut its reliance on manual validation country by country.",
      "quote": "reduce reliance on manual country-by-country validation"
    },
    "approach": {
      "testingTypes": [
        "automation"
      ],
      "mainScope": "automation",
      "tooling": [
        "Sierra"
      ]
    },
    "metrics": [
      {
        "metric": "countries in the Manhattan WMS rollout",
        "value": "15",
        "unit": "countries",
        "kind": "other",
        "numeric": 15,
        "wording": "Zensar led the end-to-end Manhattan WMS implementation for the transformation across 15 countries.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1747"
      },
      {
        "metric": "reports developed",
        "value": "600+",
        "unit": "reports",
        "kind": "other",
        "wording": "Developed 600+ reports to improve visibility, monitoring, and decision-making.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1748"
      },
      {
        "metric": "business-specific enhancements delivered",
        "value": "35+",
        "unit": "enhancements",
        "kind": "other",
        "wording": "Implemented Manhattan WMS using Zensar's standardized deployment framework and reusable accelerators across distribution centers, delivering 35+ business-specific enhancements.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1749"
      },
      {
        "metric": "inventory accuracy",
        "value": "over 95",
        "unit": "%",
        "kind": "other",
        "wording": "Zensar enabled the client to establish a standardized warehouse operating model across 15 countries, improving inventory accuracy to over 95% and strengthening visibility across its global distribution network.",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1750"
      }
    ],
    "notSaid": [
      "baseline before the engagement",
      "measurement method",
      "duration over which the metric held",
      "team size on both sides",
      "cost",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "OMS or WMS replacement",
      "dimension": "D4",
      "questionForVendor": "How many Manhattan WMS test cases were automated across the country go-lives, and what share of country validation stayed manual?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  },
  {
    "slug": "zensar-teradata-bigquery-migration",
    "vendorSlug": "zensar",
    "title": "GCP Migration Ushers in the Next Phase of Growth for an American Retail Chain",
    "sourceUrl": "https://www.zensar.com/assets/files/3AxkAvageqRTjoJwL10Iqn/GCP-Migration-Ushers-in-the-next-phase-of-growth-for-an-American-ratail-chain.pdf",
    "sourceClass": "vendor-case-study",
    "sourceEvidenceId": "ev-1774",
    "grade": "secondary",
    "corroboratingSources": [],
    "client": {
      "named": false,
      "anonymisedAs": "A large US-based multi-channel retailer",
      "segment": "enterprise-omnichannel",
      "region": "north-america",
      "country": "United States",
      "channels": [],
      "sizeIndicators": [
        "600+ stores",
        "ten distribution centers",
        "over one million SKUs"
      ]
    },
    "systems": [
      {
        "system": "data-migration",
        "platformName": "Teradata to Google BigQuery",
        "namedWhere": "named in the Overview section (Zensar's brief) and the Solution section"
      }
    ],
    "problem": {
      "paraphrase": "The retailer wanted its departmental merchandise transfer application to run fully on Google Chrome without Internet Explorer components. It also had to move the data behind it from Teradata to Google BigQuery on a short timeline, because the Teradata contract was ending. Its staff knew mainly the legacy applications, and documentation of both systems was thin.",
      "quote": "the Teradata contract was ending"
    },
    "approach": {
      "testingTypes": [
        "data-migration"
      ],
      "mainScope": "data-migration",
      "tooling": []
    },
    "metrics": [
      {
        "metric": "queries and stored procedures migrated",
        "value": "60+",
        "unit": "queries and stored procedures",
        "kind": "other",
        "wording": "Migration of 60+ queries and stored procedures",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1775"
      },
      {
        "metric": "cost reduction against on-premises EDW",
        "value": "~30",
        "unit": "%",
        "kind": "cost",
        "wording": "~30 percent reduction in costs, in comparison to on-premises enterprise data warehouse (EDW) costs",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1776"
      },
      {
        "metric": "time to insights reduction",
        "value": "40-50",
        "unit": "%",
        "kind": "time-reduction-pct",
        "wording": "40-50 percent reduction in time to insights",
        "claimedBy": "vendor",
        "verifiable": "no",
        "evidenceId": "ev-1777"
      }
    ],
    "notSaid": [
      "duration over which the metric held",
      "team size on both sides",
      "what was out of scope",
      "whether the client is still a client",
      "date of the work"
    ],
    "readingNotes": {
      "scenario": "OMS or WMS replacement",
      "dimension": "D1",
      "questionForVendor": "Which error conditions did the custom SQL assertions cover, and how many test scenarios support the claim that all were validated?"
    },
    "status": "verified",
    "verifiedOn": "2026-10-30",
    "verifiedBy": "verifier",
    "collectedOn": "2026-10-30",
    "collectedBy": "collector-case"
  }
]