{"id":7007,"date":"2026-03-23T20:56:23","date_gmt":"2026-03-23T20:56:23","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/analytics-qa-checklist\/"},"modified":"2026-03-23T20:56:23","modified_gmt":"2026-03-23T20:56:23","slug":"analytics-qa-checklist","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/analytics-qa-checklist\/","title":{"rendered":"Analytics Qa Checklist: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Analytics"},"content":{"rendered":"\n

An Analytics Qa Checklist<\/strong> is a structured set of verification steps used to confirm that your tracking, attribution, reporting, and analysis are accurate enough to make decisions with confidence. In Conversion & Measurement<\/strong>, it acts like a safety system: it helps ensure that the numbers you use to judge performance reflect real user behavior\u2014not tagging mistakes, duplicated events, missing consent signals, or broken campaign parameters. In Analytics<\/strong>, it turns data from \u201cavailable\u201d into \u201creliable.\u201d<\/p>\n\n\n\n

Modern marketing moves fast: new landing pages, A\/B tests, consent updates, and channel shifts can break measurement silently. An Analytics Qa Checklist<\/strong> matters because it reduces blind spots, prevents costly optimization based on bad data, and creates repeatable standards across teams, platforms, and markets\u2014exactly what strong Conversion & Measurement<\/strong> programs require.<\/p>\n\n\n\n

What Is Analytics Qa Checklist?<\/h2>\n\n\n\n

An Analytics Qa Checklist<\/strong> is a documented, repeatable process for validating the quality of your measurement implementation and your reported metrics. \u201cQA\u201d (quality assurance) here means checking that data collection, processing, and reporting behave as intended across browsers, devices, environments, and user journeys.<\/p>\n\n\n\n

At its core, the concept is simple: define what \u201ccorrect tracking\u201d means for your business, then verify it continuously. The business meaning is bigger than technical tagging\u2014it\u2019s about protecting decision-making. If the \u201cpurchase\u201d event fires twice, you can overestimate revenue. If paid traffic loses parameters, you can undervalue a channel. If consent is mishandled, you can lose visibility or risk compliance. An Analytics Qa Checklist<\/strong> reduces these issues.<\/p>\n\n\n\n

Within Conversion & Measurement<\/strong>, it sits between strategy (what should be measured) and reporting (what you believe happened). Inside Analytics<\/strong>, it supports trustworthy event schemas, clean dimensions, accurate conversion definitions, stable dashboards, and repeatable insights.<\/p>\n\n\n\n

Why Analytics Qa Checklist Matters in Conversion & Measurement<\/h2>\n\n\n\n

In Conversion & Measurement<\/strong>, accuracy is leverage. When measurement is clean, you can confidently shift spend, refine messaging, and improve user experience. When it isn\u2019t, every decision becomes riskier and slower.<\/p>\n\n\n\n

An Analytics Qa Checklist<\/strong> delivers strategic value by:<\/p>\n\n\n\n

    \n
  • Preventing misallocation of budget<\/strong> caused by broken attribution, missing UTMs, or duplicated conversions.<\/li>\n
  • Improving experiment integrity<\/strong> so A\/B tests reflect behavior changes, not tracking differences between variants.<\/li>\n
  • Reducing reporting disputes<\/strong> by creating shared definitions (what counts as a lead, a qualified lead, a purchase).<\/li>\n
  • Protecting performance narratives<\/strong> for stakeholders who need clear evidence of outcomes.<\/li>\n
  • Creating competitive advantage<\/strong> because teams with reliable Analytics<\/strong> iterate faster and spot real opportunities sooner.<\/li>\n<\/ul>\n\n\n\n

    Good Conversion & Measurement<\/strong> is not only about collecting more data\u2014it\u2019s about collecting the right data, consistently.<\/p>\n\n\n\n

    How Analytics Qa Checklist Works<\/h2>\n\n\n\n

    An Analytics Qa Checklist<\/strong> works as an operational workflow applied whenever tracking is created, changed, or relied upon for decisions:<\/p>\n\n\n\n

      \n
    1. \n

      Input \/ Trigger<\/strong>\n A trigger might be a new campaign, a site release, a new conversion definition, a consent banner update, a CRM integration change, or a dashboard refresh. Any of these can alter Analytics<\/strong> outputs.<\/p>\n<\/li>\n

    2. \n

      Validation \/ Analysis<\/strong>\n You verify that tags fire correctly, events contain correct parameters, sessions are attributed to the right channels, and conversions reconcile across systems (site, backend, CRM). You also validate edge cases like refunds, cross-domain journeys, and logged-in flows.<\/p>\n<\/li>\n

    3. \n

      Execution \/ Fixes<\/strong>\n If issues are found, you adjust tag rules, event naming, data layer values, channel groupings, filters, or data ingestion mappings. You document the change so the same class of issue is less likely to recur.<\/p>\n<\/li>\n

    4. \n

      Output \/ Outcome<\/strong>\n The outcome is higher confidence in reporting and better decision-making in Conversion & Measurement<\/strong>\u2014fewer false alarms, fewer inflated KPIs, and fewer \u201cwe don\u2019t trust the dashboard\u201d moments.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

      In practice, the best Analytics Qa Checklist<\/strong> is not a one-time event. It becomes a cadence: pre-launch QA, post-launch monitoring, and periodic audits.<\/p>\n\n\n\n

      Key Components of Analytics Qa Checklist<\/h2>\n\n\n\n

      A robust Analytics Qa Checklist<\/strong> typically includes the following components, tailored to your measurement stack and goals in Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

      1) Measurement plan and definitions<\/h3>\n\n\n\n

      Clear definitions for:\n– Conversions (macro vs. micro)\n– Events and parameters (names, required fields, allowed values)\n– Attribution rules (what \u201csource\/medium\u201d should look like)\n– Identity rules (user vs. device, logged-in vs. anonymous)<\/p>\n\n\n\n

      2) Tagging and event implementation checks<\/h3>\n\n\n\n

      Verification of:\n– Correct firing conditions (page, click, form submit, server response)\n– Duplicate firing prevention\n– Required parameters present (value, currency, content type, lead type)\n– Cross-domain and subdomain handling where applicable<\/p>\n\n\n\n

      3) Data quality and governance<\/h3>\n\n\n\n

      Controls for:\n– Naming conventions and versioning\n– Access management and change approvals\n– Data retention considerations\n– Internal traffic handling and bot filtering assumptions<\/p>\n\n\n\n

      4) Consent and privacy validation<\/h3>\n\n\n\n

      Checks that:\n– Consent choices correctly influence data collection behavior\n– Analytics storage and ad storage behaviors align with your policy\n– Regions and jurisdictions are handled appropriately\n– Data minimization principles are respected<\/p>\n\n\n\n

      5) Reporting and reconciliation<\/h3>\n\n\n\n

      Ensuring that dashboards reflect:\n– Correct date\/time settings\n– Stable definitions across reports\n– Alignment between Analytics<\/strong> reports and backend\/CRM totals (within expected variance)<\/p>\n\n\n\n

      Types of Analytics Qa Checklist<\/h2>\n\n\n\n

      While there isn\u2019t one official taxonomy, an Analytics Qa Checklist<\/strong> is commonly adapted by context. The most useful distinctions in Conversion & Measurement<\/strong> are:<\/p>\n\n\n\n

      Pre-launch (implementation) QA<\/h3>\n\n\n\n

      Used before releasing new tags, new site sections, or new conversions. Focuses on correctness and completeness.<\/p>\n\n\n\n

      Post-launch (smoke test) QA<\/h3>\n\n\n\n

      Performed immediately after release to confirm production behavior matches staging expectations, including edge cases and real traffic.<\/p>\n\n\n\n

      Ongoing monitoring QA<\/h3>\n\n\n\n

      A scheduled routine (weekly\/monthly) that looks for anomalies: traffic drops, conversion spikes, channel shifts, parameter drift, or missing events.<\/p>\n\n\n\n

      Campaign-specific QA<\/h3>\n\n\n\n

      Validates that UTMs, landing pages, pixels\/events, and conversion definitions work correctly for a specific initiative.<\/p>\n\n\n\n

      Audit-style QA<\/h3>\n\n\n\n

      A deeper periodic review of schema design, governance, consent handling, and cross-system reconciliation\u2014often used during replatforming or tool migrations.<\/p>\n\n\n\n

      Real-World Examples of Analytics Qa Checklist<\/h2>\n\n\n\n

      Example 1: Ecommerce checkout tracking<\/h3>\n\n\n\n

      A retailer launches a new checkout. An Analytics Qa Checklist<\/strong> confirms the \u201cpurchase\u201d event fires once per order, includes correct revenue and currency, and excludes failed payments. It also checks that Conversion & Measurement<\/strong> reports separate shipping\/tax where required and that refunds are not mistakenly counted as new revenue. This protects ROAS and merchandising decisions based on Analytics<\/strong>.<\/p>\n\n\n\n

      Example 2: B2B lead gen with CRM handoff<\/h3>\n\n\n\n

      A SaaS company runs LinkedIn and search campaigns to a demo form. The Analytics Qa Checklist<\/strong> validates that form submissions are captured, lead source fields map correctly into the CRM, and duplicate leads aren\u2019t inflating conversion counts. It also checks that \u201cqualified lead\u201d status updates are reflected in reporting. This improves Conversion & Measurement<\/strong> by connecting spend to pipeline outcomes rather than just form fills.<\/p>\n\n\n\n

      Example 3: Cross-domain journey (marketing site \u2192 app)<\/h3>\n\n\n\n

      A subscription business sends users from a marketing domain to an app domain. An Analytics Qa Checklist<\/strong> verifies cross-domain tracking continuity, correct attribution preservation, and that subscription events include plan, billing period, and discount fields. Without this, Analytics<\/strong> may show conversions as \u201cdirect,\u201d causing bad channel optimization decisions.<\/p>\n\n\n\n

      Benefits of Using Analytics Qa Checklist<\/h2>\n\n\n\n

      Using an Analytics Qa Checklist<\/strong> consistently produces tangible benefits across Conversion & Measurement<\/strong> and operations:<\/p>\n\n\n\n

        \n
      • Higher confidence in optimization:<\/strong> Teams can act on insights without second-guessing the instrumentation.<\/li>\n
      • Faster debugging and releases:<\/strong> Standard checks reduce time spent hunting for errors after stakeholders notice a discrepancy.<\/li>\n
      • Lower wasted spend:<\/strong> Accurate conversion signals prevent over-investing in channels that only look effective due to tracking issues.<\/li>\n
      • Better customer experience:<\/strong> Cleaner measurement often reveals true friction points (form errors, checkout drop-offs) rather than phantom problems.<\/li>\n
      • Improved collaboration:<\/strong> Marketers, developers, and analysts align on definitions and acceptance criteria\u2014crucial for scalable Analytics<\/strong>.<\/li>\n<\/ul>\n\n\n\n

        Challenges of Analytics Qa Checklist<\/h2>\n\n\n\n

        An Analytics Qa Checklist<\/strong> is powerful, but it\u2019s not always easy to operationalize. Common challenges include:<\/p>\n\n\n\n

          \n
        • Complex user journeys:<\/strong> Cross-device, cross-domain, logged-in vs. anonymous flows can make Analytics<\/strong> behavior hard to validate.<\/li>\n
        • Tag sprawl and inconsistent naming:<\/strong> Years of accumulated events and ad tags can introduce duplicates and conflicting definitions.<\/li>\n
        • Attribution ambiguity:<\/strong> Even with perfect tagging, Conversion & Measurement<\/strong> involves modeling assumptions and platform differences.<\/li>\n
        • Privacy and consent constraints:<\/strong> Consent choices can reduce observability and complicate comparisons over time.<\/li>\n
        • Organizational friction:<\/strong> If developers, marketing ops, and analysts don\u2019t share a process, fixes may be delayed or overwritten.<\/li>\n<\/ul>\n\n\n\n

          The goal isn\u2019t perfection\u2014it\u2019s controlled, documented, and continuously improving measurement quality.<\/p>\n\n\n\n

          Best Practices for Analytics Qa Checklist<\/h2>\n\n\n\n

          To make an Analytics Qa Checklist<\/strong> work in real teams, treat it as a product: scoped, versioned, and continuously improved.<\/p>\n\n\n\n

          Make QA criteria explicit and testable<\/h3>\n\n\n\n

          Define pass\/fail rules such as \u201cpurchase fires once per transaction,\u201d \u201cUTM parameters persist to the conversion event,\u201d or \u201clead type is always present.\u201d<\/p>\n\n\n\n

          Build QA into the release process<\/h3>\n\n\n\n

          Add QA steps to:\n– Definition of done\n– Pre-release staging validation\n– Post-release production smoke tests<\/p>\n\n\n\n

          This makes Conversion & Measurement<\/strong> reliability a shared responsibility, not an afterthought.<\/p>\n\n\n\n

          Test the full funnel, not just events<\/h3>\n\n\n\n

          Validate that key metrics in Analytics<\/strong> match expected behavior across:\n– Landing page \u2192 engagement \u2192 conversion\n– Confirmation pages and backend confirmations\n– Refunds, cancellations, and duplicate submissions<\/p>\n\n\n\n

          Reconcile with source-of-truth systems<\/h3>\n\n\n\n

          Agree on what system is authoritative for which metric (orders, revenue, qualified leads). Then document acceptable variance ranges.<\/p>\n\n\n\n

          Monitor anomalies automatically where possible<\/h3>\n\n\n\n

          Set up alerts for sudden changes in:\n– Conversion rate\n– Event volume\n– Channel mix\n– Missing parameters<\/p>\n\n\n\n

          Keep a changelog and version your checklist<\/h3>\n\n\n\n

          When definitions change (e.g., \u201cqualified lead\u201d), update the Analytics Qa Checklist<\/strong> and communicate the impact to reporting consumers.<\/p>\n\n\n\n

          Tools Used for Analytics Qa Checklist<\/h2>\n\n\n\n

          An Analytics Qa Checklist<\/strong> is supported by tool categories rather than one specific product. In Conversion & Measurement<\/strong>, common tool groups include:<\/p>\n\n\n\n

            \n
          • Analytics tools:<\/strong> Event reporting, funnel analysis, attribution views, cohort checks, and anomaly investigation.<\/li>\n
          • Tag management systems:<\/strong> Rule-based deployment, preview\/debug modes, version control, and rollback support.<\/li>\n
          • Consent management platforms:<\/strong> Consent state validation and region-specific behavior testing.<\/li>\n
          • Automation and QA utilities:<\/strong> Scheduled checks, log-based monitoring, and scripted validation of endpoints or event payloads.<\/li>\n
          • Ad platforms and campaign managers:<\/strong> Conversion configuration review, offline conversion imports, and parameter consistency checks.<\/li>\n
          • CRM systems:<\/strong> Lead lifecycle validation, deduplication logic, and revenue\/pipeline reconciliation.<\/li>\n
          • Reporting dashboards and BI tools:<\/strong> KPI definitions, calculated fields review, and stakeholder-ready monitoring in Analytics<\/strong> workflows.<\/li>\n
          • SEO tools (supporting role):<\/strong> Landing page change detection and technical changes that can affect tagging and Conversion & Measurement<\/strong> (templates, redirects, canonical changes).<\/li>\n<\/ul>\n\n\n\n

            The tools matter less than the process: define expectations, validate consistently, and document outcomes.<\/p>\n\n\n\n

            Metrics Related to Analytics Qa Checklist<\/h2>\n\n\n\n

            The success of an Analytics Qa Checklist<\/strong> can be measured. Relevant indicators include:<\/p>\n\n\n\n

              \n
            • Data completeness:<\/strong> Percentage of key events containing required parameters (e.g., value, currency, lead type).<\/li>\n
            • Duplicate rate:<\/strong> Share of conversions\/events that appear duplicated due to firing rules or user behavior edge cases.<\/li>\n
            • Attribution health:<\/strong> Percentage of conversions with \u201cunknown\u201d or \u201cdirect\u201d that should have a campaign source (tracked via rules and benchmarks).<\/li>\n
            • Reconciliation variance:<\/strong> Difference between Analytics<\/strong> conversion totals and backend\/CRM totals, tracked over time.<\/li>\n
            • Time to detect \/ time to fix:<\/strong> How quickly anomalies are spotted and resolved after a release or campaign launch.<\/li>\n
            • Dashboard stability:<\/strong> Number of KPI definition changes and stakeholder-reported discrepancies per quarter.<\/li>\n<\/ul>\n\n\n\n

              These metrics connect QA effort directly to Conversion & Measurement<\/strong> reliability and decision speed.<\/p>\n\n\n\n

              Future Trends of Analytics Qa Checklist<\/h2>\n\n\n\n

              The next evolution of Analytics Qa Checklist<\/strong> practices is being shaped by automation, privacy, and changing signal quality:<\/p>\n\n\n\n

                \n
              • AI-assisted anomaly detection:<\/strong> More teams will rely on automated alerts that identify unusual shifts in conversions, attribution, or event payload patterns.<\/li>\n
              • More server-side and hybrid tracking:<\/strong> QA will increasingly include server-generated events, deduplication logic, and validation of event integrity across client\/server paths.<\/li>\n
              • Privacy-driven measurement design:<\/strong> Consent-aware QA steps will become standard in Conversion & Measurement<\/strong>, including regional behavior testing and modeling expectations.<\/li>\n
              • Stronger schema governance:<\/strong> Event catalogs, naming standards, and validation rules will mature, making Analytics<\/strong> implementations more maintainable.<\/li>\n
              • Personalization complexity:<\/strong> As experiences vary by audience segment, QA must test multiple variants and ensure measurement remains consistent.<\/li>\n<\/ul>\n\n\n\n

                In short, Analytics Qa Checklist<\/strong> work is moving from ad hoc debugging to continuous measurement engineering.<\/p>\n\n\n\n

                Analytics Qa Checklist vs Related Terms<\/h2>\n\n\n\n

                Analytics Qa Checklist vs Measurement Plan<\/h3>\n\n\n\n

                A measurement plan defines what<\/em> you intend to measure and why<\/em> (business goals, KPIs, event definitions). An Analytics Qa Checklist<\/strong> validates whether it\u2019s actually working<\/em> in production. You need both: the plan sets direction; the checklist verifies reality in Analytics<\/strong> and Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                Analytics Qa Checklist vs Tag Audit<\/h3>\n\n\n\n

                A tag audit is typically a periodic inventory and review of what tags exist and whether they should. An Analytics Qa Checklist<\/strong> is more operational and ongoing, focusing on correctness, event payload quality, attribution, and reporting outcomes\u2014not just tag presence.<\/p>\n\n\n\n

                Analytics Qa Checklist vs Data Quality Monitoring<\/h3>\n\n\n\n

                Data quality monitoring often refers to automated checks and alerts for anomalies. An Analytics Qa Checklist<\/strong> can include monitoring, but it also covers human validation steps (journey testing, consent validation, reconciliation) and release-based QA in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                Who Should Learn Analytics Qa Checklist<\/h2>\n\n\n\n

                An Analytics Qa Checklist<\/strong> is valuable across roles because measurement is cross-functional:<\/p>\n\n\n\n

                  \n
                • Marketers:<\/strong> To trust channel performance, creative tests, and conversion optimization decisions in Conversion & Measurement<\/strong>.<\/li>\n
                • Analysts:<\/strong> To reduce time spent explaining discrepancies and increase time spent on insights and forecasting within Analytics<\/strong>.<\/li>\n
                • Agencies:<\/strong> To standardize delivery, reduce client escalations, and speed onboarding across accounts.<\/li>\n
                • Business owners and founders:<\/strong> To ensure revenue and pipeline reporting is directionally correct before scaling spend.<\/li>\n
                • Developers:<\/strong> To understand measurement acceptance criteria, avoid regressions, and implement events reliably.<\/li>\n<\/ul>\n\n\n\n

                  Summary of Analytics Qa Checklist<\/h2>\n\n\n\n

                  An Analytics Qa Checklist<\/strong> is a repeatable set of quality checks that ensures your tracking, attribution, and reporting are accurate and stable. It matters because decisions in Conversion & Measurement<\/strong> are only as good as the data behind them. Implemented well, it becomes a shared operating standard that improves trust in Analytics<\/strong>, reduces wasted spend, speeds up releases, and makes performance insights more dependable.<\/p>\n\n\n\n

                  Frequently Asked Questions (FAQ)<\/h2>\n\n\n\n

                  1) What should an Analytics Qa Checklist include at minimum?<\/h3>\n\n\n\n

                  At minimum, include checks for event firing (correct triggers and no duplicates), required parameters, campaign attribution (UTMs and referrers), consent behavior, and reconciliation against a source-of-truth system for key conversions.<\/p>\n\n\n\n

                  2) How often should I run an Analytics Qa Checklist?<\/h3>\n\n\n\n

                  Run it before and after major releases, at the start of significant campaigns, and on a recurring cadence (often weekly for monitoring and quarterly for deeper audits) depending on how fast your Conversion & Measurement<\/strong> environment changes.<\/p>\n\n\n\n

                  3) What\u2019s the difference between QA in Analytics and \u201cdebugging\u201d?<\/h3>\n\n\n\n

                  Debugging is reactive\u2014fixing what\u2019s clearly broken. QA is proactive\u2014validating expected behavior and preventing errors from reaching reports, dashboards, and optimization decisions in Analytics<\/strong>.<\/p>\n\n\n\n

                  4) How do I QA conversion tracking when consent affects data collection?<\/h3>\n\n\n\n

                  Test consent states intentionally (accept, reject, partial choices) and confirm how events behave under each state. Then document expected gaps and how your Conversion & Measurement<\/strong> reporting will interpret them.<\/p>\n\n\n\n

                  5) Why do Analytics numbers differ from CRM or backend numbers even after QA?<\/h3>\n\n\n\n

                  Differences can come from timing, attribution windows, identity resolution, blocked tracking, refunds\/cancellations, or deduplication rules. An Analytics Qa Checklist<\/strong> should document expected variance ranges and the reconciliation method.<\/p>\n\n\n\n

                  6) What are the biggest red flags that my measurement needs QA?<\/h3>\n\n\n\n

                  Sudden conversion spikes\/drops after a release, rising \u201cdirect\/none\u201d attribution, missing key parameters, duplicated purchase\/lead events, and stakeholder loss of trust in dashboards are strong signals you need a tighter Analytics Qa Checklist<\/strong> process.<\/p>\n\n\n\n

                  7) Do small businesses really need an Analytics Qa Checklist?<\/h3>\n\n\n\n

                  Yes\u2014especially if budget is tight. Even a lightweight Analytics Qa Checklist<\/strong> focused on the top 3\u20135 conversions can prevent expensive mistakes and improve Conversion & Measurement<\/strong> decision-making quickly.<\/p>\n","protected":false},"excerpt":{"rendered":"

                  An **Analytics Qa Checklist** is a structured set of verification steps used to confirm that your tracking, attribution, reporting, and analysis are accurate enough to make decisions with confidence. In **Conversion & Measurement**, it acts like a safety system: it helps ensure that the numbers you use to judge performance reflect real user behavior\u2014not tagging mistakes, duplicated events, missing consent signals, or broken campaign parameters. In **Analytics**, it turns data from \u201cavailable\u201d into \u201creliable.\u201d<\/p>\n","protected":false},"author":10235,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1887],"tags":[],"class_list":["post-7007","post","type-post","status-publish","format-standard","hentry","category-analytics"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7007","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/users\/10235"}],"replies":[{"embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/comments?post=7007"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7007\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7007"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7007"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7007"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}