{"id":7379,"date":"2026-03-24T10:38:09","date_gmt":"2026-03-24T10:38:09","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/tracking-qa-checklist\/"},"modified":"2026-03-24T10:38:09","modified_gmt":"2026-03-24T10:38:09","slug":"tracking-qa-checklist","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/tracking-qa-checklist\/","title":{"rendered":"Tracking Qa Checklist: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Tracking"},"content":{"rendered":"\n

A Tracking Qa Checklist<\/strong> is a structured set of verification steps used to confirm that marketing and product data collection is accurate, complete, and consistent. In Conversion & Measurement<\/strong>, it acts as the safeguard between what you think<\/em> you\u2019re measuring and what your tools are actually<\/em> recording. In Tracking<\/strong>, it prevents common failures like missing events, double-counted conversions, broken parameters, and misattributed revenue.<\/p>\n\n\n\n

This matters because modern measurement depends on many moving parts\u2014tags, pixels, SDKs, consent systems, data layers, redirects, and server-side integrations. A strong Tracking Qa Checklist<\/strong> helps teams trust their numbers, make faster decisions, and avoid wasted spend driven by faulty data.<\/p>\n\n\n\n

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

A Tracking Qa Checklist<\/strong> is a repeatable quality assurance process for validating analytics and advertising instrumentation across websites, apps, and backend systems. It defines what to test<\/em>, how to test it<\/em>, and what \u201ccorrect\u201d looks like<\/em> for events, conversions, attribution inputs, and reporting outputs.<\/p>\n\n\n\n

The core concept is simple: measurement should be treated like a production system, not an afterthought. Business-wise, a Tracking Qa Checklist<\/strong> protects revenue decisions by ensuring that lead counts, purchase events, funnel drop-offs, and campaign performance are grounded in reliable data.<\/p>\n\n\n\n

Within Conversion & Measurement<\/strong>, it sits between implementation and reporting: it validates that the data feeding dashboards, attribution, and optimization is trustworthy. Within Tracking<\/strong>, it is the operational discipline that keeps tagging, event schemas, and integrations stable as sites and campaigns change.<\/p>\n\n\n\n

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

In Conversion & Measurement<\/strong>, small tracking defects create big business consequences. If one form submission event fails on a high-traffic landing page, your acquisition model can overvalue one channel and underfund another for weeks.<\/p>\n\n\n\n

A strong Tracking Qa Checklist<\/strong> delivers strategic value by:<\/p>\n\n\n\n

    \n
  • Improving decision quality:<\/strong> Bids, budgets, and creative decisions rely on accurate conversion signals.<\/li>\n
  • Reducing performance volatility:<\/strong> Prevents sudden reporting drops caused by site releases, consent changes, or tag conflicts.<\/li>\n
  • Protecting attribution inputs:<\/strong> UTM parameters, click IDs, and cross-domain sessions are fragile without QA.<\/li>\n
  • Creating competitive advantage:<\/strong> Teams that trust their numbers iterate faster and waste less spend.<\/li>\n<\/ul>\n\n\n\n

    In short, Tracking<\/strong> that isn\u2019t QA\u2019d becomes guesswork, and Conversion & Measurement<\/strong> becomes storytelling instead of analysis.<\/p>\n\n\n\n

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

    A Tracking Qa Checklist<\/strong> works best as a lifecycle workflow applied before launch, after launch, and continuously.<\/p>\n\n\n\n

      \n
    1. Input \/ trigger:<\/strong> A new campaign, landing page, checkout change, app release, consent update, or analytics configuration change triggers QA. In mature teams, scheduled audits also trigger routine checks.<\/li>\n
    2. Analysis \/ expected behavior:<\/strong> The team defines expected events, parameters, and outcomes (for example: \u201cpurchase fires once per order,\u201d \u201clead includes source\/medium,\u201d \u201ccurrency is always passed\u201d).<\/li>\n
    3. Execution \/ validation:<\/strong> QA is performed using a combination of functional testing (user flows), technical inspection (requests\/payloads), and reporting validation (do numbers match expectations across tools?).<\/li>\n
    4. Output \/ outcome:<\/strong> Issues are documented, fixed, and re-tested. The result is a \u201cQA pass\u201d record, known limitations (for example, consent-based gaps), and monitoring to catch regressions.<\/li>\n<\/ol>\n\n\n\n

      In practice, the best Tracking Qa Checklist<\/strong> is not a one-time document\u2014it\u2019s a system: requirements \u2192 implementation \u2192 verification \u2192 monitoring \u2192 change control.<\/p>\n\n\n\n

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

      A complete Tracking Qa Checklist<\/strong> typically includes the following building blocks:<\/p>\n\n\n\n

      Measurement specification<\/h3>\n\n\n\n

      A written definition of what \u201ccorrect\u201d means for Conversion & Measurement<\/strong>:\n– Conversion definitions (lead, sign-up, purchase, qualified lead, etc.)\n– Event names and required parameters\n– Source\/medium rules and UTM standards\n– Cross-domain or subdomain expectations\n– Deduplication logic (especially for server + browser events)<\/p>\n\n\n\n

      Implementation review<\/h3>\n\n\n\n

      Checks across the Tracking<\/strong> stack:\n– Tag placement and firing conditions\n– Data layer or event payload structure\n– Consent gating logic (when should tags fire?)\n– Redirect behavior and parameter preservation\n– Single-page app routing and virtual pageviews\/events<\/p>\n\n\n\n

      QA test plan (flows + edge cases)<\/h3>\n\n\n\n

      A practical checklist of scenarios:\n– Happy-path conversions\n– Errors and validation failures (form errors, payment failures)\n– Logged-in vs logged-out behavior\n– Returning users vs new users\n– Multiple tabs, refreshes, and back-button behavior (common causes of double-fires)<\/p>\n\n\n\n

      Governance and ownership<\/h3>\n\n\n\n

      A clear \u201cwho does what\u201d model:\n– Marketing owns campaign parameters and conversion definitions\n– Analytics owns schema, data quality rules, and dashboards\n– Engineering owns implementation and release management\n– Compliance\/privacy owns consent requirements<\/p>\n\n\n\n

      Documentation and change log<\/h3>\n\n\n\n

      A shared record of:\n– What changed, when, and why\n– Known issues and measurement limitations\n– Version history of events and conversions<\/p>\n\n\n\n

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

      There aren\u2019t universal formal \u201ctypes,\u201d but in Conversion & Measurement<\/strong> and Tracking<\/strong>, the most useful distinctions are contextual:<\/p>\n\n\n\n

      1) Pre-launch vs post-launch vs ongoing QA<\/h3>\n\n\n\n
        \n
      • Pre-launch QA:<\/strong> Validates requirements and implementation before exposure to traffic.<\/li>\n
      • Post-launch QA:<\/strong> Confirms real-world data is flowing and matches expectations.<\/li>\n
      • Ongoing QA:<\/strong> Detects regressions from site changes, CMP updates, or platform changes.<\/li>\n<\/ul>\n\n\n\n

        2) Web vs app vs server-side QA<\/h3>\n\n\n\n
          \n
        • Web QA:<\/strong> Tags, cookies, consent behavior, SPA routing, cross-domain journeys.<\/li>\n
        • App QA:<\/strong> SDK event parity, deep links, app-to-web flows, offline buffering.<\/li>\n
        • Server-side QA:<\/strong> Event deduplication, backend-to-ad-platform signals, latency, retries.<\/li>\n<\/ul>\n\n\n\n

          3) Campaign QA vs product funnel QA<\/h3>\n\n\n\n
            \n
          • Campaign QA:<\/strong> UTMs, click IDs, landing page events, call tracking, lead routing.<\/li>\n
          • Funnel QA:<\/strong> Step-by-step validation from first touch to conversion and revenue.<\/li>\n<\/ul>\n\n\n\n

            A mature Tracking Qa Checklist<\/strong> usually combines all three lenses.<\/p>\n\n\n\n

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

            Example 1: Paid lead generation landing page<\/h3>\n\n\n\n

            A company launches a new landing page with a form and thank-you screen. The Tracking Qa Checklist<\/strong> verifies:\n– The \u201clead\u201d event fires once per successful submission (not on button click).\n– UTM parameters persist through the form and are captured with the lead.\n– Consent settings prevent marketing tags from firing before consent, while essential analytics behavior is handled as designed.\nResult: Conversion & Measurement<\/strong> reports stable lead volumes, and Tracking<\/strong> supports accurate cost-per-lead optimization.<\/p>\n\n\n\n

            Example 2: Ecommerce checkout and purchase event integrity<\/h3>\n\n\n\n

            An ecommerce team updates the checkout. QA confirms:\n– Purchase fires only on the order confirmation (not on refresh).\n– Revenue, currency, tax\/shipping, and item details are present and correctly formatted.\n– Duplicate purchases are prevented when server-side and browser events are both used.\nResult: Finance reconciliation improves, attribution improves, and Conversion & Measurement<\/strong> aligns with actual revenue.<\/p>\n\n\n\n

            Example 3: Cross-domain journey from marketing site to app signup<\/h3>\n\n\n\n

            A SaaS business sends traffic from a marketing domain to an app domain for signup. The Tracking Qa Checklist<\/strong> includes:\n– Cross-domain session continuity (so source\/medium isn\u2019t lost).\n– Signup conversion recorded once, mapped to the correct acquisition source.\n– Identity stitching rules (anonymous to logged-in) validated.\nResult: Tracking<\/strong> reflects true channel performance, and Conversion & Measurement<\/strong> stops over-crediting \u201cdirect.\u201d<\/p>\n\n\n\n

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

            A well-run Tracking Qa Checklist<\/strong> produces benefits that are both technical and commercial:<\/p>\n\n\n\n

              \n
            • Higher marketing ROI:<\/strong> Better conversion signals lead to smarter bidding and creative testing.<\/li>\n
            • Lower wasted spend:<\/strong> Fewer false positives (double-fires) and fewer false negatives (missing conversions).<\/li>\n
            • Faster troubleshooting:<\/strong> Clear test steps reduce back-and-forth between marketing and engineering.<\/li>\n
            • More reliable experimentation:<\/strong> A\/B tests depend on consistent event collection and stable attribution.<\/li>\n
            • Better customer experience:<\/strong> QA catches broken forms, confusing funnels, and error states that hurt users\u2014not just analytics.<\/li>\n<\/ul>\n\n\n\n

              In Conversion & Measurement<\/strong>, the biggest win is confidence: teams stop arguing about numbers and start improving outcomes.<\/p>\n\n\n\n

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

              Even with a solid Tracking Qa Checklist<\/strong>, there are real barriers:<\/p>\n\n\n\n

                \n
              • Complex user journeys:<\/strong> Cross-device, logged-in experiences, and offline conversions complicate validation.<\/li>\n
              • Privacy and consent constraints:<\/strong> Consent choices legitimately reduce observability, creating data gaps that QA must document rather than \u201cfix.\u201d<\/li>\n
              • Tag interference and duplication:<\/strong> Multiple teams\/tools can fire overlapping events.<\/li>\n
              • Single-page apps and dynamic content:<\/strong> Route changes and late-loaded elements can break standard event triggers.<\/li>\n
              • Release velocity:<\/strong> Frequent deployments increase regression risk unless QA is embedded into the workflow.<\/li>\n
              • Attribution discrepancies:<\/strong> Different platforms apply different attribution logic; QA can validate inputs, but outputs will still vary.<\/li>\n<\/ul>\n\n\n\n

                Good Tracking<\/strong> QA is as much about managing expectations and documenting limitations as it is about finding bugs.<\/p>\n\n\n\n

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

                To make a Tracking Qa Checklist<\/strong> effective and scalable:<\/p>\n\n\n\n

                  \n
                1. Start with a measurement spec, not tool settings.<\/strong> Define conversions, event names, parameters, and success criteria for Conversion & Measurement<\/strong> before touching tags.<\/li>\n
                2. Validate at three levels:<\/strong>\n – In-browser<\/em> (did the tag fire?)\n – In-collection<\/em> (did the platform receive the payload?)\n – In-reporting<\/em> (does it appear correctly in dashboards?)<\/li>\n
                3. Test realistic flows and edge cases.<\/strong> Refreshes, back-button, multiple tabs, coupon retries, and partial form completion often reveal double-counting.<\/li>\n
                4. Use naming conventions and schema rules.<\/strong> Consistent event\/parameter naming makes Tracking<\/strong> maintainable and reduces reporting confusion.<\/li>\n
                5. Implement change control.<\/strong> Log updates to tags, consent rules, and conversions; link changes to releases and campaigns.<\/li>\n
                6. Create automated monitoring where possible.<\/strong> Alerts for sudden drops\/spikes in key events catch regressions faster than manual checks.<\/li>\n
                7. Document known limitations.<\/strong> For example: \u201cEvents may be undercounted for users who decline consent,\u201d which is critical context for Conversion & Measurement<\/strong>.<\/li>\n<\/ol>\n\n\n\n

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

                  A Tracking Qa Checklist<\/strong> is tool-assisted, even when the checklist itself is process-driven. Common tool categories include:<\/p>\n\n\n\n

                    \n
                  • Analytics tools:<\/strong> To confirm events, parameters, and funnel behavior in Conversion & Measurement<\/strong> reporting.<\/li>\n
                  • Tag management systems:<\/strong> To inspect triggers, variables, and version history for Tracking<\/strong> changes.<\/li>\n
                  • Browser debugging tools:<\/strong> To verify requests, payloads, cookies\/storage behavior, and network calls.<\/li>\n
                  • Consent management platforms:<\/strong> To validate consent states, region rules, and tag blocking behavior.<\/li>\n
                  • Ad platforms and conversion APIs:<\/strong> To confirm conversion receipt, deduplication, and event matching quality.<\/li>\n
                  • CRM and marketing automation systems:<\/strong> To verify lead capture integrity, source fields, and lifecycle stage mapping.<\/li>\n
                  • Data warehouses and transformation tools:<\/strong> To audit event completeness, deduplicate, and create QA queries.<\/li>\n
                  • Reporting dashboards and BI tools:<\/strong> To compare key totals across sources and detect anomalies.<\/li>\n<\/ul>\n\n\n\n

                    The key is choosing tools that let you validate both Tracking<\/strong> inputs and Conversion & Measurement<\/strong> outputs.<\/p>\n\n\n\n

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

                    While QA is a process, you can still measure its effectiveness. Useful metrics include:<\/p>\n\n\n\n

                      \n
                    • Event coverage:<\/strong> Percentage of critical events firing across tested flows.<\/li>\n
                    • Parameter completeness rate:<\/strong> Share of events that include required fields (value, currency, content IDs, lead source).<\/li>\n
                    • Duplicate rate:<\/strong> Frequency of double-counted conversions (often revealed by identical order IDs or timestamps).<\/li>\n
                    • Attribution integrity checks:<\/strong> Percentage of conversions with valid source\/medium or campaign fields.<\/li>\n
                    • Discrepancy rate:<\/strong> Gap between analytics conversions and backend\/CRM records (tracked over time).<\/li>\n
                    • Data freshness\/latency:<\/strong> Time from conversion to visibility in reporting (important for optimization loops).<\/li>\n
                    • QA cycle time:<\/strong> Time from change request to QA pass\u2014an efficiency metric for the team.<\/li>\n
                    • Regression frequency:<\/strong> How often releases break key Tracking<\/strong> events.<\/li>\n<\/ul>\n\n\n\n

                      These metrics anchor Conversion & Measurement<\/strong> in operational reality, not just dashboard outputs.<\/p>\n\n\n\n

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

                      Several trends are reshaping how a Tracking Qa Checklist<\/strong> is used in Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                        \n
                      • More automation:<\/strong> Automated tests for event firing and schema validation will become more common as teams treat Tracking<\/strong> like software quality.<\/li>\n
                      • AI-assisted anomaly detection:<\/strong> Systems can flag unusual drops\/spikes, shifting QA from periodic audits to continuous monitoring.<\/li>\n
                      • Privacy-driven measurement design:<\/strong> QA will increasingly validate consent-aware designs, aggregation, and modeled conversions\u2014plus clear documentation of gaps.<\/li>\n
                      • Server-side adoption:<\/strong> More organizations will QA server-side event pipelines, deduplication, and data contracts between services.<\/li>\n
                      • Stronger governance:<\/strong> Expect more emphasis on data contracts, versioning, and ownership as measurement becomes enterprise-critical.<\/li>\n<\/ul>\n\n\n\n

                        The Tracking Qa Checklist<\/strong> is evolving from a manual checklist into a measurement reliability program.<\/p>\n\n\n\n

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

                        Tracking Qa Checklist vs tracking plan<\/h3>\n\n\n\n

                        A tracking plan defines what<\/em> you intend to collect (events, parameters, definitions). A Tracking Qa Checklist<\/strong> verifies whether it actually works<\/em> in real user flows and reporting. In Conversion & Measurement<\/strong>, you need both: the plan for clarity and the checklist for correctness.<\/p>\n\n\n\n

                        Tracking Qa Checklist vs tag audit<\/h3>\n\n\n\n

                        A tag audit is a review of installed tags and configurations (often static). A Tracking Qa Checklist<\/strong> is scenario-based and outcome-based: it tests conversions end-to-end and validates business logic, not just presence of scripts.<\/p>\n\n\n\n

                        Tracking Qa Checklist vs data quality monitoring<\/h3>\n\n\n\n

                        Monitoring detects anomalies over time (drops, spikes, missing fields). A Tracking Qa Checklist<\/strong> is the structured validation performed during changes and releases. Monitoring is the safety net; the checklist is the gate.<\/p>\n\n\n\n

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

                        A Tracking Qa Checklist<\/strong> is valuable across roles:<\/p>\n\n\n\n

                          \n
                        • Marketers:<\/strong> To ensure campaign reporting and optimization signals are trustworthy in Conversion & Measurement<\/strong>.<\/li>\n
                        • Analysts:<\/strong> To protect dashboards, attribution, and experimentation from data contamination.<\/li>\n
                        • Agencies:<\/strong> To reduce client disputes, prove performance accurately, and standardize delivery.<\/li>\n
                        • Business owners and founders:<\/strong> To make budget decisions based on reliable Tracking<\/strong>, not misleading metrics.<\/li>\n
                        • Developers:<\/strong> To implement event schemas correctly, reduce regressions, and collaborate effectively with analytics teams.<\/li>\n<\/ul>\n\n\n\n

                          If you touch performance reporting, you benefit from understanding the Tracking Qa Checklist<\/strong> mindset.<\/p>\n\n\n\n

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

                          A Tracking Qa Checklist<\/strong> is a practical QA framework for ensuring analytics and conversion instrumentation is correct, complete, and stable. It matters because modern Conversion & Measurement<\/strong> depends on complex Tracking<\/strong> systems that can break silently and distort decisions. By combining clear specifications, end-to-end testing, governance, and monitoring, a Tracking Qa Checklist<\/strong> helps teams trust their data, optimize confidently, and scale measurement without chaos.<\/p>\n\n\n\n

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

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

                          At minimum: conversion definitions, critical events list, required parameters, a set of test flows (happy path + edge cases), and a reporting validation step to confirm the data appears correctly in Conversion & Measurement<\/strong> tools.<\/p>\n\n\n\n

                          2) How often should we run a Tracking Qa Checklist?<\/h3>\n\n\n\n

                          Run it before and after major releases, when launching new campaigns, and on a scheduled cadence (monthly or quarterly) for ongoing Tracking<\/strong> health. High-change sites benefit from continuous monitoring plus targeted QA.<\/p>\n\n\n\n

                          3) What\u2019s the biggest cause of inaccurate Tracking in practice?<\/h3>\n\n\n\n

                          Common causes include event double-firing, missing parameters (like value or currency), broken UTMs due to redirects, consent misconfiguration, and SPA route changes that prevent events from triggering.<\/p>\n\n\n\n

                          4) Can a Tracking Qa Checklist fix attribution discrepancies between platforms?<\/h3>\n\n\n\n

                          It can\u2019t force platforms to agree, but it can validate attribution inputs (UTMs, click IDs, referrers, cross-domain continuity) and ensure conversions are deduplicated. That\u2019s the part you control within Tracking<\/strong>.<\/p>\n\n\n\n

                          5) Who owns the Tracking Qa Checklist\u2014marketing or engineering?<\/h3>\n\n\n\n

                          Ownership should be shared. Marketing\/analytics typically owns definitions and acceptance criteria for Conversion & Measurement<\/strong>, while engineering owns implementation and release discipline. A single accountable owner (often analytics) helps coordination.<\/p>\n\n\n\n

                          6) How do we QA tracking when consent limits what we can collect?<\/h3>\n\n\n\n

                          QA should verify that consent behavior matches your policy and implementation (what fires in each consent state) and document expected gaps. A Tracking Qa Checklist<\/strong> should treat consent-driven loss as a known constraint, not a \u201cbug.\u201d<\/p>\n\n\n\n

                          7) What\u2019s a practical first step to improve our Tracking Qa Checklist?<\/h3>\n\n\n\n

                          Create a one-page measurement spec for your top 5 conversions, then build a test script for each conversion path (including refresh\/back-button scenarios). This immediately strengthens Conversion & Measurement<\/strong> reliability and reduces future Tracking<\/strong> regressions.<\/p>\n","protected":false},"excerpt":{"rendered":"

                          A **Tracking Qa Checklist** is a structured set of verification steps used to confirm that marketing and product data collection is accurate, complete, and consistent. In **Conversion & Measurement**, it acts as the safeguard between what you *think* you\u2019re measuring and what your tools are *actually* recording. In **Tracking**, it prevents common failures like missing events, double-counted conversions, broken parameters, and misattributed revenue.<\/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":[1890],"tags":[],"class_list":["post-7379","post","type-post","status-publish","format-standard","hentry","category-tracking"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7379","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=7379"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7379\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7379"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7379"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7379"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}