{"id":6919,"date":"2026-03-23T17:36:49","date_gmt":"2026-03-23T17:36:49","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/qa-dashboard\/"},"modified":"2026-03-23T17:36:49","modified_gmt":"2026-03-23T17:36:49","slug":"qa-dashboard","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/qa-dashboard\/","title":{"rendered":"Qa Dashboard: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Analytics"},"content":{"rendered":"\n

A Qa Dashboard<\/strong> is a purpose-built view that helps teams verify whether their marketing and product measurement is working as intended. In Conversion & Measurement<\/strong>, it acts like a control panel for trust: it highlights broken tracking, suspicious spikes, missing events, and data mismatches before they mislead decisions. In Analytics<\/strong>, it provides an ongoing \u201cmeasurement health\u201d readout so marketers and analysts can confidently interpret performance, not just report it.<\/p>\n\n\n\n

A modern Conversion & Measurement<\/strong> strategy depends on fast iterations\u2014new landing pages, new ad creatives, new funnels, new consent rules, and new app releases. Each change can silently break tags, events, attribution, or revenue reporting. A well-designed Qa Dashboard<\/strong> turns measurement QA from a sporadic fire drill into a continuous operational practice that protects Analytics<\/strong> integrity and keeps optimization grounded in reality.<\/p>\n\n\n\n

What Is Qa Dashboard?<\/h2>\n\n\n\n

A Qa Dashboard<\/strong> (quality assurance dashboard) is an organized set of checks, alerts, and diagnostic metrics that validate the accuracy, completeness, and consistency of measurement data. It is typically used to confirm that:<\/p>\n\n\n\n

    \n
  • tracking is firing when it should <\/li>\n
  • key events and conversions are being recorded correctly <\/li>\n
  • data flows align across systems (ads, site\/app, CRM, payments) <\/li>\n
  • anomalies are detected early (drops, spikes, duplicates)<\/li>\n<\/ul>\n\n\n\n

    The core concept is simple: instead of assuming data is correct, you prove<\/strong> it is correct\u2014continuously. The business meaning is even more important: if your measurement is wrong, you can optimize in the wrong direction, waste budget, misreport ROI, and lose stakeholder trust.<\/p>\n\n\n\n

    In Conversion & Measurement<\/strong>, a Qa Dashboard<\/strong> sits between implementation and optimization. It verifies that the measurement foundation (events, conversions, identifiers, consent states) is stable enough to support channel decisions and funnel improvements. Inside Analytics<\/strong>, it complements performance dashboards by focusing on data quality<\/strong> and tracking health<\/strong>, not just outcomes.<\/p>\n\n\n\n

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

    In practice, teams rarely fail because they lack data. They fail because they can\u2019t trust<\/strong> the data. A Qa Dashboard<\/strong> matters in Conversion & Measurement<\/strong> because it:<\/p>\n\n\n\n

      \n
    • Prevents costly mis-optimizations:<\/strong> If a conversion event breaks, ad platforms and bidding systems may \u201clearn\u201d from bad signals, pushing spend to the wrong audiences or placements. <\/li>\n
    • Protects reporting credibility:<\/strong> Executives will discount insights if numbers change unexpectedly or don\u2019t reconcile across tools. <\/li>\n
    • Enables faster experimentation:<\/strong> When measurement checks are automated, teams ship changes with confidence and can move from idea \u2192 test \u2192 learning quicker. <\/li>\n
    • Creates a competitive advantage:<\/strong> Reliable Analytics<\/strong> supports better budget allocation, stronger lifecycle marketing, and more accurate attribution\u2014even when competitors are still troubleshooting tracking.<\/li>\n<\/ul>\n\n\n\n

      In short, a Qa Dashboard<\/strong> turns Conversion & Measurement<\/strong> into an operational discipline rather than a reactive troubleshooting function.<\/p>\n\n\n\n

      How Qa Dashboard Works<\/h2>\n\n\n\n

      A Qa Dashboard<\/strong> is more practical than theoretical. While implementations vary, it usually works as a workflow with four stages:<\/p>\n\n\n\n

        \n
      1. \n

        Input (signals and expectations)<\/strong>\n The dashboard ingests event logs, conversion counts, tag firing data, server logs, form submissions, order data, and channel metrics. Just as important, it includes expected behavior: which events should fire, what \u201cnormal\u201d ranges look like, and what success criteria are for a release or campaign.<\/p>\n<\/li>\n

      2. \n

        Processing (validation and anomaly detection)<\/strong>\n The system compares actual data against rules and baselines. Examples include: \u201cpurchase events should not drop to zero,\u201d \u201ccheckout should have fewer events than product views,\u201d or \u201cpaid clicks should not exceed landing page sessions by a large margin.\u201d This step turns raw Analytics<\/strong> into quality checks.<\/p>\n<\/li>\n

      3. \n

        Application (triage and diagnosis)<\/strong>\n When something looks wrong, the Qa Dashboard<\/strong> helps users diagnose where the break occurred\u2014tag manager changes, consent behavior, a new URL pattern, payment provider issues, or a CRM sync delay. It often includes segmentation by device, browser, traffic source, campaign, or page template.<\/p>\n<\/li>\n

      4. \n

        Output (actions and confidence)<\/strong>\n The output is not only charts; it\u2019s decision support. The dashboard produces alerts, priority queues for fixes, and a clear \u201cmeasurement health\u201d status that allows Conversion & Measurement<\/strong> teams to ship changes safely and interpret Analytics<\/strong> responsibly.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        Key Components of Qa Dashboard<\/h2>\n\n\n\n

        A strong Qa Dashboard<\/strong> is built from a few foundational elements that map directly to how measurement fails in the real world:<\/p>\n\n\n\n

        Data inputs and connectors<\/h3>\n\n\n\n
          \n
        • Website\/app event streams (page views, key actions, ecommerce steps)<\/li>\n
        • Ad platform delivery and click data<\/li>\n
        • CRM and lifecycle events (lead created, MQL, SQL, won)<\/li>\n
        • Payment\/order systems (transactions, refunds, tax, currency)<\/li>\n
        • Consent and privacy signals (opt-in status, data collection mode)<\/li>\n<\/ul>\n\n\n\n

          Validation rules and baselines<\/h3>\n\n\n\n
            \n
          • Event firing expectations (which pages\/actions should trigger events)<\/li>\n
          • Funnel logic checks (step-to-step ratios, expected drop-off bands)<\/li>\n
          • Reconciliation logic (orders vs revenue vs transactions across systems)<\/li>\n
          • Anomaly detection thresholds (drops\/spikes, duplicates, missing fields)<\/li>\n<\/ul>\n\n\n\n

            Governance and responsibilities<\/h3>\n\n\n\n
              \n
            • Ownership for each metric and event (who fixes what)<\/li>\n
            • Release notes and change tracking (what changed and when)<\/li>\n
            • Access control and documentation (what the numbers mean)<\/li>\n<\/ul>\n\n\n\n

              Operational processes<\/h3>\n\n\n\n
                \n
              • QA checklists for launches<\/li>\n
              • On-call or escalation paths for tracking incidents<\/li>\n
              • Regular audits tied to Conversion & Measurement<\/strong> goals<\/li>\n<\/ul>\n\n\n\n

                These components ensure the Qa Dashboard<\/strong> supports Analytics<\/strong> across teams, not just a single analyst.<\/p>\n\n\n\n

                Types of Qa Dashboard<\/h2>\n\n\n\n

                \u201cQa Dashboard\u201d isn\u2019t a single standardized artifact; it usually reflects the organization\u2019s measurement architecture. Common, practical variations include:<\/p>\n\n\n\n

                  \n
                1. \n

                  Tagging and event QA dashboards<\/strong>\n Focus on whether events fire correctly, parameters are populated, and naming conventions are consistent. This is often the first line of defense in Conversion & Measurement<\/strong>.<\/p>\n<\/li>\n

                2. \n

                  Funnel and conversion integrity dashboards<\/strong>\n Validate that funnel steps behave logically (e.g., add-to-cart \u2265 checkout start \u2265 purchase). These dashboards emphasize conversion paths and measurement completeness in Analytics<\/strong>.<\/p>\n<\/li>\n

                3. \n

                  Revenue and pipeline reconciliation dashboards<\/strong>\n Compare marketing conversions to orders, revenue, refunds, and CRM outcomes. They help confirm ROI and attribution inputs are grounded in reality.<\/p>\n<\/li>\n

                4. \n

                  Experiment and release QA dashboards<\/strong>\n Monitor measurement before and after a deployment or A\/B test to ensure the test itself didn\u2019t break tracking or bias data collection.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

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

                  Example 1: Lead generation campaign QA for a B2B SaaS<\/h3>\n\n\n\n

                  A team launches new landing pages and paid search ads. The Qa Dashboard<\/strong> tracks form submit events, thank-you page loads, CRM lead creation, and email nurture enrollment. It flags that form submits are up but CRM leads are flat, revealing a webhook failure. In Conversion & Measurement<\/strong>, this prevents the team from scaling spend based on inflated front-end conversions. In Analytics<\/strong>, it preserves funnel truth from click to pipeline.<\/p>\n\n\n\n

                  Example 2: Ecommerce checkout measurement after a site redesign<\/h3>\n\n\n\n

                  After redesigning checkout, purchases drop 40% in dashboards. The Qa Dashboard<\/strong> compares payment provider orders vs recorded purchase events and finds orders are stable while the \u201cpurchase\u201d event stopped firing on a new confirmation URL pattern. The team fixes the rule and backfills data where possible. This protects Conversion & Measurement<\/strong> optimization and prevents unnecessary panic-driven UX rollbacks.<\/p>\n\n\n\n

                  Example 3: Consent mode change impacting attribution and conversions<\/h3>\n\n\n\n

                  A brand updates consent banners and sees sudden shifts in channel performance. The Qa Dashboard<\/strong> separates consented vs non-consented traffic, monitors modeled vs observed conversions, and checks whether key events are suppressed under certain consent states. The result is a clear understanding of what changed, so Analytics<\/strong> interpretation stays accurate and Conversion & Measurement<\/strong> decisions remain defensible.<\/p>\n\n\n\n

                  Benefits of Using Qa Dashboard<\/h2>\n\n\n\n

                  A well-run Qa Dashboard<\/strong> delivers measurable operational and business impact:<\/p>\n\n\n\n

                    \n
                  • Higher confidence in decision-making:<\/strong> Teams spend less time debating numbers and more time improving outcomes. <\/li>\n
                  • Faster incident detection:<\/strong> Catch broken tracking in hours, not weeks\u2014especially critical in high-spend campaigns. <\/li>\n
                  • Lower wasted ad spend:<\/strong> If conversions aren\u2019t tracked correctly, automated bidding can misallocate budget. <\/li>\n
                  • Improved cross-team efficiency:<\/strong> Developers, analysts, and marketers share a common \u201csource of truth\u201d for measurement health. <\/li>\n
                  • Better customer and audience experience:<\/strong> QA often uncovers UX issues (broken forms, payment errors) surfaced through measurement signals. <\/li>\n<\/ul>\n\n\n\n

                    In Conversion & Measurement<\/strong>, these benefits compound because accurate signals improve optimization loops. In Analytics<\/strong>, they raise the reliability of reporting, forecasts, and experiments.<\/p>\n\n\n\n

                    Challenges of Qa Dashboard<\/h2>\n\n\n\n

                    A Qa Dashboard<\/strong> also comes with real constraints and risks that teams should plan for:<\/p>\n\n\n\n

                      \n
                    • Data delays and mismatched definitions:<\/strong> Systems update on different schedules; \u201cconversion\u201d may mean different things across platforms. <\/li>\n
                    • False positives and alert fatigue:<\/strong> Overly sensitive rules can overwhelm teams and reduce trust in the dashboard. <\/li>\n
                    • Attribution complexity:<\/strong> Channel-reported conversions may not match onsite events due to view-through, modeling, or deduplication. <\/li>\n
                    • Privacy and consent limitations:<\/strong> Reduced identifiers can make some validations harder, changing how Analytics<\/strong> can be reconciled. <\/li>\n
                    • Ownership gaps:<\/strong> Without clear responsibility, QA findings become \u201cinteresting\u201d rather than actionable.<\/li>\n<\/ul>\n\n\n\n

                      The goal is not perfect certainty. It\u2019s consistent, transparent confidence suitable for Conversion & Measurement<\/strong> decision-making.<\/p>\n\n\n\n

                      Best Practices for Qa Dashboard<\/h2>\n\n\n\n

                      To make a Qa Dashboard<\/strong> truly useful (not just another report), apply these practices:<\/p>\n\n\n\n

                        \n
                      1. \n

                        Start with critical journeys, not everything<\/strong>\n Cover the top revenue or lead paths first: signup, checkout, request demo, contact sales. Build outward once core Conversion & Measurement<\/strong> signals are stable.<\/p>\n<\/li>\n

                      2. \n

                        Define measurement contracts<\/strong>\n For each key event, document: when it fires, required parameters, expected volume ranges, and owner. This turns Analytics<\/strong> tracking into an enforceable spec.<\/p>\n<\/li>\n

                      3. \n

                        Use layered checks<\/strong>\n Combine:\n – event-level validation (is it firing?)\n – funnel logic validation (does it make sense?)\n – system reconciliation (does it match orders\/CRM?)<\/p>\n<\/li>\n

                      4. \n

                        Set practical thresholds and seasonality-aware baselines<\/strong>\n A \u201c20% drop day-over-day\u201d rule may be noisy. Consider weekly comparisons, channel segmentation, and known campaign calendars.<\/p>\n<\/li>\n

                      5. \n

                        Tie alerts to playbooks<\/strong>\n Every alert should answer: What does this likely mean? Who investigates? What\u2019s the first diagnostic step? This makes the Qa Dashboard<\/strong> operational.<\/p>\n<\/li>\n

                      6. \n

                        Review regularly and prune<\/strong>\n Remove checks that never trigger or don\u2019t lead to action. A lean dashboard supports faster Conversion & Measurement<\/strong> response.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                        Tools Used for Qa Dashboard<\/h2>\n\n\n\n

                        A Qa Dashboard<\/strong> is usually assembled from a stack of systems rather than a single tool. Common tool groups include:<\/p>\n\n\n\n

                          \n
                        • Analytics tools<\/strong> for event collection, exploration, and segmentation (web\/app measurement and behavioral reporting). <\/li>\n
                        • Tag management systems<\/strong> to manage client-side and server-side tags, triggers, and variables. <\/li>\n
                        • Data warehouses and pipelines<\/strong> to centralize events, join datasets, and run validation queries at scale. <\/li>\n
                        • BI and reporting dashboards<\/strong> to visualize QA checks, trends, and alert states for stakeholders. <\/li>\n
                        • Automation and alerting<\/strong> (workflow tools, messaging alerts, scheduled jobs) to notify teams when checks fail. <\/li>\n
                        • Ad platforms and campaign managers<\/strong> as reference sources for spend, clicks, and platform-reported conversions. <\/li>\n
                        • CRM and marketing automation systems<\/strong> to validate lead lifecycle, deduplication, and revenue attribution inputs.<\/li>\n<\/ul>\n\n\n\n

                          The best stacks support both Conversion & Measurement<\/strong> QA and deeper Analytics<\/strong> investigations when anomalies appear.<\/p>\n\n\n\n

                          Metrics Related to Qa Dashboard<\/h2>\n\n\n\n

                          Because a Qa Dashboard<\/strong> is about measurement health, its metrics differ from pure performance KPIs. Useful QA-oriented metrics include:<\/p>\n\n\n\n

                            \n
                          • Event coverage rate:<\/strong> % of sessions\/users where required events are present (e.g., product view on product pages). <\/li>\n
                          • Conversion completeness:<\/strong> ratio of backend orders to tracked purchases; lead form submits to CRM leads created. <\/li>\n
                          • Duplicate event rate:<\/strong> frequency of double-firing conversions, often caused by tag misconfiguration. <\/li>\n
                          • Parameter fill rate:<\/strong> % of events with required fields (currency, value, campaign IDs, product IDs). <\/li>\n
                          • Funnel sanity ratios:<\/strong> step-to-step relationships (e.g., checkout start should not exceed add-to-cart). <\/li>\n
                          • Latency and freshness:<\/strong> how delayed data is across sources, critical for timely Analytics<\/strong> and daily Conversion & Measurement<\/strong> decisions. <\/li>\n
                          • Anomaly counts and time-to-resolution:<\/strong> number of incidents per week\/month and how quickly they\u2019re fixed.<\/li>\n<\/ul>\n\n\n\n

                            These metrics make QA progress measurable, not anecdotal.<\/p>\n\n\n\n

                            Future Trends of Qa Dashboard<\/h2>\n\n\n\n

                            Several shifts are changing how a Qa Dashboard<\/strong> is designed within Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                              \n
                            • More automation and intelligent alerting:<\/strong> Rules-based checks are increasingly paired with anomaly detection that adapts to seasonality and campaign patterns. <\/li>\n
                            • Server-side and first-party measurement growth:<\/strong> As browsers restrict tracking, QA will focus more on server events, data pipelines, and consent-aware collection. <\/li>\n
                            • Privacy-driven measurement design:<\/strong> Dashboards will separate observed vs modeled performance and validate data under different consent states. <\/li>\n
                            • Experimentation at scale:<\/strong> As teams run more tests, the Qa Dashboard<\/strong> will increasingly include release monitoring and guardrails for Analytics<\/strong> integrity. <\/li>\n
                            • Data contracts and schema governance:<\/strong> Measurement schemas will be treated more like product APIs, with versioning and validation baked into deployments.<\/li>\n<\/ul>\n\n\n\n

                              The direction is clear: Qa Dashboard<\/strong> capabilities are moving from \u201cnice to have\u201d to essential infrastructure for Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                              Qa Dashboard vs Related Terms<\/h2>\n\n\n\n

                              Understanding adjacent concepts prevents teams from expecting the wrong outcomes:<\/p>\n\n\n\n

                                \n
                              • \n

                                Qa Dashboard vs Performance Dashboard<\/strong>\n A performance dashboard answers \u201cHow are we doing?\u201d (ROAS, CAC, conversion rate). A Qa Dashboard<\/strong> answers \u201cCan we trust what we\u2019re seeing?\u201d They should complement each other in Analytics<\/strong>.<\/p>\n<\/li>\n

                              • \n

                                Qa Dashboard vs Data Quality Dashboard<\/strong>\n Data quality dashboards often focus on broader enterprise data issues (missing values, duplicates, pipeline failures). A Qa Dashboard<\/strong> is typically narrower and optimized for marketing and product Conversion & Measurement<\/strong> signals like events, conversions, and funnel logic.<\/p>\n<\/li>\n

                              • \n

                                Qa Dashboard vs Tag Audit<\/strong>\n A tag audit is often a point-in-time review of tracking implementations. A Qa Dashboard<\/strong> is ongoing monitoring with baselines, alerts, and operational workflows.<\/p>\n<\/li>\n<\/ul>\n\n\n\n

                                Who Should Learn Qa Dashboard<\/h2>\n\n\n\n

                                A Qa Dashboard<\/strong> is valuable across roles because measurement touches everyone:<\/p>\n\n\n\n

                                  \n
                                • Marketers<\/strong> learn to validate conversion signals before scaling spend and to interpret Analytics<\/strong> changes responsibly. <\/li>\n
                                • Analysts<\/strong> gain a systematic way to protect data integrity and reduce time spent on repetitive troubleshooting. <\/li>\n
                                • Agencies<\/strong> use a Qa Dashboard<\/strong> to standardize onboarding, reduce client reporting disputes, and prove measurement maturity in Conversion & Measurement<\/strong>. <\/li>\n
                                • Business owners and founders<\/strong> benefit from reliable ROI reporting and fewer surprises in board-level numbers. <\/li>\n
                                • Developers<\/strong> get clearer requirements, faster debugging, and fewer ambiguous \u201ctracking is broken\u201d tickets.<\/li>\n<\/ul>\n\n\n\n

                                  Summary of Qa Dashboard<\/h2>\n\n\n\n

                                  A Qa Dashboard<\/strong> is a quality assurance view that continuously verifies whether tracking and conversion data are accurate, complete, and consistent. It matters because Conversion & Measurement<\/strong> decisions are only as good as the data behind them. By operationalizing measurement checks, a Qa Dashboard<\/strong> strengthens Analytics<\/strong> trust, speeds incident detection, supports reliable experimentation, and prevents costly optimization mistakes.<\/p>\n\n\n\n

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

                                  What is a Qa Dashboard?<\/h3>\n\n\n\n

                                  A Qa Dashboard<\/strong> is a set of monitoring views and checks that validate whether events, conversions, and related data are being captured correctly. It focuses on data reliability rather than performance alone.<\/p>\n\n\n\n

                                  How is a Qa Dashboard different from a KPI dashboard?<\/h3>\n\n\n\n

                                  A KPI dashboard reports outcomes (conversion rate, revenue, CAC). A Qa Dashboard<\/strong> verifies that the underlying tracking and data pipelines are functioning so the KPI numbers can be trusted.<\/p>\n\n\n\n

                                  Which teams own a Qa Dashboard in Conversion & Measurement?<\/h3>\n\n\n\n

                                  Ownership is shared: marketing\/analytics typically define requirements and monitor alerts, while engineering or implementation specialists fix tagging, data pipeline, or integration issues. Clear metric ownership is essential in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                  What should I include first when building a Qa Dashboard?<\/h3>\n\n\n\n

                                  Start with the highest-impact flows: primary conversion events, key funnel steps, and reconciliation against a backend source (orders, CRM leads). Then add anomaly checks and parameter validation.<\/p>\n\n\n\n

                                  How does Analytics affect what a Qa Dashboard can validate?<\/h3>\n\n\n\n

                                  Analytics<\/strong> tooling determines what you can observe (events, user properties, consent states) and how quickly. Privacy settings, modeling, and data latency can limit direct reconciliation, so dashboards should show observed vs expected behavior transparently.<\/p>\n\n\n\n

                                  How often should a Qa Dashboard be reviewed?<\/h3>\n\n\n\n

                                  High-spend or high-volume businesses often review daily with automated alerts. Others may review weekly, plus before\/after releases, campaign launches, or major site changes\u2014any moment Conversion & Measurement<\/strong> signals are likely to shift.<\/p>\n\n\n\n

                                  Can a Qa Dashboard reduce ad waste?<\/h3>\n\n\n\n

                                  Yes. By catching broken or double-counted conversions early, a Qa Dashboard<\/strong> helps prevent automated bidding and budget allocation from optimizing toward incorrect signals, improving efficiency across Conversion & Measurement<\/strong> and Analytics<\/strong>.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                  A **Qa Dashboard** is a purpose-built view that helps teams verify whether their marketing and product measurement is working as intended. In **Conversion & Measurement**, it acts like a control panel for trust: it highlights broken tracking, suspicious spikes, missing events, and data mismatches before they mislead decisions. In **Analytics**, it provides an ongoing \u201cmeasurement health\u201d readout so marketers and analysts can confidently interpret performance, not just report it.<\/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-6919","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\/6919","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=6919"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/6919\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=6919"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=6919"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=6919"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}