{"id":6896,"date":"2026-03-23T16:45:17","date_gmt":"2026-03-23T16:45:17","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/metric-tree\/"},"modified":"2026-03-23T16:45:17","modified_gmt":"2026-03-23T16:45:17","slug":"metric-tree","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/metric-tree\/","title":{"rendered":"Metric Tree: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Analytics"},"content":{"rendered":"\n

A Metric Tree<\/strong> is a structured way to connect business outcomes (like revenue, pipeline, or retention) to the measurable drivers that teams can actually influence. In Conversion & Measurement<\/strong>, it acts like a map: it links \u201cwhat success means\u201d to \u201cwhat to track,\u201d \u201cwhat to improve,\u201d and \u201cwhere to look when performance changes.\u201d In Analytics<\/strong>, it becomes the backbone for consistent reporting, diagnosis, and decision-making.<\/p>\n\n\n\n

A strong Metric Tree matters because marketing measurement is noisy: multiple channels, delayed conversion cycles, changing attribution, and privacy constraints can make it hard to know what truly caused results. A Metric Tree reduces confusion by turning high-level goals into a clear hierarchy of metrics, definitions, and relationships that teams can monitor and optimize.<\/p>\n\n\n\n

2) What Is Metric Tree?<\/h2>\n\n\n\n

A Metric Tree<\/strong> is a hierarchical model that breaks a top-level business metric into the smaller component metrics that drive it. The top of the tree is the outcome you care about most (often called a north-star metric or primary KPI). Beneath it are the leading indicators and operational inputs that influence that outcome.<\/p>\n\n\n\n

At its core, a Metric Tree answers three practical questions:<\/p>\n\n\n\n

    \n
  • What are we trying to improve?<\/strong> (the top metric)<\/li>\n
  • What drives that metric?<\/strong> (the branches)<\/li>\n
  • What can teams change day-to-day?<\/strong> (the leaves: controllable inputs)<\/li>\n<\/ul>\n\n\n\n

    From a business perspective, a Metric Tree turns strategy into measurement. It clarifies how marketing activities relate to outcomes like purchases, qualified leads, trial-to-paid conversion, or churn reduction. Within Conversion & Measurement<\/strong>, it helps you connect user actions (clicks, sign-ups, add-to-cart, form submits) to conversion goals and revenue impact. Within Analytics<\/strong>, it provides a shared structure for dashboards, experimentation, and root-cause analysis.<\/p>\n\n\n\n

    3) Why Metric Tree Matters in Conversion & Measurement<\/h2>\n\n\n\n

    In Conversion & Measurement<\/strong>, teams often track too many metrics without understanding which ones matter most. A Metric Tree forces prioritization and creates alignment across channels, funnels, and teams.<\/p>\n\n\n\n

    Key reasons it matters:<\/p>\n\n\n\n

      \n
    • Strategic clarity:<\/strong> It defines which metric is the \u201csource of truth\u201d and how supporting metrics roll up into it.<\/li>\n
    • Better decisions:<\/strong> When results move, the tree guides you to likely drivers (traffic quality, landing page conversion, lead quality, sales acceptance, retention).<\/li>\n
    • More efficient optimization:<\/strong> Instead of random tweaks, you optimize the specific branch that constrains the outcome.<\/li>\n
    • Competitive advantage:<\/strong> Organizations with a solid Metric Tree iterate faster because they diagnose issues quickly and scale what works with confidence.<\/li>\n
    • Cleaner communication:<\/strong> Executives, marketers, analysts, and developers can discuss performance using a shared measurement language grounded in Analytics<\/strong>.<\/li>\n<\/ul>\n\n\n\n

      4) How Metric Tree Works<\/h2>\n\n\n\n

      A Metric Tree is more conceptual than procedural, but it \u201cworks\u201d in practice through a repeatable operating loop:<\/p>\n\n\n\n

      1) Input \/ trigger (goal or performance change)<\/strong>\nYou start with a business goal (e.g., increase paid conversions by 15%) or a signal (e.g., revenue down 8% month-over-month).<\/p>\n\n\n\n

      2) Analysis \/ processing (decompose and quantify drivers)<\/strong>\nYou use the Metric Tree to break the top metric into drivers. For example, revenue might decompose into: sessions \u00d7 conversion rate \u00d7 average order value. Each branch can be decomposed further (sessions by channel, conversion rate by step, average order value by product mix).<\/p>\n\n\n\n

      3) Execution \/ application (choose levers and run actions)<\/strong>\nTeams select the most influential or most constrained branch and take action: improve page speed, adjust ad targeting, update onboarding, fix tracking, refine lead scoring, or run A\/B tests.<\/p>\n\n\n\n

      4) Output \/ outcome (monitor impact and learn)<\/strong>\nResults are tracked using Analytics<\/strong> dashboards and experiments. The tree helps interpret whether improvements are real, where they came from, and whether trade-offs occurred (e.g., higher conversion but lower lead quality).<\/p>\n\n\n\n

      In mature Conversion & Measurement<\/strong>, the Metric Tree becomes the structure behind regular performance reviews, growth experiments, and forecasting.<\/p>\n\n\n\n

      5) Key Components of Metric Tree<\/h2>\n\n\n\n

      A practical Metric Tree includes more than a diagram. The most effective ones combine measurement structure, data definitions, and ownership:<\/p>\n\n\n\n

      Core elements<\/h3>\n\n\n\n
        \n
      • Top metric (primary KPI):<\/strong> The outcome metric that reflects business success (revenue, pipeline, activated users, retained customers).<\/li>\n
      • Driver metrics:<\/strong> The key factors that mathematically or behaviorally influence the top metric (conversion rate, qualified lead rate, retention rate).<\/li>\n
      • Input metrics:<\/strong> Controllable \u201cleaf\u201d metrics tied to actions (landing page CTR, checkout errors, email deliverability, demo booked rate).<\/li>\n
      • Metric definitions:<\/strong> Clear calculation rules (numerator\/denominator, time windows, inclusion\/exclusion criteria).<\/li>\n
      • Segments and dimensions:<\/strong> Channel, campaign, device, geography, audience, product tier\u2014critical for diagnosing performance.<\/li>\n<\/ul>\n\n\n\n

        Systems, processes, and responsibilities<\/h3>\n\n\n\n
          \n
        • Instrumentation and tracking plan:<\/strong> Events, properties, and IDs required for accurate Analytics<\/strong>.<\/li>\n
        • Data quality checks:<\/strong> Monitoring for missing events, duplicates, bot traffic, consent changes, and schema drift.<\/li>\n
        • Governance and ownership:<\/strong> Who owns each metric branch (marketing, product, sales, data team), and how changes are approved.<\/li>\n
        • Reporting cadence:<\/strong> Weekly performance reviews, monthly business reviews, and experiment readouts aligned to Conversion & Measurement<\/strong> goals.<\/li>\n<\/ul>\n\n\n\n

          6) Types of Metric Tree<\/h2>\n\n\n\n

          There aren\u2019t universal \u201cofficial\u201d types, but there are common approaches depending on the business model and measurement maturity. In practice, teams use different Metric Tree designs for different questions:<\/p>\n\n\n\n

            \n
          • North-star Metric Tree:<\/strong> Starts with one primary metric (e.g., activated users) and decomposes into drivers across acquisition, activation, and retention.<\/li>\n
          • Funnel Metric Tree:<\/strong> Maps each funnel step (visit \u2192 signup \u2192 activate \u2192 purchase) and breaks each step into rate-based drivers and causes.<\/li>\n
          • Channel Performance Metric Tree:<\/strong> Starts with a top outcome (revenue or pipeline) and decomposes by channel contribution and efficiency (paid search, organic, email, partners).<\/li>\n
          • Unit economics Metric Tree:<\/strong> Connects revenue to cost drivers (CAC, payback period, margin) to support budget allocation decisions in Conversion & Measurement<\/strong>.<\/li>\n
          • Customer lifecycle Metric Tree:<\/strong> Organizes metrics by stage (awareness, consideration, conversion, onboarding, retention) to support lifecycle marketing and product-led growth.<\/li>\n<\/ul>\n\n\n\n

            7) Real-World Examples of Metric Tree<\/h2>\n\n\n\n

            Example 1: E-commerce revenue diagnosis<\/h3>\n\n\n\n

            Top metric: Revenue<\/strong>\nBranches: Sessions \u00d7 Conversion Rate \u00d7 Average Order Value\nSub-branches:\n– Sessions \u2192 channel mix, brand vs non-brand search, paid spend efficiency\n– Conversion Rate \u2192 product page add-to-cart rate, checkout completion rate, payment error rate\n– Average Order Value \u2192 discount rate, bundle attach rate, shipping threshold behavior<\/p>\n\n\n\n

            In Analytics<\/strong>, this Metric Tree makes it clear whether a revenue drop is caused by traffic loss, funnel friction, or basket size changes\u2014each requiring a different fix in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

            Example 2: B2B pipeline growth and lead quality<\/h3>\n\n\n\n

            Top metric: Sales-qualified pipeline<\/strong>\nBranches: Leads \u00d7 MQL rate \u00d7 SQL rate \u00d7 average deal size\nSub-branches:\n– Leads \u2192 channel volume, landing page conversion, event registrations\n– MQL rate \u2192 lead scoring model, form field completeness, persona fit\n– SQL rate \u2192 sales follow-up speed, meeting booked rate, disqualification reasons<\/p>\n\n\n\n

            A Metric Tree prevents the common trap of optimizing for lead volume while quality declines. It keeps Conversion & Measurement<\/strong> tied to downstream outcomes, not vanity metrics.<\/p>\n\n\n\n

            Example 3: SaaS trial-to-paid conversion improvement<\/h3>\n\n\n\n

            Top metric: New paid subscriptions<\/strong>\nBranches: Trials started \u00d7 activation rate \u00d7 trial-to-paid rate\nSub-branches:\n– Trials started \u2192 pricing page CTR, signup completion, authentication errors\n– Activation rate \u2192 first key action completion, onboarding engagement, time-to-value\n– Trial-to-paid \u2192 paywall friction, value messaging, plan fit, support interactions<\/p>\n\n\n\n

            This Metric Tree helps teams prioritize: if activation is the constraint, more top-of-funnel spend won\u2019t help. Analytics<\/strong> reveals which activation steps predict purchase and where drop-offs occur.<\/p>\n\n\n\n

            8) Benefits of Using Metric Tree<\/h2>\n\n\n\n

            A well-maintained Metric Tree delivers tangible operational benefits:<\/p>\n\n\n\n

              \n
            • Faster root-cause analysis:<\/strong> When a KPI moves, you know where to look first.<\/li>\n
            • Better experimentation:<\/strong> Hypotheses map to specific branches, making tests easier to design and interpret in Analytics<\/strong>.<\/li>\n
            • More efficient spend:<\/strong> Budget shifts become evidence-based, improving ROI across Conversion & Measurement<\/strong> programs.<\/li>\n
            • Cross-team alignment:<\/strong> Marketing, product, sales, and data teams share definitions and targets.<\/li>\n
            • Improved customer experience:<\/strong> Optimizing driver metrics often reduces friction (faster checkout, clearer onboarding, better messaging).<\/li>\n<\/ul>\n\n\n\n

              9) Challenges of Metric Tree<\/h2>\n\n\n\n

              Metric trees fail when they become theoretical or disconnected from real data and decisions. Common barriers include:<\/p>\n\n\n\n

                \n
              • Ambiguous definitions:<\/strong> If \u201cconversion\u201d or \u201cqualified lead\u201d means different things to different teams, Analytics<\/strong> reporting becomes inconsistent.<\/li>\n
              • Missing instrumentation:<\/strong> You can\u2019t build reliable branches without consistent tracking, identity resolution, and event taxonomy.<\/li>\n
              • Over-complexity:<\/strong> Trees with too many layers become hard to maintain and harder to act on.<\/li>\n
              • Attribution limitations:<\/strong> Some drivers are correlated but not causal; a Metric Tree must be paired with experiments and careful interpretation.<\/li>\n
              • Organizational ownership gaps:<\/strong> If no team owns a branch, issues persist and Conversion & Measurement<\/strong> stalls.<\/li>\n<\/ul>\n\n\n\n

                10) Best Practices for Metric Tree<\/h2>\n\n\n\n

                To make a Metric Tree actionable (not decorative), use these practices:<\/p>\n\n\n\n

                  \n
                • Start with one top metric per objective:<\/strong> Avoid multiple \u201cprimary\u201d KPIs for the same initiative.<\/li>\n
                • Use rate-based drivers when possible:<\/strong> Rates (conversion rate, activation rate) reveal efficiency changes better than raw counts.<\/li>\n
                • Limit depth to what you can act on:<\/strong> Two to four levels is often enough for clarity and ownership.<\/li>\n
                • Document definitions next to the tree:<\/strong> Include formulas, time windows, and segmentation rules.<\/li>\n
                • Assign owners to branches:<\/strong> Every major driver should have a team accountable for improvement.<\/li>\n
                • Validate with historical data:<\/strong> Use Analytics<\/strong> to see whether the proposed drivers explain past changes.<\/li>\n
                • Review and prune quarterly:<\/strong> Products change, channels change, and tracking changes\u2014your Metric Tree should evolve with them.<\/li>\n
                • Pair with experimentation:<\/strong> Use A\/B testing, holdouts, or quasi-experiments to distinguish causation from correlation in Conversion & Measurement<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                  11) Tools Used for Metric Tree<\/h2>\n\n\n\n

                  A Metric Tree is a framework, but it relies on tooling to implement and maintain it across Conversion & Measurement<\/strong> and Analytics<\/strong> workflows:<\/p>\n\n\n\n

                    \n
                  • Analytics tools:<\/strong> Web\/app analytics and event-based measurement to track behaviors and funnel steps.<\/li>\n
                  • Tag management and instrumentation systems:<\/strong> Centralized control of tags, event schemas, and deployment processes.<\/li>\n
                  • Product analytics and experimentation platforms:<\/strong> Funnel analysis, cohorts, feature impact, and test measurement.<\/li>\n
                  • Data warehouse and transformation tools:<\/strong> Unified datasets, modeled metrics, and consistent calculations at scale.<\/li>\n
                  • BI and reporting dashboards:<\/strong> Metric Tree-aligned scorecards, drill-downs, and alerts for changes in key branches.<\/li>\n
                  • CRM and marketing automation systems:<\/strong> Lead stages, lifecycle attribution inputs, and conversion feedback loops.<\/li>\n
                  • Ad platforms and campaign systems:<\/strong> Spend, impressions, clicks, and on-platform conversion signals to connect acquisition to outcomes.<\/li>\n
                  • SEO tools:<\/strong> Visibility, query intent, and landing page performance inputs that feed organic growth branches in Conversion & Measurement<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                    12) Metrics Related to Metric Tree<\/h2>\n\n\n\n

                    A Metric Tree typically combines outcome metrics, driver metrics, and diagnostic metrics. Common categories include:<\/p>\n\n\n\n

                    Performance and conversion metrics<\/h3>\n\n\n\n
                      \n
                    • Conversion rate (by step and overall)<\/li>\n
                    • Cost per acquisition (CPA) or cost per lead (CPL)<\/li>\n
                    • Activation rate and time-to-value<\/li>\n
                    • Trial-to-paid conversion rate<\/li>\n
                    • Retention rate and churn rate<\/li>\n<\/ul>\n\n\n\n

                      ROI and financial efficiency metrics<\/h3>\n\n\n\n
                        \n
                      • Customer acquisition cost (CAC)<\/li>\n
                      • Customer lifetime value (LTV) or LTV:CAC ratio<\/li>\n
                      • Payback period<\/li>\n
                      • Contribution margin (where applicable)<\/li>\n
                      • Pipeline velocity (for B2B)<\/li>\n<\/ul>\n\n\n\n

                        Engagement and quality metrics<\/h3>\n\n\n\n
                          \n
                        • Qualified lead rate and sales acceptance rate<\/li>\n
                        • Bounce rate \/ engagement rate (interpreted carefully)<\/li>\n
                        • Session depth or key event completion<\/li>\n
                        • Refund rate, chargeback rate, or cancellation reasons<\/li>\n<\/ul>\n\n\n\n

                          The point isn\u2019t to track everything\u2014it\u2019s to place the right metrics on the right branches so Analytics<\/strong> can explain outcomes and guide Conversion & Measurement<\/strong> priorities.<\/p>\n\n\n\n

                          13) Future Trends of Metric Tree<\/h2>\n\n\n\n

                          Metric Tree usage is evolving as measurement environments change:<\/p>\n\n\n\n

                            \n
                          • AI-assisted diagnosis:<\/strong> Automated anomaly detection and narrative insights will increasingly highlight which branches explain KPI shifts, speeding analysis in Analytics<\/strong>.<\/li>\n
                          • More experimentation and incrementality:<\/strong> As attribution becomes less reliable, Metric Tree branches will be validated through holdouts and causal approaches.<\/li>\n
                          • Privacy-driven measurement changes:<\/strong> Consent requirements and reduced identifier access will push teams toward first-party data strategies and modeled measurement.<\/li>\n
                          • Personalization and segmentation at scale:<\/strong> Trees will be used more dynamically, with branches by audience, lifecycle stage, or intent segment to support targeted Conversion & Measurement<\/strong>.<\/li>\n
                          • Metric standardization across teams:<\/strong> Organizations will invest more in metric layers, governance, and semantic definitions so the Metric Tree remains consistent across tools.<\/li>\n<\/ul>\n\n\n\n

                            14) Metric Tree vs Related Terms<\/h2>\n\n\n\n

                            Metric Tree vs KPI framework<\/h3>\n\n\n\n

                            A KPI framework is a set of important metrics and targets. A Metric Tree<\/strong> goes further by showing how metrics relate<\/strong> (drivers, inputs, rollups) and how changes in one area should affect others\u2014making it more diagnostic and operational in Analytics<\/strong>.<\/p>\n\n\n\n

                            Metric Tree vs funnel metrics<\/h3>\n\n\n\n

                            Funnel metrics describe step-by-step conversion (e.g., visit \u2192 checkout \u2192 purchase). A Metric Tree can include funnel metrics, but also includes non-funnel drivers like pricing, retention, lead quality, or average order value\u2014making it broader for Conversion & Measurement<\/strong> strategy.<\/p>\n\n\n\n

                            Metric Tree vs driver tree \/ impact tree<\/h3>\n\n\n\n

                            These are closely related concepts. \u201cDriver tree\u201d often emphasizes mathematical decomposition (e.g., revenue = traffic \u00d7 conversion \u00d7 AOV). \u201cImpact tree\u201d often emphasizes interventions and experiments (what action changes what metric). A Metric Tree can incorporate both, blending decomposition with practical levers and measurement ownership.<\/p>\n\n\n\n

                            15) Who Should Learn Metric Tree<\/h2>\n\n\n\n
                              \n
                            • Marketers:<\/strong> To connect channel performance to business outcomes and avoid optimizing vanity metrics in Conversion & Measurement<\/strong>.<\/li>\n
                            • Analysts and data teams:<\/strong> To standardize definitions, improve diagnostic workflows, and build trusted Analytics<\/strong> reporting layers.<\/li>\n
                            • Agencies and consultants:<\/strong> To communicate strategy clearly, align stakeholders, and prove impact beyond surface-level metrics.<\/li>\n
                            • Business owners and founders:<\/strong> To understand what truly drives growth and where to invest limited resources.<\/li>\n
                            • Developers and product teams:<\/strong> To instrument events correctly, support experimentation, and ensure measurement reflects real user behavior.<\/li>\n<\/ul>\n\n\n\n

                              16) Summary of Metric Tree<\/h2>\n\n\n\n

                              A Metric Tree<\/strong> is a hierarchical model that breaks a top business outcome into the driver and input metrics that influence it. It matters because it creates clarity, accountability, and speed in decision-making\u2014especially when measurement is complex. In Conversion & Measurement<\/strong>, it ties marketing and product actions to conversions and revenue. In Analytics<\/strong>, it provides a consistent structure for reporting, diagnosis, experimentation, and long-term performance improvement.<\/p>\n\n\n\n

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

                              1) What is a Metric Tree and when should I use it?<\/h3>\n\n\n\n

                              A Metric Tree is a hierarchy that connects a top KPI (like revenue or pipeline) to the drivers and inputs that influence it. Use it when multiple teams need shared measurement, when performance changes need fast diagnosis, or when you want Conversion & Measurement<\/strong> optimization tied to business outcomes.<\/p>\n\n\n\n

                              2) How do I pick the top metric for a Metric Tree?<\/h3>\n\n\n\n

                              Choose the metric that best represents success for the objective and time horizon (e.g., new paid subscriptions for acquisition, retained customers for retention). The top metric should be meaningful to leadership and measurable reliably in Analytics<\/strong>.<\/p>\n\n\n\n

                              3) Does a Metric Tree replace attribution?<\/h3>\n\n\n\n

                              No. A Metric Tree complements attribution by showing the internal drivers of outcomes (rates, steps, quality measures). Attribution estimates \u201cwhere conversions came from,\u201d while a Metric Tree helps explain \u201cwhy conversions changed\u201d and what to fix in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                              4) What\u2019s the difference between leading and lagging metrics in a Metric Tree?<\/h3>\n\n\n\n

                              Lagging metrics are outcomes (revenue, pipeline created). Leading metrics are earlier signals (activation, add-to-cart rate, demo booked rate) that tend to move first and can be influenced quickly. A good Metric Tree mixes both so Analytics<\/strong> can detect issues early.<\/p>\n\n\n\n

                              5) How can Analytics teams support a Metric Tree without creating metric chaos?<\/h3>\n\n\n\n

                              Create a shared metric dictionary, standardize calculations in a governed data model, and build dashboards aligned to the tree\u2019s branches. This reduces conflicting definitions and keeps Analytics<\/strong> consistent across teams.<\/p>\n\n\n\n

                              6) How often should we update our Metric Tree?<\/h3>\n\n\n\n

                              Review it quarterly or whenever major changes occur (new pricing, funnel redesign, new channels, tracking changes). Conversion & Measurement<\/strong> evolves, and the Metric Tree should reflect current strategy and instrumentation reality.<\/p>\n\n\n\n

                              7) What\u2019s a common mistake when building a Metric Tree?<\/h3>\n\n\n\n

                              Overbuilding it. If the tree has too many layers or includes metrics no one owns, it won\u2019t drive action. Keep the Metric Tree focused on controllable drivers, clear definitions, and decisions you\u2019ll actually make using Analytics<\/strong>.<\/p>\n","protected":false},"excerpt":{"rendered":"

                              A **Metric Tree** is a structured way to connect business outcomes (like revenue, pipeline, or retention) to the measurable drivers that teams can actually influence. In **Conversion & Measurement**, it acts like a map: it links \u201cwhat success means\u201d to \u201cwhat to track,\u201d \u201cwhat to improve,\u201d and \u201cwhere to look when performance changes.\u201d In **Analytics**, it becomes the backbone for consistent reporting, diagnosis, and decision-making.<\/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-6896","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\/6896","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=6896"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/6896\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=6896"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=6896"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=6896"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}