{"id":10566,"date":"2026-03-29T14:38:05","date_gmt":"2026-03-29T14:38:05","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/paid-social-experiment\/"},"modified":"2026-03-29T14:38:05","modified_gmt":"2026-03-29T14:38:05","slug":"paid-social-experiment","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/paid-social-experiment\/","title":{"rendered":"Paid Social Experiment: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Paid Social"},"content":{"rendered":"\n

A Paid Social Experiment<\/strong> is a structured way to test a change in your social ad strategy\u2014such as creative, targeting, bidding, placements, or landing pages\u2014and measure the impact with as much rigor as possible. In the context of Paid Marketing<\/strong>, it turns \u201cwe think this will work\u201d into \u201cwe can prove what worked, for whom, and why.\u201d Within Paid Social<\/strong>, experimentation is how teams improve performance while reducing the risk of making budget-heavy decisions based on intuition.<\/p>\n\n\n\n

This matters because social platforms change quickly: algorithms evolve, audiences saturate, privacy constraints reduce visibility, and creative fatigue is constant. A well-run Paid Social Experiment<\/strong> gives marketers a repeatable method to learn, adapt, and scale what actually drives revenue and long-term customer value.<\/p>\n\n\n\n

What Is Paid Social Experiment?<\/h2>\n\n\n\n

A Paid Social Experiment<\/strong> is a planned, measurable test conducted in Paid Social<\/strong> campaigns to evaluate the causal or comparative impact of a specific change. The change might be a new creative concept, a different audience strategy, a revised offer, a new optimization event, or a landing page update. The key idea is control: you compare outcomes against a baseline so you can attribute performance differences to the change, not random fluctuation.<\/p>\n\n\n\n

At its core, a Paid Social Experiment<\/strong> answers questions like:<\/p>\n\n\n\n

    \n
  • If we switch from broad targeting to interest stacks, does cost per acquisition improve?<\/li>\n
  • If we change the first three seconds of the video, do we lift qualified leads?<\/li>\n
  • If we optimize for a deeper event, do we increase downstream revenue or just reduce volume?<\/li>\n<\/ul>\n\n\n\n

    From a business standpoint, this is decision support. In Paid Marketing<\/strong>, budgets are finite and opportunity costs are real; experiments help you allocate spend to the highest-confidence strategies. Inside Paid Social<\/strong>, it also provides a learning system that improves creative and media decisions over time, not just one campaign.<\/p>\n\n\n\n

    Why Paid Social Experiment Matters in Paid Marketing<\/h2>\n\n\n\n

    A Paid Social Experiment<\/strong> creates strategic leverage. Instead of optimizing only for short-term platform metrics, you build an evidence-based roadmap for growth.<\/p>\n\n\n\n

    Key reasons it matters in Paid Marketing<\/strong>:<\/p>\n\n\n\n

      \n
    • Better budget allocation:<\/strong> Experiments help you justify moving spend toward tactics with demonstrated impact, rather than the loudest internal opinion.<\/li>\n
    • Faster learning cycles:<\/strong> Social performance can change weekly. Experimentation turns that volatility into a learning advantage.<\/li>\n
    • Reduced risk at scale:<\/strong> Scaling a campaign amplifies mistakes. A Paid Social Experiment<\/strong> de-risks changes before you roll them out across accounts and geographies.<\/li>\n
    • Competitive advantage:<\/strong> Many competitors \u201coptimize\u201d without testing. Teams that run clean experiments discover durable insights (audience-message fit, offer elasticity, creative patterns) that compound over time.<\/li>\n
    • Improved cross-team alignment:<\/strong> When product, analytics, and growth teams disagree, a well-defined test can resolve debates with data.<\/li>\n<\/ul>\n\n\n\n

      Ultimately, experimentation makes Paid Social<\/strong> more predictable and more defensible to stakeholders\u2014especially when performance dips and you need to explain why.<\/p>\n\n\n\n

      How Paid Social Experiment Works<\/h2>\n\n\n\n

      In practice, a Paid Social Experiment<\/strong> follows a workflow that balances scientific rigor with platform realities:<\/p>\n\n\n\n

        \n
      1. \n

        Trigger and hypothesis<\/strong>\n – You identify a problem or opportunity (rising CPA, stagnant scale, poor lead quality, new product launch).\n – You write a hypothesis: \u201cIf we change X, we expect Y, because Z.\u201d\n Example: \u201cIf we use a stronger price-anchor in the first frame, we expect higher purchase conversion rate because it qualifies intent earlier.\u201d<\/p>\n<\/li>\n

      2. \n

        Design and measurement plan<\/strong>\n – Define what changes (the \u201ctreatment\u201d) and what stays constant.\n – Pick primary success metrics (one or two) and guardrails (to prevent harmful tradeoffs).\n – Decide how to split traffic: A\/B split, geo split, holdout, time-based comparison (least preferred), or platform experiment tools.<\/p>\n<\/li>\n

      3. \n

        Execution in Paid Social<\/strong>\n – Launch the test with clean naming, stable budgets, and controlled variables.\n – Ensure tracking is functioning and attribution settings are consistent.\n – Run long enough to gather meaningful data, factoring in conversion lag.<\/p>\n<\/li>\n

      4. \n

        Analysis and decision<\/strong>\n – Compare outcomes using the pre-defined metric(s).\n – Check for statistical noise, seasonality, and audience overlap.\n – Decide: adopt, iterate, or reject\u2014then document learnings and next tests.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        This is how a Paid Social Experiment<\/strong> becomes operational: not a one-off test, but a repeatable system embedded in your Paid Marketing<\/strong> process.<\/p>\n\n\n\n

        Key Components of Paid Social Experiment<\/h2>\n\n\n\n

        A high-quality Paid Social Experiment<\/strong> relies on several building blocks:<\/p>\n\n\n\n

        Clear hypotheses and change control<\/h3>\n\n\n\n

        You need a single primary change whenever possible. If you change creative, audience, and landing page simultaneously, you may win\u2014but you won\u2019t know why.<\/p>\n\n\n\n

        Reliable data inputs<\/h3>\n\n\n\n
          \n
        • Ad platform delivery and engagement data<\/li>\n
        • Conversion events (pixel\/server-side)<\/li>\n
        • CRM or backend revenue (for quality and LTV)<\/li>\n
        • Time-to-convert distributions (conversion lag)<\/li>\n<\/ul>\n\n\n\n

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

          In mature Paid Social<\/strong> teams, experimentation has owners:\n– Media buyer: test setup, delivery monitoring\n– Creative strategist: concept and iteration plan\n– Analyst: measurement design and interpretation\n– Growth lead: prioritization and rollout decisions<\/p>\n\n\n\n

          Metrics and guardrails<\/h3>\n\n\n\n

          A Paid Social Experiment<\/strong> should define:\n– Primary metric (e.g., CPA, revenue per visitor, qualified lead rate)\n– Secondary diagnostics (CTR, CVR, frequency, CPM)\n– Guardrails (refund rate, lead spam rate, ROAS floor)<\/p>\n\n\n\n

          Documentation<\/h3>\n\n\n\n

          Experiment logs (hypothesis, setup, dates, results, learnings) prevent repeated mistakes and accelerate onboarding.<\/p>\n\n\n\n

          Types of Paid Social Experiment<\/h2>\n\n\n\n

          \u201cTypes\u201d are best thought of as common experimentation contexts within Paid Social<\/strong> and Paid Marketing<\/strong>:<\/p>\n\n\n\n

            \n
          1. \n

            Creative experiments<\/strong>\n – New hooks, formats (video vs static), UGC vs studio, offer framing, length, first-frame changes.\n – Often the highest leverage because creative drives both performance and learning.<\/p>\n<\/li>\n

          2. \n

            Audience and targeting experiments<\/strong>\n – Broad vs interest vs lookalike-style models (where available), retargeting windows, exclusions, and segmentation by funnel stage.<\/p>\n<\/li>\n

          3. \n

            Bidding\/optimization experiments<\/strong>\n – Optimizing for different events (add-to-cart vs purchase), value-based optimization (when supported), cost controls, or delivery objectives.<\/p>\n<\/li>\n

          4. \n

            Placement and channel mix experiments<\/strong>\n – Feed vs stories vs reels-like placements, or shifting budget between multiple social platforms as part of Paid Marketing<\/strong> allocation.<\/p>\n<\/li>\n

          5. \n

            Landing page and funnel experiments<\/strong>\n – Page speed, message match, form length, pricing presentation, trial vs demo flow.<\/p>\n<\/li>\n

          6. \n

            Incrementality-style experiments (holdout)<\/strong>\n – Testing whether Paid Social<\/strong> is driving net-new outcomes versus capturing conversions that would have happened anyway.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

            Real-World Examples of Paid Social Experiment<\/h2>\n\n\n\n

            Example 1: Ecommerce creative refresh to reduce creative fatigue<\/h3>\n\n\n\n

            A direct-to-consumer brand sees rising CPA and frequency. They run a Paid Social Experiment<\/strong> comparing:\n– Control: existing best-performing video\n– Treatment: new UGC-style video with a stronger first 2-second hook and clearer price point<\/p>\n\n\n\n

            Primary metric: purchase CPA. Guardrails: conversion rate and return rate (from backend).\nOutcome: Treatment lowers CPA by 18% and increases conversion rate without raising refunds. The brand scales the new concept and turns the hook into a creative template for future Paid Social<\/strong> production.<\/p>\n\n\n\n

            Example 2: B2B lead quality test using CRM-qualified leads<\/h3>\n\n\n\n

            A SaaS company wants more pipeline, not just cheaper leads. They run a Paid Social Experiment<\/strong>:\n– Control: optimize for leads (form submits)\n– Treatment: optimize for a deeper event (qualified lead indicator), while keeping creative constant<\/p>\n\n\n\n

            Primary metric: cost per qualified lead (from CRM). Secondary metrics: lead volume, sales acceptance rate.\nOutcome: Treatment raises CPL but reduces cost per qualified lead by 25%. In Paid Marketing<\/strong> terms, efficiency improves because spend aligns with revenue outcomes rather than top-of-funnel volume.<\/p>\n\n\n\n

            Example 3: Retargeting window experiment to improve efficiency<\/h3>\n\n\n\n

            A subscription app tests retargeting windows:\n– Control: 30-day site retargeting\n– Treatment: 7-day high-intent retargeting with stronger urgency messaging<\/p>\n\n\n\n

            Primary metric: ROAS or cost per subscription start. Guardrails: frequency cap adherence and user complaints\/ad fatigue indicators.\nOutcome: 7-day window improves efficiency and reduces wasted impressions, freeing budget for prospecting in the broader Paid Social<\/strong> plan.<\/p>\n\n\n\n

            Benefits of Using Paid Social Experiment<\/h2>\n\n\n\n

            A well-executed Paid Social Experiment<\/strong> can deliver:<\/p>\n\n\n\n

              \n
            • Performance improvements:<\/strong> Higher conversion rate, better ROAS, lower CPA, improved qualified lead rates.<\/li>\n
            • Cost savings:<\/strong> Fewer wasted impressions, faster elimination of underperforming audiences\/creatives, better control of scaling mistakes.<\/li>\n
            • Operational efficiency:<\/strong> Clearer prioritization and less reactive \u201ctweaking,\u201d which is common in Paid Social<\/strong> when teams chase daily fluctuations.<\/li>\n
            • Better customer experience:<\/strong> More relevant ads, tighter message-match, improved landing page clarity, and fewer annoying retargeting sequences.<\/li>\n
            • Stronger forecasting and planning:<\/strong> Experiment results create benchmarks that inform Paid Marketing<\/strong> budget models and growth targets.<\/li>\n<\/ul>\n\n\n\n

              Challenges of Paid Social Experiment<\/h2>\n\n\n\n

              Experimentation in Paid Social<\/strong> is powerful, but it\u2019s not frictionless:<\/p>\n\n\n\n

                \n
              • Attribution limitations:<\/strong> Privacy changes, modeled conversions, and cross-device behavior can blur measurement.<\/li>\n
              • Platform volatility:<\/strong> Auction dynamics shift; a result may degrade when scaled or when competitors change spend.<\/li>\n
              • Learning phase and delivery instability:<\/strong> Some tests fail because ad sets never stabilize or don\u2019t spend evenly.<\/li>\n
              • Audience overlap and contamination:<\/strong> If users are exposed to both variants, results can be diluted.<\/li>\n
              • Small sample sizes:<\/strong> Low conversion volume makes it hard to separate signal from noise.<\/li>\n
              • Misleading success metrics:<\/strong> Lower CPA can come from lower-quality leads; higher CTR can come from clickbait creative.<\/li>\n<\/ul>\n\n\n\n

                A Paid Social Experiment<\/strong> should be designed to minimize these risks, not pretend they don\u2019t exist.<\/p>\n\n\n\n

                Best Practices for Paid Social Experiment<\/h2>\n\n\n\n

                To make experimentation reliable and repeatable:<\/p>\n\n\n\n

                  \n
                1. Prioritize by impact and confidence<\/strong>\n – Focus on big levers first: creative concepts, offer, optimization event, landing page friction.<\/li>\n
                2. Keep variables tight<\/strong>\n – Change one major factor per experiment whenever possible.<\/li>\n
                3. Define primary metrics and guardrails upfront<\/strong>\n – Decide what \u201cwin\u201d means before you launch.<\/li>\n
                4. Run tests long enough<\/strong>\n – Account for conversion lag; avoid ending tests after a single good day.<\/li>\n
                5. Control budgets and delivery<\/strong>\n – Keep budgets stable during the test to reduce confounding factors.<\/li>\n
                6. Validate tracking before spending<\/strong>\n – Ensure events fire correctly and match backend totals directionally.<\/li>\n
                7. Document and operationalize learnings<\/strong>\n – Turn wins into templates (creative briefs, audience playbooks) across Paid Marketing<\/strong> efforts.<\/li>\n
                8. Scale deliberately<\/strong>\n – After a win, roll out in stages to confirm performance holds under higher spend.<\/li>\n<\/ol>\n\n\n\n

                  Tools Used for Paid Social Experiment<\/h2>\n\n\n\n

                  A Paid Social Experiment<\/strong> typically uses a stack of tool categories rather than one \u201cexperiment tool\u201d:<\/p>\n\n\n\n

                    \n
                  • Ad platform experiment and reporting features:<\/strong> For split tests, audience comparisons, and placement analysis inside Paid Social<\/strong> interfaces.<\/li>\n
                  • Analytics tools:<\/strong> To evaluate onsite behavior, funnel drop-off, and post-click quality beyond platform metrics.<\/li>\n
                  • Tagging and event management systems:<\/strong> To manage pixels, server-side events, and consistent conversion definitions.<\/li>\n
                  • CRM and sales systems:<\/strong> Essential for lead qualification, pipeline attribution, and revenue-based evaluation in Paid Marketing<\/strong>.<\/li>\n
                  • Data warehouses and BI dashboards:<\/strong> For joining platform spend with conversions, cohorts, and profitability.<\/li>\n
                  • Automation and workflow tools:<\/strong> For naming conventions, experiment logs, approvals, and repeatable QA.<\/li>\n<\/ul>\n\n\n\n

                    The goal is not tooling complexity\u2014it\u2019s measurement credibility and operational consistency.<\/p>\n\n\n\n

                    Metrics Related to Paid Social Experiment<\/h2>\n\n\n\n

                    Metrics should match the test goal and funnel stage. Common metrics include:<\/p>\n\n\n\n

                    Performance and efficiency<\/h3>\n\n\n\n
                      \n
                    • CPA \/ cost per lead<\/li>\n
                    • ROAS (with caution, depending on attribution)<\/li>\n
                    • Cost per qualified lead \/ cost per opportunity<\/li>\n
                    • Revenue per click or per session<\/li>\n<\/ul>\n\n\n\n

                      Conversion behavior<\/h3>\n\n\n\n
                        \n
                      • Click-through rate (CTR)<\/li>\n
                      • Conversion rate (CVR)<\/li>\n
                      • Add-to-cart rate, checkout initiation rate, form completion rate<\/li>\n
                      • Time-to-convert (lag) and assisted conversions (where measurable)<\/li>\n<\/ul>\n\n\n\n

                        Auction and delivery diagnostics<\/h3>\n\n\n\n
                          \n
                        • CPM and CPC<\/li>\n
                        • Frequency and reach<\/li>\n
                        • Impression share-like indicators (where available)<\/li>\n
                        • Placement-level performance<\/li>\n<\/ul>\n\n\n\n

                          Quality and brand guardrails<\/h3>\n\n\n\n
                            \n
                          • Refund rate \/ churn rate (for subscriptions)<\/li>\n
                          • Lead spam rate, invalid leads, sales acceptance rate<\/li>\n
                          • Negative feedback or engagement quality signals<\/li>\n<\/ul>\n\n\n\n

                            A strong Paid Social Experiment<\/strong> uses a small set of decisive metrics, supported by diagnostics to explain \u201cwhy.\u201d<\/p>\n\n\n\n

                            Future Trends of Paid Social Experiment<\/h2>\n\n\n\n

                            Several forces are reshaping experimentation in Paid Marketing<\/strong> and Paid Social<\/strong>:<\/p>\n\n\n\n

                              \n
                            • AI-assisted creative iteration:<\/strong> Faster generation of variants increases the need for disciplined test design so teams don\u2019t drown in options.<\/li>\n
                            • Automation-driven delivery:<\/strong> As platforms automate targeting and bidding, experiments shift from micro-targeting tests toward creative, offers, and conversion optimization strategy.<\/li>\n
                            • Privacy and measurement changes:<\/strong> Modeled conversions and limited tracking increase interest in incrementality approaches, geo tests, and stronger first-party data.<\/li>\n
                            • Personalization at scale:<\/strong> More dynamic creative and segment-specific landing experiences require experiment frameworks that can handle many variants responsibly.<\/li>\n
                            • Higher standards for \u201cproof\u201d:<\/strong> Finance and leadership teams increasingly ask what Paid Social<\/strong> truly contributes. Expect more focus on lift, profitability, and retention\u2014not just platform ROAS.<\/li>\n<\/ul>\n\n\n\n

                              A modern Paid Social Experiment<\/strong> is evolving from \u201cwhich ad wins?\u201d to \u201cwhich strategy creates durable incremental growth?\u201d<\/p>\n\n\n\n

                              Paid Social Experiment vs Related Terms<\/h2>\n\n\n\n

                              Paid Social Experiment vs A\/B testing<\/h3>\n\n\n\n

                              A\/B testing is a specific method (two variants compared). A Paid Social Experiment<\/strong> is broader: it can include A\/B tests, holdouts, geo splits, or structured comparisons across audiences, bids, and funnels. In other words, A\/B testing is one tool inside the experimentation toolkit.<\/p>\n\n\n\n

                              Paid Social Experiment vs Incrementality testing<\/h3>\n\n\n\n

                              Incrementality testing asks a stricter question: did ads cause additional conversions compared to no ads? A Paid Social Experiment<\/strong> might simply compare two tactics within ongoing spend, while incrementality requires a credible control group (holdout) and often longer timeframes.<\/p>\n\n\n\n

                              Paid Social Experiment vs campaign optimization<\/h3>\n\n\n\n

                              Campaign optimization is the day-to-day process of improving performance (budget shifts, pausing ads, refreshing creative). A Paid Social Experiment<\/strong> is a deliberate test with a measurement plan. Optimization without experiments can improve results, but it\u2019s more prone to false conclusions.<\/p>\n\n\n\n

                              Who Should Learn Paid Social Experiment<\/h2>\n\n\n\n
                                \n
                              • Marketers and growth leads:<\/strong> To make better budget decisions and build a repeatable learning system in Paid Marketing<\/strong>.<\/li>\n
                              • Paid media specialists:<\/strong> To move beyond reactive tweaks and justify strategy changes with evidence in Paid Social<\/strong> accounts.<\/li>\n
                              • Analysts:<\/strong> To design measurement approaches that are credible under real-world constraints (attribution, lag, noise).<\/li>\n
                              • Agencies:<\/strong> To standardize experimentation across clients and demonstrate value beyond reporting.<\/li>\n
                              • Business owners and founders:<\/strong> To understand what\u2019s scalable, what\u2019s luck, and where money is actually made.<\/li>\n
                              • Developers and technical teams:<\/strong> To improve event instrumentation, data pipelines, and conversion quality signals that experiments depend on.<\/li>\n<\/ul>\n\n\n\n

                                Summary of Paid Social Experiment<\/h2>\n\n\n\n

                                A Paid Social Experiment<\/strong> is a structured test used to measure the impact of a change in Paid Social<\/strong> campaigns. It matters because it turns uncertainty into learning, supports smarter decisions in Paid Marketing<\/strong>, and reduces the risk of scaling the wrong tactics. When designed with clear hypotheses, clean measurement, and strong documentation, experimentation becomes a durable growth capability\u2014not just a one-time performance hack.<\/p>\n\n\n\n

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

                                1) What is a Paid Social Experiment?<\/h3>\n\n\n\n

                                A Paid Social Experiment<\/strong> is a planned test in social advertising where you change one key element (creative, audience, optimization, offer, or landing page) and compare results to a baseline to determine what caused the performance difference.<\/p>\n\n\n\n

                                2) How long should a Paid Social Experiment run?<\/h3>\n\n\n\n

                                Long enough to capture stable delivery and conversion lag. Many tests require at least 1\u20132 weeks, but the right duration depends on spend, conversion volume, and how quickly customers typically convert after clicking.<\/p>\n\n\n\n

                                3) What\u2019s the most important metric in Paid Social experiments?<\/h3>\n\n\n\n

                                The metric that best represents the business goal. For ecommerce it\u2019s often purchase CPA or profit-based ROAS; for B2B it may be cost per qualified lead or cost per opportunity sourced from CRM data.<\/p>\n\n\n\n

                                4) Can I run experiments inside a single Paid Social campaign?<\/h3>\n\n\n\n

                                Yes, if you can isolate variants and prevent overlap or biased delivery. In practice, many teams separate ad sets (or campaigns) to control budget and targeting more cleanly.<\/p>\n\n\n\n

                                5) How do I avoid misleading results in Paid Social?<\/h3>\n\n\n\n

                                Predefine your hypothesis and primary metric, keep variables controlled, run the test long enough, and use guardrails for quality (refund rate, churn, lead qualification). Also document audience overlap risks and attribution limitations.<\/p>\n\n\n\n

                                6) Are Paid Marketing experiments only for large budgets?<\/h3>\n\n\n\n

                                No. Smaller advertisers can still run a Paid Social Experiment<\/strong> by focusing on high-impact changes (creative and landing pages), keeping tests simple, and using longer durations to accumulate sufficient data.<\/p>\n\n\n\n

                                7) What should I do after I find a winning variant?<\/h3>\n\n\n\n

                                Validate it with a follow-up test if the win is small, then scale gradually. Turn the insight into a repeatable playbook (creative template, audience rule, landing page pattern) so it benefits the broader Paid Marketing<\/strong> strategy.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                A **Paid Social Experiment** is a structured way to test a change in your social ad strategy\u2014such as creative, targeting, bidding, placements, or landing pages\u2014and measure the impact with as much rigor as possible. In the context of **Paid Marketing**, it turns \u201cwe think this will work\u201d into \u201cwe can prove what worked, for whom, and why.\u201d Within **Paid Social**, experimentation is how teams improve performance while reducing the risk of making budget-heavy decisions based on intuition.<\/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":[1910],"tags":[],"class_list":["post-10566","post","type-post","status-publish","format-standard","hentry","category-paid-social"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/10566","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=10566"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/10566\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=10566"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=10566"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=10566"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}