{"id":7124,"date":"2026-03-24T01:11:12","date_gmt":"2026-03-24T01:11:12","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/control-group\/"},"modified":"2026-03-24T01:11:12","modified_gmt":"2026-03-24T01:11:12","slug":"control-group","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/control-group\/","title":{"rendered":"Control Group: What It Is, Key Features, Benefits, Use Cases, and How It Fits in CRO"},"content":{"rendered":"\n

In Conversion & Measurement<\/strong>, a Control Group<\/strong> is the audience segment that does not<\/em> receive a change, treatment, or marketing intervention\u2014so you can isolate what actually caused a performance shift. In practical CRO<\/strong> work, it\u2019s how you separate \u201cthis improved because we changed something\u201d from \u201cthis improved because seasonality, audience mix, or randomness helped us.\u201d<\/p>\n\n\n\n

Modern marketing is full of confounders: platform algorithm changes, rising CPCs, shifting demand, tracking loss, and multi-touch journeys. A well-designed Control Group<\/strong> anchors your analysis in causality, making Conversion & Measurement<\/strong> decisions more defensible, budgets more efficient, and CRO<\/strong> roadmaps more credible.<\/p>\n\n\n\n

\n\n\n\n

What Is Control Group?<\/h2>\n\n\n\n

A Control Group<\/strong> is the baseline group used for comparison against a \u201ctreatment\u201d group that receives a new experience, message, offer, or targeting approach. The core concept is simple: if the two groups are comparable, then differences in outcomes can be attributed\u2014more confidently\u2014to the intervention.<\/p>\n\n\n\n

In business terms, a Control Group<\/strong> helps you measure incrementality<\/em>: the added conversions, revenue, or retention you got because you did something different, not just because conditions changed. This matters in Conversion & Measurement<\/strong> because many metrics (like ROAS or conversion rate) can rise or fall for reasons unrelated to your change.<\/p>\n\n\n\n

Within CRO<\/strong>, the Control Group<\/strong> is the \u201cbefore\u201d or \u201cbusiness as usual\u201d condition\u2014often the existing page, funnel, or message\u2014used to evaluate whether a new variant truly lifts performance.<\/p>\n\n\n\n

\n\n\n\n

Why Control Group Matters in Conversion & Measurement<\/h2>\n\n\n\n

A Control Group<\/strong> turns marketing from \u201cperformance reporting\u201d into true Conversion & Measurement<\/strong>. Without it, you may optimize toward noise, over-credit campaigns, or ship product changes that look good in dashboards but don\u2019t increase real outcomes.<\/p>\n\n\n\n

Strategically, it provides:<\/p>\n\n\n\n

    \n
  • Causal confidence<\/strong>: You can claim lift with evidence, not just correlation.<\/li>\n
  • Budget protection<\/strong>: It reduces wasted spend on tactics that merely shift attribution rather than create incremental conversions.<\/li>\n
  • Faster learning loops<\/strong>: Teams can make fewer, better decisions instead of debating whose interpretation is right.<\/li>\n
  • Competitive advantage<\/strong>: Organizations with rigorous Control Group<\/strong> practices iterate faster and scale winners with less risk.<\/li>\n<\/ul>\n\n\n\n

    In CRO<\/strong>, this discipline is especially valuable because small percentage lifts can be costly to \u201cfind,\u201d easy to misread, and hard to reproduce without a stable baseline.<\/p>\n\n\n\n

    \n\n\n\n

    How Control Group Works<\/h2>\n\n\n\n

    A Control Group<\/strong> is more conceptual than a step-by-step tool, but it follows a consistent real-world workflow in Conversion & Measurement<\/strong> and CRO<\/strong>.<\/p>\n\n\n\n

      \n
    1. \n

      Input \/ trigger<\/strong>\n You introduce a change: a landing page redesign, a new email sequence, a bidding strategy, a pricing test, or a personalization rule.<\/p>\n<\/li>\n

    2. \n

      Analysis \/ design<\/strong>\n You define who will be held constant (the Control Group<\/strong>) and who receives the change (treatment). You set success metrics, guardrails, and the minimum sample size needed to detect a meaningful effect.<\/p>\n<\/li>\n

    3. \n

      Execution \/ exposure<\/strong>\n You run both conditions at the same time (ideally), ensuring the Control Group<\/strong> is not accidentally exposed to the treatment. Randomization, segmentation rules, or geo splits are used to keep groups comparable.<\/p>\n<\/li>\n

    4. \n

      Output \/ outcome<\/strong>\n You compare outcomes and quantify lift\u2014conversion rate, revenue per visitor, retention, or cost efficiency\u2014plus statistical confidence. The result informs whether to ship, iterate, or stop.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

      The key is that the Control Group<\/strong> is not \u201cno marketing.\u201d It\u2019s the baseline experience you would have delivered anyway, which makes the comparison meaningful for CRO<\/strong> and practical Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

      \n\n\n\n

      Key Components of Control Group<\/h2>\n\n\n\n

      A reliable Control Group<\/strong> depends on design, data quality, and governance\u2014not just a testing tool.<\/p>\n\n\n\n

      Design and process elements<\/h3>\n\n\n\n
        \n
      • Randomization or matching<\/strong>: Ensures groups are comparable and reduces bias.<\/li>\n
      • Eligibility rules<\/strong>: Defines who can enter the test (new users vs returning, specific geos, device types, etc.).<\/li>\n
      • Exposure control<\/strong>: Prevents contamination (control users seeing the treatment).<\/li>\n
      • Test duration and sample size<\/strong>: Avoids underpowered tests and premature conclusions.<\/li>\n<\/ul>\n\n\n\n

        Data inputs and systems<\/h3>\n\n\n\n
          \n
        • Event tracking<\/strong>: Page views, add-to-cart, purchase, lead submit, activation milestones.<\/li>\n
        • Identity resolution<\/strong>: User IDs, device IDs, or hashed identifiers (where permitted) to avoid double-counting.<\/li>\n
        • Attribution context<\/strong>: Helps interpret results, even if attribution isn\u2019t the method used to prove incrementality.<\/li>\n<\/ul>\n\n\n\n

          Governance and responsibilities<\/h3>\n\n\n\n
            \n
          • Experiment owner<\/strong> (often a CRO lead): defines hypotheses, metrics, and launch criteria. <\/li>\n
          • Analytics partner<\/strong>: validates tracking, computes lift, and checks validity. <\/li>\n
          • Engineering or martech<\/strong>: implements splits, ensures consistent exposure, and fixes instrumentation.<\/li>\n<\/ul>\n\n\n\n

            In Conversion & Measurement<\/strong>, the strongest Control Group<\/strong> setups include both methodological rigor and operational guardrails.<\/p>\n\n\n\n

            \n\n\n\n

            Types of Control Group<\/h2>\n\n\n\n

            \u201cTypes\u201d often describe how the baseline is created and protected. The best approach depends on channel constraints, traffic volume, and the risk of cross-exposure.<\/p>\n\n\n\n

            1) Randomized experiment control (classic A\/B control)<\/h3>\n\n\n\n

            Users are randomly assigned to control vs treatment at the user\/session level. This is the standard in CRO<\/strong> for on-site tests and in-product experiments.<\/p>\n\n\n\n

            2) Holdout control (incrementality holdout)<\/h3>\n\n\n\n

            A fixed percentage of eligible users is deliberately held back from a campaign or feature. This is common in lifecycle messaging, personalization, and ad measurement within Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

            3) Geo-based control (geo split \/ matched markets)<\/h3>\n\n\n\n

            Regions are assigned as control vs treatment. This is useful when user-level randomization is hard, such as certain offline campaigns or walled-garden constraints.<\/p>\n\n\n\n

            4) Time-based control (before\/after baseline)<\/h3>\n\n\n\n

            You compare performance before the change vs after the change. This is weaker than concurrent controls because seasonality and external factors can dominate, but it can be improved with careful adjustments.<\/p>\n\n\n\n

            5) Synthetic or matched control<\/h3>\n\n\n\n

            You construct a \u201ccontrol-like\u201d baseline using matched cohorts or statistical techniques when true randomization isn\u2019t possible. This approach is increasingly relevant as privacy and tracking constraints reshape Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

            \n\n\n\n

            Real-World Examples of Control Group<\/h2>\n\n\n\n

            Example 1: Landing page test for lead generation (CRO)<\/h3>\n\n\n\n

            A SaaS company redesigns a pricing page to reduce friction and increase demo requests. Half of eligible visitors see the current page (Control Group<\/strong>), and half see the new layout. Primary KPI: demo-submit conversion rate. Guardrails: bounce rate and lead quality (SQL rate). The test shows a lift in submits but a drop in SQL rate\u2014so the team iterates on copy to improve qualification. This is CRO<\/strong> supported by rigorous Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

            Example 2: Email holdout to measure incremental revenue (Conversion & Measurement)<\/h3>\n\n\n\n

            A retailer launches a new \u201cabandoned browse\u201d email program. To prove incrementality, 10% of eligible users are assigned to a Control Group<\/strong> that receives no browse emails for the test period. By comparing revenue per eligible user, the team finds the campaign produces smaller lift than last-click reports suggested, preventing overspend and guiding smarter segmentation.<\/p>\n\n\n\n

            Example 3: Paid search creative test with geo control<\/h3>\n\n\n\n

            A multi-location service business tests new ad messaging in selected cities while similar cities remain the Control Group<\/strong>. The team tracks qualified leads and booked appointments, adjusting for baseline differences. The result informs rollout decisions with stronger Conversion & Measurement<\/strong> than platform-only attribution.<\/p>\n\n\n\n

            \n\n\n\n

            Benefits of Using Control Group<\/h2>\n\n\n\n

            A thoughtfully designed Control Group<\/strong> improves decision quality and business outcomes.<\/p>\n\n\n\n

              \n
            • More accurate lift measurement<\/strong>: You estimate incremental conversions rather than inflated attributed conversions.<\/li>\n
            • Better ROI and lower waste<\/strong>: Spend shifts from \u201clooks good in reports\u201d to \u201cprovably works.\u201d<\/li>\n
            • Higher-quality CRO wins<\/strong>: You avoid shipping changes that harm downstream metrics like retention, refunds, or lead quality.<\/li>\n
            • Improved customer experience<\/strong>: Testing reveals what helps users, not just what increases clicks.<\/li>\n
            • Organizational alignment<\/strong>: A credible Control Group<\/strong> reduces stakeholder debates and increases trust in Conversion & Measurement<\/strong> outputs.<\/li>\n<\/ul>\n\n\n\n
              \n\n\n\n

              Challenges of Control Group<\/h2>\n\n\n\n

              A Control Group<\/strong> can fail or mislead if design and data are weak.<\/p>\n\n\n\n

                \n
              • Contamination<\/strong>: Control users get exposed to treatment through retargeting, forwarding links, cross-device behavior, or shared accounts.<\/li>\n
              • Sample ratio mismatch<\/strong>: The split is not what you intended due to bugs, eligibility filtering, or delivery constraints.<\/li>\n
              • Underpowered tests<\/strong>: Too little traffic or too short a run time causes false negatives (or unstable positives).<\/li>\n
              • Changing environments<\/strong>: Pricing changes, outages, or competitor moves can distort results, especially for time-based controls.<\/li>\n
              • Ethical and business constraints<\/strong>: Holding out users may reduce short-term revenue, even if it improves long-term learning.<\/li>\n
              • Measurement limitations<\/strong>: Tracking loss, cookie restrictions, and identity gaps can make Conversion & Measurement<\/strong> noisier, raising the bar for sound Control Group<\/strong> design.<\/li>\n<\/ul>\n\n\n\n
                \n\n\n\n

                Best Practices for Control Group<\/h2>\n\n\n\n

                Strong CRO<\/strong> and Conversion & Measurement<\/strong> programs treat the Control Group<\/strong> as a product-quality artifact, not a checkbox.<\/p>\n\n\n\n

                Design for validity<\/h3>\n\n\n\n
                  \n
                • Use concurrent<\/strong> control and treatment whenever possible.<\/li>\n
                • Randomize at the right level (user-level is often better than session-level for lifecycle effects).<\/li>\n
                • Define eligibility rules upfront to avoid cherry-picking.<\/li>\n<\/ul>\n\n\n\n

                  Protect the split<\/h3>\n\n\n\n
                    \n
                  • Prevent cross-exposure with clear suppression logic (e.g., exclude the Control Group<\/strong> from campaign audiences).<\/li>\n
                  • Use consistent identifiers to keep the same user in the same condition.<\/li>\n<\/ul>\n\n\n\n

                    Measure what matters<\/h3>\n\n\n\n
                      \n
                    • Choose a primary KPI and a small set of guardrails (quality, refunds, churn, support tickets).<\/li>\n
                    • Prefer incremental metrics per eligible user over vanity metrics.<\/li>\n<\/ul>\n\n\n\n

                      Operate with discipline<\/h3>\n\n\n\n
                        \n
                      • Predefine stop conditions and minimum sample size to reduce \u201cpeeking.\u201d<\/li>\n
                      • Document hypotheses, test setup, and results so learnings compound across CRO<\/strong> cycles.<\/li>\n<\/ul>\n\n\n\n

                        Scale responsibly<\/h3>\n\n\n\n
                          \n
                        • When rolling out winners, monitor for regression and segment effects; a win in one audience may not generalize.<\/li>\n<\/ul>\n\n\n\n
                          \n\n\n\n

                          Tools Used for Control Group<\/h2>\n\n\n\n

                          A Control Group<\/strong> is enabled by a stack of measurement and activation systems. In Conversion & Measurement<\/strong> and CRO<\/strong>, tool categories matter more than brand names.<\/p>\n\n\n\n

                            \n
                          • Experimentation platforms<\/strong>: Create randomized splits, manage variants, and report results for on-site or in-product testing.<\/li>\n
                          • Analytics tools<\/strong>: Track events, build funnels, segment cohorts, and validate that the Control Group<\/strong> and treatment behave comparably.<\/li>\n
                          • Tag management and server-side tracking<\/strong>: Improve data quality and reduce instrumentation errors that can corrupt control comparisons.<\/li>\n
                          • Ad platforms and audience managers<\/strong>: Set up holdouts, exclusions, or geo splits; manage suppression so the Control Group<\/strong> stays unexposed.<\/li>\n
                          • CRM and marketing automation<\/strong>: Enforce suppression lists, holdout assignments, and lifecycle logic for email\/SMS\/push tests.<\/li>\n
                          • Data warehouses and BI dashboards<\/strong>: Compute incrementality, run deeper analyses (like LTV impact), and create governance-ready reporting.<\/li>\n<\/ul>\n\n\n\n
                            \n\n\n\n

                            Metrics Related to Control Group<\/h2>\n\n\n\n

                            Metrics should reflect both impact and confidence. In CRO<\/strong>, small lifts require careful interpretation.<\/p>\n\n\n\n

                            Impact metrics (lift and value)<\/h3>\n\n\n\n
                              \n
                            • Conversion rate lift<\/strong>: Difference between treatment and Control Group<\/strong> conversion rate.<\/li>\n
                            • Revenue per visitor \/ per user<\/strong>: Captures value beyond conversion count.<\/li>\n
                            • Average order value (AOV)<\/strong> and units per transaction<\/strong>: Detects tradeoffs.<\/li>\n
                            • Incremental ROAS or incremental CAC<\/strong>: Especially useful in paid media Conversion & Measurement<\/strong>.<\/li>\n
                            • Retention or repeat purchase lift<\/strong>: Important when changes affect long-term value.<\/li>\n<\/ul>\n\n\n\n

                              Quality and guardrail metrics<\/h3>\n\n\n\n
                                \n
                              • Lead quality<\/strong> (MQL\u2192SQL rate, close rate)<\/li>\n
                              • Refunds, cancellations, churn<\/strong><\/li>\n
                              • Time to convert<\/strong> and funnel step drop-offs<\/li>\n
                              • Support contacts or complaint rate<\/strong><\/li>\n<\/ul>\n\n\n\n

                                Statistical and validity metrics<\/h3>\n\n\n\n
                                  \n
                                • Confidence intervals<\/strong>: Communicate uncertainty more clearly than a single number.<\/li>\n
                                • P-value (used carefully)<\/strong>: Helps assess whether observed differences are likely due to chance.<\/li>\n
                                • Power and minimum detectable effect (MDE)<\/strong>: Ensures the test can realistically detect the lift you care about.<\/li>\n
                                • Balance checks<\/strong>: Verify the Control Group<\/strong> and treatment are similar on key attributes.<\/li>\n<\/ul>\n\n\n\n
                                  \n\n\n\n

                                  Future Trends of Control Group<\/h2>\n\n\n\n

                                  The role of Control Group<\/strong> design is expanding as marketing measurement changes.<\/p>\n\n\n\n

                                    \n
                                  • Privacy-first measurement<\/strong>: With reduced user-level tracking, more brands will rely on incrementality tests, geo experiments, and modeled outcomes in Conversion & Measurement<\/strong>.<\/li>\n
                                  • Automation and always-on experimentation<\/strong>: CRO<\/strong> programs will increasingly run continuous tests with persistent control logic and automated guardrails.<\/li>\n
                                  • AI-assisted test design<\/strong>: AI can help propose hypotheses, estimate sample sizes, detect anomalies, and flag when the Control Group<\/strong> is contaminated\u2014but it won\u2019t replace the need for sound experimental design.<\/li>\n
                                  • Causal inference adoption<\/strong>: Techniques like matched controls and synthetic baselines will become more common where randomization is limited.<\/li>\n
                                  • Personalization at scale<\/strong>: As experiences become more individualized, defining a stable Control Group<\/strong> will require clearer governance and smarter segmentation.<\/li>\n<\/ul>\n\n\n\n
                                    \n\n\n\n

                                    Control Group vs Related Terms<\/h2>\n\n\n\n

                                    Control Group vs A\/B test<\/h3>\n\n\n\n

                                    An A\/B test is the overall experiment method; the Control Group<\/strong> is the baseline condition within that method. In CRO<\/strong>, you can\u2019t interpret an A\/B test without a clearly defined control.<\/p>\n\n\n\n

                                    Control Group vs Holdout group<\/h3>\n\n\n\n

                                    A holdout group is a specific kind of Control Group<\/strong> where users are excluded from an intervention (often a campaign) to measure incrementality. It\u2019s common in Conversion & Measurement<\/strong> for lifecycle and paid media evaluation.<\/p>\n\n\n\n

                                    Control Group vs Baseline period (before\/after)<\/h3>\n\n\n\n

                                    A baseline period compares time windows, not concurrent groups. It can be useful when testing isn\u2019t possible, but it\u2019s more vulnerable to seasonality and external changes than a true Control Group<\/strong>.<\/p>\n\n\n\n

                                    \n\n\n\n

                                    Who Should Learn Control Group<\/h2>\n\n\n\n
                                      \n
                                    • Marketers<\/strong> benefit by proving which campaigns create incremental growth and which only shift credit in Conversion & Measurement<\/strong>.<\/li>\n
                                    • Analysts<\/strong> use Control Group<\/strong> methods to produce causal insights, improve forecasting, and prevent misleading conclusions.<\/li>\n
                                    • Agencies<\/strong> gain credibility by tying strategy to incrementality and measurable CRO<\/strong> outcomes, not just reported platform metrics.<\/li>\n
                                    • Business owners and founders<\/strong> can allocate budget with more confidence and avoid scaling tactics that don\u2019t truly move the business.<\/li>\n
                                    • Developers and martech teams<\/strong> enable reliable experimentation by implementing consistent assignment, clean tracking, and exposure control.<\/li>\n<\/ul>\n\n\n\n
                                      \n\n\n\n

                                      Summary of Control Group<\/h2>\n\n\n\n

                                      A Control Group<\/strong> is the baseline audience segment that doesn\u2019t receive a change, allowing you to measure what your intervention truly caused. It matters because Conversion & Measurement<\/strong> without a reliable baseline often turns into correlation and guesswork. In CRO<\/strong>, the Control Group<\/strong> is essential for determining whether a new page, flow, or message produces real lift\u2014while protecting quality and long-term value.<\/p>\n\n\n\n

                                      \n\n\n\n

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

                                      1) What is a Control Group in marketing measurement?<\/h3>\n\n\n\n

                                      A Control Group<\/strong> is a set of users (or regions) that does not receive the tested change, so you can compare outcomes against the treated group and estimate incremental impact in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                      2) Do I always need a Control Group for CRO?<\/h3>\n\n\n\n

                                      For most CRO<\/strong> decisions that involve claiming \u201cthis change improved conversions,\u201d yes. If you can\u2019t run a true Control Group<\/strong>, use the strongest alternative available (geo split, matched cohorts) and be explicit about limitations.<\/p>\n\n\n\n

                                      3) How big should a Control Group be?<\/h3>\n\n\n\n

                                      It depends on traffic volume, expected lift, and risk. Common splits include 50\/50 for classic CRO<\/strong> A\/B tests or 5\u201320% holdouts for lifecycle programs. The right answer comes from power and sample size planning.<\/p>\n\n\n\n

                                      4) What\u2019s the difference between a Control Group and a holdout?<\/h3>\n\n\n\n

                                      A holdout is a Control Group<\/strong> that is intentionally excluded from an intervention (like an email or ad campaign) to measure incrementality. It\u2019s a practical pattern within Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                      5) How do I prevent the Control Group from being \u201ccontaminated\u201d?<\/h3>\n\n\n\n

                                      Use strict suppression logic, consistent user identifiers, and clear audience rules across channels. Also audit retargeting and automation flows to ensure the Control Group<\/strong> cannot accidentally receive the treatment.<\/p>\n\n\n\n

                                      6) Can a Control Group be unethical or harmful?<\/h3>\n\n\n\n

                                      It can be, depending on context. Holding back safety improvements, critical information, or legally required disclosures is not appropriate. In CRO<\/strong> and Conversion & Measurement<\/strong>, design tests that respect user welfare and compliance constraints.<\/p>\n\n\n\n

                                      7) What should I do if results conflict with attribution reports?<\/h3>\n\n\n\n

                                      Trust the method that measures incrementality more directly. A Control Group<\/strong> comparison often reveals that attribution over-credits certain channels; use the insight to recalibrate budgets, targeting, and CRO<\/strong> priorities.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                      In **Conversion & Measurement**, a **Control Group** is the audience segment that does *not* receive a change, treatment, or marketing intervention\u2014so you can isolate what actually caused a performance shift. In practical **CRO** work, it\u2019s how you separate \u201cthis improved because we changed something\u201d from \u201cthis improved because seasonality, audience mix, or randomness helped us.\u201d<\/p>\n","protected":false},"author":10235,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1889],"tags":[],"class_list":["post-7124","post","type-post","status-publish","format-standard","hentry","category-cro"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7124","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=7124"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7124\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7124"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7124"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7124"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}