{"id":7126,"date":"2026-03-24T01:15:20","date_gmt":"2026-03-24T01:15:20","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/conversion-lift\/"},"modified":"2026-03-24T01:15:20","modified_gmt":"2026-03-24T01:15:20","slug":"conversion-lift","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/conversion-lift\/","title":{"rendered":"Conversion Lift: What It Is, Key Features, Benefits, Use Cases, and How It Fits in CRO"},"content":{"rendered":"\n

Conversion Lift is the measurable increase in conversions that can be attributed to a specific change\u2014such as a new landing page, an ad campaign, a pricing tweak, or a checkout redesign\u2014compared to what would have happened without that change. In Conversion & Measurement<\/strong>, it\u2019s one of the most practical ways to move from \u201cthis performed well\u201d to \u201cthis caused improvement.\u201d<\/p>\n\n\n\n

For CRO<\/strong> practitioners, Conversion Lift turns optimization into evidence-based decision-making. Instead of relying on intuition, you quantify incremental impact, prioritize what to scale, and protect your roadmap from false wins caused by seasonality, channel mix shifts, or random variance. As tracking becomes harder and budgets face greater scrutiny, Conversion Lift has become a core idea in modern Conversion & Measurement<\/strong> strategy.<\/p>\n\n\n\n

What Is Conversion Lift?<\/h2>\n\n\n\n

Conversion Lift is the incremental improvement in conversion outcomes attributable to an intervention. The intervention could be:<\/p>\n\n\n\n

    \n
  • A test variant in an A\/B test<\/li>\n
  • A new audience strategy in paid media<\/li>\n
  • A revised onboarding flow<\/li>\n
  • A product change that reduces friction<\/li>\n<\/ul>\n\n\n\n

    Beginner-friendly definition: Conversion Lift is the difference between your conversion rate (or conversions) with a change versus without it, after accounting for a fair baseline.<\/strong><\/p>\n\n\n\n

    The core concept is causality. Conversion Lift is not just \u201cwe saw conversions go up.\u201d It\u2019s \u201cconversions went up because of this change,\u201d supported by sound Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

    From a business perspective, Conversion Lift connects experiments and optimizations to revenue, pipeline, sign-ups, subscriptions, or qualified leads. Within Conversion & Measurement<\/strong>, it sits alongside attribution, incrementality testing, and experiment design. Inside CRO<\/strong>, it is the foundation for deciding whether a change truly improved user outcomes or simply coincided with a good week.<\/p>\n\n\n\n

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

    Conversion Lift matters because marketing and product performance are noisy. Competitors launch promotions, your brand gets press, a holiday hits, or one channel suddenly under-delivers. Without a lift-focused mindset, teams often mistake correlation for causation.<\/p>\n\n\n\n

    Strategically, Conversion Lift helps you:<\/p>\n\n\n\n

      \n
    • Prove incremental value<\/strong>: Demonstrate what your actions added beyond the baseline.<\/li>\n
    • Allocate budget confidently<\/strong>: Shift spend or engineering time to changes with verified lift.<\/li>\n
    • Prioritize the CRO backlog<\/strong>: Focus on experiments that produce meaningful improvements, not superficial gains.<\/li>\n
    • Protect decisions from bias<\/strong>: Reduce \u201cwinner\u2019s curse\u201d where early results look better than reality.<\/li>\n
    • Build a compounding advantage<\/strong>: Repeated, validated lift creates durable performance improvements.<\/li>\n<\/ul>\n\n\n\n

      In Conversion & Measurement<\/strong>, lift is also a communication tool. It translates complex analysis into an outcome executives understand: incremental conversions, incremental revenue, and incremental efficiency.<\/p>\n\n\n\n

      How Conversion Lift Works<\/h2>\n\n\n\n

      Conversion Lift is both a concept and a practical measurement approach. In real work, it typically follows a workflow:<\/p>\n\n\n\n

        \n
      1. \n

        Input \/ Trigger: a change you want to evaluate<\/strong>\n This could be an A\/B test (new CTA, shorter form), a campaign (new creative, new targeting), or a product update (fewer checkout steps). In CRO<\/strong>, this is often framed as a hypothesis tied to a user friction point.<\/p>\n<\/li>\n

      2. \n

        Analysis \/ Processing: define a baseline and a comparison<\/strong>\n A baseline might be a control group, a holdout audience, or a pre-change period adjusted for trends. Strong Conversion & Measurement<\/strong> ensures the comparison is fair\u2014similar users, similar timing, minimal confounding variables.<\/p>\n<\/li>\n

      3. \n

        Execution \/ Application: run the experiment or controlled rollout<\/strong>\n You expose a subset of users to the change (treatment) while another subset does not receive it (control). In many CRO programs, this is done via randomized A\/B tests; in paid media, it may be a geo test or audience holdout.<\/p>\n<\/li>\n

      4. \n

        Output \/ Outcome: compute lift and decide what to do<\/strong>\n Lift is calculated as the difference in conversion outcomes between treatment and control, often expressed as a percentage or absolute change. Then you decide whether to ship, iterate, or stop\u2014ideally considering statistical uncertainty and business impact, not just a point estimate.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        The most important practical point: Conversion Lift is only as reliable as the baseline and the quality of your Conversion & Measurement<\/strong> setup.<\/p>\n\n\n\n

        Key Components of Conversion Lift<\/h2>\n\n\n\n

        Conversion Lift depends on a few foundational elements that blend analytics, experimentation discipline, and organizational clarity:<\/p>\n\n\n\n

        Data inputs<\/h3>\n\n\n\n
          \n
        • Traffic and user behavior data (sessions, events, funnels)<\/li>\n
        • Conversion definitions (purchase, lead, activation, retention proxy)<\/li>\n
        • Audience and channel metadata (source\/medium, campaign, device)<\/li>\n
        • Time context (seasonality, promotions, outages)<\/li>\n<\/ul>\n\n\n\n

          Measurement and experimentation processes<\/h3>\n\n\n\n
            \n
          • Hypothesis framing and test planning (what change, which audience, expected impact)<\/li>\n
          • Randomization and allocation (who sees control vs treatment)<\/li>\n
          • Sample size and duration planning to avoid underpowered results<\/li>\n
          • QA processes for tracking and variant delivery<\/li>\n<\/ul>\n\n\n\n

            Metrics and reporting<\/h3>\n\n\n\n
              \n
            • Primary conversion metric (the outcome you\u2019re lifting)<\/li>\n
            • Guardrail metrics (bounce rate, refund rate, lead quality, latency)<\/li>\n
            • Segmentation (new vs returning, mobile vs desktop, geo, intent level)<\/li>\n
            • Uncertainty reporting (confidence intervals or credible intervals)<\/li>\n<\/ul>\n\n\n\n

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

              In mature CRO<\/strong> and Conversion & Measurement<\/strong> teams, roles are explicit:\n– Marketers\/product managers define goals and hypotheses\n– Analysts validate design, power, and interpretation\n– Developers ensure correct implementation and tracking\n– Stakeholders agree on decision rules before viewing results<\/p>\n\n\n\n

              Types of Conversion Lift<\/h2>\n\n\n\n

              Conversion Lift doesn\u2019t have one universal taxonomy, but there are useful distinctions that show up in practice:<\/p>\n\n\n\n

              Absolute vs relative lift<\/h3>\n\n\n\n
                \n
              • Absolute lift<\/strong>: Control converts at 4%, treatment at 5% \u2192 absolute lift = +1 percentage point.<\/li>\n
              • Relative lift<\/strong>: (5% – 4%) \/ 4% = +25% relative lift.<\/li>\n<\/ul>\n\n\n\n

                Both matter in Conversion & Measurement<\/strong>. Relative lift is great for comparison; absolute lift is often better for forecasting volume and revenue.<\/p>\n\n\n\n

                Conversion-rate lift vs conversion-volume lift<\/h3>\n\n\n\n
                  \n
                • Rate lift<\/strong> measures efficiency (conversion rate improvement).<\/li>\n
                • Volume lift<\/strong> measures total incremental conversions, which depends on traffic and reach.<\/li>\n<\/ul>\n\n\n\n

                  In CRO<\/strong>, rate lift is common for on-site tests. For paid campaigns, volume lift may be the main business outcome.<\/p>\n\n\n\n

                  Short-term lift vs long-term lift<\/h3>\n\n\n\n

                  A change can improve immediate conversion but harm downstream metrics (higher churn, more refunds, lower lead quality). Mature Conversion & Measurement<\/strong> includes both immediate and lagging indicators.<\/p>\n\n\n\n

                  Experiment lift vs incrementality lift in media<\/h3>\n\n\n\n

                  On-site A\/B testing is often straightforward randomization. In advertising, lift is frequently measured through holdouts, geo experiments, or matched-market tests because user-level randomness can be difficult across platforms. The principle\u2014incremental impact\u2014remains the same.<\/p>\n\n\n\n

                  Real-World Examples of Conversion Lift<\/h2>\n\n\n\n

                  Example 1: SaaS landing page CRO test<\/h3>\n\n\n\n

                  A SaaS company tests a new pricing page emphasizing annual savings and adds clearer plan comparison. Control conversion to \u201cStart Trial\u201d is 3.2%; treatment is 3.8%. The Conversion Lift is +0.6 percentage points (about +18.75% relative). In Conversion & Measurement<\/strong>, the team also checks guardrails: trial-to-paid rate and support tickets. The lift is only accepted once downstream quality holds.<\/p>\n\n\n\n

                  Example 2: Ecommerce checkout optimization<\/h3>\n\n\n\n

                  An ecommerce brand reduces checkout steps from three pages to one and adds express payment options. Conversions rise, but the CRO<\/strong> team validates Conversion Lift using an A\/B test across devices and segments. They also monitor refunds and chargebacks as guardrails. The final readout includes lift by device: desktop sees modest gain, mobile sees a larger lift, guiding future mobile-first work.<\/p>\n\n\n\n

                  Example 3: Paid media audience holdout<\/h3>\n\n\n\n

                  A brand launches a retargeting campaign and wants to know incremental impact, not just attributed conversions. They create a holdout group that does not see retargeting ads. The treatment group converts at a higher rate; the difference is the Conversion Lift attributable to the campaign. This strengthens Conversion & Measurement<\/strong> by separating \u201cpeople who would have purchased anyway\u201d from true incremental buyers.<\/p>\n\n\n\n

                  Benefits of Using Conversion Lift<\/h2>\n\n\n\n

                  When teams consistently measure and act on Conversion Lift, they gain benefits beyond \u201cbetter reporting\u201d:<\/p>\n\n\n\n

                    \n
                  • Higher performance with less waste<\/strong>: You invest in changes that actually move conversions.<\/li>\n
                  • Lower customer acquisition cost (CAC)<\/strong>: Verified lift improves efficiency across channels and pages.<\/li>\n
                  • Faster learning loops<\/strong>: CRO<\/strong> programs become systematic\u2014test, learn, scale.<\/li>\n
                  • Better customer experience<\/strong>: Lift often comes from removing friction, clarifying value, and improving usability.<\/li>\n
                  • More credible stakeholder alignment<\/strong>: Conversion Lift gives a shared language for decisions in Conversion & Measurement<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                    Challenges of Conversion Lift<\/h2>\n\n\n\n

                    Conversion Lift is powerful, but it\u2019s easy to get wrong without rigor:<\/p>\n\n\n\n

                      \n
                    • Underpowered tests<\/strong>: Small sample sizes produce noisy \u201cwins\u201d that don\u2019t replicate.<\/li>\n
                    • Confounding variables<\/strong>: Promotions, channel shifts, or product outages can bias results if not controlled.<\/li>\n
                    • Tracking gaps<\/strong>: Missing events, inconsistent conversion definitions, and cross-domain issues weaken Conversion & Measurement<\/strong>.<\/li>\n
                    • Novelty effects<\/strong>: New experiences can temporarily boost engagement; lift may decay over time.<\/li>\n
                    • Metric mismatch<\/strong>: Optimizing for a top-of-funnel conversion can reduce downstream quality (e.g., more leads, worse close rate).<\/li>\n
                    • Segment instability<\/strong>: Lift may be positive overall but negative for high-value cohorts, creating hidden revenue loss.<\/li>\n<\/ul>\n\n\n\n

                      The practical takeaway for CRO<\/strong>: treat lift as a decision input, not a vanity metric.<\/p>\n\n\n\n

                      Best Practices for Conversion Lift<\/h2>\n\n\n\n

                      Design for causality<\/h3>\n\n\n\n
                        \n
                      • Prefer randomized experiments when feasible.<\/li>\n
                      • Define control and treatment clearly before launch.<\/li>\n
                      • Avoid changing multiple major variables at once unless you\u2019re running a multivariate strategy intentionally.<\/li>\n<\/ul>\n\n\n\n

                        Choose the right primary and guardrail metrics<\/h3>\n\n\n\n
                          \n
                        • Pick a single primary conversion event for decision-making.<\/li>\n
                        • Add guardrails tied to business health: revenue per visitor, churn, refund rate, lead-to-opportunity rate, time to first value.<\/li>\n<\/ul>\n\n\n\n

                          Plan sample size and duration<\/h3>\n\n\n\n
                            \n
                          • Estimate baseline conversion rate and minimum detectable effect.<\/li>\n
                          • Run tests long enough to cover day-of-week patterns and typical demand cycles.<\/li>\n<\/ul>\n\n\n\n

                            Validate instrumentation<\/h3>\n\n\n\n
                              \n
                            • QA events, deduplication, and funnel steps.<\/li>\n
                            • Ensure variant exposure is tracked accurately (who saw what, when).<\/li>\n<\/ul>\n\n\n\n

                              Interpret results like a business operator<\/h3>\n\n\n\n
                                \n
                              • Convert lift into expected incremental conversions and revenue.<\/li>\n
                              • Consider uncertainty, not just point estimates.<\/li>\n
                              • Segment carefully (device, intent, new\/returning) but avoid \u201csegment fishing\u201d without a plan.<\/li>\n<\/ul>\n\n\n\n

                                Scale with a learning system<\/h3>\n\n\n\n

                                A strong Conversion & Measurement<\/strong> practice documents:\n– Hypotheses and outcomes\n– Context (traffic sources, seasonality, changes during test)\n– Final decisions and follow-up checks\nThis turns CRO<\/strong> into an asset that compounds.<\/p>\n\n\n\n

                                Tools Used for Conversion Lift<\/h2>\n\n\n\n

                                Conversion Lift is not tied to one product category, but it commonly relies on an ecosystem of tools within Conversion & Measurement<\/strong> and CRO<\/strong>:<\/p>\n\n\n\n

                                  \n
                                • Analytics tools<\/strong>: Event and funnel analysis, cohorting, segmentation, and pathing to understand where lift is occurring.<\/li>\n
                                • Experimentation platforms<\/strong>: A\/B testing, feature flagging, and rollout controls to manage treatments vs controls.<\/li>\n
                                • Tag management and tracking<\/strong>: Governance over pixels, event schemas, and consent-aware tracking.<\/li>\n
                                • Ad platforms and measurement suites<\/strong>: Holdouts, geo experiments, and campaign reporting to estimate incremental lift.<\/li>\n
                                • CRM and marketing automation<\/strong>: Lead quality validation (MQL to SQL), lifecycle stages, and downstream attribution checks.<\/li>\n
                                • Data warehouse and BI dashboards<\/strong>: Centralized reporting, reproducible lift calculations, and stakeholder-ready views.<\/li>\n<\/ul>\n\n\n\n

                                  The key is interoperability: if your tools can\u2019t reconcile exposure, conversions, and cohorts, Conversion Lift becomes harder to trust.<\/p>\n\n\n\n

                                  Metrics Related to Conversion Lift<\/h2>\n\n\n\n

                                  Conversion Lift is usually expressed using a conversion rate or conversion count, but strong Conversion & Measurement<\/strong> ties it to business impact:<\/p>\n\n\n\n

                                    \n
                                  • Conversion rate (CVR)<\/strong>: Primary efficiency metric for CRO<\/strong>.<\/li>\n
                                  • Incremental conversions<\/strong>: Treatment conversions minus expected conversions from baseline.<\/li>\n
                                  • Revenue per visitor \/ session<\/strong>: Captures price and basket effects, not just conversion.<\/li>\n
                                  • Average order value (AOV)<\/strong> and profit per visitor<\/strong>: Ensures lift is economically meaningful.<\/li>\n
                                  • Customer acquisition cost (CAC)<\/strong> and cost per acquisition (CPA)<\/strong>: For paid efforts, lift must justify spend.<\/li>\n
                                  • Lead quality metrics<\/strong>: SQL rate, close rate, pipeline per lead; essential for B2B.<\/li>\n
                                  • Retention or churn proxies<\/strong>: Activation rate, repeat purchase rate, renewal rate\u2014prevents short-term lift from harming long-term value.<\/li>\n
                                  • Statistical uncertainty<\/strong>: Confidence intervals (or similar) to communicate reliability.<\/li>\n<\/ul>\n\n\n\n

                                    Future Trends of Conversion Lift<\/h2>\n\n\n\n

                                    Several shifts are changing how Conversion Lift is measured and operationalized in Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                                      \n
                                    • Privacy and consent constraints<\/strong>: Less user-level tracking increases reliance on first-party data, modeled measurement, and controlled experiments.<\/li>\n
                                    • More incrementality testing in media<\/strong>: As attribution becomes less deterministic, holdouts and geo tests become more important to estimate true lift.<\/li>\n
                                    • AI-assisted experimentation<\/strong>: AI can propose hypotheses, detect anomalies, and automate segmentation\u2014while still requiring human judgment for causality and ethics.<\/li>\n
                                    • Personalization with guardrails<\/strong>: More dynamic experiences can create lift, but they also increase risk of bias, overfitting, and inconsistent customer experiences.<\/li>\n
                                    • Server-side and hybrid measurement<\/strong>: Improving data quality and governance to make lift calculations more reliable across devices and platforms.<\/li>\n<\/ul>\n\n\n\n

                                      In short: Conversion Lift is evolving from \u201cnice-to-have testing output\u201d to a central pillar of modern Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                      Conversion Lift vs Related Terms<\/h2>\n\n\n\n

                                      Conversion Lift vs Conversion Rate<\/h3>\n\n\n\n

                                      Conversion rate is a snapshot metric (what percentage converted). Conversion Lift is the change attributable to an intervention<\/strong>. You can have a higher conversion rate this month with zero true lift if demand increased for unrelated reasons.<\/p>\n\n\n\n

                                      Conversion Lift vs Attribution<\/h3>\n\n\n\n

                                      Attribution assigns credit to channels or touchpoints. Conversion Lift asks whether an action caused incremental conversions. In Conversion & Measurement<\/strong>, lift often challenges attribution by revealing that some \u201ccredited\u201d conversions would have happened anyway.<\/p>\n\n\n\n

                                      Conversion Lift vs Uplift Modeling<\/h3>\n\n\n\n

                                      Uplift modeling predicts which users are most likely to be influenced by a treatment. Conversion Lift is the measured outcome of the treatment. In practice, uplift modeling can help target where lift is likely, while experiments confirm actual Conversion Lift.<\/p>\n\n\n\n

                                      Who Should Learn Conversion Lift<\/h2>\n\n\n\n
                                        \n
                                      • Marketers<\/strong> benefit by proving which campaigns and messages create incremental impact, strengthening Conversion & Measurement<\/strong> beyond platform-reported numbers.<\/li>\n
                                      • Analysts<\/strong> use Conversion Lift to bring causal discipline to reporting, experimentation, and forecasting.<\/li>\n
                                      • Agencies<\/strong> can differentiate with credible performance narratives and stronger CRO<\/strong> roadmaps tied to measurable lift.<\/li>\n
                                      • Business owners and founders<\/strong> gain clarity on what really drives growth, avoiding spend based on misleading attribution.<\/li>\n
                                      • Developers<\/strong> enable reliable lift measurement by implementing clean event schemas, experiment bucketing, and performance-safe testing.<\/li>\n<\/ul>\n\n\n\n

                                        Summary of Conversion Lift<\/h2>\n\n\n\n

                                        Conversion Lift is the incremental increase in conversions caused by a specific change, measured against a trustworthy baseline. It matters because it separates real performance gains from noise, making Conversion & Measurement<\/strong> more credible and decisions more profitable. In CRO<\/strong>, Conversion Lift is the backbone of experimentation: it tells you what to ship, what to iterate, and what not to scale.<\/p>\n\n\n\n

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

                                        1) What is Conversion Lift in simple terms?<\/h3>\n\n\n\n

                                        Conversion Lift is the extra conversions you gained because you made a change, compared to what would have happened without that change. It\u2019s a causal concept used in Conversion & Measurement<\/strong> to validate impact.<\/p>\n\n\n\n

                                        2) Is Conversion Lift the same as an increase in conversion rate?<\/h3>\n\n\n\n

                                        Not necessarily. Your conversion rate can rise due to seasonality or traffic quality changes. Conversion Lift requires a baseline comparison (like a control group) to show the increase was caused by the intervention.<\/p>\n\n\n\n

                                        3) How do you calculate Conversion Lift?<\/h3>\n\n\n\n

                                        A common approach is:\nLift = (Treatment conversion rate \u2212 Control conversion rate) \/ Control conversion rate\nYou can also report absolute lift (difference in percentage points) and incremental conversions (difference in conversion counts), depending on your Conversion & Measurement<\/strong> goals.<\/p>\n\n\n\n

                                        4) How does Conversion Lift relate to CRO?<\/h3>\n\n\n\n

                                        CRO<\/strong> is the practice of improving conversion performance through research and experimentation. Conversion Lift is the evidence that a CRO change truly improved outcomes, not just metrics that moved coincidentally.<\/p>\n\n\n\n

                                        5) What\u2019s a good Conversion Lift benchmark?<\/h3>\n\n\n\n

                                        There isn\u2019t a universal benchmark. A \u201cgood\u201d lift depends on baseline conversion rate, traffic volume, margin, and implementation cost. In Conversion & Measurement<\/strong>, it\u2019s better to evaluate lift by expected incremental profit and confidence than by a generic percentage target.<\/p>\n\n\n\n

                                        6) Why might measured lift not replicate after launch?<\/h3>\n\n\n\n

                                        Common reasons include underpowered tests, novelty effects, different traffic mix after rollout, tracking discrepancies, or running the test during an unusual period. Strong CRO<\/strong> practices include post-launch monitoring to confirm lift persists.<\/p>\n\n\n\n

                                        7) Can you measure Conversion Lift without A\/B testing?<\/h3>\n\n\n\n

                                        Yes, but it\u2019s harder. You can use holdout groups, geo experiments, or matched comparisons\u2014especially in paid media. The goal remains the same: credible Conversion & Measurement<\/strong> that estimates incremental impact.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                        Conversion Lift is the measurable increase in conversions that can be attributed to a specific change\u2014such as a new landing page, an ad campaign, a pricing tweak, or a checkout redesign\u2014compared to what would have happened without that change. In **Conversion & Measurement**, it\u2019s one of the most practical ways to move from \u201cthis performed well\u201d to \u201cthis caused improvement.\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-7126","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\/7126","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=7126"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7126\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7126"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7126"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7126"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}