{"id":7047,"date":"2026-03-23T22:22:44","date_gmt":"2026-03-23T22:22:44","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/incrementality-measurement\/"},"modified":"2026-03-23T22:22:44","modified_gmt":"2026-03-23T22:22:44","slug":"incrementality-measurement","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/incrementality-measurement\/","title":{"rendered":"Incrementality Measurement: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Attribution"},"content":{"rendered":"\n

Incrementality Measurement is the discipline of quantifying what marketing actually causes<\/em>\u2014the conversions, revenue, and behaviors that would not<\/strong> have happened without a specific campaign, channel, or tactic. In modern Conversion & Measurement<\/strong>, it answers the hardest question: \u201cDid this marketing activity create new value, or did it just claim credit for demand that already existed?\u201d<\/p>\n\n\n\n

This is where Incrementality Measurement intersects directly with Attribution<\/strong>. Attribution assigns credit across touchpoints; Incrementality Measurement tests whether that credited impact is real, net-new lift. As privacy changes reduce tracking fidelity and customer journeys become more complex, Incrementality Measurement has become a cornerstone of credible Conversion & Measurement<\/strong> strategy\u2014especially for paid media, promotions, and upper-funnel activity.<\/p>\n\n\n\n

What Is Incrementality Measurement?<\/h2>\n\n\n\n

Incrementality Measurement is a structured approach to estimating the causal lift<\/strong> of marketing. Instead of asking \u201cWhich channel got the last click?\u201d it asks: \u201cWhat is the difference in outcomes between a world where we run this marketing and a world where we don\u2019t?\u201d<\/strong><\/p>\n\n\n\n

At its core, Incrementality Measurement compares a treatment<\/strong> (people exposed to marketing) with a control<\/strong> (similar people not exposed). The difference between the two groups\u2014after accounting for randomness and bias\u2014is incremental impact.<\/p>\n\n\n\n

From a business perspective, Incrementality Measurement translates marketing activity into decisions about:\n– Budget allocation (what to scale vs. cut)\n– Efficient customer acquisition (what actually drives new customers)\n– Profitability (incremental revenue vs. incremental cost)\n– Risk management (avoiding over-investment in non-incremental channels)<\/p>\n\n\n\n

Within Conversion & Measurement<\/strong>, it sits alongside tracking, experimentation, analytics, and reporting. Within Attribution<\/strong>, it acts as a reality check: a channel can look \u201chigh-performing\u201d in attribution reports while producing little to no incremental conversions once you test causality.<\/p>\n\n\n\n

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

Incrementality Measurement matters because many marketing signals are correlated<\/em> with conversions without causing<\/em> them. Brand demand, seasonality, pricing changes, and returning customers can all inflate performance dashboards. Strong Conversion & Measurement<\/strong> requires distinguishing correlation from causation.<\/p>\n\n\n\n

Key reasons it\u2019s strategically important:<\/p>\n\n\n\n

    \n
  • It prevents wasted spend.<\/strong> If a campaign mostly reaches people who would convert anyway, the incremental lift is low even if attributed conversions are high.<\/li>\n
  • It improves decision quality.<\/strong> Teams can prioritize actions that generate net-new outcomes rather than optimizing to vanity efficiency metrics.<\/li>\n
  • It clarifies channel roles.<\/strong> Incrementality Measurement reveals when a channel is primarily capturing existing demand (harvesting) versus creating new demand (growing).<\/li>\n
  • It strengthens competitive advantage.<\/strong> Organizations that measure incrementality can invest with confidence, defend budgets with evidence, and iterate faster than competitors relying solely on platform-reported results.<\/li>\n<\/ul>\n\n\n\n

    In short, Incrementality Measurement upgrades Conversion & Measurement<\/strong> from \u201ccounting conversions\u201d to \u201cproving business impact,\u201d and it makes Attribution<\/strong> more trustworthy by validating which credited touchpoints truly move outcomes.<\/p>\n\n\n\n

    How Incrementality Measurement Works<\/h2>\n\n\n\n

    Incrementality Measurement is often implemented through experiments or quasi-experiments. While exact designs vary, practical workflows tend to follow four stages:<\/p>\n\n\n\n

    1) Input: Define the decision and the intervention<\/h3>\n\n\n\n

    Start with a clear decision you want to inform, such as:\n– Should we increase paid social budget by 20%?\n– Does retargeting drive incremental purchases or just capture existing intent?\n– Are affiliate coupons adding revenue or discounting conversions we would have gotten anyway?<\/p>\n\n\n\n

    Define the intervention precisely: audience, geography, time window, creative, frequency, and the primary conversion event(s) used in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

    2) Analysis setup: Create a counterfactual<\/h3>\n\n\n\n

    Incrementality Measurement requires a counterfactual\u2014what would have happened without the marketing. Common approaches include:\n– Randomized holdouts (A\/B test style)\n– Geo experiments (region-based control vs. test)\n– Time-based experiments (on\/off with careful controls)\n– Causal inference techniques (matching, synthetic controls)<\/p>\n\n\n\n

    The goal is to produce a control group similar enough to the treatment group that the difference in outcomes can be interpreted causally.<\/p>\n\n\n\n

    3) Execution: Run the test and ensure separation<\/h3>\n\n\n\n

    You run the campaign while maintaining separation between treatment and control. This is often the hardest operational part:\n– Preventing \u201cspillover\u201d (control group gets exposed anyway)\n– Avoiding budget re-optimization that breaks the design\n– Maintaining consistent measurement definitions and tagging<\/p>\n\n\n\n

    This stage is where Conversion & Measurement<\/strong> governance matters: stable conversion definitions, clean data collection, and clear ownership.<\/p>\n\n\n\n

    4) Output: Estimate lift and translate it into business metrics<\/h3>\n\n\n\n

    Incrementality Measurement typically outputs:\n– Incremental conversions (lift)\n– Incremental revenue or profit\n– Incremental cost per acquisition (iCPA) or cost per incremental conversion\n– Incremental return on ad spend (iROAS)<\/p>\n\n\n\n

    These results then inform Attribution<\/strong> calibration, budget shifts, creative changes, and channel strategy.<\/p>\n\n\n\n

    Key Components of Incrementality Measurement<\/h2>\n\n\n\n

    Incrementality Measurement works best when its components are explicitly designed, owned, and documented:<\/p>\n\n\n\n

    Data inputs<\/h3>\n\n\n\n
      \n
    • Conversion events (purchase, lead, subscription, activation)<\/li>\n
    • Revenue, margin, and refund\/cancellation signals (where applicable)<\/li>\n
    • Exposure\/impression or eligibility data (who could have been targeted)<\/li>\n
    • Audience attributes (new vs. returning, lifecycle stage, region)<\/li>\n
    • Timing signals (seasonality, promotions, product launches)<\/li>\n<\/ul>\n\n\n\n

      Processes and governance<\/h3>\n\n\n\n
        \n
      • A testing roadmap tied to budget decisions<\/li>\n
      • Pre-registered hypotheses (what you expect and why)<\/li>\n
      • Guardrails for platform optimizations (to avoid invalidating holdouts)<\/li>\n
      • Consistent definitions inside Conversion & Measurement<\/strong> documentation<\/li>\n<\/ul>\n\n\n\n

        Measurement methodology<\/h3>\n\n\n\n
          \n
        • Randomization strategy (user-level or geo-level)<\/li>\n
        • Statistical approach (confidence intervals, power analysis)<\/li>\n
        • Validation checks (balance, contamination, pre-trend similarity)<\/li>\n<\/ul>\n\n\n\n

          Team responsibilities<\/h3>\n\n\n\n
            \n
          • Marketing owns the business question and execution constraints<\/li>\n
          • Analytics\/data science owns design, analysis, and interpretation<\/li>\n
          • Engineering\/data teams ensure event quality and identity resolution<\/li>\n
          • Finance partners on profit and incrementality-to-P&L translation<\/li>\n<\/ul>\n\n\n\n

            Incrementality Measurement is not just an analysis\u2014it\u2019s a cross-functional operating model within Conversion & Measurement<\/strong> and a necessary companion to Attribution<\/strong> reporting.<\/p>\n\n\n\n

            Types of Incrementality Measurement<\/h2>\n\n\n\n

            Incrementality Measurement doesn\u2019t have a single \u201cstandard\u201d method, but there are common approaches suited to different constraints:<\/p>\n\n\n\n

            1) Randomized controlled experiments (holdouts)<\/h3>\n\n\n\n

            The cleanest approach: randomly withhold ads from a portion of the eligible audience. This is powerful for validating Attribution<\/strong> claims in channels like paid social, display, and retargeting.<\/p>\n\n\n\n

            2) Geo incrementality tests<\/h3>\n\n\n\n

            Split markets or regions into test and control. Useful when user-level holdouts aren\u2019t feasible (e.g., offline media, retail, some CTV\/radio scenarios). Strong geo design is a major pillar of enterprise Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

            3) Time-based and on\/off testing (with caution)<\/h3>\n\n\n\n

            Turning campaigns on and off can be informative but is more exposed to confounders (seasonality, competitor activity). It\u2019s best used with careful controls or as directional evidence.<\/p>\n\n\n\n

            4) Observational causal inference<\/h3>\n\n\n\n

            When experimentation isn\u2019t possible, techniques like matching or synthetic control can approximate incrementality. This can support Attribution<\/strong> decisions, but it requires stronger assumptions and transparent limitations.<\/p>\n\n\n\n

            Real-World Examples of Incrementality Measurement<\/h2>\n\n\n\n

            Example 1: Retargeting that looks great in Attribution but isn\u2019t incremental<\/h3>\n\n\n\n

            A DTC brand sees retargeting with a very low CPA in their Attribution<\/strong> dashboard. They run an Incrementality Measurement holdout: 15% of eligible site visitors are excluded from retargeting for 4 weeks. Result: overall purchases barely change; many \u201cretargeting conversions\u201d still happen through direct or email. The conclusion: retargeting is mostly capturing existing intent. The brand reduces spend, tightens frequency caps, and reallocates budget to prospecting with better incremental lift\u2014improving Conversion & Measurement<\/strong> outcomes.<\/p>\n\n\n\n

            Example 2: Paid search brand terms vs. incremental new customers<\/h3>\n\n\n\n

            A SaaS company suspects brand search ads cannibalize organic clicks. They run an Incrementality Measurement geo test: in matched regions, brand search ads are paused while other regions remain active. They measure total signups and paid vs. organic mix. Outcome: total signups remain nearly flat while paid signups drop and organic increases. Incremental lift is small; they keep brand search for defense in specific competitor-heavy regions only. This aligns Attribution<\/strong> with actual causal value.<\/p>\n\n\n\n

            Example 3: Promo campaigns and true profit lift<\/h3>\n\n\n\n

            A retailer runs a 20% discount campaign and sees a spike in conversions. Incrementality Measurement compares exposed vs. holdout audiences while tracking margin. Incremental orders increase modestly, but profit declines due to discounting customers who would have purchased anyway. The retailer shifts to targeted offers for price-sensitive segments, improving profit per incremental conversion\u2014an advanced Conversion & Measurement<\/strong> application beyond simple conversion counting.<\/p>\n\n\n\n

            Benefits of Using Incrementality Measurement<\/h2>\n\n\n\n

            Incrementality Measurement delivers benefits that typical reporting and Attribution<\/strong> cannot reliably provide:<\/p>\n\n\n\n

              \n
            • More accurate ROI decisions:<\/strong> iROAS and incremental profit are closer to true business impact than attributed ROAS.<\/li>\n
            • Cost savings:<\/strong> eliminate or reduce spend in low-incrementality tactics (common with over-targeted retargeting and coupon-heavy affiliate programs).<\/li>\n
            • Better budget allocation:<\/strong> shift investment to channels and creatives that create net-new demand.<\/li>\n
            • Improved customer experience:<\/strong> reduced ad fatigue when you stop over-serving likely converters.<\/li>\n
            • Stronger stakeholder alignment:<\/strong> finance and leadership trust results grounded in causal evidence, strengthening Conversion & Measurement<\/strong> credibility.<\/li>\n<\/ul>\n\n\n\n

              Challenges of Incrementality Measurement<\/h2>\n\n\n\n

              Incrementality Measurement is powerful, but it\u2019s not \u201cfree\u201d:<\/p>\n\n\n\n

              Technical and data challenges<\/h3>\n\n\n\n
                \n
              • Identity and deduplication issues (especially across devices)<\/li>\n
              • Event loss or inconsistent tagging in Conversion & Measurement<\/strong><\/li>\n
              • Limited visibility due to privacy changes and walled gardens<\/li>\n<\/ul>\n\n\n\n

                Design and execution risks<\/h3>\n\n\n\n
                  \n
                • Contamination\/spillover (control users still see ads)<\/li>\n
                • Biased controls (non-random exclusion, uneven markets)<\/li>\n
                • Insufficient sample size (underpowered tests give inconclusive results)<\/li>\n<\/ul>\n\n\n\n

                  Strategic and organizational barriers<\/h3>\n\n\n\n
                    \n
                  • Teams fear tests that may show low incrementality<\/li>\n
                  • Platform optimization algorithms can conflict with holdout designs<\/li>\n
                  • Misinterpretation: lift may be real but short-term metrics may not capture long-term value (e.g., brand building)<\/li>\n<\/ul>\n\n\n\n

                    A mature program treats Incrementality Measurement as a continuous learning system that complements Attribution<\/strong>, rather than a one-time \u201cgotcha.\u201d<\/p>\n\n\n\n

                    Best Practices for Incrementality Measurement<\/h2>\n\n\n\n
                      \n
                    • Start with high-spend, high-uncertainty areas.<\/strong> Retargeting, brand search, affiliates, and broad prospecting are common starting points in Conversion & Measurement<\/strong>.<\/li>\n
                    • Define one primary metric and a few guardrails.<\/strong> Example: primary = incremental purchases; guardrails = AOV, profit, churn, unsubscribe rate.<\/li>\n
                    • Pre-plan sample size and duration.<\/strong> Use power analysis where possible; avoid stopping early because results \u201clook good.\u201d<\/li>\n
                    • Control for seasonality and promos.<\/strong> Run tests long enough to smooth weekly cycles, and avoid major site changes mid-test.<\/li>\n
                    • Measure incrementality at the decision level.<\/strong> Sometimes the right unit is geo, sometimes audience segment, sometimes channel budget tier.<\/li>\n
                    • Document assumptions.<\/strong> Especially when using observational methods; transparency increases trust in Attribution<\/strong> and Conversion & Measurement<\/strong> outputs.<\/li>\n
                    • Operationalize learnings.<\/strong> Convert results into rules: frequency caps, audience exclusions, brand bidding policies, budget floors\/ceilings.<\/li>\n<\/ul>\n\n\n\n

                      Tools Used for Incrementality Measurement<\/h2>\n\n\n\n

                      Incrementality Measurement is methodology-driven, but tools make it operational inside Conversion & Measurement<\/strong> and Attribution<\/strong> workflows:<\/p>\n\n\n\n

                        \n
                      • Analytics tools:<\/strong> event tracking, funnel analysis, cohorting, and experimentation readouts.<\/li>\n
                      • Experimentation platforms:<\/strong> A\/B testing systems that can randomize exposure or eligibility (web\/app) and track conversion impact.<\/li>\n
                      • Ad platforms and ad servers:<\/strong> to implement holdouts, geo targeting, frequency caps, and to collect exposure logs where available.<\/li>\n
                      • CRM systems and marketing automation:<\/strong> to connect experiments with lifecycle outcomes (lead quality, retention, LTV).<\/li>\n
                      • Data warehouses and pipelines:<\/strong> to unify spend, exposure, and conversion data and to compute incremental lift reliably.<\/li>\n
                      • BI and reporting dashboards:<\/strong> to publish incremental KPIs (iCPA, iROAS) alongside conventional Attribution<\/strong> metrics for decision-makers.<\/li>\n<\/ul>\n\n\n\n

                        The best toolset is the one that ensures consistent definitions, repeatable tests, and audit-ready results in your Conversion & Measurement<\/strong> stack.<\/p>\n\n\n\n

                        Metrics Related to Incrementality Measurement<\/h2>\n\n\n\n

                        Incrementality Measurement typically revolves around \u201cincremental\u201d versions of familiar metrics:<\/p>\n\n\n\n

                          \n
                        • Incremental conversions (lift):<\/strong> additional conversions caused by marketing.<\/li>\n
                        • Lift percentage:<\/strong> incremental conversions divided by control conversions (or baseline).<\/li>\n
                        • Incremental revenue:<\/strong> net-new revenue attributable to the intervention.<\/li>\n
                        • Incremental profit \/ contribution margin:<\/strong> more decision-relevant than revenue for promo-heavy businesses.<\/li>\n
                        • iCPA (incremental cost per acquisition):<\/strong> spend divided by incremental conversions.<\/li>\n
                        • iROAS (incremental return on ad spend):<\/strong> incremental revenue divided by spend.<\/li>\n
                        • New-customer lift:<\/strong> incremental first-time buyers or qualified new leads.<\/li>\n
                        • Downstream lift:<\/strong> retention, repeat purchase, activation, churn reduction\u2014important when Attribution<\/strong> focuses too narrowly on immediate conversions.<\/li>\n<\/ul>\n\n\n\n

                          Future Trends of Incrementality Measurement<\/h2>\n\n\n\n

                          Incrementality Measurement is evolving quickly within Conversion & Measurement<\/strong> as the industry adapts:<\/p>\n\n\n\n

                            \n
                          • Privacy-first measurement:<\/strong> more reliance on aggregated data, modeled conversions, and experimentation as user-level tracking declines.<\/li>\n
                          • More automation in experimentation:<\/strong> platforms increasingly support built-in holdouts and geo tests, making incrementality easier to run at scale.<\/li>\n
                          • Blended measurement frameworks:<\/strong> teams combine Incrementality Measurement with media mix modeling and calibrated Attribution<\/strong> to cover both short- and long-term effects.<\/li>\n
                          • AI-assisted analysis:<\/strong> faster design iteration, anomaly detection, and scenario planning\u2014while still requiring human governance and causal rigor.<\/li>\n
                          • Incrementality beyond paid media:<\/strong> more focus on email, pricing, onsite personalization, and product-led growth changes as measurable interventions.<\/li>\n<\/ul>\n\n\n\n

                            The direction is clear: robust Conversion & Measurement<\/strong> strategies will treat Incrementality Measurement as a standard practice, not an advanced luxury.<\/p>\n\n\n\n

                            Incrementality Measurement vs Related Terms<\/h2>\n\n\n\n

                            Incrementality Measurement vs Attribution<\/h3>\n\n\n\n

                            Attribution<\/strong> allocates credit among touchpoints; Incrementality Measurement estimates causal lift. Attribution can be directionally helpful for optimization, but it can over-credit channels that capture existing intent. Incrementality Measurement validates whether attributed conversions represent net-new outcomes.<\/p>\n\n\n\n

                            Incrementality Measurement vs A\/B Testing<\/h3>\n\n\n\n

                            A\/B testing is a broader experimentation method that can be used for product, UX, pricing, and messaging. Incrementality Measurement often uses A\/B testing principles, but specifically to quantify the incremental business impact of marketing exposure or spend.<\/p>\n\n\n\n

                            Incrementality Measurement vs Media Mix Modeling (MMM)<\/h3>\n\n\n\n

                            MMM estimates how different marketing channels contribute to outcomes over time using aggregated data and statistical modeling. Incrementality Measurement is usually more targeted and causal via experiments. Many organizations use both: MMM for strategic allocation and Incrementality Measurement for channel- or tactic-level validation within Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                            Who Should Learn Incrementality Measurement<\/h2>\n\n\n\n
                              \n
                            • Marketers:<\/strong> to understand what truly drives growth, not just what gets credited in Attribution<\/strong> reports.<\/li>\n
                            • Analysts and data scientists:<\/strong> to design valid experiments, quantify lift, and communicate uncertainty clearly.<\/li>\n
                            • Agencies:<\/strong> to prove impact credibly, defend strategy, and avoid optimizing to misleading platform metrics.<\/li>\n
                            • Business owners and founders:<\/strong> to allocate budgets efficiently and connect marketing to profit, not just volume.<\/li>\n
                            • Developers and data engineers:<\/strong> to build reliable event pipelines, identity resolution, and experimentation infrastructure that supports Conversion & Measurement<\/strong> at scale.<\/li>\n<\/ul>\n\n\n\n

                              Summary of Incrementality Measurement<\/h2>\n\n\n\n

                              Incrementality Measurement is the practice of estimating the causal, net-new impact<\/strong> of marketing by comparing outcomes with and without an intervention. It matters because modern Conversion & Measurement<\/strong> can be distorted by correlation, tracking gaps, and biased Attribution<\/strong>. By focusing on lift, iCPA, and iROAS, Incrementality Measurement helps teams spend smarter, reduce waste, and align marketing results with real business outcomes\u2014while making Attribution more accurate and decision-ready.<\/p>\n\n\n\n

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

                              1) What is Incrementality Measurement in simple terms?<\/h3>\n\n\n\n

                              Incrementality Measurement is measuring the extra conversions or revenue a campaign creates beyond what would have happened anyway, typically by comparing a test group to a control group.<\/p>\n\n\n\n

                              2) How is Incrementality Measurement different from Attribution?<\/h3>\n\n\n\n

                              Attribution assigns credit for conversions across touchpoints; Incrementality Measurement tests whether marketing exposure actually caused additional conversions. They work best together in a complete Conversion & Measurement<\/strong> approach.<\/p>\n\n\n\n

                              3) Do I always need a control group to measure incrementality?<\/h3>\n\n\n\n

                              A control group (or a credible counterfactual like matched geos) is the most reliable path. If you can\u2019t run one, you can use causal inference methods, but results depend more on assumptions and should be treated with more caution.<\/p>\n\n\n\n

                              4) What channels benefit most from Incrementality Measurement?<\/h3>\n\n\n\n

                              Channels that often look strong in Attribution<\/strong> but may have low causal lift are common candidates: retargeting, brand search, affiliates\/coupons, and some high-frequency display campaigns. It\u2019s also valuable for upper-funnel channels where click-based measurement is weak.<\/p>\n\n\n\n

                              5) What\u2019s the difference between ROAS and incremental ROAS (iROAS)?<\/h3>\n\n\n\n

                              ROAS uses attributed revenue divided by spend. iROAS uses incremental<\/strong> revenue divided by spend, reflecting revenue that was caused by the campaign\u2014often more accurate for budgeting decisions in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                              6) How long should an incrementality test run?<\/h3>\n\n\n\n

                              Long enough to reach adequate sample size and cover typical cycles (often at least 2\u20134 weeks for many businesses, longer for low-volume conversions). The right duration depends on traffic, conversion rate, and expected lift.<\/p>\n\n\n\n

                              7) Can Incrementality Measurement be used for SEO or content marketing?<\/h3>\n\n\n\n

                              Direct holdout tests are harder for SEO because you can\u2019t easily \u201cturn off\u201d organic exposure for a randomized audience. However, Incrementality Measurement concepts can still inform SEO by using geo comparisons, careful time-based analysis, and by integrating results into broader Conversion & Measurement<\/strong> and Attribution<\/strong> planning.<\/p>\n","protected":false},"excerpt":{"rendered":"

                              Incrementality Measurement is the discipline of quantifying what marketing *actually causes*\u2014the conversions, revenue, and behaviors that would **not** have happened without a specific campaign, channel, or tactic. In modern **Conversion & Measurement**, it answers the hardest question: \u201cDid this marketing activity create new value, or did it just claim credit for demand that already existed?\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":[1888],"tags":[],"class_list":["post-7047","post","type-post","status-publish","format-standard","hentry","category-attribution"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7047","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=7047"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7047\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7047"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7047"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7047"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}