{"id":7086,"date":"2026-03-23T23:48:01","date_gmt":"2026-03-23T23:48:01","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/attribution-incrementality\/"},"modified":"2026-03-23T23:48:01","modified_gmt":"2026-03-23T23:48:01","slug":"attribution-incrementality","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/attribution-incrementality\/","title":{"rendered":"Attribution Incrementality: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Attribution"},"content":{"rendered":"\n

Attribution Incrementality is the practice of measuring how much additional (or \u201cincremental\u201d) business outcome a marketing activity truly causes, beyond what would have happened anyway. In Conversion & Measurement<\/strong>, it answers the most important question that traditional Attribution<\/strong> often struggles with: Did this channel create new conversions, or did it just get credit for conversions that were already likely to happen?<\/em><\/p>\n\n\n\n

Modern customer journeys are fragmented across devices, platforms, and privacy-restricted environments. As a result, click-based and exposure-based Attribution<\/strong> can over-credit certain touchpoints\u2014especially retargeting, brand search, and last-touch channels. Attribution Incrementality<\/strong> matters because it brings causal thinking to Conversion & Measurement<\/strong>, helping teams invest in what genuinely grows revenue rather than what merely looks good in reports.<\/p>\n\n\n\n

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

Attribution Incrementality<\/strong> is a measurement approach that estimates the causal lift<\/em> generated by a marketing tactic, channel, campaign, or touchpoint. Instead of assigning credit based on who touched the user last (or first, or most), it aims to quantify the conversions, revenue, or other outcomes that occurred because<\/em> the marketing activity happened.<\/p>\n\n\n\n

The core concept is the counterfactual<\/strong>: what would have occurred if the user (or market) had not been exposed to that marketing activity. The difference between observed results and the counterfactual is incremental impact.<\/p>\n\n\n\n

From a business perspective, Attribution Incrementality turns Attribution<\/strong> from a \u201ccredit assignment\u201d exercise into an \u201cinvestment decision\u201d tool. Within Conversion & Measurement<\/strong>, it supports budgeting, channel strategy, bid optimization, and forecasting by grounding decisions in incremental outcomes rather than attributed outcomes.<\/p>\n\n\n\n

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

In practical Conversion & Measurement<\/strong>, teams need to decide where to spend the next dollar, not just where the last dollar appeared to work. Attribution Incrementality<\/strong> improves that decision in several ways:<\/p>\n\n\n\n

    \n
  • Reduces wasted spend:<\/strong> It reveals channels that harvest demand (capture conversions already in motion) rather than create demand.<\/li>\n
  • Improves budget allocation:<\/strong> When incremental ROI is known, budgets can shift toward activities that actually add conversions.<\/li>\n
  • Builds resilience to tracking gaps:<\/strong> Privacy changes and limited identifiers weaken user-level Attribution<\/strong>; incrementality can be estimated with experiments and aggregated methods.<\/li>\n
  • Creates competitive advantage:<\/strong> Organizations that measure incremental lift can scale faster because they avoid false positives and optimize sooner.<\/li>\n<\/ul>\n\n\n\n

    In short, Attribution Incrementality<\/strong> strengthens Attribution<\/strong> by validating whether credited touchpoints truly drove incremental results.<\/p>\n\n\n\n

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

    Attribution Incrementality<\/strong> is more practical than theoretical when you view it as a repeatable measurement loop:<\/p>\n\n\n\n

      \n
    1. \n

      Input (what you change or test)<\/strong>\n You introduce a controlled difference in marketing exposure\u2014such as pausing a campaign in certain regions, reducing bids for a subset of users, or holding out a portion of the audience from ads.<\/p>\n<\/li>\n

    2. \n

      Analysis (how you estimate causality)<\/strong>\n You compare outcomes between exposed and non-exposed groups (or between before\/after periods with controls) while accounting for confounders like seasonality, pricing, and baseline demand.<\/p>\n<\/li>\n

    3. \n

      Execution (how you apply findings)<\/strong>\n You translate incremental lift into actions: reallocate budget, adjust targeting, refine creative, change frequency caps, or modify bidding rules.<\/p>\n<\/li>\n

    4. \n

      Output (what you get)<\/strong>\n You produce incrementality-adjusted metrics such as incremental conversions, incremental revenue, and incremental ROAS\u2014feeding them back into Conversion & Measurement<\/strong> dashboards and planning.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

      The key difference from many Attribution<\/strong> models is that Attribution Incrementality prioritizes causal impact<\/em> over path credit<\/em>.<\/p>\n\n\n\n

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

      A robust Attribution Incrementality program typically includes the following elements:<\/p>\n\n\n\n

      Data inputs<\/h3>\n\n\n\n
        \n
      • Conversion events (orders, leads, signups) with consistent definitions<\/li>\n
      • Revenue and margin data (ideally contribution margin, not just top-line revenue)<\/li>\n
      • Spend, impressions, clicks, reach, and frequency by channel<\/li>\n
      • Contextual factors: seasonality, promos, pricing, inventory, and site\/app changes<\/li>\n<\/ul>\n\n\n\n

        Measurement processes<\/h3>\n\n\n\n
          \n
        • Experiment design (holdouts, geo tests, or controlled pauses)<\/li>\n
        • Baseline modeling (estimating expected conversions without the marketing activity)<\/li>\n
        • Statistical inference (confidence intervals, significance, and power planning)<\/li>\n
        • Documentation and QA for repeatability in Conversion & Measurement<\/strong><\/li>\n<\/ul>\n\n\n\n

          Governance and responsibilities<\/h3>\n\n\n\n
            \n
          • Clear ownership between marketing, analytics, and finance<\/li>\n
          • A test calendar to avoid overlapping experiments<\/li>\n
          • Standard definitions for \u201cincremental\u201d vs \u201cattributed\u201d<\/li>\n
          • Decision rules: what lift threshold justifies scaling or cutting spend<\/li>\n<\/ul>\n\n\n\n

            Systems and pipelines<\/h3>\n\n\n\n
              \n
            • Event collection and tagging standards<\/li>\n
            • Identity and consent-aware data handling<\/li>\n
            • Reporting layers that can show both Attribution<\/strong> results and incrementality results side by side<\/li>\n<\/ul>\n\n\n\n

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

              \u201cAttribution Incrementality\u201d isn\u2019t a single model; it\u2019s an umbrella for approaches that estimate causal lift. The most useful distinctions are:<\/p>\n\n\n\n

              1) Experiment-based incrementality<\/h3>\n\n\n\n

              This is the gold standard when feasible.<\/p>\n\n\n\n

                \n
              • Randomized holdout tests:<\/strong> A portion of users do not receive ads; outcomes are compared.<\/li>\n
              • Geo experiments:<\/strong> Regions are assigned different spend levels or on\/off conditions.<\/li>\n
              • Ghost ads \/ PSA tests:<\/strong> Some platforms simulate ad eligibility without serving the ad to estimate lift.<\/li>\n<\/ul>\n\n\n\n

                Best for validating whether a channel drives incremental conversions in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                2) Quasi-experimental incrementality<\/h3>\n\n\n\n

                Used when randomization is difficult.<\/p>\n\n\n\n

                  \n
                • Difference-in-differences:<\/strong> Compare changes over time between a test group and a control group.<\/li>\n
                • Synthetic controls:<\/strong> Build a \u201cvirtual control\u201d from multiple regions or segments to match baseline trends.<\/li>\n
                • Matched markets:<\/strong> Pair similar regions and vary spend in one.<\/li>\n<\/ul>\n\n\n\n

                  These approaches can support Attribution<\/strong> decisions when platform experiments aren\u2019t available.<\/p>\n\n\n\n

                  3) Model-assisted incrementality (aggregated)<\/h3>\n\n\n\n

                  Helpful for long time horizons and multi-channel evaluation.<\/p>\n\n\n\n

                    \n
                  • Aggregated statistical models that estimate contribution while accounting for seasonality and external factors<\/li>\n
                  • Often used alongside, not instead of, experimental validation<\/li>\n<\/ul>\n\n\n\n

                    In mature Conversion & Measurement<\/strong> setups, teams mix experiment-based and model-assisted incrementality to balance precision and coverage.<\/p>\n\n\n\n

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

                    Example 1: Brand search \u201cperformance\u201d that isn\u2019t incremental<\/h3>\n\n\n\n

                    A retailer sees strong last-click Attribution<\/strong> for brand search. To assess Attribution Incrementality, they run a geo test reducing brand search bids in matched regions while keeping other media constant. Conversions drop slightly, but far less than attributed conversions suggested. The result: brand search captures existing demand, and incremental ROAS is lower than reports implied. Budget shifts toward prospecting and merchandising improvements.<\/p>\n\n\n\n

                    Example 2: Retargeting frequency that cannibalizes organic conversions<\/h3>\n\n\n\n

                    An e-commerce brand runs heavy retargeting and sees excellent CPA in Attribution<\/strong> dashboards. They implement a user holdout where 10% of eligible visitors are excluded from retargeting. The holdout converts at nearly the same rate as exposed users, indicating low lift. In Conversion & Measurement<\/strong>, the team tightens retargeting windows, adds frequency caps, and redirects spend to acquisition campaigns with higher incremental lift.<\/p>\n\n\n\n

                    Example 3: Upper-funnel video that creates incremental demand<\/h3>\n\n\n\n

                    A subscription app doubts whether video ads \u201cwork\u201d because last-touch Attribution<\/strong> shows minimal credit. They run a regional lift test with increased video reach in test markets. Branded traffic and trial starts rise meaningfully versus controls, and downstream paid search becomes more efficient. Attribution Incrementality reveals that video drove incremental demand that click-based Attribution<\/strong> missed.<\/p>\n\n\n\n

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

                    When implemented well, Attribution Incrementality delivers benefits that standard Attribution<\/strong> alone cannot:<\/p>\n\n\n\n

                      \n
                    • More accurate ROI:<\/strong> Incremental ROAS reflects real business impact, not just credit allocation.<\/li>\n
                    • Better budget efficiency:<\/strong> Spend moves from low-lift channels to high-lift channels.<\/li>\n
                    • Stronger forecasting:<\/strong> Incrementality curves (lift vs spend) support planning and scenario modeling in Conversion & Measurement<\/strong>.<\/li>\n
                    • Reduced internal conflict:<\/strong> Teams align around causal evidence rather than debating which Attribution<\/strong> model is \u201cright.\u201d<\/li>\n
                    • Improved customer experience:<\/strong> Less unnecessary retargeting and better sequencing reduces ad fatigue and improves relevance.<\/li>\n<\/ul>\n\n\n\n

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

                      Attribution Incrementality is powerful, but it\u2019s not frictionless:<\/p>\n\n\n\n

                        \n
                      • Experiment feasibility:<\/strong> Some channels or platforms limit holdouts, or the business can\u2019t risk turning off revenue-driving campaigns.<\/li>\n
                      • Statistical power:<\/strong> Small budgets, low conversion volume, or short test windows can produce inconclusive results.<\/li>\n
                      • Contamination and spillover:<\/strong> Users travel between regions; channels interact; one campaign\u2019s change can affect others.<\/li>\n
                      • Operational complexity:<\/strong> Coordinating tests across teams, calendars, and platforms requires strong governance in Conversion & Measurement<\/strong>.<\/li>\n
                      • Time-to-learn:<\/strong> Incrementality results can take longer than daily Attribution<\/strong> reporting, which challenges fast-paced optimization cycles.<\/li>\n
                      • Misinterpretation risk:<\/strong> A \u201cno lift detected\u201d outcome may reflect insufficient power rather than truly zero incrementality.<\/li>\n<\/ul>\n\n\n\n

                        Best Practices for Attribution Incrementality<\/h2>\n\n\n\n

                        To make Attribution Incrementality reliable and actionable, focus on disciplined execution:<\/p>\n\n\n\n

                          \n
                        1. \n

                          Start with high-risk areas of over-crediting<\/strong>\n Prioritize channels where traditional Attribution<\/strong> commonly overstates impact: retargeting, brand search, affiliate\/coupon, and high-frequency display.<\/p>\n<\/li>\n

                        2. \n

                          Define success metrics before the test<\/strong>\n Choose primary outcomes (incremental conversions, incremental revenue, profit) and guardrails (CAC, margin, churn) so Conversion & Measurement<\/strong> decisions don\u2019t drift.<\/p>\n<\/li>\n

                        3. \n

                          Use clean, stable conversion definitions<\/strong>\n Consistent event definitions and deduplication rules reduce noise and prevent \u201clift\u201d from being a tracking artifact.<\/p>\n<\/li>\n

                        4. \n

                          Control for seasonality and major changes<\/strong>\n Avoid running tests during product launches, major promos, pricing changes, or site migrations unless those factors are explicitly modeled.<\/p>\n<\/li>\n

                        5. \n

                          Measure beyond immediate conversions<\/strong>\n Where relevant, include downstream outcomes (repeat purchase, retention, LTV). This prevents optimizing Attribution Incrementality<\/strong> for short-term wins only.<\/p>\n<\/li>\n

                        6. \n

                          Operationalize results<\/strong>\n Document learnings, create channel-specific incrementality benchmarks, and update planning assumptions. The goal is not a one-off study; it\u2019s a durable Conversion & Measurement<\/strong> capability.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

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

                          Attribution Incrementality is enabled by a stack of capabilities rather than a single tool:<\/p>\n\n\n\n

                            \n
                          • Analytics tools:<\/strong> Event measurement, funnel analysis, cohorting, and conversion QA to support Conversion & Measurement<\/strong> integrity.<\/li>\n
                          • Experimentation platforms and frameworks:<\/strong> Audience splits, geo testing, and measurement readouts for incrementality studies.<\/li>\n
                          • Ad platforms:<\/strong> Campaign controls, lift studies (when available), and reporting exports needed to design and monitor tests.<\/li>\n
                          • CRM and marketing automation:<\/strong> Downstream conversion linkage (lead quality, pipeline, retention) to validate incremental value beyond top-of-funnel.<\/li>\n
                          • Data warehouse and transformation pipelines:<\/strong> Centralized spend + performance + conversion datasets for consistent analysis across channels.<\/li>\n
                          • Reporting dashboards and BI:<\/strong> Executive-ready views that compare classic Attribution<\/strong> metrics with incrementality-adjusted outcomes.<\/li>\n
                          • SEO tools (supporting context):<\/strong> Organic trend monitoring to ensure paid tests don\u2019t get misread when organic demand shifts.<\/li>\n<\/ul>\n\n\n\n

                            The most important \u201ctool\u201d is often process: a repeatable testing workflow with clear ownership and documentation.<\/p>\n\n\n\n

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

                            Because Attribution Incrementality is about causal lift, the best metrics emphasize incremental outcomes per unit of spend<\/em>:<\/p>\n\n\n\n

                              \n
                            • Incremental conversions:<\/strong> Additional conversions caused by the marketing activity.<\/li>\n
                            • Incremental revenue:<\/strong> Additional revenue attributable to causal impact.<\/li>\n
                            • Incremental profit \/ contribution margin:<\/strong> More decision-useful than revenue when margins vary.<\/li>\n
                            • Incremental ROAS (iROAS):<\/strong> Incremental revenue divided by spend; a cornerstone metric in Conversion & Measurement<\/strong>.<\/li>\n
                            • Incremental CPA \/ CAC (iCPA\/iCAC):<\/strong> Spend divided by incremental conversions or customers.<\/li>\n
                            • Lift percentage:<\/strong> Relative increase vs control baseline.<\/li>\n
                            • Confidence intervals \/ significance:<\/strong> Essential for interpreting whether measured lift is reliable.<\/li>\n
                            • Diminishing returns curves:<\/strong> Incremental lift as spend increases, supporting budget optimization beyond simplistic Attribution<\/strong> reporting.<\/li>\n<\/ul>\n\n\n\n

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

                              Several forces are pushing Attribution Incrementality<\/strong> toward broader adoption in Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                                \n
                              • Privacy-driven aggregation:<\/strong> As user-level identifiers fade, incrementality methods that work with aggregated data become more important.<\/li>\n
                              • Automation and continuous testing:<\/strong> Always-on experiments, automated holdouts, and faster iteration cycles will make incrementality more operational.<\/li>\n
                              • AI-assisted design and analysis:<\/strong> Better power planning, anomaly detection, and model selection will reduce the manual burden\u2014while still requiring human governance.<\/li>\n
                              • Cross-channel planning maturity:<\/strong> Organizations are moving from channel-by-channel Attribution<\/strong> debates to portfolio management where incrementality and marginal returns guide spend.<\/li>\n
                              • More focus on profit, not just revenue:<\/strong> Incrementality measurement increasingly ties to margin, payback periods, and LTV to improve business outcomes.<\/li>\n<\/ul>\n\n\n\n

                                The direction is clear: Attribution Incrementality is becoming a core competency, not an advanced side project.<\/p>\n\n\n\n

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

                                Attribution Incrementality vs Multi-Touch Attribution<\/h3>\n\n\n\n

                                Multi-touch Attribution<\/strong> assigns credit across touchpoints in a user journey based on rules or statistical models. Attribution Incrementality<\/strong> asks whether those touchpoints caused<\/em> additional conversions. Multi-touch Attribution<\/strong> can be useful for journey insights, but it may still misstate causal impact without incrementality validation.<\/p>\n\n\n\n

                                Attribution Incrementality vs Marketing Mix Modeling<\/h3>\n\n\n\n

                                Marketing Mix Modeling<\/strong> uses aggregated, time-based data to estimate channel contributions while controlling for external factors. It can be incrementality-oriented, but it\u2019s typically less granular and slower to update. Attribution Incrementality often relies on experiments for sharper causal inference, while mix models provide broader, strategic coverage in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                Attribution Incrementality vs Lift Studies<\/h3>\n\n\n\n

                                A lift study<\/strong> is usually a specific experiment that measures lift (often on one platform). Attribution Incrementality is the broader discipline of designing, interpreting, and operationalizing lift measurement across channels, aligning it with Attribution<\/strong> and business decision-making.<\/p>\n\n\n\n

                                Who Should Learn Attribution Incrementality<\/h2>\n\n\n\n
                                  \n
                                • Marketers:<\/strong> To invest in channels that truly grow demand and to avoid optimizing toward misleading Attribution<\/strong> signals.<\/li>\n
                                • Analysts and data scientists:<\/strong> To design experiments, validate assumptions, and translate results into decision-ready insights for Conversion & Measurement<\/strong>.<\/li>\n
                                • Agencies:<\/strong> To prove incremental value, defend strategy, and retain clients by connecting spend to causal outcomes.<\/li>\n
                                • Business owners and founders:<\/strong> To understand what\u2019s actually driving growth and to scale marketing with less risk.<\/li>\n
                                • Developers and martech teams:<\/strong> To implement measurement foundations (event quality, data pipelines, experimentation infrastructure) that make Attribution Incrementality feasible.<\/li>\n<\/ul>\n\n\n\n

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

                                  Attribution Incrementality<\/strong> measures the additional conversions or revenue that marketing truly causes, not just what Attribution<\/strong> systems assign credit for. It fits at the heart of Conversion & Measurement<\/strong> because it supports better budgeting, smarter optimization, and more reliable ROI assessment in a complex, privacy-constrained ecosystem. By validating causal impact, Attribution Incrementality strengthens Attribution<\/strong> and helps teams scale what genuinely works.<\/p>\n\n\n\n

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

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

                                  Attribution Incrementality is the measurement of how many extra conversions (or how much extra revenue) happened because of a marketing activity, compared to what would have happened without it.<\/p>\n\n\n\n

                                  2) Why isn\u2019t standard Attribution enough?<\/h3>\n\n\n\n

                                  Standard Attribution<\/strong> often assigns credit based on clicks or touchpoints, which can overvalue channels that capture existing demand. Incrementality focuses on causation, making it more reliable for budget decisions in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                  3) How do you measure incrementality without turning campaigns off?<\/h3>\n\n\n\n

                                  You can use holdout splits, geo experiments, matched market approaches, or quasi-experimental methods that change exposure for a subset while maintaining business continuity.<\/p>\n\n\n\n

                                  4) Which channels most often fail incrementality tests?<\/h3>\n\n\n\n

                                  Retargeting, brand search, and coupon\/affiliate placements are common candidates because they frequently intercept users who were already likely to convert, leading to inflated Attribution<\/strong> credit.<\/p>\n\n\n\n

                                  5) What\u2019s a good primary metric for Attribution Incrementality?<\/h3>\n\n\n\n

                                  Incremental ROAS (iROAS) is a strong primary metric because it ties spend to causal revenue impact. Many teams also track incremental conversions and incremental profit.<\/p>\n\n\n\n

                                  6) How often should teams run incrementality studies?<\/h3>\n\n\n\n

                                  A practical cadence is quarterly for major channels and whenever there\u2019s a significant strategy shift (new targeting, creative overhaul, budget step-change). Mature Conversion & Measurement<\/strong> programs may run smaller continuous tests.<\/p>\n\n\n\n

                                  7) Can Attribution Incrementality and multi-touch Attribution work together?<\/h3>\n\n\n\n

                                  Yes. Multi-touch Attribution<\/strong> can explain journeys and touchpoint relationships, while incrementality validates which channels truly add conversions. Together they provide both narrative insight and causal proof.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                  Attribution Incrementality is the practice of measuring how much additional (or \u201cincremental\u201d) business outcome a marketing activity truly causes, beyond what would have happened anyway. In **Conversion & Measurement**, it answers the most important question that traditional **Attribution** often struggles with: *Did this channel create new conversions, or did it just get credit for conversions that were already likely to happen?*<\/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-7086","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\/7086","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=7086"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7086\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7086"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7086"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7086"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}