{"id":7042,"date":"2026-03-23T22:11:56","date_gmt":"2026-03-23T22:11:56","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/ghost-ads\/"},"modified":"2026-03-23T22:11:56","modified_gmt":"2026-03-23T22:11:56","slug":"ghost-ads","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/ghost-ads\/","title":{"rendered":"Ghost Ads: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Attribution"},"content":{"rendered":"\n

Ghost Ads are a measurement technique used to understand what advertising actually<\/em> causes\u2014rather than what advertising merely correlates with<\/em>. In modern Conversion & Measurement<\/strong>, where privacy constraints, cross-device behavior, and walled-garden platforms complicate tracking, Ghost Ads help teams estimate incremental impact with less bias than many traditional approaches.<\/p>\n\n\n\n

In the context of Attribution<\/strong>, Ghost Ads are especially valuable because they create a credible \u201cwhat would have happened anyway\u201d comparison. That counterfactual is the missing ingredient in many reporting stacks that rely on last-click, view-through, or modeled credit allocation. When used correctly, Ghost Ads can reveal whether spend is driving new conversions or simply capturing demand that would have converted without ads.<\/p>\n\n\n\n

What Is Ghost Ads?<\/h2>\n\n\n\n

Ghost Ads<\/strong> are \u201cplacebo\u201d or \u201ccounterfactual\u201d ad exposures recorded for a control group that was eligible<\/em> to see an ad but did not actually receive<\/em> the real ad experience. The key idea is to log a synthetic (ghost) impression event so you can compare outcomes between:<\/p>\n\n\n\n

    \n
  • People who truly saw the ad (treatment group)<\/li>\n
  • People who were eligible and would have seen an ad, but instead received a ghost exposure record (control group)<\/li>\n<\/ul>\n\n\n\n

    Business-wise, Ghost Ads answer a high-stakes question: How many conversions are incremental because of advertising?<\/strong> This is a core concern in Conversion & Measurement<\/strong> because it influences budgets, bidding strategies, creative decisions, and channel mix.<\/p>\n\n\n\n

    Within Attribution<\/strong>, Ghost Ads serve as a reality check. They don\u2019t replace attribution modeling; they help validate it. If your attribution reports show strong performance but Ghost Ads-based lift is small, your \u201ccredited\u201d conversions may be largely non-incremental.<\/p>\n\n\n\n

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

    Ghost Ads matter because many common measurement methods systematically over-credit ads:<\/p>\n\n\n\n

      \n
    • Users who are already likely to buy are more likely to be targeted, retargeted, and to click.<\/li>\n
    • Platforms optimize delivery toward predicted converters, which increases apparent performance even when incrementality is low.<\/li>\n
    • View-through and last-touch Attribution<\/strong> can count conversions that would have happened anyway.<\/li>\n<\/ul>\n\n\n\n

      In Conversion & Measurement<\/strong>, the strategic importance of Ghost Ads is that they help separate causation<\/strong> from selection bias<\/strong>. That enables better decisions on:<\/p>\n\n\n\n

        \n
      • Scaling or cutting spend with confidence<\/li>\n
      • Preventing overinvestment in retargeting that \u201charvests\u201d existing demand<\/li>\n
      • Comparing prospecting vs. remarketing using a consistent causal lens<\/li>\n
      • Evaluating creative and audience strategies based on incremental outcomes<\/li>\n<\/ul>\n\n\n\n

        Teams that understand Ghost Ads often gain a competitive advantage by reallocating budget toward tactics that truly generate new conversions, not just new tracking events.<\/p>\n\n\n\n

        How Ghost Ads Works<\/h2>\n\n\n\n

        Ghost Ads are more of a measurement design than a single workflow, but in practice they follow a clear sequence:<\/p>\n\n\n\n

          \n
        1. \n

          Trigger (eligibility to be shown an ad)<\/strong>\n A user matches targeting criteria and enters an ad auction or delivery decision point. They are \u201celigible\u201d to see your ad.<\/p>\n<\/li>\n

        2. \n

          Assignment (treatment vs. control)<\/strong>\n The platform or experiment system assigns the user to:\n – Treatment:<\/strong> the real ad is served\n – Control:<\/strong> the real ad is withheld, but a Ghost Ads<\/strong> exposure is logged as if an impression occurred (or as if the user was in-scope for an impression)<\/p>\n<\/li>\n

        3. \n

          Execution (ad delivery and logging)<\/strong>\n – Treatment users experience the ad normally.\n – Control users do not see the ad (or may see a neutral alternative such as a PSA), but the system records a ghost impression event to preserve comparability.<\/p>\n<\/li>\n

        4. \n

          Outcome (conversion comparison and lift)<\/strong>\n You compare conversion rates (and revenue) between treatment and control. The difference is incremental lift<\/strong>, which feeds back into Conversion & Measurement<\/strong> decisions and helps calibrate Attribution<\/strong> assumptions.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

          The defining feature is that Ghost Ads attempt to measure what ads change<\/em>, not just what ads touch<\/em>.<\/p>\n\n\n\n

          Key Components of Ghost Ads<\/h2>\n\n\n\n

          A robust Ghost Ads setup typically involves the following components:<\/p>\n\n\n\n

          Experimental design and governance<\/h3>\n\n\n\n
            \n
          • Clear hypothesis (e.g., \u201cProspecting increases first-time purchases by X%\u201d)<\/li>\n
          • Defined treatment\/control logic and exclusion rules<\/li>\n
          • Ownership across marketing, analytics, and engineering to prevent \u201canalysis-only\u201d experiments with weak implementation<\/li>\n<\/ul>\n\n\n\n

            Delivery and eligibility logic<\/h3>\n\n\n\n
              \n
            • A reliable mechanism to identify eligible users at the moment of ad delivery or auction participation<\/li>\n
            • Consistent assignment rules to prevent leakage (control users accidentally seeing the real ad)<\/li>\n<\/ul>\n\n\n\n

              Measurement instrumentation<\/h3>\n\n\n\n
                \n
              • Event logging for exposures (real and ghost) and conversions<\/li>\n
              • Consistent conversion definitions aligned to Conversion & Measurement<\/strong> standards (e.g., purchase, lead qualified, subscription activated)<\/li>\n<\/ul>\n\n\n\n

                Data quality and analysis framework<\/h3>\n\n\n\n
                  \n
                • Identity strategy (logged-in IDs, device graphs, or aggregated methods depending on privacy constraints)<\/li>\n
                • Statistical methods (confidence intervals, power calculations, minimum detectable effect)<\/li>\n
                • Guardrails to interpret lift alongside spend, frequency, and audience overlap<\/li>\n<\/ul>\n\n\n\n

                  Organizational alignment<\/h3>\n\n\n\n

                  Ghost Ads influence budget and channel decisions, so teams need agreed-upon rules for:\n– How incrementality results will be used\n– When results override standard Attribution<\/strong> reports\n– How often tests are repeated as campaigns and targeting evolve<\/p>\n\n\n\n

                  Types of Ghost Ads<\/h2>\n\n\n\n

                  \u201cGhost Ads\u201d isn\u2019t always implemented the same way. The most useful distinctions are:<\/p>\n\n\n\n

                  Ghost impressions vs. ghost eligibility logs<\/h3>\n\n\n\n
                    \n
                  • Ghost impression:<\/strong> logs an impression event for control users as if an ad was served. <\/li>\n
                  • Eligibility log:<\/strong> records that a user was eligible to be served an ad at a given time, even if no impression is logged.\nBoth aim to create a comparable baseline for Attribution<\/strong> and lift analysis within Conversion & Measurement<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                    Blank control vs. neutral control<\/h3>\n\n\n\n
                      \n
                    • Blank control:<\/strong> the user sees nothing from your campaign in that slot. <\/li>\n
                    • Neutral control (e.g., PSA):<\/strong> the user sees a non-commercial or non-brand message to account for \u201cad experience\u201d effects.\nNeutral controls can reduce bias when the experience of seeing any<\/em> ad changes behavior.<\/li>\n<\/ul>\n\n\n\n

                      Auction-based holdout vs. randomized holdout<\/h3>\n\n\n\n
                        \n
                      • Auction-based holdout:<\/strong> control assignment occurs at the time a user would enter an auction, preserving auction dynamics. <\/li>\n
                      • Randomized holdout:<\/strong> users are assigned earlier (e.g., at audience creation).\nAuction-based designs often better reflect real delivery conditions, improving Conversion & Measurement<\/strong> validity.<\/li>\n<\/ul>\n\n\n\n

                        Real-World Examples of Ghost Ads<\/h2>\n\n\n\n

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

                        An eCommerce brand sees strong ROAS in platform Attribution<\/strong> for cart-abandoner retargeting. They run a Ghost Ads-based holdout where eligible cart abandoners are split into treatment and control (with ghost exposures logged for control).<\/p>\n\n\n\n

                        Result: conversions are nearly the same in both groups, meaning the campaign captures demand that would convert anyway. The brand shifts budget from retargeting to prospecting and onsite UX improvements\u2014an immediate Conversion & Measurement<\/strong> win that pure attribution reports failed to surface.<\/p>\n\n\n\n

                        Example 2: Measuring incrementality for a new market launch<\/h3>\n\n\n\n

                        A subscription app launches in a new region and wants to know whether paid social is driving first-time subscriptions or simply accelerating organic installs. They run Ghost Ads with a neutral control.<\/p>\n\n\n\n

                        Result: significant lift in first-time subscriptions, but only at moderate frequency. The team uses the findings to cap frequency, focus creative on onboarding benefits, and recalibrate Attribution<\/strong> expectations for upper-funnel campaigns.<\/p>\n\n\n\n

                        Example 3: Testing creative impact independent of algorithmic bias<\/h3>\n\n\n\n

                        A B2B company compares two creatives. Standard reporting shows Creative A \u201cwins,\u201d but delivery skewed toward high-intent segments. They use Ghost Ads-style randomized holdouts per creative cohort.<\/p>\n\n\n\n

                        Result: Creative B produces higher incremental qualified leads even though it looked weaker in last-touch Attribution<\/strong>. The marketing team updates their creative testing process to prioritize incrementality within Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                        Benefits of Using Ghost Ads<\/h2>\n\n\n\n

                        When implemented well, Ghost Ads can deliver practical advantages:<\/p>\n\n\n\n

                          \n
                        • More accurate incrementality measurement:<\/strong> clarifies true causal impact instead of relying solely on observational Attribution<\/strong>.<\/li>\n
                        • Budget efficiency:<\/strong> helps cut spend that inflates reported performance without creating new conversions.<\/li>\n
                        • Better channel strategy:<\/strong> supports smarter prospecting\/retargeting balance and reduces over-crediting of lower-funnel touchpoints.<\/li>\n
                        • Improved decision-making under privacy limits:<\/strong> even when user-level tracking is reduced, structured lift tests can still inform Conversion & Measurement<\/strong> direction.<\/li>\n
                        • Healthier customer experience:<\/strong> avoiding excessive retargeting can reduce ad fatigue and brand annoyance while preserving conversions.<\/li>\n<\/ul>\n\n\n\n

                          Challenges of Ghost Ads<\/h2>\n\n\n\n

                          Ghost Ads are powerful, but not trivial. Common challenges include:<\/p>\n\n\n\n

                            \n
                          • Access and feasibility:<\/strong> not all ad environments support true ghost exposure logging or clean holdouts.<\/li>\n
                          • Statistical power:<\/strong> if conversions are low or effects are small, you may need large samples or longer test windows.<\/li>\n
                          • Interference and contamination:<\/strong> users in the control may still be exposed via other campaigns, channels, or devices, complicating Attribution<\/strong> interpretation.<\/li>\n
                          • Complexity in multi-touch reality:<\/strong> Ghost Ads can estimate lift for a campaign or channel, but it doesn\u2019t automatically allocate credit across every touchpoint.<\/li>\n
                          • Operational friction:<\/strong> marketing teams must accept results that may contradict platform-reported performance, which can be uncomfortable but necessary for credible Conversion & Measurement<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                            Best Practices for Ghost Ads<\/h2>\n\n\n\n

                            To get reliable results, apply these proven practices:<\/p>\n\n\n\n

                              \n
                            1. \n

                              Start with a clear decision the test will drive<\/strong>\n For example: \u201cShould we scale retargeting by 30%?\u201d Ghost Ads should answer a business question, not just produce a chart.<\/p>\n<\/li>\n

                            2. \n

                              Define conversions and windows tightly<\/strong>\n Align to your Conversion & Measurement<\/strong> taxonomy (primary vs. secondary conversions, attribution windows, revenue recognition).<\/p>\n<\/li>\n

                            3. \n

                              Prevent leakage between groups<\/strong>\n Ensure control users are excluded from the ad delivery path, and validate with exposure diagnostics.<\/p>\n<\/li>\n

                            4. \n

                              Run power and duration planning<\/strong>\n Estimate minimum detectable lift and required sample size. Underpowered Ghost Ads tests often create false \u201cno effect\u201d conclusions.<\/p>\n<\/li>\n

                            5. \n

                              Control for overlapping campaigns<\/strong>\n If multiple campaigns target the same users, isolate the variable you\u2019re testing or document overlap so Attribution<\/strong> interpretation remains honest.<\/p>\n<\/li>\n

                            6. \n

                              Use incrementality as a calibration layer<\/strong>\n Don\u2019t throw out attribution models; use Ghost Ads results to adjust expectations, bidding rules, and reporting narratives across Conversion & Measurement<\/strong>.<\/p>\n<\/li>\n

                            7. \n

                              Repeat tests as algorithms and audiences change<\/strong>\n Incrementality is not permanent. Refresh lift benchmarks when targeting, creative, or pricing shifts.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                              Tools Used for Ghost Ads<\/h2>\n\n\n\n

                              Ghost Ads typically require a combination of systems rather than a single tool:<\/p>\n\n\n\n

                                \n
                              • Ad platforms with built-in lift testing<\/strong>: some platforms support conversion lift experiments, holdouts, or similar frameworks that approximate Ghost Ads.<\/li>\n
                              • Analytics tools<\/strong>: for validating conversion events, cohort behavior, and downstream outcomes aligned to Conversion & Measurement<\/strong>.<\/li>\n
                              • Tag management and server-side tracking<\/strong>: improves event reliability and reduces measurement loss, strengthening lift calculations and Attribution<\/strong> alignment.<\/li>\n
                              • Data warehouses and ELT pipelines<\/strong>: unify exposure logs, conversions, and cost data for robust analysis and auditing.<\/li>\n
                              • Experimentation frameworks<\/strong>: for randomized assignment, governance, and statistical evaluation beyond what ad platforms provide.<\/li>\n
                              • Reporting dashboards<\/strong>: to communicate incrementality alongside standard KPIs so leadership can compare Ghost Ads findings to routine Attribution<\/strong> reports.<\/li>\n<\/ul>\n\n\n\n

                                Metrics Related to Ghost Ads<\/h2>\n\n\n\n

                                Ghost Ads-based analysis commonly focuses on causal and efficiency metrics:<\/p>\n\n\n\n

                                  \n
                                • Incremental conversions (lift):<\/strong> additional conversions caused by the ads vs. control<\/li>\n
                                • Incremental conversion rate (iCVR):<\/strong> conversion rate difference between treatment and control<\/li>\n
                                • Incremental revenue \/ profit:<\/strong> revenue lift minus incremental cost (ideally contribution margin-aware)<\/li>\n
                                • Incremental CPA (iCPA):<\/strong> spend divided by incremental conversions (often more honest than platform CPA)<\/li>\n
                                • Incremental ROAS (iROAS):<\/strong> incremental revenue divided by spend<\/li>\n
                                • Confidence intervals and significance:<\/strong> to quantify uncertainty, critical for responsible Conversion & Measurement<\/strong><\/li>\n
                                • Reach and frequency in test groups:<\/strong> to interpret lift relative to exposure intensity<\/li>\n
                                • Contamination rate:<\/strong> how often control users were exposed elsewhere, affecting Attribution<\/strong> conclusions<\/li>\n<\/ul>\n\n\n\n

                                  Future Trends of Ghost Ads<\/h2>\n\n\n\n

                                  Several trends are shaping how Ghost Ads evolves within Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                                    \n
                                  • Privacy-driven aggregation:<\/strong> as user-level identifiers decline, Ghost Ads-style incrementality testing becomes more important for validating performance beyond granular tracking.<\/li>\n
                                  • Clean-room workflows:<\/strong> more organizations will analyze exposure and conversion signals in privacy-preserving environments, changing how lift studies are executed and audited.<\/li>\n
                                  • Automation in experimentation:<\/strong> platforms and internal tools will increasingly automate holdout creation, power calculations, and continuous testing.<\/li>\n
                                  • AI-assisted optimization (with guardrails):<\/strong> AI can recommend budget shifts, but Ghost Ads-like lift results will remain essential to prevent automated systems from optimizing to biased Attribution<\/strong> signals.<\/li>\n
                                  • Blended measurement stacks:<\/strong> teams will combine incrementality tests, media mix modeling, and attribution models\u2014using Ghost Ads results as a calibration point for broader Conversion & Measurement<\/strong> strategy.<\/li>\n<\/ul>\n\n\n\n

                                    Ghost Ads vs Related Terms<\/h2>\n\n\n\n

                                    Ghost Ads vs Dark Posts (Unpublished Ads)<\/h3>\n\n\n\n

                                    Dark posts are ads not visible on a public page feed. They are a delivery format. Ghost Ads<\/strong> are a measurement construct designed to estimate causal lift. Dark posts can be used in a campaign that is later evaluated with Ghost Ads, but they are not the same thing.<\/p>\n\n\n\n

                                    Ghost Ads vs Conversion Lift Studies<\/h3>\n\n\n\n

                                    Conversion lift is the broader category of experiments that estimate incremental impact. Ghost Ads<\/strong> are one way to implement lift measurement\u2014specifically by logging synthetic exposures for control users to improve comparability and reduce bias in Attribution<\/strong>-adjacent reporting.<\/p>\n\n\n\n

                                    Ghost Ads vs View-Through Attribution<\/h3>\n\n\n\n

                                    View-through Attribution<\/strong> credits conversions after an impression, often without proving causality. Ghost Ads attempt to measure whether the impression changed<\/em> conversion probability by comparing against a counterfactual control group.<\/p>\n\n\n\n

                                    Who Should Learn Ghost Ads<\/h2>\n\n\n\n

                                    Ghost Ads are worth learning for roles that touch performance decisions:<\/p>\n\n\n\n

                                      \n
                                    • Marketers:<\/strong> to understand true incremental growth and avoid optimizing to misleading Attribution<\/strong> KPIs.<\/li>\n
                                    • Analysts:<\/strong> to design credible experiments, quantify uncertainty, and improve Conversion & Measurement<\/strong> integrity.<\/li>\n
                                    • Agencies:<\/strong> to defend strategy with causal evidence and avoid over-promising based on platform-reported results.<\/li>\n
                                    • Business owners and founders:<\/strong> to allocate budget across channels based on what drives incremental customers.<\/li>\n
                                    • Developers and data engineers:<\/strong> to support experiment assignment, event reliability, and data pipelines that make Ghost Ads analyses trustworthy.<\/li>\n<\/ul>\n\n\n\n

                                      Summary of Ghost Ads<\/h2>\n\n\n\n

                                      Ghost Ads<\/strong> are a measurement approach that logs counterfactual ad exposure for a control group to estimate incremental impact. They matter because modern Conversion & Measurement<\/strong> is prone to bias when relying solely on observational reporting. Used thoughtfully, Ghost Ads strengthen decision-making by quantifying lift and helping calibrate Attribution<\/strong> models, budgets, and optimization strategies around what truly causes conversions.<\/p>\n\n\n\n

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

                                      1) What are Ghost Ads used for?<\/h3>\n\n\n\n

                                      Ghost Ads are used to estimate incremental conversions by comparing outcomes between people who saw real ads and a control group that was eligible but withheld from seeing them. This supports more reliable Conversion & Measurement<\/strong> than observational reporting alone.<\/p>\n\n\n\n

                                      2) Do Ghost Ads replace Attribution models?<\/h3>\n\n\n\n

                                      No. Ghost Ads complement Attribution<\/strong> by providing lift benchmarks that can validate or challenge credited conversions. Attribution helps allocate credit; Ghost Ads help confirm causality.<\/p>\n\n\n\n

                                      3) Are Ghost Ads the same as ad fraud \u201cghost impressions\u201d?<\/h3>\n\n\n\n

                                      No. In fraud contexts, \u201cghost impressions\u201d imply fake delivery. In Conversion & Measurement<\/strong>, Ghost Ads refer to intentionally logged counterfactual exposures for controlled experiments.<\/p>\n\n\n\n

                                      4) When should I run a Ghost Ads-style lift test?<\/h3>\n\n\n\n

                                      Run one when you\u2019re making a budget or strategy decision and suspect bias\u2014common cases include heavy retargeting, brand campaigns, new channel launches, or major creative changes that standard Attribution<\/strong> may misread.<\/p>\n\n\n\n

                                      5) What conversions work best for Ghost Ads measurement?<\/h3>\n\n\n\n

                                      Primary conversions with clear definitions (purchase, qualified lead, subscription) work best. Ambiguous goals can dilute lift and complicate Conversion & Measurement<\/strong> interpretation.<\/p>\n\n\n\n

                                      6) Why might Ghost Ads show low lift even when platform reporting looks great?<\/h3>\n\n\n\n

                                      Platform Attribution<\/strong> can over-credit ads due to targeting and optimization toward likely converters. Ghost Ads reveal whether conversions increased versus a comparable control group.<\/p>\n\n\n\n

                                      7) How do I act on Ghost Ads results?<\/h3>\n\n\n\n

                                      Use lift to adjust budgets, frequency caps, audience strategy, and creative testing. If incremental impact is low, reallocate spend toward higher-lift tactics or improve onsite conversion drivers\u2014then retest to confirm changes in Conversion & Measurement<\/strong> outcomes.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                      Ghost Ads are a measurement technique used to understand what advertising *actually* causes\u2014rather than what advertising merely *correlates with*. In modern **Conversion & Measurement**, where privacy constraints, cross-device behavior, and walled-garden platforms complicate tracking, Ghost Ads help teams estimate incremental impact with less bias than many traditional approaches.<\/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-7042","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\/7042","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=7042"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7042\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7042"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7042"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7042"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}