{"id":10814,"date":"2026-03-30T00:01:43","date_gmt":"2026-03-30T00:01:43","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/recency-window\/"},"modified":"2026-03-30T00:01:43","modified_gmt":"2026-03-30T00:01:43","slug":"recency-window","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/recency-window\/","title":{"rendered":"Recency Window: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Programmatic Advertising"},"content":{"rendered":"\n

Recency Window is a foundational concept in Paid Marketing<\/strong> that helps teams decide how far back in time<\/em> a user action should remain eligible to influence targeting, bidding, personalization, and measurement. In Programmatic Advertising<\/strong>, where decisions are automated and occur in milliseconds, the Recency Window acts like a time-based filter: a purchase yesterday might be highly meaningful, while a site visit six months ago may be irrelevant (or even misleading) for today\u2019s ad decision.<\/p>\n\n\n\n

Getting the Recency Window right matters because user intent decays over time\u2014sometimes quickly, sometimes slowly\u2014depending on product, category, and buying cycle. A well-chosen Recency Window improves efficiency, protects budgets, and makes messaging feel timely rather than repetitive. A poorly chosen window can inflate frequency, waste spend on stale audiences, and distort attribution and incrementality conclusions.<\/p>\n\n\n\n

What Is Recency Window?<\/h2>\n\n\n\n

A Recency Window<\/strong> is the defined time period during which a user event (such as a site visit, product view, add-to-cart, lead form start, or purchase) is considered \u201crecent enough\u201d to qualify the user for a marketing action. That action might be inclusion in an audience, eligibility for an ad sequence, a bid adjustment, a suppression rule, or a conversion crediting rule.<\/p>\n\n\n\n

At its core, Recency Window answers a simple question: \u201cHow long should this behavior remain actionable?\u201d<\/strong> The business meaning is even more direct: it is a lever that controls relevance and cost. In Paid Marketing<\/strong>, it commonly determines who gets retargeted, how aggressively, and for how long. Inside Programmatic Advertising<\/strong>, it shapes audience segments, real-time bidding logic, and dynamic creative rules that depend on \u201crecent\u201d behavior.<\/p>\n\n\n\n

Recency Window is not just about targeting. Many teams also apply Recency Window thinking to measurement\u2014e.g., how many days after an ad exposure should a conversion be counted. While platforms may use different terms (view-through windows, attribution windows), the underlying concept is the same: time boundaries affect decisions and reported performance.<\/p>\n\n\n\n

Why Recency Window Matters in Paid Marketing<\/h2>\n\n\n\n

Recency Window has outsized impact because it controls both scale and precision<\/em>. In Paid Marketing<\/strong>, every additional day you keep a user eligible expands the audience pool, but it often reduces intent density. That tradeoff directly influences core outcomes:<\/p>\n\n\n\n

    \n
  • Higher conversion rates through better intent alignment:<\/strong> A 1\u20133 day window for cart abandoners often performs differently than a 30-day window of casual browsers.<\/li>\n
  • Lower wasted spend:<\/strong> Serving ads to users whose intent has decayed increases cost without adding incremental conversions.<\/li>\n
  • Improved customer experience:<\/strong> A sensible Recency Window prevents \u201chaunting\u201d people with ads long after they\u2019ve moved on\u2014or already bought.<\/li>\n
  • Competitive advantage:<\/strong> In Programmatic Advertising<\/strong>, teams that tune recency by funnel stage often outbid competitors only when it matters, rather than everywhere all the time.<\/li>\n<\/ul>\n\n\n\n

    Strategically, Recency Window also helps unify teams around a shared definition of \u201chot,\u201d \u201cwarm,\u201d and \u201ccold\u201d audiences\u2014reducing guesswork across media buying, lifecycle marketing, and analytics.<\/p>\n\n\n\n

    How Recency Window Works<\/h2>\n\n\n\n

    In practice, Recency Window works as a ruleset applied to event data. A helpful workflow view looks like this:<\/p>\n\n\n\n

      \n
    1. \n

      Input \/ Trigger (Event capture)<\/strong>\n Users generate events: page views, product views, searches, form submissions, app opens, purchases, subscription renewals, or offline events uploaded later. These events are timestamped and tied to an identifier (cookie, device ID, hashed email, or platform ID, depending on privacy and consent).<\/p>\n<\/li>\n

    2. \n

      Processing (Eligibility filtering by time)<\/strong>\n Systems evaluate whether each event falls within the defined Recency Window. For example, \u201cinclude users who viewed product X in the last 7 days\u201d or \u201cexclude purchasers in the last 30 days.\u201d<\/p>\n<\/li>\n

    3. \n

      Execution (Activation in campaigns)<\/strong>\n The filtered audience is used in Paid Marketing<\/strong> actions: retargeting, sequential messaging, bid multipliers, frequency controls, or suppression. In Programmatic Advertising<\/strong>, this can happen via a DSP using audience segments from a DMP\/CDP or via platform-native audiences.<\/p>\n<\/li>\n

    4. \n

      Output \/ Outcome (Performance and learning)<\/strong>\n Campaigns produce results (CTR, CVR, CPA, ROAS). Analysts compare windows (e.g., 3-day vs 14-day) and adjust based on lift, cost, and saturation. Over time, the Recency Window becomes a tested parameter rather than a guess.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

      Because users behave differently by category and funnel stage, many mature programs manage multiple Recency Window rules simultaneously, each tied to a specific intent signal.<\/p>\n\n\n\n

      Key Components of Recency Window<\/h2>\n\n\n\n

      A Recency Window strategy is built from several practical components:<\/p>\n\n\n\n

      Data inputs<\/h3>\n\n\n\n
        \n
      • First-party behavioral events:<\/strong> product views, add-to-cart, checkout starts, content consumption, search queries.<\/li>\n
      • Conversion and lifecycle events:<\/strong> purchases, renewals, churn signals, lead qualification stages.<\/li>\n
      • Contextual metadata:<\/strong> product category, price tier, inventory status, geography, device, channel source.<\/li>\n<\/ul>\n\n\n\n

        Systems and processes<\/h3>\n\n\n\n
          \n
        • Tagging and event taxonomy:<\/strong> consistent naming and parameters so \u201cviewed product\u201d means the same across teams.<\/li>\n
        • Identity and consent handling:<\/strong> eligibility should respect consent choices and privacy requirements.<\/li>\n
        • Audience building logic:<\/strong> inclusion and exclusion rules based on event time and attributes.<\/li>\n
        • Activation pipelines:<\/strong> pushing audiences into Programmatic Advertising<\/strong> and other Paid Marketing<\/strong> channels with predictable refresh rates.<\/li>\n<\/ul>\n\n\n\n

          Metrics and feedback loops<\/h3>\n\n\n\n
            \n
          • Decay analysis:<\/strong> how conversion probability changes over days since last event.<\/li>\n
          • Frequency and saturation monitoring:<\/strong> ensuring longer windows don\u2019t cause excessive repetition.<\/li>\n
          • Holdouts or lift tests:<\/strong> validating that broader windows add incremental value rather than just claiming credit.<\/li>\n<\/ul>\n\n\n\n

            Governance and ownership<\/h3>\n\n\n\n
              \n
            • Marketing owns intent strategy; analytics validates; engineering ensures data quality.<\/strong>\n Without clear responsibility, Recency Window choices often default to platform settings, which may not reflect your business reality.<\/li>\n<\/ul>\n\n\n\n

              Types of Recency Window<\/h2>\n\n\n\n

              Recency Window doesn\u2019t have one universal taxonomy, but in Paid Marketing<\/strong> and Programmatic Advertising<\/strong> you\u2019ll see useful distinctions based on purpose and signal strength:<\/p>\n\n\n\n

              1) Intent-based windows (by funnel stage)<\/h3>\n\n\n\n
                \n
              • High-intent signals:<\/strong> add-to-cart, checkout start \u2192 short windows (hours to ~7 days)<\/li>\n
              • Mid-intent signals:<\/strong> product view, pricing page visit \u2192 medium windows (7\u201330 days)<\/li>\n
              • Low-intent signals:<\/strong> blog views, broad category browsing \u2192 longer windows (30\u201390 days), if used at all<\/li>\n<\/ul>\n\n\n\n

                2) Inclusion vs suppression windows<\/h3>\n\n\n\n
                  \n
                • Inclusion Recency Window:<\/strong> \u201ctarget users who did X in the last N days\u201d<\/li>\n
                • Suppression Recency Window:<\/strong> \u201cexclude users who purchased in the last N days\u201d to avoid wasting spend or hurting experience<\/li>\n<\/ul>\n\n\n\n

                  3) Channel-specific windows<\/h3>\n\n\n\n

                  Different channels saturate differently:\n– Display retargeting<\/strong> often needs tighter Recency Window controls to avoid overexposure.\n– Paid social<\/strong> can tolerate broader windows in some categories, but creative fatigue may rise.\n– Video<\/strong> may use longer windows for consideration, paired with frequency caps.<\/p>\n\n\n\n

                  4) Event-specific windows<\/h3>\n\n\n\n

                  The same user can belong to multiple windows depending on what they did. A \u201cvisited site\u201d window might be 30 days, while a \u201ccart abandoned\u201d window might be 3 days\u2014both can coexist with prioritization rules.<\/p>\n\n\n\n

                  Real-World Examples of Recency Window<\/h2>\n\n\n\n

                  Example 1: Ecommerce retargeting with funnel-tier windows<\/h3>\n\n\n\n

                  An ecommerce brand uses Programmatic Advertising<\/strong> for dynamic retargeting:\n– Cart abandoners:<\/strong> Recency Window = 1\u20133 days, higher bids, urgency creative\n– Product viewers:<\/strong> Recency Window = 7\u201314 days, moderate bids, benefit-driven creative\n– Category browsers:<\/strong> Recency Window = 30 days, lighter bids, discovery creative<\/p>\n\n\n\n

                  This setup improves Paid Marketing<\/strong> efficiency because budget concentrates on the highest-probability users while still maintaining scale upstream.<\/p>\n\n\n\n

                  Example 2: B2B lead generation with qualification-aware recency<\/h3>\n\n\n\n

                  A SaaS company runs Paid Marketing<\/strong> to generate demos and trials:\n– Visitors to pricing page: Recency Window = 14 days\n– Started form but didn\u2019t submit: Recency Window = 3\u20137 days\n– Submitted lead: suppression Recency Window = 30\u201390 days (to prevent duplicate acquisition spend)\n– Converted to customer: suppression Recency Window = 180 days, plus upsell handled separately<\/p>\n\n\n\n

                  In Programmatic Advertising<\/strong>, these windows prevent expensive retargeting of already-converted accounts and keep prospecting focused.<\/p>\n\n\n\n

                  Example 3: Subscription renewals and win-back timing<\/h3>\n\n\n\n

                  A subscription business uses Recency Window logic for win-back:\n– \u201cCanceled within last 7 days\u201d: short window, service-recovery messaging\n– \u201cCanceled 30\u201390 days ago\u201d: longer window, promotional offer testing\n– \u201cCanceled >180 days\u201d: exclude or treat as cold prospecting<\/p>\n\n\n\n

                  Here the Recency Window directly ties to lifecycle stage, reducing wasted impressions and improving message fit.<\/p>\n\n\n\n

                  Benefits of Using Recency Window<\/h2>\n\n\n\n

                  A disciplined Recency Window approach delivers compounding benefits across performance and experience:<\/p>\n\n\n\n

                    \n
                  • Higher ROAS \/ lower CPA:<\/strong> concentrating spend on recent, higher-intent users typically improves efficiency in Paid Marketing<\/strong>.<\/li>\n
                  • Better relevance and lower fatigue:<\/strong> users see ads that match what they just did<\/em>, not what they did weeks ago.<\/li>\n
                  • Cleaner measurement:<\/strong> clearer boundaries reduce \u201cstale intent\u201d conversions that would have happened anyway, improving incrementality interpretation.<\/li>\n
                  • Smarter bidding in Programmatic Advertising:<\/strong> recency-based bid multipliers help pay more only when propensity is high.<\/li>\n
                  • Operational clarity:<\/strong> teams can standardize \u201chot\/warm\/cold\u201d definitions and align creative, budgets, and frequency controls.<\/li>\n<\/ul>\n\n\n\n

                    Challenges of Recency Window<\/h2>\n\n\n\n

                    Despite its simplicity, Recency Window can be tricky in real deployments:<\/p>\n\n\n\n

                      \n
                    • Signal loss and identity gaps:<\/strong> cookie churn, device switching, and consent constraints can shorten usable history, affecting Programmatic Advertising<\/strong> audience stability.<\/li>\n
                    • Data latency:<\/strong> if events arrive late (server-to-server delays, offline uploads), users may miss the intended Recency Window and underperform.<\/li>\n
                    • Overlapping audiences and prioritization:<\/strong> a user may qualify for multiple windows; without rules, campaigns can cannibalize or overfrequency.<\/li>\n
                    • Attribution confusion:<\/strong> different platforms apply different default windows for view-through\/click-through reporting, complicating comparisons.<\/li>\n
                    • Creative mismatch:<\/strong> longer windows require different messaging; using the same \u201cyou forgot this\u201d creative for 30 days can damage brand perception.<\/li>\n
                    • Seasonality and product cycle variance:<\/strong> windows that work in peak season may be inefficient off-peak.<\/li>\n<\/ul>\n\n\n\n

                      Best Practices for Recency Window<\/h2>\n\n\n\n

                      Use these proven practices to make Recency Window a controllable, testable lever:<\/p>\n\n\n\n

                        \n
                      1. \n

                        Start with intent decay, not guesses<\/strong>\n Analyze conversion rate by \u201cdays since last event.\u201d Your Recency Window should map to the point where marginal performance drops below your threshold.<\/p>\n<\/li>\n

                      2. \n

                        Use multiple windows by funnel stage<\/strong>\n In Paid Marketing<\/strong>, a single window for all visitors is usually suboptimal. Create tiers (e.g., 1\u20133, 7, 14, 30 days) aligned to behaviors.<\/p>\n<\/li>\n

                      3. \n

                        Pair recency with frequency controls<\/strong>\n A longer Recency Window without frequency caps can lead to saturation. Manage both eligibility duration and exposure rate.<\/p>\n<\/li>\n

                      4. \n

                        Build suppression rules early<\/strong>\n Add suppression Recency Window logic for purchasers, converters, and customer support cases where ads would be inappropriate.<\/p>\n<\/li>\n

                      5. \n

                        Refresh audiences at the right cadence<\/strong>\n If your Recency Window is 1 day but your audience updates every 24\u201348 hours, you\u2019ll miss the moment. Ensure the refresh rate matches the window\u2019s intent.<\/p>\n<\/li>\n

                      6. \n

                        Test windows with holdouts when possible<\/strong>\n Especially in Programmatic Advertising<\/strong>, run controlled experiments (geo splits, audience holdouts, PSA control) to confirm incremental value.<\/p>\n<\/li>\n

                      7. \n

                        Document definitions and keep them consistent<\/strong>\n \u201c7-day visitors\u201d should mean the same across dashboards, buying platforms, and reports\u2014avoid silent changes.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                        Tools Used for Recency Window<\/h2>\n\n\n\n

                        Recency Window is implemented through a combination of data, activation, and measurement tooling. Common categories include:<\/p>\n\n\n\n

                          \n
                        • Analytics tools:<\/strong> to analyze intent decay, cohort performance by recency, and conversion lag.<\/li>\n
                        • Tag management \/ event collection:<\/strong> to ensure timestamps, event names, and parameters are accurate and consistent.<\/li>\n
                        • Customer data platforms (CDPs) or data warehouses:<\/strong> to unify events, resolve identities, and build recency-based segments.<\/li>\n
                        • Ad platforms and DSPs (Programmatic Advertising):<\/strong> to activate audiences, apply recency filters, and manage bidding and frequency.<\/li>\n
                        • CRM systems:<\/strong> to sync lifecycle stages (lead status, opportunity stage, customer status) into suppression or inclusion windows.<\/li>\n
                        • Reporting dashboards and BI:<\/strong> to monitor performance by window, detect saturation, and align stakeholders on results.<\/li>\n<\/ul>\n\n\n\n

                          The key is not the brand of tool, but whether your stack can (1) timestamp reliably, (2) segment by time, (3) activate quickly, and (4) measure outcomes consistently.<\/p>\n\n\n\n

                          Metrics Related to Recency Window<\/h2>\n\n\n\n

                          To manage Recency Window effectively, track metrics that reveal both value and risk:<\/p>\n\n\n\n

                            \n
                          • Conversion rate (CVR) by recency bucket:<\/strong> 0\u20131 days, 2\u20133, 4\u20137, 8\u201314, etc.<\/li>\n
                          • CPA \/ ROAS by recency bucket:<\/strong> confirms where efficiency drops off.<\/li>\n
                          • Incremental lift by window:<\/strong> via tests or modeled lift, to avoid optimizing only for credited conversions.<\/li>\n
                          • Frequency and reach:<\/strong> longer windows can inflate frequency; monitor distribution, not just averages.<\/li>\n
                          • Time-to-convert (conversion lag):<\/strong> helps align Recency Window with real purchase cycles.<\/li>\n
                          • Audience size and churn:<\/strong> indicates whether eligibility rules are too tight or too broad.<\/li>\n
                          • Creative fatigue indicators:<\/strong> CTR decline over time, rising CPMs, or falling engagement among older recency cohorts.<\/li>\n<\/ul>\n\n\n\n

                            Future Trends of Recency Window<\/h2>\n\n\n\n

                            Recency Window is evolving as Paid Marketing<\/strong> adapts to automation and privacy:<\/p>\n\n\n\n

                              \n
                            • AI-driven recency personalization:<\/strong> instead of one fixed Recency Window, models will set eligibility and bids based on predicted propensity given time since event.<\/li>\n
                            • Greater use of first-party data:<\/strong> as third-party identifiers decline, server-side events and authenticated signals will shape recency segmentation.<\/li>\n
                            • More emphasis on incrementality:<\/strong> advertisers will pressure Programmatic Advertising<\/strong> strategies to prove lift, leading to tighter, evidence-based windows.<\/li>\n
                            • Real-time and near-real-time activation:<\/strong> shorter windows (hours) become more valuable when pipelines can refresh audiences quickly.<\/li>\n
                            • Privacy-aware measurement shifts:<\/strong> aggregated reporting and consent constraints may reduce granularity, requiring smarter cohorting and experimentation rather than user-level assumptions.<\/li>\n<\/ul>\n\n\n\n

                              In short, Recency Window won\u2019t disappear\u2014it will become more dynamic, model-informed, and integrated with lifecycle data.<\/p>\n\n\n\n

                              Recency Window vs Related Terms<\/h2>\n\n\n\n

                              Recency Window vs Attribution Window<\/h3>\n\n\n\n
                                \n
                              • Recency Window<\/strong>: determines how long a user action keeps them eligible for targeting or segmentation.<\/li>\n
                              • Attribution window<\/strong>: determines how long after an ad interaction a conversion can be credited to that ad.\nThey\u2019re related, but one governs activation<\/em>, the other governs crediting<\/em>.<\/li>\n<\/ul>\n\n\n\n

                                Recency Window vs Lookback Window<\/h3>\n\n\n\n

                                A lookback window<\/strong> often refers to how far back a system checks for events to build an audience or compute a metric. In practice, it can be synonymous, but \u201clookback\u201d is frequently used in analytics contexts, while Recency Window is used more in Paid Marketing<\/strong> activation rules.<\/p>\n\n\n\n

                                Recency Window vs Membership Duration<\/h3>\n\n\n\n

                                Membership duration<\/strong> is the operational setting that controls how long someone stays in an audience list after qualifying. It is effectively the implementation of a Recency Window inside a platform. The concept is strategic; membership duration is the knob.<\/p>\n\n\n\n

                                Who Should Learn Recency Window<\/h2>\n\n\n\n
                                  \n
                                • Marketers:<\/strong> to improve retargeting relevance, manage suppression, and stop wasting spend on stale intent in Paid Marketing<\/strong>.<\/li>\n
                                • Analysts:<\/strong> to quantify intent decay, validate incremental value, and prevent reporting artifacts caused by mismatched windows.<\/li>\n
                                • Agencies:<\/strong> to standardize audience frameworks across clients and justify budget allocation decisions in Programmatic Advertising<\/strong>.<\/li>\n
                                • Business owners and founders:<\/strong> to understand why results change when audiences expand or shrink\u2014and how to control efficiency.<\/li>\n
                                • Developers and data engineers:<\/strong> to build reliable event pipelines, timestamps, refresh schedules, and privacy-compliant identity handling that make Recency Window execution possible.<\/li>\n<\/ul>\n\n\n\n

                                  Summary of Recency Window<\/h2>\n\n\n\n

                                  Recency Window is the time boundary that determines whether a user\u2019s past behavior is still \u201crecent enough\u201d to influence targeting, bidding, sequencing, suppression, and sometimes measurement. It matters because intent decays, and Paid Marketing<\/strong> performance depends on showing the right message at the right time\u2014not simply reaching the largest possible audience. In Programmatic Advertising<\/strong>, Recency Window is a practical lever that shapes automated decisions at scale, improving efficiency, relevance, and the quality of insights you take from campaign data.<\/p>\n\n\n\n

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

                                  1) What is a Recency Window in simple terms?<\/h3>\n\n\n\n

                                  A Recency Window is the number of days (or hours) after a user action during which that action remains eligible to trigger ads, audience membership, bid changes, or suppression in Paid Marketing<\/strong>.<\/p>\n\n\n\n

                                  2) How do I choose the right Recency Window for retargeting?<\/h3>\n\n\n\n

                                  Base it on intent decay: analyze conversion rate by days since the event (view, cart, lead). Choose the window where marginal performance drops below your CPA\/ROAS targets, then test adjacent windows (shorter and longer).<\/p>\n\n\n\n

                                  3) Does Programmatic Advertising use Recency Window automatically?<\/h3>\n\n\n\n

                                  Many Programmatic Advertising<\/strong> platforms support recency-based audiences and membership durations, but they don\u2019t know your true buying cycle. You still need to define and test Recency Window rules to match your product and customer behavior.<\/p>\n\n\n\n

                                  4) Should I use different Recency Window settings for different events?<\/h3>\n\n\n\n

                                  Yes. High-intent events (checkout start) typically need short Recency Window settings, while lower-intent events (content views) may justify longer windows or a different messaging strategy.<\/p>\n\n\n\n

                                  5) What\u2019s the difference between a Recency Window and frequency capping?<\/h3>\n\n\n\n

                                  Recency Window controls how long<\/strong> someone is eligible after an event. Frequency capping controls how often<\/strong> they see ads during that eligibility period. Strong Paid Marketing<\/strong> programs manage both.<\/p>\n\n\n\n

                                  6) Can a Recency Window be too short?<\/h3>\n\n\n\n

                                  Yes. If it\u2019s too short, you may miss converters who need more time, reduce audience scale, and cause unstable delivery\u2014especially if your audience refresh cadence is slow or your sales cycle is longer.<\/p>\n\n\n\n

                                  7) How do privacy changes affect Recency Window strategies?<\/h3>\n\n\n\n

                                  With less durable identifiers and stricter consent requirements, audience histories can shrink and recency signals may be harder to maintain. This increases the importance of first-party event collection, fast refresh rates, and aggregated testing to validate which Recency Window settings truly drive lift.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                  Recency Window is a foundational concept in **Paid Marketing** that helps teams decide *how far back in time* a user action should remain eligible to influence targeting, bidding, personalization, and measurement. In **Programmatic Advertising**, where decisions are automated and occur in milliseconds, the Recency Window acts like a time-based filter: a purchase yesterday might be highly meaningful, while a site visit six months ago may be irrelevant (or even misleading) for today\u2019s ad decision.<\/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":[1911],"tags":[],"class_list":["post-10814","post","type-post","status-publish","format-standard","hentry","category-programmatic-advertising"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/10814","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=10814"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/10814\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=10814"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=10814"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=10814"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}