{"id":7102,"date":"2026-03-24T00:24:08","date_gmt":"2026-03-24T00:24:08","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/attribution-target-audience\/"},"modified":"2026-03-24T00:24:08","modified_gmt":"2026-03-24T00:24:08","slug":"attribution-target-audience","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/attribution-target-audience\/","title":{"rendered":"Attribution Target Audience: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Attribution"},"content":{"rendered":"\n

Attribution Target Audience is the defined group of people (or accounts) you want to include when you evaluate marketing impact\u2014who is \u201cin scope\u201d for crediting conversions to channels, campaigns, and touchpoints. In Conversion & Measurement<\/strong>, this concept is foundational because every report, model, and optimization decision depends on which users you\u2019re measuring and how consistently you can recognize them across sessions and platforms.<\/p>\n\n\n\n

Modern Attribution<\/strong> is no longer just \u201cwhich ad got the last click.\u201d Privacy restrictions, fragmented identities, and multi-touch journeys mean your measurement is only as reliable as the audience definition behind it. A clearly specified Attribution Target Audience<\/strong> helps teams align strategy, data collection, and decision-making\u2014so performance improvements are real, repeatable, and explainable.<\/p>\n\n\n\n

What Is Attribution Target Audience?<\/h2>\n\n\n\n

Attribution Target Audience<\/strong> is the explicit definition of the users, customers, or accounts for whom you will assign conversion credit across marketing touchpoints. It states who counts<\/em> in your attribution analysis and, just as importantly, who does not.<\/p>\n\n\n\n

At its core, the concept is about scope<\/strong> and eligibility<\/strong>:<\/p>\n\n\n\n

    \n
  • Scope: which segments, markets, products, or lifecycle stages are included<\/li>\n
  • Eligibility: which identities or events can be reliably tied to a person or account for Attribution<\/strong><\/li>\n<\/ul>\n\n\n\n

    In business terms, Attribution Target Audience<\/strong> is the measurement boundary that makes your KPIs interpretable. For example, if you\u2019re evaluating paid search, are you measuring all visitors, only new prospects, only logged-in users, or only high-intent leads? Each choice changes outcomes, budget decisions, and stakeholder conclusions.<\/p>\n\n\n\n

    Within Conversion & Measurement<\/strong>, this term sits between strategy and instrumentation. It informs tagging plans, identity resolution, conversion definitions, and reporting. Inside Attribution<\/strong>, it determines whether your model is explaining performance for the right<\/em> population and whether comparisons over time are valid.<\/p>\n\n\n\n

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

    A well-defined Attribution Target Audience<\/strong> drives better strategy because it makes your analysis comparable, actionable, and tied to real business goals. Without it, teams unintentionally compare apples to oranges\u2014different user groups, different tracking coverage, and different intent levels.<\/p>\n\n\n\n

    Key business value in Conversion & Measurement<\/strong> includes:<\/p>\n\n\n\n

      \n
    • Cleaner optimization signals:<\/strong> Media buying and channel evaluation work best when the measured audience matches the audience you\u2019re trying to grow.<\/li>\n
    • Reduced reporting conflict:<\/strong> When marketing, sales, and finance disagree on \u201cwhat happened,\u201d the disagreement is often rooted in different audience scopes.<\/li>\n
    • More credible performance narratives:<\/strong> Leadership trusts Attribution<\/strong> results when the measured population is explicit and stable.<\/li>\n
    • Competitive advantage:<\/strong> Companies that define and refine their Attribution Target Audience<\/strong> can allocate budget faster, test more confidently, and scale winning channels with fewer surprises.<\/li>\n<\/ul>\n\n\n\n

      How Attribution Target Audience Works<\/h2>\n\n\n\n

      In practice, Attribution Target Audience<\/strong> is less a single feature and more a repeatable workflow that connects business intent to measurement execution.<\/p>\n\n\n\n

        \n
      1. \n

        Input \/ Trigger: define the decision you need to make<\/strong>\n Examples: \u201cWhich channels acquire new customers profitably?\u201d or \u201cWhich touchpoints influence enterprise demos?\u201d The decision determines the right Attribution Target Audience<\/strong> (e.g., net-new buyers vs. all purchasers).<\/p>\n<\/li>\n

      2. \n

        Analysis \/ Processing: specify inclusion rules and identity requirements<\/strong>\n You define segment rules (geo, product line, lifecycle stage), identity standards (user ID, hashed email, account ID), and the conversion events that matter. This is where Conversion & Measurement<\/strong> teams align event schemas and tracking coverage.<\/p>\n<\/li>\n

      3. \n

        Execution \/ Application: collect data and run Attribution consistently<\/strong>\n You implement tracking (client-side and\/or server-side), connect CRM and analytics data, and run the chosen Attribution<\/strong> approach (multi-touch, last-touch, data-driven, or blended with experiments).<\/p>\n<\/li>\n

      4. \n

        Output \/ Outcome: make decisions with documented scope<\/strong>\n Reports and dashboards clearly state the Attribution Target Audience<\/strong> so stakeholders know what the results represent. Budget changes, creative decisions, and lifecycle messaging are then grounded in a consistent measurement frame.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        Key Components of Attribution Target Audience<\/h2>\n\n\n\n

        A reliable Attribution Target Audience<\/strong> depends on both marketing strategy and measurement plumbing. The most important components are:<\/p>\n\n\n\n

        Audience definition and governance<\/h3>\n\n\n\n
          \n
        • Written inclusion\/exclusion rules (new vs returning, prospects vs customers, regions, device types)<\/li>\n
        • Change control (when the definition changes, how it\u2019s communicated and versioned)<\/li>\n
        • Ownership (marketing analytics, growth, or revops) and stakeholder sign-off<\/li>\n<\/ul>\n\n\n\n

          Data inputs<\/h3>\n\n\n\n
            \n
          • Web\/app behavioral events (page views, product views, add-to-cart, lead form starts)<\/li>\n
          • Conversion events (purchases, subscriptions, demo requests, qualified leads)<\/li>\n
          • Identity signals (login ID, CRM contact ID, email, account domain)<\/li>\n
          • Campaign metadata (UTM parameters, referrers, ad platform IDs)<\/li>\n<\/ul>\n\n\n\n

            Systems and processes in Conversion & Measurement<\/h3>\n\n\n\n
              \n
            • Tag management and event instrumentation<\/li>\n
            • Consent and preference management (where applicable)<\/li>\n
            • Data modeling and validation routines<\/li>\n
            • Reporting definitions (how conversions, revenue, and cohorts are computed)<\/li>\n<\/ul>\n\n\n\n

              Measurement rules inside Attribution<\/h3>\n\n\n\n
                \n
              • Lookback windows (how far back to assign credit)<\/li>\n
              • Cross-device logic (person vs device vs household interpretation)<\/li>\n
              • Channel inclusion (do you include email, organic, referrals, offline?)<\/li>\n
              • Deduplication rules (when multiple systems claim the same conversion)<\/li>\n<\/ul>\n\n\n\n

                Types of Attribution Target Audience<\/h2>\n\n\n\n

                There aren\u2019t universal \u201cofficial\u201d types of Attribution Target Audience<\/strong>, but there are practical distinctions that meaningfully change Attribution<\/strong> outcomes.<\/p>\n\n\n\n

                1) Lifecycle-based audiences<\/h3>\n\n\n\n
                  \n
                • Prospecting audience:<\/strong> net-new users or first-time buyers <\/li>\n
                • Retention audience:<\/strong> existing customers, renewals, repeat purchases\nThis is crucial in Conversion & Measurement<\/strong> because acquisition and retention often have different channels, conversion windows, and success metrics.<\/li>\n<\/ul>\n\n\n\n

                  2) Identity-strength audiences<\/h3>\n\n\n\n
                    \n
                  • Authenticated audience:<\/strong> logged-in users with stable IDs (high confidence) <\/li>\n
                  • Anonymous audience:<\/strong> cookie\/device-based or modeled (lower confidence)\nThis distinction affects match rates, cross-device visibility, and the reliability of multi-touch Attribution<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                    3) Account-based vs person-based audiences (B2B)<\/h3>\n\n\n\n
                      \n
                    • Account-level audience:<\/strong> target accounts, buying committees, firmographics <\/li>\n
                    • Person-level audience:<\/strong> individual leads and contacts\nIn B2B Conversion & Measurement<\/strong>, attribution often needs to connect marketing touches to opportunity creation and pipeline influence, not just form fills.<\/li>\n<\/ul>\n\n\n\n

                      4) Market\/product scope audiences<\/h3>\n\n\n\n
                        \n
                      • Single product line or offer:<\/strong> isolates impact for a specific initiative <\/li>\n
                      • All products\/regions:<\/strong> broader view for executive budgeting\nNarrow scopes improve clarity; broad scopes improve strategic coverage.<\/li>\n<\/ul>\n\n\n\n

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

                        Example 1: Ecommerce brand separating acquisition from repeat purchase<\/h3>\n\n\n\n

                        A retailer notices paid social looks \u201cgreat\u201d under last-click Attribution<\/strong>, but most conversions are returning customers. They redefine the Attribution Target Audience<\/strong> to \u201cfirst-time purchasers in the last 30 days,\u201d using customer IDs from the commerce system. In Conversion & Measurement<\/strong>, they track first-purchase status at conversion time and re-run reports. Result: spend shifts toward channels that actually grow new customers, not just harvest demand.<\/p>\n\n\n\n

                        Example 2: B2B SaaS measuring pipeline, not just leads<\/h3>\n\n\n\n

                        A SaaS company has high lead volume from content syndication, but low sales acceptance. They set the Attribution Target Audience<\/strong> to \u201cICP accounts with sales-accepted leads\u201d and measure influence through opportunity creation. Their Conversion & Measurement<\/strong> approach connects website events to CRM contacts and accounts, then applies Attribution<\/strong> to pipeline value rather than form submissions. Result: fewer leads, higher pipeline efficiency, and clearer channel accountability.<\/p>\n\n\n\n

                        Example 3: Mobile app balancing privacy constraints with modeled insights<\/h3>\n\n\n\n

                        An app marketer can\u2019t fully observe user paths due to platform privacy limits. They define the Attribution Target Audience<\/strong> as \u201cnew installs with onboarding completion within 7 days,\u201d and blend deterministic signals with aggregated\/model-based measurement. In Conversion & Measurement<\/strong>, they prioritize event quality (install, signup, activation) and use experiments to validate incrementality alongside Attribution<\/strong> reporting.<\/p>\n\n\n\n

                        Benefits of Using Attribution Target Audience<\/h2>\n\n\n\n

                        A disciplined Attribution Target Audience<\/strong> improves performance and decision quality across the funnel:<\/p>\n\n\n\n

                          \n
                        • Higher ROI from better budget allocation:<\/strong> Credit is assigned within the right cohort, reducing overinvestment in channels that mainly reach already-converted users.<\/li>\n
                        • Lower wasted spend and fewer false positives:<\/strong> You avoid \u201cwinning\u201d based on segments you didn\u2019t intend to optimize for.<\/li>\n
                        • Faster learning cycles:<\/strong> Tests are easier to interpret when your Conversion & Measurement<\/strong> scope is stable.<\/li>\n
                        • Improved customer experience:<\/strong> Messaging aligns with lifecycle stage\u2014prospects see education, customers see onboarding or upsell\u2014because measurement segments match activation segments.<\/li>\n
                        • Better cross-team alignment:<\/strong> Marketing, product, and sales share a common lens for interpreting Attribution<\/strong> outputs.<\/li>\n<\/ul>\n\n\n\n

                          Challenges of Attribution Target Audience<\/h2>\n\n\n\n

                          Even well-designed audience scopes face real constraints:<\/p>\n\n\n\n

                            \n
                          • Identity fragmentation:<\/strong> Cross-device journeys and limited identifiers can make it hard to consistently recognize who belongs in the Attribution Target Audience<\/strong>.<\/li>\n
                          • Tracking coverage gaps:<\/strong> Ad blockers, consent choices, and browser\/platform limits reduce observable touchpoints, impacting Attribution<\/strong> completeness.<\/li>\n
                          • Changing definitions over time:<\/strong> If the business redefines \u201cnew customer\u201d or changes qualification criteria, trend lines break unless Conversion & Measurement<\/strong> teams version definitions.<\/li>\n
                          • Selection bias:<\/strong> Over-reliance on logged-in users may skew results toward high-intent segments and underrepresent top-of-funnel behavior.<\/li>\n
                          • Organizational misalignment:<\/strong> Different teams may quietly use different audience scopes, producing conflicting dashboards and mistrust in Attribution<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                            Best Practices for Attribution Target Audience<\/h2>\n\n\n\n

                            To make Attribution Target Audience<\/strong> durable and decision-ready:<\/p>\n\n\n\n

                              \n
                            1. \n

                              Write the definition down and version it<\/strong>\n Include inclusion rules, conversion events, windows, and identity logic. Treat it as a measurement contract in Conversion & Measurement<\/strong>.<\/p>\n<\/li>\n

                            2. \n

                              Choose the audience based on the decision, not the data you happen to have<\/strong>\n If your goal is new customer growth, don\u2019t default to \u201call purchasers\u201d because it\u2019s easiest.<\/p>\n<\/li>\n

                            3. \n

                              Separate reporting views for acquisition vs retention<\/strong>\n Two clean views often beat one muddy blended view, especially for Attribution<\/strong> comparisons.<\/p>\n<\/li>\n

                            4. \n

                              Validate with experiments or holdouts when stakes are high<\/strong>\n Attribution models can be directionally useful, but incrementality testing helps confirm causal impact for your Attribution Target Audience<\/strong>.<\/p>\n<\/li>\n

                            5. \n

                              Monitor identity and data quality metrics<\/strong>\n Track match rate, event loss, deduplication rates, and schema changes so Conversion & Measurement<\/strong> doesn\u2019t drift silently.<\/p>\n<\/li>\n

                            6. \n

                              Align attribution windows with real buying cycles<\/strong>\n A 7-day window may fit impulse purchases; enterprise deals may need longer windows and account-based logic.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                              Tools Used for Attribution Target Audience<\/h2>\n\n\n\n

                              Attribution Target Audience<\/strong> is operationalized through a stack of systems that define audiences, collect signals, and report outcomes in Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                                \n
                              • Analytics tools:<\/strong> event collection, path analysis, cohorting, and conversion reporting<\/li>\n
                              • Tag management and server-side tracking systems:<\/strong> consistent event firing and governance<\/li>\n
                              • Ad platforms and campaign managers:<\/strong> audience targeting, conversion APIs, and campaign metadata<\/li>\n
                              • CRM systems and marketing automation:<\/strong> lifecycle stage, lead status, opportunity and revenue linkage<\/li>\n
                              • Customer data platforms (CDPs) or identity layers:<\/strong> profile unification and audience building<\/li>\n
                              • Data warehouses and transformation pipelines:<\/strong> joining ad, web, app, and CRM data for analysis-ready datasets<\/li>\n
                              • Reporting dashboards \/ BI:<\/strong> scoped, versioned reporting for stakeholders<\/li>\n
                              • SEO tools (contextual):<\/strong> when organic acquisition is part of the Attribution Target Audience<\/strong>, SEO data supports channel-mix interpretation in Attribution<\/strong><\/li>\n<\/ul>\n\n\n\n

                                Metrics Related to Attribution Target Audience<\/h2>\n\n\n\n

                                Because the audience scope changes what \u201cgood\u201d looks like, track metrics that confirm both performance and measurement integrity:<\/p>\n\n\n\n

                                Performance and ROI metrics<\/h3>\n\n\n\n
                                  \n
                                • Conversion rate (within the defined Attribution Target Audience<\/strong>)<\/li>\n
                                • Cost per acquisition (CPA) or cost per qualified lead (CPL\/CPQL)<\/li>\n
                                • Return on ad spend (ROAS) or marketing ROI<\/li>\n
                                • Customer acquisition cost (CAC) and payback period<\/li>\n
                                • Lifetime value (LTV) and LTV:CAC (when lifecycle data is reliable)<\/li>\n<\/ul>\n\n\n\n

                                  Attribution quality and efficiency metrics<\/h3>\n\n\n\n
                                    \n
                                  • Identity match rate \/ stitched-user rate<\/li>\n
                                  • Share of conversions with attributable touchpoints (coverage)<\/li>\n
                                  • Assisted conversions and path length<\/li>\n
                                  • Time to convert (median\/percentiles)<\/li>\n
                                  • Model stability (how often credit allocation shifts due to data changes)<\/li>\n<\/ul>\n\n\n\n

                                    Funnel quality metrics (especially B2B)<\/h3>\n\n\n\n
                                      \n
                                    • Lead-to-MQL, MQL-to-SQL, SQL-to-opportunity rates<\/li>\n
                                    • Pipeline created and pipeline influenced within the Attribution Target Audience<\/strong><\/li>\n<\/ul>\n\n\n\n

                                      Future Trends of Attribution Target Audience<\/h2>\n\n\n\n

                                      Several shifts are changing how Attribution Target Audience<\/strong> is defined and measured in Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                                        \n
                                      • More first-party and server-side data strategies:<\/strong> To maintain measurement continuity, teams will rely more on durable identifiers and controlled data collection.<\/li>\n
                                      • Modeled and aggregated measurement becoming standard:<\/strong> As user-level visibility declines, Attribution<\/strong> increasingly blends observed data with statistical modeling and incrementality tests.<\/li>\n
                                      • Greater emphasis on consent-aware segmentation:<\/strong> Audience eligibility will be tied to consent states and regional privacy requirements, impacting who is measurable.<\/li>\n
                                      • Automation in audience governance:<\/strong> Expect more automated checks for schema changes, event loss, and audience drift\u2014so the Attribution Target Audience<\/strong> remains consistent over time.<\/li>\n
                                      • Personalization tied to measurable cohorts:<\/strong> Teams will prioritize experiences they can measure reliably, tightening the link between activation audiences and measurement audiences in Conversion & Measurement<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                                        Attribution Target Audience vs Related Terms<\/h2>\n\n\n\n

                                        Attribution Target Audience vs Target Audience<\/h3>\n\n\n\n

                                        A general target audience<\/strong> is who you want to reach with marketing. Attribution Target Audience<\/strong> is who you will measure and assign credit for<\/em> in Attribution<\/strong>. They often overlap, but they don\u2019t have to\u2014measurement constraints may limit who is observable.<\/p>\n\n\n\n

                                        Attribution Target Audience vs Conversion Audience<\/h3>\n\n\n\n

                                        A conversion audience<\/strong> is the set of users who completed a conversion event. Attribution Target Audience<\/strong> includes converters and non-converters who were eligible to convert (the denominator), enabling valid conversion rates and channel comparisons in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                        Attribution Target Audience vs Attribution Model<\/h3>\n\n\n\n

                                        An Attribution model<\/strong> defines how credit is assigned<\/em> across touchpoints (last-click, position-based, data-driven, etc.). Attribution Target Audience<\/strong> defines who the model is applied to<\/em>. You can keep the same model but change the audience and get very different insights.<\/p>\n\n\n\n

                                        Who Should Learn Attribution Target Audience<\/h2>\n\n\n\n
                                          \n
                                        • Marketers:<\/strong> to ensure channel performance reflects the customers you\u2019re trying to acquire or retain, not just the easiest-to-track users.<\/li>\n
                                        • Analysts:<\/strong> to design valid comparisons, reduce bias, and communicate scope clearly in Conversion & Measurement<\/strong> dashboards.<\/li>\n
                                        • Agencies:<\/strong> to align client reporting with business goals and avoid optimization that inflates vanity results.<\/li>\n
                                        • Business owners and founders:<\/strong> to make budget decisions based on measurement that reflects the real growth objective.<\/li>\n
                                        • Developers and data engineers:<\/strong> to implement identity, event schemas, and data pipelines that keep Attribution Target Audience<\/strong> definitions enforceable and auditable.<\/li>\n<\/ul>\n\n\n\n

                                          Summary of Attribution Target Audience<\/h2>\n\n\n\n

                                          Attribution Target Audience<\/strong> is the defined population for which you measure and assign conversion credit across marketing touchpoints. It matters because Conversion & Measurement<\/strong> results depend on clear scope, consistent identity logic, and stable conversion definitions. By explicitly defining who is included, you make Attribution<\/strong> outputs more credible, comparable, and actionable\u2014improving budget allocation, experimentation, and cross-team alignment.<\/p>\n\n\n\n

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

                                          1) What does \u201cAttribution Target Audience\u201d mean in practice?<\/h3>\n\n\n\n

                                          It means you have a written, enforceable definition of who is eligible for your attribution analysis\u2014such as \u201cnew customers in the US,\u201d \u201cICP accounts,\u201d or \u201clogged-in users\u201d\u2014and you apply Attribution<\/strong> reporting only within that scope.<\/p>\n\n\n\n

                                          2) How is Attribution Target Audience different from a campaign\u2019s targeting?<\/h3>\n\n\n\n

                                          Campaign targeting is who you try to reach. Attribution Target Audience<\/strong> is who you measure outcomes for in Conversion & Measurement<\/strong>. You might target broadly but measure narrowly (e.g., only qualified leads).<\/p>\n\n\n\n

                                          3) Can I have more than one Attribution Target Audience?<\/h3>\n\n\n\n

                                          Yes. Many teams maintain separate scopes for acquisition vs retention, or for different product lines. The key is to keep each Attribution Target Audience<\/strong> versioned and consistently reported.<\/p>\n\n\n\n

                                          4) What\u2019s the biggest mistake teams make with Attribution Target Audience?<\/h3>\n\n\n\n

                                          They let tooling limitations define the audience (e.g., only last-click, only logged-in users) without acknowledging bias. That produces confident-looking Attribution<\/strong> reports that don\u2019t represent the true market opportunity.<\/p>\n\n\n\n

                                          5) How do privacy changes affect Attribution Target Audience?<\/h3>\n\n\n\n

                                          They can reduce observable identifiers and touchpoints, shrinking or skewing the measurable audience. In Conversion & Measurement<\/strong>, this often requires more first-party identity, modeled measurement, and stronger governance around audience definitions.<\/p>\n\n\n\n

                                          6) Which metrics tell me my Attribution Target Audience is reliable?<\/h3>\n\n\n\n

                                          Look at identity match rate, attributable-touchpoint coverage, event loss rates, and deduplication consistency. If these shift, your Attribution<\/strong> outputs may change even if marketing performance didn\u2019t.<\/p>\n\n\n\n

                                          7) Do I need experiments if I already have Attribution reports?<\/h3>\n\n\n\n

                                          If budget decisions are significant, experiments (like geo tests or holdouts) help validate incrementality for your Attribution Target Audience<\/strong>. Attribution is useful for direction and optimization, while experiments help confirm causal impact.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                          Attribution Target Audience is the defined group of people (or accounts) you want to include when you evaluate marketing impact\u2014who is \u201cin scope\u201d for crediting conversions to channels, campaigns, and touchpoints. In **Conversion & Measurement**, this concept is foundational because every report, model, and optimization decision depends on which users you\u2019re measuring and how consistently you can recognize them across sessions and platforms.<\/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-7102","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\/7102","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=7102"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7102\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7102"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7102"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7102"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}