{"id":11032,"date":"2026-03-30T07:46:41","date_gmt":"2026-03-30T07:46:41","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/audience-observation\/"},"modified":"2026-03-30T07:46:41","modified_gmt":"2026-03-30T07:46:41","slug":"audience-observation","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/audience-observation\/","title":{"rendered":"Audience Observation: What It Is, Key Features, Benefits, Use Cases, and How It Fits in SEM \/ Paid Search"},"content":{"rendered":"\n

Audience Observation is the disciplined practice of adding audience context to your campaigns so you can learn how different people behave\u2014without automatically narrowing who can see your ads. In Paid Marketing<\/strong>, it\u2019s the bridge between \u201cwe think this persona will convert\u201d and \u201cwe can prove which segments actually drive profit.\u201d In SEM \/ Paid Search<\/strong>, Audience Observation is especially valuable because search intent is visible, but the person behind the query is not\u2014unless you intentionally observe audience signals alongside keywords.<\/p>\n\n\n\n

Modern Paid Marketing<\/strong> is increasingly automated, privacy-constrained, and margin-sensitive. Audience Observation helps teams make better decisions about bidding, creative, landing pages, and budget allocation based on segment-level evidence rather than assumptions. It\u2019s also one of the safest ways to explore audiences: you can gather insights first, then decide when to shift from learning to targeted execution.<\/p>\n\n\n\n

What Is Audience Observation?<\/h2>\n\n\n\n

Audience Observation<\/strong> is the process of attaching audience segments to a campaign or ad group primarily to measure performance differences, not necessarily to restrict reach. You\u2019re \u201cobserving\u201d how identified segments (for example, returning visitors, high-intent users, certain lifecycle stages, or customer-match lists) respond compared to everyone else.<\/p>\n\n\n\n

The core concept is simple: keep the campaign\u2019s reach behavior the same, but enrich reporting and optimization with audience-level breakdowns. Business-wise, Audience Observation turns anonymous traffic into actionable patterns\u2014revealing where you\u2019re overpaying, where you\u2019re under-investing, and where messaging or landing pages are misaligned.<\/p>\n\n\n\n

Within Paid Marketing<\/strong>, Audience Observation sits at the intersection of measurement, segmentation, and optimization. Within SEM \/ Paid Search<\/strong>, it helps you understand which audiences convert on which query themes, what they\u2019re worth, and how that should influence bids, match types, and ad messaging.<\/p>\n\n\n\n

Why Audience Observation Matters in Paid Marketing<\/h2>\n\n\n\n

Audience Observation matters because performance is rarely uniform across your market. Two users can search the same keyword but have very different likelihoods to convert, very different order values, and very different sensitivity to price or trust signals. In Paid Marketing<\/strong>, those differences determine whether you scale profitably or burn budget.<\/p>\n\n\n\n

Key business outcomes Audience Observation supports include:<\/p>\n\n\n\n

    \n
  • More efficient spend<\/strong> by identifying high-value segments that justify higher bids or budgets.<\/li>\n
  • Better creative and offer alignment<\/strong> by revealing which messages resonate with which audiences.<\/li>\n
  • Improved conversion quality<\/strong> by highlighting segments that generate refunds, low LTV, or poor sales acceptance.<\/li>\n
  • Sharper competitive advantage<\/strong> because you\u2019re learning from your own first-party signals rather than relying on generic persona templates.<\/li>\n<\/ul>\n\n\n\n

    In SEM \/ Paid Search<\/strong>, where competition is often auction-driven and CPCs can rise quickly, Audience Observation becomes a practical method to protect ROI: you learn where you can pay more confidently and where you should tighten relevance or reduce exposure.<\/p>\n\n\n\n

    How Audience Observation Works<\/h2>\n\n\n\n

    Audience Observation is partly a platform feature and partly a workflow discipline. In practice, it works like an experimentation loop:<\/p>\n\n\n\n

      \n
    1. \n

      Inputs (signals and segments)<\/strong>\n You bring audience signals into the campaign environment\u2014such as remarketing lists, customer lists, lifecycle stages, geographic clusters, device categories, or on-site behaviors. The goal is coverage and clarity: segments must be large enough to measure and meaningful enough to act on.<\/p>\n<\/li>\n

    2. \n

      Analysis (compare performance by segment)<\/strong>\n You examine how each observed segment performs against core goals (leads, sales, revenue, qualified pipeline) and efficiency constraints (CPA, ROAS, margin). In SEM \/ Paid Search<\/strong>, this often includes segment performance by query theme, match type, or landing page.<\/p>\n<\/li>\n

    3. \n

      Execution (use insights to optimize)<\/strong>\n Based on what you learn, you take controlled actions: adjust bids, change budgets, split campaigns, tailor ad copy, create dedicated landing pages, refine negative keywords, or shift conversion goals. Audience Observation does not force \u201ctargeting,\u201d but it often informs when targeting is appropriate.<\/p>\n<\/li>\n

    4. \n

      Outcomes (measurable lift and better decisions)<\/strong>\n The result should be improved profitability, less wasted spend, and clearer prioritization across accounts. Even when automation is doing the bidding, Audience Observation helps humans decide what the machine should optimize for and where guardrails are needed.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

      Key Components of Audience Observation<\/h2>\n\n\n\n

      Effective Audience Observation depends on both data quality and organizational habits. The most important components include:<\/p>\n\n\n\n

      Data inputs<\/h3>\n\n\n\n
        \n
      • First-party audiences<\/strong>: site visitors, product viewers, cart abandoners, lead stages, customer lists.<\/li>\n
      • Contextual signals<\/strong>: device, location, time of day, day of week, language, and sometimes content\/context categories.<\/li>\n
      • Intent and funnel proxies<\/strong>: pages viewed, engagement depth, repeat visits, and conversion history.<\/li>\n<\/ul>\n\n\n\n

        Measurement foundation<\/h3>\n\n\n\n
          \n
        • Accurate conversion tracking<\/strong> (leads, sales, offline outcomes when possible).<\/li>\n
        • Attribution approach<\/strong> that matches decision-making (not perfect, but consistent).<\/li>\n
        • UTM or campaign naming discipline<\/strong> to keep analysis reliable.<\/li>\n<\/ul>\n\n\n\n

          Processes and governance<\/h3>\n\n\n\n
            \n
          • Segment definitions<\/strong> documented in plain language (what qualifies a user for a segment and why it matters).<\/li>\n
          • Minimum data thresholds<\/strong> to avoid reacting to noise.<\/li>\n
          • Ownership<\/strong> across marketing, analytics, and sales operations for lifecycle and quality signals.<\/li>\n<\/ul>\n\n\n\n

            Decision framework<\/h3>\n\n\n\n
              \n
            • Clear rules for how Audience Observation insights translate into action\u2014bid changes, new creative tests, landing page work, or budget shifts\u2014so insights don\u2019t die in reporting.<\/li>\n<\/ul>\n\n\n\n

              Types of Audience Observation<\/h2>\n\n\n\n

              Audience Observation doesn\u2019t have rigid \u201cofficial\u201d types everywhere, but in Paid Marketing<\/strong> and SEM \/ Paid Search<\/strong>, several practical distinctions matter:<\/p>\n\n\n\n

              Observation vs targeting<\/h3>\n\n\n\n
                \n
              • Observation<\/strong>: you measure segment performance while keeping reach broad (no restriction). <\/li>\n
              • Targeting<\/strong>: you restrict delivery to specific segments.\nObservation is ideal for learning; targeting is ideal once you\u2019re confident the segment warrants focused spend or tailored messaging.<\/li>\n<\/ul>\n\n\n\n

                Exploratory vs diagnostic observation<\/h3>\n\n\n\n
                  \n
                • Exploratory<\/strong>: you\u2019re looking for new pockets of efficiency or growth (e.g., \u201cWhich lifecycle stage has the highest ROAS?\u201d).<\/li>\n
                • Diagnostic<\/strong>: you\u2019re investigating a performance issue (e.g., \u201cWhy did CPA spike\u2014did a segment mix change?\u201d).<\/li>\n<\/ul>\n\n\n\n

                  First-party vs partner\/aggregated audiences<\/h3>\n\n\n\n
                    \n
                  • First-party<\/strong> observation uses your own customer and site behavior signals and is often the most durable under privacy constraints.<\/li>\n
                  • Aggregated<\/strong> or modeled audiences can still be useful, but should be validated carefully and treated as directional.<\/li>\n<\/ul>\n\n\n\n

                    Funnel-stage observation<\/h3>\n\n\n\n

                    In SEM \/ Paid Search<\/strong>, you can observe audiences by funnel stage\u2014new prospects, engaged visitors, leads in nurture, customers\u2014and then align bids and messaging to realistic intent.<\/p>\n\n\n\n

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

                    Example 1: E-commerce brand separating value, not just volume<\/h3>\n\n\n\n

                    A retailer runs non-brand search campaigns for \u201crunning shoes.\u201d With Audience Observation, they compare performance across:\n– returning visitors,\n– past purchasers,\n– new visitors who viewed size guide pages.<\/p>\n\n\n\n

                    They discover returning visitors have a higher conversion rate but lower average order value, while size-guide viewers convert less frequently but purchase higher-margin products. In SEM \/ Paid Search<\/strong>, they respond by testing higher bids for high-margin segments, tailoring ad copy to \u201cfree returns \/ fit guarantee,\u201d and building landing pages that reduce sizing friction. This is Paid Marketing<\/strong> optimization driven by segment economics, not just CTR.<\/p>\n\n\n\n

                    Example 2: B2B SaaS improving lead quality with lifecycle observation<\/h3>\n\n\n\n

                    A SaaS company observes audiences by CRM stage (new lead, marketing-qualified, sales-qualified) and by company size inferred from form fields. Audience Observation shows small companies convert on \u201cpricing\u201d keywords but rarely become sales-qualified, while mid-market leads convert more on \u201cintegration\u201d queries and progress further in the funnel. In Paid Marketing<\/strong>, the team shifts budget toward integration-intent ad groups and rewrites ads to emphasize security and implementation. In SEM \/ Paid Search<\/strong>, they also refine negatives to reduce low-quality \u201cfree\u201d intent.<\/p>\n\n\n\n

                    Example 3: Local services controlling waste from low-intent segments<\/h3>\n\n\n\n

                    A home services provider observes performance by geography radius, device, and \u201crecent site visitors.\u201d They find mobile clicks outside a core service area drive calls but with low booking rates, while recent visitors on desktop book at a much higher rate. Audience Observation informs tighter geo bid adjustments, stronger location qualifiers in ad copy, and a dedicated booking page for returning visitors. The result is lower wasted spend and more booked jobs from the same budget.<\/p>\n\n\n\n

                    Benefits of Using Audience Observation<\/h2>\n\n\n\n

                    When implemented consistently, Audience Observation improves both learning speed and financial outcomes:<\/p>\n\n\n\n

                      \n
                    • Performance improvements<\/strong>: better conversion rates and stronger ROAS because bids and messaging align with real segment behavior.<\/li>\n
                    • Cost savings<\/strong>: reduced spend on segments that click but don\u2019t convert or convert poorly downstream.<\/li>\n
                    • Efficiency gains<\/strong>: faster diagnosis of performance changes (seasonality vs audience mix vs tracking issues).<\/li>\n
                    • Better audience experience<\/strong>: ads and landing pages become more relevant, reducing friction and improving trust\u2014especially important in SEM \/ Paid Search<\/strong>, where users expect immediate relevance.<\/li>\n<\/ul>\n\n\n\n

                      Challenges of Audience Observation<\/h2>\n\n\n\n

                      Audience Observation is powerful, but it\u2019s not automatic truth. Common challenges include:<\/p>\n\n\n\n

                        \n
                      • Insufficient data volume<\/strong>: small segments can create misleading swings in CPA or ROAS.<\/li>\n
                      • Attribution limitations<\/strong>: segment performance may look better or worse depending on attribution model, conversion lag, or cross-device behavior.<\/li>\n
                      • Audience overlap<\/strong>: users can belong to multiple segments, complicating conclusions.<\/li>\n
                      • Privacy constraints<\/strong>: shrinking addressability and consent requirements can reduce audience match rates and reporting granularity.<\/li>\n
                      • Organizational misalignment<\/strong>: without shared definitions of \u201cquality,\u201d Paid Marketing<\/strong> teams may optimize for leads that sales won\u2019t accept.<\/li>\n<\/ul>\n\n\n\n

                        Best Practices for Audience Observation<\/h2>\n\n\n\n

                        To get reliable, actionable insights, use these best practices:<\/p>\n\n\n\n

                          \n
                        1. \n

                          Start with decisions, not dashboards<\/strong>\n Define what you would change if you learned \u201cSegment A is 30% more valuable than Segment B.\u201d Audience Observation should feed concrete actions.<\/p>\n<\/li>\n

                        2. \n

                          Use clear thresholds for action<\/strong>\n Require minimum conversions, spend, or revenue per segment before adjusting bids or budgets. This reduces over-optimization.<\/p>\n<\/li>\n

                        3. \n

                          Normalize for intent where possible<\/strong>\n In SEM \/ Paid Search<\/strong>, compare segments within similar query themes (brand vs non-brand, pricing vs informational) to avoid mixing intent with audience effects.<\/p>\n<\/li>\n

                        4. \n

                          Tie observation to creative and landing page testing<\/strong>\n If a segment underperforms, don\u2019t only lower bids\u2014test message fit, trust signals, and page speed. Audience Observation is often a relevance problem, not just a bidding problem.<\/p>\n<\/li>\n

                        5. \n

                          Validate with incrementality thinking<\/strong>\n Especially for remarketing-style segments, ask: \u201cAre we capturing demand that would have converted anyway?\u201d Use holdouts or time-based tests where feasible.<\/p>\n<\/li>\n

                        6. \n

                          Document segment definitions and changes<\/strong>\n Keep a simple log of audience definitions, membership rules, and major campaign changes so insights remain interpretable over time.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                          Tools Used for Audience Observation<\/h2>\n\n\n\n

                          Audience Observation is enabled by a stack of systems rather than a single tool. Common tool categories include:<\/p>\n\n\n\n

                            \n
                          • Ad platforms for search and auctions<\/strong>: where you apply audiences in observation mode, review segment reporting, and adjust bids\/budgets in SEM \/ Paid Search<\/strong>.<\/li>\n
                          • Web analytics tools<\/strong>: to understand on-site behavior by segment (engagement, funnel drop-off, assisted conversions).<\/li>\n
                          • Tag management systems<\/strong>: to deploy and govern tracking tags and audience pixels consistently.<\/li>\n
                          • CRM and marketing automation systems<\/strong>: to define lifecycle stages, sync lead\/customer lists, and measure downstream quality.<\/li>\n
                          • Data warehouses \/ CDPs (where applicable)<\/strong>: to unify first-party data and create stable audience definitions.<\/li>\n
                          • Reporting dashboards \/ BI tools<\/strong>: to monitor segment performance, run cohort views, and share insights across teams.<\/li>\n
                          • SEO tools (supporting role)<\/strong>: useful for aligning observed audience interests with content and query patterns, improving Paid Marketing<\/strong> keyword and landing-page strategy.<\/li>\n<\/ul>\n\n\n\n

                            Metrics Related to Audience Observation<\/h2>\n\n\n\n

                            Because Audience Observation is about segment-level differences, metrics should be comparable and decision-oriented:<\/p>\n\n\n\n

                              \n
                            • Conversion rate (CVR)<\/strong> by observed audience<\/li>\n
                            • Cost per acquisition (CPA)<\/strong> and cost per lead (CPL)<\/strong> by segment<\/li>\n
                            • Return on ad spend (ROAS)<\/strong> or profit per click<\/strong> where margin data exists<\/li>\n
                            • Average order value (AOV)<\/strong> and customer lifetime value (LTV)<\/strong> proxies<\/li>\n
                            • Lead quality metrics<\/strong>: sales-qualified rate, opportunity rate, win rate (crucial in B2B)<\/li>\n
                            • Click-through rate (CTR)<\/strong> and engagement rate<\/strong> as relevance indicators (not end goals)<\/li>\n
                            • Impression share \/ lost impression share<\/strong> to see whether high-value segments are being under-served<\/li>\n
                            • New customer rate<\/strong> (or share of first-time buyers) to avoid optimizing only for existing demand<\/li>\n<\/ul>\n\n\n\n

                              In SEM \/ Paid Search<\/strong>, it\u2019s often useful to pair outcome metrics (CPA\/ROAS) with diagnostic metrics (CTR, search terms, landing page CVR) to pinpoint what\u2019s driving segment gaps.<\/p>\n\n\n\n

                              Future Trends of Audience Observation<\/h2>\n\n\n\n

                              Audience Observation is evolving as measurement and automation change:<\/p>\n\n\n\n

                                \n
                              • More modeled and aggregated reporting<\/strong>: as identifiers become less available, observation may rely more on modeled conversions and aggregated segment insights.<\/li>\n
                              • Stronger first-party emphasis<\/strong>: CRM-connected audiences and on-site behavior signals will become more important for durable Paid Marketing<\/strong> performance.<\/li>\n
                              • Automation-informed observation<\/strong>: bidding systems increasingly use audience signals implicitly; the human role shifts toward auditing outcomes, setting goals, and supplying high-quality conversion data.<\/li>\n
                              • Personalization with guardrails<\/strong>: teams will use Audience Observation to decide where personalization is worth it (and where it adds complexity without lift).<\/li>\n
                              • Incrementality and experimentation<\/strong>: as attribution becomes noisier, holdout tests and geo experiments will become more central to validating segment-driven decisions in SEM \/ Paid Search<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                                Audience Observation vs Related Terms<\/h2>\n\n\n\n

                                Audience Observation vs audience targeting<\/h3>\n\n\n\n
                                  \n
                                • Audience Observation<\/strong> is primarily measurement-first: learn how segments perform while maintaining broader eligibility.<\/li>\n
                                • Audience targeting<\/strong> is delivery-first: restrict who sees ads.\nA practical sequence is observe \u2192 validate \u2192 selectively target.<\/li>\n<\/ul>\n\n\n\n

                                  Audience Observation vs segmentation<\/h3>\n\n\n\n
                                    \n
                                  • Segmentation<\/strong> is the broader strategy of dividing a market into meaningful groups (personas, behaviors, value tiers).<\/li>\n
                                  • Audience Observation<\/strong> is the campaign-level practice of measuring those groups inside Paid Marketing<\/strong> systems to drive optimization.<\/li>\n<\/ul>\n\n\n\n

                                    Audience Observation vs remarketing<\/h3>\n\n\n\n
                                      \n
                                    • Remarketing<\/strong> is a tactic to reach prior visitors\/customers with tailored messaging.<\/li>\n
                                    • Audience Observation<\/strong> can include remarketing lists, but it doesn\u2019t require focusing spend on them; it can simply measure how those users behave within SEM \/ Paid Search<\/strong> campaigns.<\/li>\n<\/ul>\n\n\n\n

                                      Who Should Learn Audience Observation<\/h2>\n\n\n\n
                                        \n
                                      • Marketers<\/strong> benefit because Audience Observation makes optimization more scientific and reduces waste in Paid Marketing<\/strong>.<\/li>\n
                                      • Analysts<\/strong> gain a structured way to turn segment data into decisions, not just reports.<\/li>\n
                                      • Agencies<\/strong> can use Audience Observation to diagnose performance, justify strategy changes, and communicate value clearly to clients.<\/li>\n
                                      • Business owners and founders<\/strong> get visibility into which customers are profitable and which campaigns attract the wrong buyers.<\/li>\n
                                      • Developers and technical teams<\/strong> play a key role in reliable tracking, audience definitions, and data integrations that make observation accurate.<\/li>\n<\/ul>\n\n\n\n

                                        Summary of Audience Observation<\/h2>\n\n\n\n

                                        Audience Observation is a measurement-driven approach to understanding how different audience segments perform in your campaigns, especially within Paid Marketing<\/strong> and SEM \/ Paid Search<\/strong>. It helps teams learn before they restrict reach, uncover which segments drive profitable outcomes, and translate those insights into better bidding, messaging, landing pages, and budget allocation. Done well, Audience Observation reduces wasted spend, improves conversion quality, and makes optimization decisions more evidence-based.<\/p>\n\n\n\n

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

                                        1) What is Audience Observation and when should I use it?<\/h3>\n\n\n\n

                                        Audience Observation is attaching audience segments to campaigns to measure performance by segment without necessarily limiting reach. Use it when you want insights before committing budget to audience-only targeting, or when you need to explain performance changes by audience mix.<\/p>\n\n\n\n

                                        2) How is Audience Observation used in SEM \/ Paid Search campaigns?<\/h3>\n\n\n\n

                                        In SEM \/ Paid Search<\/strong>, Audience Observation is used to compare CPA, ROAS, and conversion rates across segments like new vs returning visitors, customer lists, or lifecycle stages. Those insights inform bid adjustments, keyword expansion\/pruning, and more relevant ad and landing page experiences.<\/p>\n\n\n\n

                                        3) Does Audience Observation improve results even if bidding is automated?<\/h3>\n\n\n\n

                                        Yes\u2014because automation still depends on inputs and goals. Audience Observation helps you choose better conversion actions, detect low-quality segments, and decide where budgets and creative should change, improving Paid Marketing<\/strong> outcomes even with automated bidding.<\/p>\n\n\n\n

                                        4) What audiences are most useful to observe first?<\/h3>\n\n\n\n

                                        Start with high-signal first-party groups: returning visitors, cart\/lead-stage audiences, customers (if allowed and available), and core geo\/device splits. These segments are usually large enough to measure and directly tied to business value.<\/p>\n\n\n\n

                                        5) How long does it take to get meaningful Audience Observation insights?<\/h3>\n\n\n\n

                                        It depends on traffic and conversion volume. A common rule is to wait until each key segment has enough conversions to reduce randomness. For many accounts, that\u2019s weeks\u2014not days\u2014especially for higher-consideration offers.<\/p>\n\n\n\n

                                        6) What\u2019s a common mistake when implementing Audience Observation?<\/h3>\n\n\n\n

                                        Overreacting to small sample sizes and making bid changes based on a handful of conversions. Another common issue is observing segments without aligning conversion tracking to true business outcomes, which can optimize Paid Marketing<\/strong> toward the wrong goal.<\/p>\n\n\n\n

                                        7) Should I always switch from observation to targeting once I find a strong segment?<\/h3>\n\n\n\n

                                        Not always. If the segment performs well but is too small, targeting may limit volume. Often the best move is to keep observing, apply modest bid\/value adjustments, and improve relevance through creative and landing pages while maintaining broader reach.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                        Audience Observation is the disciplined practice of adding audience context to your campaigns so you can learn how different people behave\u2014without automatically narrowing who can see your ads. In **Paid Marketing**, it\u2019s the bridge between \u201cwe think this persona will convert\u201d and \u201cwe can prove which segments actually drive profit.\u201d In **SEM \/ Paid Search**, Audience Observation is especially valuable because search intent is visible, but the person behind the query is not\u2014unless you intentionally observe audience signals alongside keywords.<\/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":[1913],"tags":[],"class_list":["post-11032","post","type-post","status-publish","format-standard","hentry","category-sem-paid-search"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/11032","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=11032"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/11032\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=11032"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=11032"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=11032"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}