{"id":7017,"date":"2026-03-23T21:18:09","date_gmt":"2026-03-23T21:18:09","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/analytics-target-audience\/"},"modified":"2026-03-23T21:18:09","modified_gmt":"2026-03-23T21:18:09","slug":"analytics-target-audience","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/analytics-target-audience\/","title":{"rendered":"Analytics Target Audience: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Analytics"},"content":{"rendered":"\n

Modern marketing teams don\u2019t just measure performance\u2014they measure performance for specific groups of people. Analytics Target Audience<\/strong> is the practice of defining, analyzing, and using a clearly described audience segment inside your measurement stack so your reporting, optimization, and decisions reflect who is actually driving outcomes.<\/p>\n\n\n\n

In Conversion & Measurement<\/strong>, the goal is not only to count conversions, but to understand which<\/em> users convert, why<\/em> they convert, and what<\/em> changes improve conversion rates. Analytics Target Audience<\/strong> connects those dots by turning raw user data into actionable audience definitions that can be tracked consistently over time.<\/p>\n\n\n\n

Within Analytics<\/strong>, this concept is the bridge between \u201cwe have data\u201d and \u201cwe can make decisions.\u201d It helps you move from generic, site-wide averages to insights that directly guide budgeting, creative, UX changes, and targeting.<\/p>\n\n\n\n

1) What Is Analytics Target Audience?<\/h2>\n\n\n\n

Analytics Target Audience<\/strong> is a defined group of users (or accounts) identified in your analytics and measurement environment based on shared attributes, behaviors, intents, or value signals\u2014so you can measure performance and optimize experiences for that group specifically.<\/p>\n\n\n\n

At a beginner level, think of it as \u201cthe audience segment you care about most in your reporting.\u201d At an advanced level, it\u2019s a governed, reusable segmentation layer that standardizes how teams interpret results across channels and time.<\/p>\n\n\n\n

From a business perspective, Analytics Target Audience<\/strong> answers questions like:<\/p>\n\n\n\n

    \n
  • Which segment is most likely to convert or renew?<\/li>\n
  • Which traffic sources produce the highest-value customers?<\/li>\n
  • Which users are getting stuck before a key funnel step?<\/li>\n<\/ul>\n\n\n\n

    In Conversion & Measurement<\/strong>, it sits at the intersection of segmentation, funnel analysis, attribution inputs, and experimentation. In Analytics<\/strong>, it\u2019s a core mechanism for turning metrics into decisions\u2014because averages can hide critical segment-level truths.<\/p>\n\n\n\n

    2) Why Analytics Target Audience Matters in Conversion & Measurement<\/h2>\n\n\n\n

    A strong Analytics Target Audience<\/strong> strategy improves decision quality. When you know which audience you\u2019re measuring, you reduce the risk of optimizing for the wrong outcomes\u2014such as maximizing low-quality leads or driving \u201cengagement\u201d that never produces revenue.<\/p>\n\n\n\n

    Key reasons it matters in Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

      \n
    • Strategic clarity:<\/strong> It aligns teams on who success is for (e.g., qualified buyers vs. all visitors).<\/li>\n
    • Better optimization:<\/strong> UX and campaign changes can be evaluated by impact on the segment that matters, not site-wide noise.<\/li>\n
    • Budget efficiency:<\/strong> Spend can be shifted toward sources and messages that perform best for the defined audience.<\/li>\n
    • Competitive advantage:<\/strong> Organizations that segment well learn faster, personalize more effectively, and waste less spend.<\/li>\n<\/ul>\n\n\n\n

      In Analytics<\/strong>, segmentation is often the difference between insights and dashboards. Analytics Target Audience<\/strong> makes your measurement system decision-ready rather than purely descriptive.<\/p>\n\n\n\n

      3) How Analytics Target Audience Works<\/h2>\n\n\n\n

      Analytics Target Audience<\/strong> is both conceptual and operational. In practice, it works as a workflow that converts user signals into a measurable, repeatable segment.<\/p>\n\n\n\n

        \n
      1. \n

        Input (signals and identifiers)<\/strong>\n You start with data inputs such as acquisition source, device, geography, content consumed, on-site events, CRM attributes, or product usage signals. In Analytics<\/strong>, these inputs come from tags, SDK events, server logs, CRM syncs, and consented identity signals.<\/p>\n<\/li>\n

      2. \n

        Analysis (segmentation logic)<\/strong>\n You define rules: \u201cusers who viewed pricing twice,\u201d \u201cleads with company size 200+,\u201d or \u201creturning visitors from organic search who watched a demo.\u201d This is where Analytics Target Audience<\/strong> becomes a standardized definition rather than an ad-hoc filter.<\/p>\n<\/li>\n

      3. \n

        Execution (measurement and activation)<\/strong>\n The segment is used in Conversion & Measurement<\/strong> activities like funnel reporting, cohort analysis, experiment targeting, or channel performance comparisons. In some organizations, it also powers downstream activation (e.g., personalization or remarketing), but measurement is the foundation.<\/p>\n<\/li>\n

      4. \n

        Output (decisions and outcomes)<\/strong>\n You get segment-specific KPIs, insights, and trends: conversion rate by segment, CAC by segment, drop-off points, or LTV by acquisition cohort. The output is better prioritization\u2014what to fix, what to scale, and what to stop.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        4) Key Components of Analytics Target Audience<\/h2>\n\n\n\n

        A reliable Analytics Target Audience<\/strong> depends on several building blocks working together:<\/p>\n\n\n\n

          \n
        • Data collection layer:<\/strong> Event tracking, pageview tracking, product instrumentation, and campaign parameters. Without consistent data capture, segments drift or break.<\/li>\n
        • Identity and deduplication:<\/strong> Anonymous vs. known users, cross-device stitching (where permitted), and account-level mapping for B2B. This affects accuracy in Analytics<\/strong>.<\/li>\n
        • Segmentation rules and documentation:<\/strong> Clear definitions, inclusion\/exclusion criteria, and versioning (so stakeholders know what changed and when).<\/li>\n
        • Governance and ownership:<\/strong> A named owner (often marketing ops, analytics, or growth) who approves changes and ensures consistency across Conversion & Measurement<\/strong> reporting.<\/li>\n
        • Data quality controls:<\/strong> Bot filtering, internal traffic exclusions, consent-aware data handling, and validation checks.<\/li>\n
        • Reporting surfaces:<\/strong> Dashboards and analysis views that make the segment easy to use across teams\u2014without re-creating it repeatedly.<\/li>\n<\/ul>\n\n\n\n

          5) Types of Analytics Target Audience<\/h2>\n\n\n\n

          \u201cTypes\u201d of Analytics Target Audience<\/strong> are usually practical distinctions rather than formal categories. Common, useful approaches include:<\/p>\n\n\n\n

          Behavioral audiences<\/h3>\n\n\n\n

          Defined by actions taken: video watched, features used, number of sessions, scroll depth, or checkout steps reached. These are powerful in Conversion & Measurement<\/strong> because they map directly to intent.<\/p>\n\n\n\n

          Acquisition-based audiences<\/h3>\n\n\n\n

          Defined by where users came from: organic search, paid social, email, referrals, partner campaigns, or specific campaign groupings. This helps connect channel strategy to real outcomes in Analytics<\/strong>.<\/p>\n\n\n\n

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

          Top-of-funnel (new visitors), mid-funnel (pricing viewers), bottom-of-funnel (checkout starters), and post-conversion (customers, repeat purchasers). These audiences support cleaner funnel reporting.<\/p>\n\n\n\n

          Value-based audiences<\/h3>\n\n\n\n

          Defined by predicted or observed value: high-LTV customers, high-AOV purchasers, low-churn cohorts, or \u201cqualified lead\u201d criteria. These segments reduce the risk of optimizing for volume instead of value.<\/p>\n\n\n\n

          Account-based audiences (B2B)<\/h3>\n\n\n\n

          Defined at the company\/account level: target accounts, industry segments, or account engagement thresholds\u2014often requiring CRM alignment and careful identity rules.<\/p>\n\n\n\n

          6) Real-World Examples of Analytics Target Audience<\/h2>\n\n\n\n

          Example 1: E-commerce \u201chigh intent\u201d segment for funnel optimization<\/h3>\n\n\n\n

          A retailer defines an Analytics Target Audience<\/strong> as users who viewed a product page twice within seven days and added at least one item to cart. In Conversion & Measurement<\/strong>, they track cart-to-checkout drop-off for this segment separately from general visitors. The analysis reveals mobile payment friction affecting this audience disproportionately, leading to a checkout change that lifts conversions where it matters most.<\/p>\n\n\n\n

          Example 2: SaaS \u201cqualified trial\u201d segment aligned to product usage<\/h3>\n\n\n\n

          A SaaS company defines an Analytics Target Audience<\/strong> for trial users who complete key activation events (e.g., invite a teammate, connect an integration, create a first project). In Analytics<\/strong>, they compare activation-to-paid conversion rate by acquisition source, identifying that one channel drives many trials but few activated users. The team reallocates budget and updates messaging to attract better-fit prospects.<\/p>\n\n\n\n

          Example 3: Content-led B2B segment for lead quality measurement<\/h3>\n\n\n\n

          A B2B publisher defines an Analytics Target Audience<\/strong> as visitors who consume two or more in-depth guides and then visit a solution page. In Conversion & Measurement<\/strong>, they measure form-fill rate and sales acceptance rate for this segment. They find that this audience produces fewer leads but higher downstream quality, shaping content strategy and lead scoring alignment.<\/p>\n\n\n\n

          7) Benefits of Using Analytics Target Audience<\/h2>\n\n\n\n

          Implementing Analytics Target Audience<\/strong> well creates both performance and operational benefits:<\/p>\n\n\n\n

            \n
          • Higher conversion impact:<\/strong> Optimizations are evaluated on the segment most likely to convert, improving signal-to-noise in Conversion & Measurement<\/strong>.<\/li>\n
          • Lower wasted spend:<\/strong> Channel and campaign decisions focus on high-quality segments, reducing spend on low-intent traffic.<\/li>\n
          • Faster learning cycles:<\/strong> Experiments become easier to interpret when you can isolate the audience the change was designed for.<\/li>\n
          • Better customer experience:<\/strong> Personalization and UX improvements become more relevant when grounded in segment behavior.<\/li>\n
          • Cross-team alignment:<\/strong> Product, marketing, and sales share a consistent definition of \u201cthe right audience,\u201d reducing reporting disputes in Analytics<\/strong> reviews.<\/li>\n<\/ul>\n\n\n\n

            8) Challenges of Analytics Target Audience<\/h2>\n\n\n\n

            Despite its value, Analytics Target Audience<\/strong> can fail or mislead if not implemented carefully:<\/p>\n\n\n\n

              \n
            • Incomplete or inconsistent tracking:<\/strong> Missing events, duplicated events, or inconsistent naming can break audience definitions and distort Analytics<\/strong> outputs.<\/li>\n
            • Identity limitations:<\/strong> Cookie restrictions, consent choices, cross-device behavior, and walled-garden measurement reduce audience continuity.<\/li>\n
            • Small sample sizes:<\/strong> Narrow segments may produce unstable metrics, especially for weekly reporting or experiment analysis.<\/li>\n
            • Over-segmentation:<\/strong> Too many audiences can fragment insights and create analysis paralysis.<\/li>\n
            • Misaligned definitions:<\/strong> If marketing defines \u201cqualified\u201d differently than sales or product, Conversion & Measurement<\/strong> becomes contentious.<\/li>\n
            • Privacy and governance risks:<\/strong> Segment definitions must respect consent and data minimization principles; overly granular segments can create compliance concerns.<\/li>\n<\/ul>\n\n\n\n

              9) Best Practices for Analytics Target Audience<\/h2>\n\n\n\n

              To make Analytics Target Audience<\/strong> trustworthy and scalable, use these practices:<\/p>\n\n\n\n

                \n
              1. \n

                Start with a decision, not a dashboard<\/strong>\n Define the key decisions the audience will inform (budget shifts, funnel changes, onboarding improvements). This anchors Conversion & Measurement<\/strong> work to outcomes.<\/p>\n<\/li>\n

              2. \n

                Write clear segment definitions<\/strong>\n Document inclusion\/exclusion criteria, time windows (e.g., \u201clast 30 days\u201d), and required events. Version changes so Analytics<\/strong> trends remain interpretable.<\/p>\n<\/li>\n

              3. \n

                Use layered segmentation<\/strong>\n Combine broad and narrow views (e.g., \u201cAll prospects\u201d \u2192 \u201cHigh intent\u201d \u2192 \u201cHigh intent from organic search\u201d). This prevents overfitting while enabling deep diagnosis.<\/p>\n<\/li>\n

              4. \n

                Validate with reality checks<\/strong>\n Compare audience counts to CRM totals, backend orders, or product databases where possible. Investigate sudden spikes\/drops before acting.<\/p>\n<\/li>\n

              5. \n

                Focus on stability and reusability<\/strong>\n Prefer segments built on durable signals (activation events, purchases, lead qualification) rather than brittle proxies (single pageviews).<\/p>\n<\/li>\n

              6. \n

                Measure both rate and volume<\/strong>\n In Conversion & Measurement<\/strong>, a segment with a high conversion rate but tiny volume may be less impactful than a slightly lower-rate segment with large volume.<\/p>\n<\/li>\n

              7. \n

                Create an ownership model<\/strong>\n Assign owners for data collection, audience definitions, and reporting. This reduces drift and improves trust in Analytics<\/strong> outputs.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                10) Tools Used for Analytics Target Audience<\/h2>\n\n\n\n

                Analytics Target Audience<\/strong> is implemented across a stack rather than in a single tool. Common tool groups include:<\/p>\n\n\n\n

                  \n
                • Analytics tools:<\/strong> Web and product analytics platforms for segmentation, funnels, cohorts, and event analysis.<\/li>\n
                • Tag management and instrumentation:<\/strong> Systems to standardize event collection, enforce naming conventions, and manage releases.<\/li>\n
                • Data warehouses and pipelines:<\/strong> Central storage and transformation for joining marketing, product, and CRM data to enrich audiences.<\/li>\n
                • CRM systems:<\/strong> Lead\/customer attributes that strengthen segments (industry, lifecycle stage, account tier) and connect measurement to revenue.<\/li>\n
                • Marketing automation tools:<\/strong> Email and lifecycle systems that may use the segment for nurturing while feeding engagement data back into Analytics<\/strong>.<\/li>\n
                • Ad platforms and activation systems:<\/strong> Where permitted, segments can inform targeting and exclusions; measurement must remain consistent with Conversion & Measurement<\/strong> goals.<\/li>\n
                • Reporting dashboards and BI tools:<\/strong> Executive views of segment KPIs, with drill-down paths for analysts.<\/li>\n<\/ul>\n\n\n\n

                  The most important principle is interoperability: the audience definition should be consistent across Analytics<\/strong> and downstream reporting to avoid conflicting \u201ctruths.\u201d<\/p>\n\n\n\n

                  11) Metrics Related to Analytics Target Audience<\/h2>\n\n\n\n

                  You evaluate Analytics Target Audience<\/strong> with metrics that show both performance and audience quality:<\/p>\n\n\n\n

                    \n
                  • Segment conversion rate:<\/strong> Purchases, sign-ups, demo requests, or qualified lead submissions for the audience.<\/li>\n
                  • Funnel step completion and drop-off:<\/strong> Step-by-step progression for the segment, critical for Conversion & Measurement<\/strong> diagnostics.<\/li>\n
                  • Revenue per user \/ average order value:<\/strong> Especially important for value-based optimization.<\/li>\n
                  • Customer acquisition cost (CAC) by segment:<\/strong> Spend divided by segment-specific conversions or customers.<\/li>\n
                  • Lead quality indicators:<\/strong> Sales acceptance rate, opportunity creation rate, or qualification rate for the segment.<\/li>\n
                  • Retention and repeat behavior:<\/strong> Repeat purchases, renewal rate, churn rate, or cohort retention for segment members.<\/li>\n
                  • Time to convert:<\/strong> How long it takes the segment to reach a conversion event, which affects attribution interpretation in Analytics<\/strong>.<\/li>\n
                  • Incrementality \/ experiment lift (when available):<\/strong> Whether changes truly improved outcomes for the target segment.<\/li>\n<\/ul>\n\n\n\n

                    A strong Analytics Target Audience<\/strong> setup makes these metrics more actionable because they are tied to a defined group, not blended averages.<\/p>\n\n\n\n

                    12) Future Trends of Analytics Target Audience<\/h2>\n\n\n\n

                    Several trends are reshaping how Analytics Target Audience<\/strong> evolves inside Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                      \n
                    • AI-assisted segmentation:<\/strong> Machine learning can propose high-performing segments, detect patterns, and surface drivers of conversion\u2014while teams still need governance and interpretability.<\/li>\n
                    • More first-party and modeled measurement:<\/strong> As identifiers become less available, Analytics<\/strong> increasingly relies on first-party data, consented identity, and statistical modeling to estimate audience behavior.<\/li>\n
                    • Real-time personalization expectations:<\/strong> Faster segmentation updates enable near-real-time experience changes, but require higher data quality and clearer guardrails.<\/li>\n
                    • Privacy-by-design segmentation:<\/strong> Audience definitions will increasingly avoid sensitive attributes, emphasize aggregation, and incorporate consent states as part of the segment logic.<\/li>\n
                    • Shift toward value optimization:<\/strong> More teams will prioritize LTV, retention, and margin by audience segment\u2014not just top-line conversion counts\u2014bringing Conversion & Measurement<\/strong> closer to finance outcomes.<\/li>\n<\/ul>\n\n\n\n

                      13) Analytics Target Audience vs Related Terms<\/h2>\n\n\n\n

                      Understanding nearby concepts helps prevent misuse:<\/p>\n\n\n\n

                      Analytics Target Audience vs Buyer Persona<\/h3>\n\n\n\n

                      A buyer persona is a qualitative profile (motivations, pain points, context). Analytics Target Audience<\/strong> is a measurable segment based on actual observed data and rules in your Analytics<\/strong> environment. Personas inspire messaging; analytics audiences validate and optimize performance.<\/p>\n\n\n\n

                      Analytics Target Audience vs Market Segment<\/h3>\n\n\n\n

                      Market segments are broad strategic groupings (industry, demographics, needs). Analytics Target Audience<\/strong> is operational and measurement-driven\u2014often narrower and defined by behaviors and conversion intent. Market segments guide positioning; analytics audiences guide Conversion & Measurement<\/strong> actions.<\/p>\n\n\n\n

                      Analytics Target Audience vs Remarketing Audience<\/h3>\n\n\n\n

                      A remarketing audience is built primarily for ad targeting and re-engagement. Analytics Target Audience<\/strong> is built primarily for analysis and decision-making, though it may be activated in channels. The key difference is purpose: measurement integrity vs media activation.<\/p>\n\n\n\n

                      14) Who Should Learn Analytics Target Audience<\/h2>\n\n\n\n

                      Analytics Target Audience<\/strong> is valuable across roles because it improves how teams define success:<\/p>\n\n\n\n

                        \n
                      • Marketers:<\/strong> Build smarter campaigns, optimize landing pages, and report outcomes that reflect business value within Conversion & Measurement<\/strong>.<\/li>\n
                      • Analysts:<\/strong> Create reusable, governed segments that make insights consistent and scalable in Analytics<\/strong>.<\/li>\n
                      • Agencies:<\/strong> Deliver clearer performance narratives, segment-based optimizations, and better client trust.<\/li>\n
                      • Business owners and founders:<\/strong> Understand which customers drive growth and where to focus product and marketing investment.<\/li>\n
                      • Developers and implementation teams:<\/strong> Instrument events and identities correctly so segments are accurate, privacy-aware, and stable over time.<\/li>\n<\/ul>\n\n\n\n

                        15) Summary of Analytics Target Audience<\/h2>\n\n\n\n

                        Analytics Target Audience<\/strong> is a defined, measurable audience segment used inside your measurement environment to evaluate performance and guide optimization. It matters because it replaces misleading averages with segment-specific insights that improve decisions, efficiency, and growth outcomes.<\/p>\n\n\n\n

                        Within Conversion & Measurement<\/strong>, it strengthens funnel analysis, experimentation, attribution inputs, and KPI reporting. Within Analytics<\/strong>, it provides the structure that turns data into repeatable, business-aligned learning.<\/p>\n\n\n\n

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

                        1) What is an Analytics Target Audience?<\/h3>\n\n\n\n

                        An Analytics Target Audience<\/strong> is a specific group of users defined by attributes and behaviors in your measurement setup so you can analyze conversions, funnels, and outcomes for that group separately from overall traffic.<\/p>\n\n\n\n

                        2) How is Analytics Target Audience different from a persona?<\/h3>\n\n\n\n

                        Personas are descriptive and qualitative; Analytics Target Audience<\/strong> is rules-based and measurable in data. Personas guide creative direction, while analytics audiences validate what actually converts in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                        3) Which data is most important for building an Analytics Target Audience?<\/h3>\n\n\n\n

                        Prioritize durable signals: key events (add-to-cart, start checkout, activation actions), lifecycle stage, acquisition source, and value indicators like purchases or qualified lead status. These signals produce more stable Analytics<\/strong> insights than one-off pageviews.<\/p>\n\n\n\n

                        4) Can I use Analytics Target Audience for both reporting and advertising?<\/h3>\n\n\n\n

                        Yes, but treat reporting as the source of truth. If you activate the segment in ad platforms, keep definitions consistent and be aware that platform-side audiences may not match your Analytics<\/strong> counts due to privacy constraints and identity differences.<\/p>\n\n\n\n

                        5) What should I do if the segment is too small to analyze?<\/h3>\n\n\n\n

                        Broaden the definition (longer time window, fewer constraints), use layered segmentation (broad \u2192 narrow), or switch from weekly to monthly views. In Conversion & Measurement<\/strong>, small samples can create misleading swings.<\/p>\n\n\n\n

                        6) How does privacy affect Analytics Target Audience?<\/h3>\n\n\n\n

                        Consent choices, limited identifiers, and data minimization reduce what you can observe and connect across sessions\/devices. Build segments that work with aggregated, consented signals and document limitations in Analytics<\/strong> reporting.<\/p>\n\n\n\n

                        7) What are the most useful KPIs to track by Analytics Target Audience?<\/h3>\n\n\n\n

                        Start with conversion rate, funnel drop-off by step, revenue per user, CAC by segment, and retention\/renewal indicators. These metrics connect Conversion & Measurement<\/strong> work directly to growth and profitability.<\/p>\n","protected":false},"excerpt":{"rendered":"

                        Modern marketing teams don\u2019t just measure performance\u2014they measure performance for specific groups of people. **Analytics Target Audience** is the practice of defining, analyzing, and using a clearly described audience segment inside your measurement stack so your reporting, optimization, and decisions reflect who is actually driving outcomes.<\/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":[1887],"tags":[],"class_list":["post-7017","post","type-post","status-publish","format-standard","hentry","category-analytics"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7017","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=7017"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7017\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7017"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7017"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7017"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}