{"id":6980,"date":"2026-03-23T19:58:16","date_gmt":"2026-03-23T19:58:16","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/user-scoped-dimension\/"},"modified":"2026-03-23T19:58:16","modified_gmt":"2026-03-23T19:58:16","slug":"user-scoped-dimension","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/user-scoped-dimension\/","title":{"rendered":"User-scoped Dimension: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Analytics"},"content":{"rendered":"\n

A User-scoped Dimension<\/strong> is one of the most powerful ideas in Conversion & Measurement<\/strong> because it changes what<\/em> you\u2019re measuring: instead of describing a single pageview, click, or session, it describes a person (or pseudonymous user)<\/strong> and carries that context across many interactions. In Analytics<\/strong>, this is the difference between analyzing isolated events and understanding behavior over time\u2014acquisition, engagement, retention, and conversion paths.<\/p>\n\n\n\n

As marketing becomes more fragmented across devices, channels, and touchpoints, a strong Conversion & Measurement<\/strong> strategy increasingly depends on user-level context. A well-designed User-scoped Dimension<\/strong> helps teams segment audiences correctly, attribute outcomes more realistically, personalize experiences, and report performance in a way that aligns with how customers actually behave.<\/p>\n\n\n\n

\n\n\n\n

What Is User-scoped Dimension?<\/h2>\n\n\n\n

A User-scoped Dimension<\/strong> is an attribute assigned to a user identity that remains available for analysis across multiple sessions and events. Think of it as a \u201cprofile field\u201d used for segmentation and reporting in Analytics<\/strong>\u2014for example, a user\u2019s subscription tier, lifecycle stage, region, or acquisition cohort.<\/p>\n\n\n\n

The core concept<\/h3>\n\n\n\n

Most measurement data has a \u201cscope\u201d that determines how long a value is considered relevant:<\/p>\n\n\n\n

    \n
  • Event scope<\/strong>: applies to one action (e.g., a click)<\/li>\n
  • Session scope<\/strong>: applies to a visit (e.g., traffic source for that session)<\/li>\n
  • User scope<\/strong>: applies to the user across time (e.g., customer type)<\/li>\n<\/ul>\n\n\n\n

    A User-scoped Dimension<\/strong> is the user-scope version: it\u2019s meant to represent something about the user that should persist (at least until it\u2019s updated).<\/p>\n\n\n\n

    The business meaning<\/h3>\n\n\n\n

    In practical business terms, this dimension answers questions like:<\/p>\n\n\n\n

      \n
    • Do free users convert differently than paid users?<\/li>\n
    • Are returning customers responding better than first-time visitors?<\/li>\n
    • Which lifecycle stage drives the highest lead-to-opportunity rate?<\/li>\n<\/ul>\n\n\n\n

      These are Conversion & Measurement<\/strong> questions, but they\u2019re hard to answer reliably without user-level attributes.<\/p>\n\n\n\n

      Where it fits in Conversion & Measurement<\/h3>\n\n\n\n

      A User-scoped Dimension<\/strong> sits at the intersection of segmentation and outcome measurement. It helps you compare conversion performance across meaningful user groups, not just across channels or campaigns.<\/p>\n\n\n\n

      Its role inside Analytics<\/h3>\n\n\n\n

      In Analytics<\/strong>, user-level dimensions enable:<\/p>\n\n\n\n

        \n
      • Cohort analysis (behavior over time)<\/li>\n
      • Retention and frequency analysis<\/li>\n
      • Cross-session conversion path analysis<\/li>\n
      • Audience building for activation and experimentation<\/li>\n<\/ul>\n\n\n\n
        \n\n\n\n

        Why User-scoped Dimension Matters in Conversion & Measurement<\/h2>\n\n\n\n

        A User-scoped Dimension<\/strong> matters because many marketing decisions are actually decisions about audience quality<\/em>, not just traffic volume. When you can analyze outcomes by user attributes, your Conversion & Measurement<\/strong> work becomes more diagnostic and less guesswork.<\/p>\n\n\n\n

        Key business value includes:<\/p>\n\n\n\n

          \n
        • More accurate segmentation:<\/strong> You can separate \u201cnew vs returning,\u201d \u201ctrial vs paid,\u201d \u201chigh intent vs low intent,\u201d and measure each group\u2019s conversion propensity.<\/li>\n
        • Better lifecycle optimization:<\/strong> You can see where conversions stall (activation, repeat purchase, renewal) and tailor messaging.<\/li>\n
        • Stronger attribution context:<\/strong> Channel performance looks different when you separate high-LTV users from one-time bargain hunters.<\/li>\n
        • Competitive advantage:<\/strong> Teams that operationalize user-level measurement in Analytics<\/strong> can optimize toward long-term value, not short-term click metrics.<\/li>\n<\/ul>\n\n\n\n
          \n\n\n\n

          How User-scoped Dimension Works<\/h2>\n\n\n\n

          A User-scoped Dimension<\/strong> is less about a single workflow and more about disciplined implementation and consistent interpretation. In practice, it typically works like this:<\/p>\n\n\n\n

            \n
          1. \n

            Input \/ capture<\/strong>\n A user attribute is collected or derived. This may come from:\n – Account creation (e.g., \u201cindustry\u201d)\n – CRM status (e.g., \u201clead stage\u201d)\n – Behavioral rules (e.g., \u201cengaged user = true\u201d after threshold)\n – Consent-aware first-party data (e.g., \u201cmarketing_opt_in\u201d)<\/p>\n<\/li>\n

          2. \n

            Processing \/ assignment<\/strong>\n The attribute is associated with a user identifier (logged-in ID or pseudonymous device\/app ID). In Analytics<\/strong>, the platform stores or references that attribute as user-level context.<\/p>\n<\/li>\n

          3. \n

            Application \/ activation<\/strong>\n The User-scoped Dimension<\/strong> is used to:\n – Segment reports and funnels\n – Build audiences for remarketing or lifecycle messaging\n – Power personalization rules or experiment targeting\n – Filter dashboards for stakeholders<\/p>\n<\/li>\n

          4. \n

            Output \/ outcome<\/strong>\n The organization gets clearer Conversion & Measurement<\/strong> insights such as:\n – Conversion rate by customer type\n – Retention by acquisition cohort\n – LTV proxies by lifecycle stage<\/p>\n<\/li>\n<\/ol>\n\n\n\n

            The \u201cmagic\u201d is consistency: user-level fields must be defined, populated, and maintained in a way that doesn\u2019t drift across teams.<\/p>\n\n\n\n

            \n\n\n\n

            Key Components of User-scoped Dimension<\/h2>\n\n\n\n

            A high-quality User-scoped Dimension<\/strong> program relies on more than just a field name in a tool. The major components include:<\/p>\n\n\n\n

            Data inputs and identity<\/h3>\n\n\n\n
              \n
            • User identifiers:<\/strong> logged-in user ID, hashed identifiers, or pseudonymous IDs (implemented with privacy and consent in mind)<\/li>\n
            • Attribute sources:<\/strong> product database, CRM, subscription system, support platform, surveys, or behavioral rules<\/li>\n<\/ul>\n\n\n\n

              Systems and processes<\/h3>\n\n\n\n
                \n
              • Event collection pipeline:<\/strong> app\/web tracking that can attach user attributes at the time of events or via updates<\/li>\n
              • Data model governance:<\/strong> definitions, allowed values, and update rules<\/li>\n
              • QA and validation:<\/strong> tests to ensure correct population and stability over time<\/li>\n<\/ul>\n\n\n\n

                Team responsibilities<\/h3>\n\n\n\n
                  \n
                • Marketing\/CRM:<\/strong> defines lifecycle segments and activation use cases<\/li>\n
                • Analytics team:<\/strong> enforces measurement design, data quality, and reporting logic<\/li>\n
                • Engineering\/data:<\/strong> implements identity stitching and data pipelines<\/li>\n
                • Privacy\/legal:<\/strong> ensures compliant collection, retention, and access<\/li>\n<\/ul>\n\n\n\n

                  In Conversion & Measurement<\/strong>, governance is what prevents \u201csegment chaos,\u201d where every team defines \u201cactive user\u201d differently.<\/p>\n\n\n\n

                  \n\n\n\n

                  Types of User-scoped Dimension<\/h2>\n\n\n\n

                  \u201cTypes\u201d of User-scoped Dimension<\/strong> are usually best understood as practical distinctions rather than strict categories:<\/p>\n\n\n\n

                  Stable vs mutable attributes<\/h3>\n\n\n\n
                    \n
                  • Stable:<\/strong> country (usually), signup date, original acquisition cohort <\/li>\n
                  • Mutable:<\/strong> subscription tier, lifecycle stage, marketing consent status<\/li>\n<\/ul>\n\n\n\n

                    Mutable attributes are common in Analytics<\/strong>, but they require careful interpretation because historical analysis may be affected by updates.<\/p>\n\n\n\n

                    Declared vs inferred attributes<\/h3>\n\n\n\n
                      \n
                    • Declared:<\/strong> user provides it (industry, preferences)<\/li>\n
                    • Inferred:<\/strong> computed from behavior (high-intent segment, churn risk bucket)<\/li>\n<\/ul>\n\n\n\n

                      Inferred fields can be powerful for Conversion & Measurement<\/strong>, but you must document the rules and monitor drift.<\/p>\n\n\n\n

                      First-party vs enriched attributes<\/h3>\n\n\n\n
                        \n
                      • First-party:<\/strong> collected directly from your product\/site<\/li>\n
                      • Enriched:<\/strong> appended from internal models or data partnerships (where permitted)<\/li>\n<\/ul>\n\n\n\n

                        For evergreen measurement, prioritize first-party attributes with clear consent and provenance.<\/p>\n\n\n\n

                        \n\n\n\n

                        Real-World Examples of User-scoped Dimension<\/h2>\n\n\n\n

                        Example 1: SaaS lifecycle stage for lead-to-paid measurement<\/h3>\n\n\n\n

                        A SaaS company defines a User-scoped Dimension<\/strong> called lifecycle_stage<\/code> with values like: visitor, trial, activated, paid, churned. In Conversion & Measurement<\/strong>, they measure:\n– Activation rate by acquisition channel\n– Trial-to-paid conversion by persona\n– Re-activation campaigns for churned users<\/p>\n\n\n\n

                        In Analytics<\/strong>, funnels become more meaningful because users are compared within the same lifecycle context.<\/p>\n\n\n\n

                        Example 2: Ecommerce customer type for repeat purchase optimization<\/h3>\n\n\n\n

                        An ecommerce brand sets a User-scoped Dimension<\/strong> called customer_type<\/code>: first_time, returning, VIP. They find:\n– VIP users convert well even from generic campaigns\n– First-time users need trust signals and shipping clarity\nThey adjust creative and onsite experiences accordingly, improving Conversion & Measurement<\/strong> outcomes while reducing wasted spend.<\/p>\n\n\n\n

                        Example 3: B2B account segment for pipeline quality<\/h3>\n\n\n\n

                        A B2B company assigns account_segment<\/code> (SMB, mid_market, enterprise) as a User-scoped Dimension<\/strong> after form submission or enrichment. In Analytics<\/strong>, they analyze:\n– Demo request conversion rate by segment\n– Down-funnel conversion by segment (when integrated with CRM outcomes)\nThis prevents optimizing to cheap leads that never become pipeline.<\/p>\n\n\n\n

                        \n\n\n\n

                        Benefits of Using User-scoped Dimension<\/h2>\n\n\n\n

                        Implementing User-scoped Dimension<\/strong> well can unlock measurable improvements:<\/p>\n\n\n\n

                          \n
                        • Higher conversion efficiency:<\/strong> Better segmentation reveals which audiences need different offers, landing pages, or nurturing.<\/li>\n
                        • Lower acquisition waste:<\/strong> You can avoid over-investing in sources that bring low-quality users, improving Conversion & Measurement<\/strong> ROI.<\/li>\n
                        • Stronger personalization:<\/strong> User-level attributes make personalization less guessy and more systematic.<\/li>\n
                        • Better reporting clarity:<\/strong> Stakeholders can understand performance by customer reality (tier, stage, cohort), not just by channel labels.<\/li>\n
                        • Improved experimentation:<\/strong> A\/B tests become more reliable when you can analyze results by user segments in Analytics<\/strong>.<\/li>\n<\/ul>\n\n\n\n
                          \n\n\n\n

                          Challenges of User-scoped Dimension<\/h2>\n\n\n\n

                          A User-scoped Dimension<\/strong> is powerful precisely because it\u2019s user-level\u2014and that introduces real risks and complexity:<\/p>\n\n\n\n

                          Identity and stitching issues<\/h3>\n\n\n\n

                          If users switch devices, clear cookies, or browse anonymously, user identity may fragment. That can dilute segment accuracy and distort Conversion & Measurement<\/strong> conclusions.<\/p>\n\n\n\n

                          Timing and backfill limitations<\/h3>\n\n\n\n

                          Some platforms won\u2019t retroactively apply updated user properties to past events, or they may apply them in ways that change historical interpretation. You must understand how your Analytics<\/strong> system handles changes.<\/p>\n\n\n\n

                          Governance and definition drift<\/h3>\n\n\n\n

                          If \u201cVIP\u201d means one thing in CRM and another thing in dashboards, teams will make contradictory decisions. A User-scoped Dimension<\/strong> needs controlled definitions and owners.<\/p>\n\n\n\n

                          Privacy and compliance constraints<\/h3>\n\n\n\n

                          User-level attributes can increase privacy risk if they\u2019re sensitive, unnecessary, or collected without proper consent. Good Conversion & Measurement<\/strong> includes data minimization and clear retention policies.<\/p>\n\n\n\n

                          \n\n\n\n

                          Best Practices for User-scoped Dimension<\/h2>\n\n\n\n

                          To make User-scoped Dimension<\/strong> reliable and scalable:<\/p>\n\n\n\n

                            \n
                          1. \n

                            Start with business questions, not fields<\/strong>\n Define the conversion decisions you want to improve (activation, repeat purchase, upsell), then select the minimum user attributes needed.<\/p>\n<\/li>\n

                          2. \n

                            Write a measurement specification<\/strong>\n Document:\n – Field name and description\n – Allowed values (controlled vocabulary)\n – Data source of truth\n – Update rules (when it changes)\n – Known caveats for Analytics<\/strong> interpretation<\/p>\n<\/li>\n

                          3. \n

                            Prefer controlled values over free text<\/strong>\n Free text creates messy reporting. Use enumerated values and version them when changes occur.<\/p>\n<\/li>\n

                          4. \n

                            Design for change over time<\/strong>\n For mutable attributes, consider tracking both:\n – current value (user-level)\n – change events (event-level) for historical analysis<\/p>\n<\/li>\n

                          5. \n

                            Validate in three places<\/strong>\n – Collection (is it sent?)\n – Storage (is it persisted as user-scoped?)\n – Reporting (does segmentation behave as expected?)<\/p>\n<\/li>\n

                          6. \n

                            Review quarterly<\/strong>\n In Conversion & Measurement<\/strong>, old segments often stop matching the business. Audit usage and retire unused dimensions.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                            \n\n\n\n

                            Tools Used for User-scoped Dimension<\/h2>\n\n\n\n

                            A User-scoped Dimension<\/strong> typically spans multiple tool categories in Analytics<\/strong> and measurement operations:<\/p>\n\n\n\n

                              \n
                            • Analytics tools:<\/strong> collect events, store user properties, and provide segmentation, funnels, and cohorts.<\/li>\n
                            • Tag management and SDK frameworks:<\/strong> standardize how user attributes are captured across web and apps.<\/li>\n
                            • Customer data platforms and data pipelines:<\/strong> unify identity, resolve duplicates, and distribute user attributes consistently.<\/li>\n
                            • CRM systems:<\/strong> provide lifecycle stage, lead status, and customer metadata that can enrich Conversion & Measurement<\/strong> reporting.<\/li>\n
                            • Marketing automation tools:<\/strong> use user segments for nurture, winback, and retention campaigns.<\/li>\n
                            • Ad platforms:<\/strong> activate audiences built from user-level attributes (with privacy-safe practices).<\/li>\n
                            • Reporting dashboards \/ BI:<\/strong> combine Analytics<\/strong> data with revenue, margin, and customer success metrics.<\/li>\n<\/ul>\n\n\n\n

                              The key is consistency: the same definition of a user attribute should travel across collection, analysis, and activation.<\/p>\n\n\n\n

                              \n\n\n\n

                              Metrics Related to User-scoped Dimension<\/h2>\n\n\n\n

                              A User-scoped Dimension<\/strong> is not a metric by itself, but it powers better metrics by segment. Common metrics to pair with user-level dimensions include:<\/p>\n\n\n\n

                                \n
                              • Conversion rate by user segment:<\/strong> signup rate, purchase rate, demo request rate<\/li>\n
                              • Activation rate:<\/strong> % of users reaching a defined \u201caha\u201d milestone<\/li>\n
                              • Retention and churn:<\/strong> returning users, cohort retention curves, renewal rates<\/li>\n
                              • Time to conversion:<\/strong> how long it takes different segments to convert<\/li>\n
                              • Average order value \/ revenue per user (where available):<\/strong> interpreted carefully with privacy and aggregation rules<\/li>\n
                              • Customer acquisition cost (CAC) proxies by segment:<\/strong> blended spend vs conversions by segment for Conversion & Measurement<\/strong><\/li>\n
                              • Engagement depth:<\/strong> sessions per user, key actions per user, feature adoption by segment<\/li>\n<\/ul>\n\n\n\n

                                In Analytics<\/strong>, segmentation is often the difference between \u201cwe improved conversion rate\u201d and \u201cwe improved conversion rate for the users who matter.\u201d<\/p>\n\n\n\n

                                \n\n\n\n

                                Future Trends of User-scoped Dimension<\/h2>\n\n\n\n

                                Several trends are shaping how User-scoped Dimension<\/strong> evolves within Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                                  \n
                                • Privacy-first identity:<\/strong> More emphasis on first-party identifiers, consent signals, and modeled\/aggregated measurement. User attributes will need stricter data minimization and clearer purpose limitation.<\/li>\n
                                • AI-assisted segmentation:<\/strong> Machine learning will increasingly create inferred user attributes (propensity, churn risk). Teams will need transparency, monitoring, and bias checks in Analytics<\/strong> workflows.<\/li>\n
                                • Cross-platform measurement maturity:<\/strong> Businesses will push for consistent user attributes across web, app, email, and offline touchpoints\u2014driving stronger governance and data contracts.<\/li>\n
                                • Real-time personalization expectations:<\/strong> Faster pipelines will make user-level attributes usable for immediate onsite\/app personalization, not just reporting.<\/li>\n
                                • More emphasis on quality over volume:<\/strong> Conversion & Measurement<\/strong> will shift further toward LTV proxies and retention-based goals, where user-scoped context is essential.<\/li>\n<\/ul>\n\n\n\n
                                  \n\n\n\n

                                  User-scoped Dimension vs Related Terms<\/h2>\n\n\n\n

                                  User-scoped Dimension vs Event-scoped dimension<\/h3>\n\n\n\n
                                    \n
                                  • User-scoped Dimension:<\/strong> describes the user across time (e.g., membership tier)<\/li>\n
                                  • Event-scoped dimension:<\/strong> describes a single interaction (e.g., button name)<\/li>\n<\/ul>\n\n\n\n

                                    Use user-scope for persistent attributes; use event-scope for context of a specific action in Analytics<\/strong>.<\/p>\n\n\n\n

                                    User-scoped Dimension vs Session-scoped dimension<\/h3>\n\n\n\n
                                      \n
                                    • Session-scoped:<\/strong> describes a visit (e.g., campaign for that session)<\/li>\n
                                    • User-scoped:<\/strong> describes the person (e.g., acquisition cohort)<\/li>\n<\/ul>\n\n\n\n

                                      In Conversion & Measurement<\/strong>, session-scope helps you understand how<\/em> a visit happened, while user-scope helps you understand who<\/em> is converting.<\/p>\n\n\n\n

                                      User-scoped Dimension vs Metric<\/h3>\n\n\n\n

                                      A User-scoped Dimension<\/strong> is a category used to slice data; a metric is a number you measure (conversions, revenue, retention). Dimensions explain differences<\/em> in metrics.<\/p>\n\n\n\n

                                      \n\n\n\n

                                      Who Should Learn User-scoped Dimension<\/h2>\n\n\n\n
                                        \n
                                      • Marketers:<\/strong> to build audience strategies, improve personalization, and measure beyond last-click outcomes in Conversion & Measurement<\/strong>.<\/li>\n
                                      • Analysts:<\/strong> to design clean segmentation, cohorts, and funnel reporting in Analytics<\/strong>.<\/li>\n
                                      • Agencies:<\/strong> to deliver better measurement frameworks, performance insights, and reporting that clients can act on.<\/li>\n
                                      • Business owners and founders:<\/strong> to understand which customers drive sustainable growth, not just which campaigns drive cheap clicks.<\/li>\n
                                      • Developers and data engineers:<\/strong> to implement identity, data contracts, and reliable pipelines that make user-level measurement trustworthy.<\/li>\n<\/ul>\n\n\n\n
                                        \n\n\n\n

                                        Summary of User-scoped Dimension<\/h2>\n\n\n\n

                                        A User-scoped Dimension<\/strong> is a user-level attribute used for segmentation and reporting across multiple sessions and events. It matters because modern Conversion & Measurement<\/strong> depends on understanding outcomes by audience quality, lifecycle stage, and behavior over time. Implemented with strong governance and privacy discipline, user-level dimensions strengthen Analytics<\/strong> by enabling cohorts, retention analysis, meaningful funnels, and more actionable marketing optimization.<\/p>\n\n\n\n

                                        \n\n\n\n

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

                                        1) What is a User-scoped Dimension in plain language?<\/h3>\n\n\n\n

                                        It\u2019s a label or attribute attached to a user (not just a visit or click) that lets you analyze behavior and conversions across time, such as \u201creturning customer\u201d or \u201csubscription tier.\u201d<\/p>\n\n\n\n

                                        2) How is a User-scoped Dimension different from a session attribute?<\/h3>\n\n\n\n

                                        Session attributes describe a single visit (like the campaign for that visit). A User-scoped Dimension<\/strong> describes the person and can persist across many visits, which is often more useful for lifecycle Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                        3) Does changing a User-scoped Dimension affect historical reporting?<\/h3>\n\n\n\n

                                        It can. Some Analytics<\/strong> systems treat user attributes as \u201ccurrent state,\u201d while others retain historical states differently. For critical analysis, consider also tracking change events so you can reconstruct history reliably.<\/p>\n\n\n\n

                                        4) Which user attributes are most useful for Conversion & Measurement?<\/h3>\n\n\n\n

                                        Common high-impact choices include lifecycle stage, customer type (new\/returning\/VIP), acquisition cohort, subscription tier, and consent status\u2014provided each is clearly defined and consistently populated.<\/p>\n\n\n\n

                                        5) How do I avoid messy segmentation in Analytics?<\/h3>\n\n\n\n

                                        Use controlled values, document definitions, assign ownership, and audit dimensions regularly. Also align CRM, product, and Analytics<\/strong> naming so \u201ctrial user\u201d means the same thing everywhere.<\/p>\n\n\n\n

                                        6) What are the biggest implementation risks?<\/h3>\n\n\n\n

                                        Identity fragmentation (users across devices), inconsistent definitions, and privacy\/compliance issues. Address these with a clear measurement spec, strong governance, and consent-aware data collection practices.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                        A **User-scoped Dimension** is one of the most powerful ideas in **Conversion & Measurement** because it changes *what* you\u2019re measuring: instead of describing a single pageview, click, or session, it describes a **person (or pseudonymous user)** and carries that context across many interactions. In **Analytics**, this is the difference between analyzing isolated events and understanding behavior over time\u2014acquisition, engagement, retention, and conversion paths.<\/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-6980","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\/6980","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=6980"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/6980\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=6980"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=6980"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=6980"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}