{"id":6861,"date":"2026-03-23T15:27:40","date_gmt":"2026-03-23T15:27:40","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/event-scoped-dimension\/"},"modified":"2026-03-23T15:27:40","modified_gmt":"2026-03-23T15:27:40","slug":"event-scoped-dimension","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/event-scoped-dimension\/","title":{"rendered":"Event-scoped Dimension: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Analytics"},"content":{"rendered":"\n

An Event-scoped Dimension<\/strong> is a way to describe what was true about a specific interaction at the moment it happened<\/em>. In Conversion & Measurement<\/strong>, that matters because most decisions\u2014creative, targeting, UX, offer design, funnel fixes\u2014depend on understanding which<\/em> events occurred and under what conditions<\/em>.<\/p>\n\n\n\n

In modern Analytics<\/strong>, measurement has shifted from pageview-centric reporting to event-based models where clicks, submissions, purchases, scrolls, and errors are all captured as events. An Event-scoped Dimension<\/strong> is the context you attach to those events so you can segment performance, diagnose drop-offs, and attribute outcomes with more precision.<\/p>\n\n\n\n

\n\n\n\n

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

An Event-scoped Dimension<\/strong> is an attribute (often categorical text) that applies to one event occurrence<\/em> rather than to a user, a session, or a product record. If an event is \u201cform_submit,\u201d then the Event-scoped Dimension might be \u201cform_id,\u201d \u201cform_location,\u201d or \u201clead_type\u201d\u2014values that describe that specific submission.<\/p>\n\n\n\n

The core concept is scope: event-scoped<\/strong> means the dimension\u2019s value is evaluated and stored at the event level. If the same user triggers the same event multiple times, the Event-scoped Dimension<\/strong> can vary each time (for example, different button labels or different on-page positions).<\/p>\n\n\n\n

From a business perspective, this is what turns raw interaction logs into decision-ready insights. Instead of only knowing that \u201csignups increased,\u201d you can learn which plan name, CTA text, page section, or campaign context<\/em> drove the lift.<\/p>\n\n\n\n

Within Conversion & Measurement<\/strong>, an Event-scoped Dimension<\/strong> sits at the heart of funnel analysis, conversion attribution, experimentation readouts, and UX optimization. Inside Analytics<\/strong>, it enables meaningful segmentation of event counts, conversion rates, revenue, and engagement by the context that actually influenced behavior.<\/p>\n\n\n\n

\n\n\n\n

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

An Event-scoped Dimension<\/strong> improves strategic decision-making because it connects outcomes to controllable levers. Marketers rarely optimize \u201cevents\u201d in the abstract; they optimize variants\u2014messages, placements, audiences, and flows. Event-level dimensions let you isolate those variants without guessing.<\/p>\n\n\n\n

Business value typically shows up in three ways:<\/p>\n\n\n\n

    \n
  • Sharper diagnosis:<\/strong> You can pinpoint which step, element, or error condition is responsible for conversion leakage.<\/li>\n
  • Better budget allocation:<\/strong> In Conversion & Measurement<\/strong>, tying event outcomes to campaign context helps distinguish high-intent actions from low-quality clicks.<\/li>\n
  • Faster iteration loops:<\/strong> Product, marketing, and growth teams can make changes and validate impact in Analytics<\/strong> without waiting for complex data science pipelines.<\/li>\n<\/ul>\n\n\n\n

    Competitive advantage comes from measurement clarity. Teams that instrument Event-scoped Dimension<\/strong> well can detect emerging issues earlier, personalize experiences more confidently, and build a more reliable performance narrative across channels.<\/p>\n\n\n\n

    \n\n\n\n

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

    In practice, an Event-scoped Dimension<\/strong> \u201cworks\u201d when event instrumentation, data handling, and reporting all agree on what the dimension means and when it should be captured.<\/p>\n\n\n\n

    A typical workflow looks like this:<\/p>\n\n\n\n

      \n
    1. \n

      Input \/ trigger (collection)<\/strong>\n A user action occurs\u2014click, view, add-to-cart, checkout start, form submit, video play, or an error. The tracking implementation sends an event along with parameters such as cta_text<\/code>, page_section<\/code>, or payment_method<\/code>. Those parameters become candidates for an Event-scoped Dimension<\/strong>.<\/p>\n<\/li>\n

    2. \n

      Processing (definition and schema)<\/strong>\n The measurement system (or your team\u2019s data layer standards) determines which parameters are treated as dimensions and how values are formatted. Good Conversion & Measurement<\/strong> practice also includes validation rules (allowed values, casing, null handling) so dimensions remain trustworthy.<\/p>\n<\/li>\n

    3. \n

      Execution \/ application (reporting and activation)<\/strong>\n In Analytics<\/strong>, you use the Event-scoped Dimension<\/strong> to break down metrics: conversion rate by cta_text<\/code>, revenue by coupon_code<\/code>, or errors by error_type<\/code>. In some organizations, the same event-level attributes are also used for audience building, lifecycle messaging, or experimentation analysis.<\/p>\n<\/li>\n

    4. \n

      Output \/ outcome (decisions and improvements)<\/strong>\n The result is actionable insight: remove a friction point, adjust creative, redesign a page section, prioritize a bug fix, or refine attribution logic. Over time, these decisions compound into measurable gains in Conversion & Measurement<\/strong> performance.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

      \n\n\n\n

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

      Implementing an Event-scoped Dimension<\/strong> reliably is less about a single setting and more about disciplined measurement design across people and systems.<\/p>\n\n\n\n

      Key components commonly include:<\/p>\n\n\n\n

        \n
      • Tracking plan and event taxonomy:<\/strong> A documented list of events and their associated Event-scoped Dimension<\/strong> fields, written in business language and mapped to KPIs in Conversion & Measurement<\/strong>.<\/li>\n
      • Data layer or instrumentation contract:<\/strong> A consistent way to pass event attributes from the site\/app to collection endpoints (web tags, mobile SDKs, server events).<\/li>\n
      • Naming conventions and value standards:<\/strong> Rules for casing, separators, enumerated lists (e.g., pricing_page<\/code> vs PricingPage<\/code>), and versioning for evolving UX.<\/li>\n
      • Quality assurance processes:<\/strong> Debugging, test environments, and automated checks to ensure event parameters populate correctly.<\/li>\n
      • Governance and ownership:<\/strong> Clear responsibility across marketing, product, engineering, and Analytics<\/strong> for adding, deprecating, and maintaining dimensions.<\/li>\n
      • Storage and reporting layer:<\/strong> Whether you report directly in an analytics UI, a data warehouse, or BI dashboards, the system must preserve event-level granularity.<\/li>\n<\/ul>\n\n\n\n
        \n\n\n\n

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

        \u201cTypes\u201d aren\u2019t always formalized, but in real-world Analytics<\/strong> practice, Event-scoped Dimension<\/strong> design usually falls into useful distinctions:<\/p>\n\n\n\n

        1) Standard vs custom dimensions<\/h3>\n\n\n\n
          \n
        • Standard<\/strong>: Common, predefined event attributes (like page context or device context) that are broadly consistent.<\/li>\n
        • Custom<\/strong>: Business-specific attributes like plan_tier<\/code>, lead_source_detail<\/code>, or checkout_step_name<\/code>.<\/li>\n<\/ul>\n\n\n\n

          2) Raw vs derived (calculated) dimensions<\/h3>\n\n\n\n
            \n
          • Raw<\/strong>: Captured directly at event time (e.g., button_text<\/code>).<\/li>\n
          • Derived<\/strong>: Computed later from one or more fields (e.g., mapping many utm_campaign<\/code> patterns into a normalized campaign_theme<\/code> for Conversion & Measurement<\/strong> reporting).<\/li>\n<\/ul>\n\n\n\n

            3) Low-cardinality vs high-cardinality dimensions<\/h3>\n\n\n\n
              \n
            • Low-cardinality<\/strong>: Limited set of values (e.g., device_type<\/code>: desktop\/tablet\/mobile).<\/li>\n
            • High-cardinality<\/strong>: Many unique values (e.g., free-text search queries or full URLs). High cardinality can create performance, privacy, and reporting limitations, so it requires special care.<\/li>\n<\/ul>\n\n\n\n

              4) Behavior context vs marketing context<\/h3>\n\n\n\n
                \n
              • Behavior context<\/strong>: UI element, step number, error type, feature flag variant.<\/li>\n
              • Marketing context<\/strong>: Campaign tags, creative IDs, landing page group, channel group\u2014often central to Conversion & Measurement<\/strong> attribution.<\/li>\n<\/ul>\n\n\n\n
                \n\n\n\n

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

                Example 1: Ecommerce checkout optimization<\/h3>\n\n\n\n

                A retailer tracks checkout_error<\/code> events with an Event-scoped Dimension<\/strong> called error_type<\/code> (e.g., payment_declined<\/code>, address_invalid<\/code>, shipping_unavailable<\/code>). In Analytics<\/strong>, they segment checkout completion rate by error_type<\/code> and discover a spike in payment_declined<\/code> on one payment method. In Conversion & Measurement<\/strong>, that finding justifies prioritizing a payment provider fix over generic CRO changes.<\/p>\n\n\n\n

                Example 2: SaaS signup funnel and intent quality<\/h3>\n\n\n\n

                A SaaS company tracks trial_start<\/code> with Event-scoped Dimension<\/strong> fields plan_selected<\/code>, signup_method<\/code> (email\/SSO), and cta_location<\/code> (pricing page header vs product tour). In Analytics<\/strong>, they compare downstream activation (e.g., first key action) by cta_location<\/code> to learn which entry point brings higher-quality trials. In Conversion & Measurement<\/strong>, they shift spend toward campaigns that land users on the better-performing CTA path.<\/p>\n\n\n\n

                Example 3: Content engagement and lead capture<\/h3>\n\n\n\n

                A publisher tracks newsletter_subscribe<\/code> with Event-scoped Dimension<\/strong> fields article_category<\/code>, paywall_state<\/code> (free\/soft\/hard), and form_variant<\/code> (A\/B test variant). In Analytics<\/strong>, they evaluate subscription rate by article_category<\/code> and form_variant<\/code>, then apply the best-performing variant to high-opportunity categories. This connects editorial decisions directly to Conversion & Measurement<\/strong> outcomes.<\/p>\n\n\n\n

                \n\n\n\n

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

                When implemented thoughtfully, an Event-scoped Dimension<\/strong> creates measurable improvements in day-to-day marketing and product work:<\/p>\n\n\n\n

                  \n
                • More precise funnel insights:<\/strong> You can identify which step variant or UI element drives drop-off, not just that drop-off exists.<\/li>\n
                • Better attribution narratives:<\/strong> In Conversion & Measurement<\/strong>, event-level context helps explain why<\/em> a channel performs well (or poorly), beyond last-touch summaries.<\/li>\n
                • Faster debugging and incident response:<\/strong> Error or latency dimensions shorten time-to-detection and time-to-fix, protecting revenue.<\/li>\n
                • Improved experimentation readouts:<\/strong> Event segmentation by variant, placement, or message reduces ambiguity in A\/B test results.<\/li>\n
                • Operational efficiency:<\/strong> Cleaner Analytics<\/strong> reduces manual reporting, reconciliation meetings, and ad hoc \u201cwhat changed?\u201d investigations.<\/li>\n
                • Audience and experience improvements:<\/strong> Knowing event context supports smarter personalization and lifecycle messaging\u2014without relying on guesswork.<\/li>\n<\/ul>\n\n\n\n
                  \n\n\n\n

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

                  An Event-scoped Dimension<\/strong> can also introduce complexity, especially as event volume and stakeholders grow.<\/p>\n\n\n\n

                  Common challenges include:<\/p>\n\n\n\n

                    \n
                  • Inconsistent naming and value hygiene:<\/strong> Slight differences (HeaderCTA<\/code> vs header_cta<\/code>) fragment reporting and weaken Conversion & Measurement<\/strong> conclusions.<\/li>\n
                  • High-cardinality blowups:<\/strong> Capturing uncontrolled text (full URLs, free-form inputs) can degrade reporting usefulness and complicate governance.<\/li>\n
                  • Missing or null values:<\/strong> Dimensions that are optional or poorly implemented create biased analyses in Analytics<\/strong>, because \u201cunknown\u201d may correlate with specific devices, browsers, or flows.<\/li>\n
                  • Cross-domain and cross-device complexity:<\/strong> Event context can be lost when users move between systems or devices, creating gaps in measurement.<\/li>\n
                  • Privacy and compliance constraints:<\/strong> Event-level data can become sensitive if it contains personal or uniquely identifying information. That risk must be managed with strict policies and minimization.<\/li>\n
                  • Stakeholder misinterpretation:<\/strong> Teams may treat event-scoped values as user traits, leading to incorrect segmentation and misguided optimization.<\/li>\n<\/ul>\n\n\n\n
                    \n\n\n\n

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

                    Strong Event-scoped Dimension<\/strong> practice is as much about governance as it is about tagging.<\/p>\n\n\n\n

                      \n
                    1. \n

                      Start from decisions, not data<\/strong>\n Define the business questions first: \u201cWhich CTA location produces the highest trial-to-activation rate?\u201d Then design the Event-scoped Dimension<\/strong> fields needed to answer them.<\/p>\n<\/li>\n

                    2. \n

                      Use a consistent taxonomy<\/strong>\n Standardize event names and dimension keys. Keep them readable, stable, and documented so Analytics<\/strong> consumers interpret them the same way.<\/p>\n<\/li>\n

                    3. \n

                      Control cardinality deliberately<\/strong>\n Prefer enumerated values over raw text when possible (e.g., cta_location<\/code> as a small set of known positions). For necessary high-cardinality fields, consider hashing, bucketing, or alternative storage outside standard reporting.<\/p>\n<\/li>\n

                    4. \n

                      Validate at collection time<\/strong>\n Add QA checks: required fields for key conversion events, allowed values, and formatting rules. This protects Conversion & Measurement<\/strong> reporting from silent breakage.<\/p>\n<\/li>\n

                    5. \n

                      Version changes and communicate<\/strong>\n If a UX redesign changes what cta_location<\/code> means, version it or document a clear \u201ceffective date\u201d so trend analysis remains trustworthy in Analytics<\/strong>.<\/p>\n<\/li>\n

                    6. \n

                      Minimize sensitive data<\/strong>\n Avoid capturing personally identifiable information in event dimensions. Apply consent-aware collection and retention policies aligned with privacy requirements.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                      \n\n\n\n

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

                      An Event-scoped Dimension<\/strong> typically spans multiple tool categories, even in vendor-neutral stacks:<\/p>\n\n\n\n

                        \n
                      • Analytics tools:<\/strong> Event exploration, funnel reporting, segmentation, cohorting, and anomaly detection using event-level dimensions.<\/li>\n
                      • Tag management and SDK tooling:<\/strong> Web tags, mobile SDKs, and server-side event collection that attach parameters at event time.<\/li>\n
                      • Data warehouses and pipelines:<\/strong> Storage of raw event logs, transformations to create derived dimensions, and modeling for Conversion & Measurement<\/strong> dashboards.<\/li>\n
                      • Reporting dashboards \/ BI:<\/strong> Operational dashboards that track conversion KPIs by Event-scoped Dimension values (e.g., by form variant or checkout step).<\/li>\n
                      • CRM and marketing automation:<\/strong> Using event context to route leads, trigger lifecycle messaging, or suppress irrelevant campaigns.<\/li>\n
                      • Ad platforms and measurement layers:<\/strong> Using campaign and creative metadata as Event-scoped Dimension<\/strong> inputs for attribution and incrementality analysis.<\/li>\n
                      • SEO tools (supporting role):<\/strong> While SEO platforms aren\u2019t usually where event dimensions live, they help interpret landing page behavior when Analytics<\/strong> ties event conversions to page groups and query intent.<\/li>\n<\/ul>\n\n\n\n
                        \n\n\n\n

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

                        An Event-scoped Dimension<\/strong> is most powerful when it segments metrics that matter. Common metrics to analyze by<\/em> event-scoped values include:<\/p>\n\n\n\n

                          \n
                        • Conversion & Measurement core metrics:<\/strong> conversion rate, cost per conversion, revenue per visitor, average order value, lead-to-qualified rate.<\/li>\n
                        • Funnel metrics:<\/strong> step completion rate, abandonment rate by step name, time between steps, re-entry rate.<\/li>\n
                        • Engagement metrics:<\/strong> click-through rate on CTAs by placement, video completion rate by content type, scroll depth event rate by template.<\/li>\n
                        • Quality metrics:<\/strong> refund rate by coupon, churn risk signals by onboarding path (where appropriate and privacy-safe).<\/li>\n
                        • Operational metrics:<\/strong> error rate by error type, latency percentiles by device category, failed payment rate by method.<\/li>\n<\/ul>\n\n\n\n

                          The key is interpretability: the Event-scoped Dimension<\/strong> should explain why<\/em> the metric changes, not just correlate with it.<\/p>\n\n\n\n

                          \n\n\n\n

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

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

                            \n
                          • Privacy-driven minimization and consent-aware measurement:<\/strong> Expect more emphasis on capturing only what is necessary, with stricter governance around event-level fields.<\/li>\n
                          • Server-side and first-party collection patterns:<\/strong> More teams will capture event context on the server to improve reliability and reduce client-side loss, changing how Analytics<\/strong> pipelines validate dimensions.<\/li>\n
                          • Automation in schema management:<\/strong> Tooling is improving for detecting new parameters, flagging cardinality spikes, and enforcing tracking contracts\u2014reducing manual oversight.<\/li>\n
                          • AI-assisted analysis (not autopilot measurement):<\/strong> AI will increasingly help interpret event-scoped breakdowns, surface anomalies, and propose hypotheses, while teams still need disciplined dimension design to avoid misleading outputs.<\/li>\n
                          • Personalization and experimentation maturity:<\/strong> As testing becomes continuous, Event-scoped Dimension<\/strong> fields like variant_id<\/code>, feature_flag<\/code>, and experience_group<\/code> will be central to Conversion & Measurement<\/strong> learning loops.<\/li>\n<\/ul>\n\n\n\n
                            \n\n\n\n

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

                            Clarity on scope prevents reporting mistakes in Analytics<\/strong>.<\/p>\n\n\n\n

                            Event-scoped Dimension vs User-scoped dimension<\/h3>\n\n\n\n

                            A user-scoped dimension describes a persistent attribute of the user (e.g., membership tier). An Event-scoped Dimension<\/strong> describes the context of a single interaction (e.g., which CTA was clicked). If you treat an event-scoped value as a user trait, you can mistakenly attribute later conversions to the wrong experience.<\/p>\n\n\n\n

                            Event-scoped Dimension vs Session-scoped dimension<\/h3>\n\n\n\n

                            A session-scoped dimension applies to a bounded visit or session (e.g., session source\/medium). An Event-scoped Dimension<\/strong> can vary multiple times within the same session (e.g., multiple cta_location<\/code> clicks). In Conversion & Measurement<\/strong>, you often need both: session context for acquisition, event context for UX optimization.<\/p>\n\n\n\n

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

                            A metric is numeric (counts, revenue, time). A dimension is descriptive (labels, categories). An Event-scoped Dimension<\/strong> doesn\u2019t measure performance by itself; it explains<\/em> performance by slicing metrics into meaningful groups in Analytics<\/strong>.<\/p>\n\n\n\n

                            \n\n\n\n

                            Who Should Learn Event-scoped Dimension<\/h2>\n\n\n\n
                              \n
                            • Marketers:<\/strong> To improve Conversion & Measurement<\/strong> by understanding which messages, placements, and offers drive action\u2014not just which channels deliver traffic.<\/li>\n
                            • Analysts:<\/strong> To design clean segmentation, avoid scope errors, and create trustworthy Analytics<\/strong> dashboards and narratives.<\/li>\n
                            • Agencies:<\/strong> To implement scalable measurement frameworks across clients, proving impact with defensible event-level insights.<\/li>\n
                            • Business owners and founders:<\/strong> To connect product changes and marketing spend to real conversion drivers, reducing reliance on intuition.<\/li>\n
                            • Developers:<\/strong> To implement event schemas, maintain tracking contracts, and ensure event context is captured accurately and safely.<\/li>\n<\/ul>\n\n\n\n
                              \n\n\n\n

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

                              An Event-scoped Dimension<\/strong> is an event-level attribute that describes the context of a specific user interaction. It matters because modern Conversion & Measurement<\/strong> depends on understanding which<\/em> experiences cause outcomes, not just whether outcomes happened. In Analytics<\/strong>, event-scoped dimensions enable segmentation, funnel diagnostics, experimentation analysis, and clearer attribution. When governed well\u2014consistent naming, controlled values, strong QA\u2014they become one of the highest-leverage building blocks in an event-based measurement strategy.<\/p>\n\n\n\n

                              \n\n\n\n

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

                              1) What is an Event-scoped Dimension in simple terms?<\/h3>\n\n\n\n

                              An Event-scoped Dimension<\/strong> is a label attached to a single event occurrence\u2014like the button text on a click or the error type on a failed checkout\u2014so you can break down Analytics<\/strong> metrics by the context of that interaction.<\/p>\n\n\n\n

                              2) How does Event-scoped Dimension improve Conversion & Measurement?<\/h3>\n\n\n\n

                              It helps you identify which specific variants (CTA location, form type, plan selected, coupon used) drive conversions or cause drop-offs, making optimization work more precise and less speculative.<\/p>\n\n\n\n

                              3) What\u2019s the difference between event-scoped and user-scoped dimensions?<\/h3>\n\n\n\n

                              User-scoped dimensions describe the user over time; an Event-scoped Dimension<\/strong> describes one interaction at one moment. Confusing the two can produce incorrect conclusions in Analytics<\/strong> reporting.<\/p>\n\n\n\n

                              4) Can Event-scoped Dimension values change for the same user?<\/h3>\n\n\n\n

                              Yes. That\u2019s the point of event scope. The same user can click different CTAs, submit different forms, or encounter different errors, each with different Event-scoped Dimension<\/strong> values.<\/p>\n\n\n\n

                              5) What should I avoid capturing as an Event-scoped Dimension?<\/h3>\n\n\n\n

                              Avoid personal or uniquely identifying data, uncontrolled free text, and excessively high-cardinality values unless you have a clear need and governance plan. This protects privacy and keeps Conversion & Measurement<\/strong> reports usable.<\/p>\n\n\n\n

                              6) How do I choose which Event-scoped Dimension fields to implement first?<\/h3>\n\n\n\n

                              Start with your highest-impact conversion events (purchase, lead submit, trial start) and add dimensions that answer immediate optimization questions\u2014like cta_location<\/code>, form_variant<\/code>, checkout_step<\/code>, or payment_method<\/code>\u2014then expand as your Analytics<\/strong> maturity grows.<\/p>\n","protected":false},"excerpt":{"rendered":"

                              An **Event-scoped Dimension** is a way to describe *what was true about a specific interaction at the moment it happened*. In **Conversion & Measurement**, that matters because most decisions\u2014creative, targeting, UX, offer design, funnel fixes\u2014depend on understanding *which* events occurred and *under what conditions*.<\/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-6861","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\/6861","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=6861"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/6861\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=6861"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=6861"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=6861"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}