{"id":6992,"date":"2026-03-23T20:24:20","date_gmt":"2026-03-23T20:24:20","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/analytics-benchmark\/"},"modified":"2026-03-23T20:24:20","modified_gmt":"2026-03-23T20:24:20","slug":"analytics-benchmark","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/analytics-benchmark\/","title":{"rendered":"Analytics Benchmark: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Analytics"},"content":{"rendered":"\n

An Analytics Benchmark<\/strong> is a reference point you use to judge whether performance is strong, weak, improving, or declining. In Conversion & Measurement<\/strong>, it turns raw numbers into decisions by answering a practical question: \u201cCompared to what?\u201d<\/em> Without a benchmark, metrics like conversion rate, cost per lead, or retention can look \u201cgood\u201d or \u201cbad\u201d based on gut feel rather than evidence.<\/p>\n\n\n\n

In modern Analytics<\/strong>, benchmarking is essential because marketing runs across many channels, devices, and journeys. An Analytics Benchmark<\/strong> helps teams set realistic targets, detect issues early, prioritize optimization work, and communicate performance clearly to stakeholders. It\u2019s one of the most reliable ways to connect measurement to action.<\/p>\n\n\n\n

\n\n\n\n

What Is Analytics Benchmark?<\/h2>\n\n\n\n

An Analytics Benchmark<\/strong> is a standard or comparison baseline used to evaluate performance over time, across segments, or against peers. It can be built from your own historical data (internal) or sourced from industry, market, or aggregated datasets (external). The core concept is simple: a metric becomes meaningful when you compare it to a stable reference.<\/p>\n\n\n\n

From a business perspective, an Analytics Benchmark<\/strong> supports decisions such as:<\/p>\n\n\n\n

    \n
  • Whether a campaign is scalable<\/li>\n
  • Whether a landing page change improved outcomes<\/li>\n
  • Whether a channel is underperforming relative to expectations<\/li>\n
  • Whether a new market launch is tracking to plan<\/li>\n<\/ul>\n\n\n\n

    In Conversion & Measurement<\/strong>, benchmarks typically anchor goal setting and optimization. They help define what \u201csuccess\u201d looks like for acquisition, activation, revenue, and retention. Within Analytics<\/strong>, benchmarking is the layer that transforms reporting into performance management.<\/p>\n\n\n\n

    \n\n\n\n

    Why Analytics Benchmark Matters in Conversion & Measurement<\/h2>\n\n\n\n

    In Conversion & Measurement<\/strong>, teams often have plenty of data but limited clarity. An Analytics Benchmark<\/strong> provides that clarity by creating context.<\/p>\n\n\n\n

    Key reasons it matters:<\/p>\n\n\n\n

      \n
    • Strategic focus:<\/strong> Benchmarks turn \u201cwe need more leads\u201d into \u201cwe need to raise qualified lead conversion from 2.1% to 2.6% in the next quarter,\u201d which is more actionable.<\/li>\n
    • Business value:<\/strong> When you benchmark conversion and cost metrics, you can quantify the revenue impact of incremental improvements.<\/li>\n
    • Marketing outcomes:<\/strong> Benchmarks help identify which channel, audience, or funnel step deserves optimization\u2014especially when multiple metrics move at once.<\/li>\n
    • Competitive advantage:<\/strong> External benchmarking can reveal where you\u2019re behind the market and where you\u2019re already strong, guiding investment and messaging.<\/li>\n<\/ul>\n\n\n\n

      In short, an Analytics Benchmark<\/strong> helps you stop debating opinions and start aligning teams around measurable performance in Analytics<\/strong> programs.<\/p>\n\n\n\n

      \n\n\n\n

      How Analytics Benchmark Works<\/h2>\n\n\n\n

      An Analytics Benchmark<\/strong> is both conceptual and practical. In day-to-day Conversion & Measurement<\/strong>, it usually follows a simple workflow:<\/p>\n\n\n\n

        \n
      1. \n

        Input (define the scope and metric)<\/strong>\n – Choose a KPI (e.g., purchase conversion rate, lead-to-opportunity rate, churn, ROAS).\n – Define the context: channel, device, region, product line, or funnel stage.\n – Ensure tracking and definitions are consistent (what counts as a \u201cconversion\u201d?).<\/p>\n<\/li>\n

      2. \n

        Processing (create the benchmark)<\/strong>\n – Internal approach: compute baseline using historical performance (e.g., last 90 days, last quarter, same season last year).\n – External approach: compare to industry norms or peer sets (when available and methodologically compatible).\n – Segment benchmarks: establish different baselines by traffic type, intent, or audience quality.<\/p>\n<\/li>\n

      3. \n

        Execution (apply the benchmark to decisions)<\/strong>\n – Set targets and guardrails (e.g., \u201cPause ad sets below X after Y spend.\u201d)\n – Prioritize experiments where the gap to benchmark is largest and fixable.\n – Diagnose funnel issues by comparing steps (view \u2192 click \u2192 add-to-cart \u2192 purchase).<\/p>\n<\/li>\n

      4. \n

        Output (interpretation and action)<\/strong>\n – A performance narrative: \u201cWe\u2019re above benchmark on CTR but below benchmark on landing-page conversion.\u201d\n – A plan: optimize page speed, adjust offer, refine targeting, or improve lead qualification.\n – Monitoring: alerts and dashboards that show deviation from benchmark over time.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        This is why an Analytics Benchmark<\/strong> is not just a number\u2014it\u2019s a repeatable decision system inside Analytics<\/strong> and Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

        \n\n\n\n

        Key Components of Analytics Benchmark<\/h2>\n\n\n\n

        A reliable Analytics Benchmark<\/strong> depends on more than a single report. The strongest programs include:<\/p>\n\n\n\n

        Data and tracking foundations<\/h3>\n\n\n\n
          \n
        • Clear event\/goal definitions (e.g., signup, purchase, qualified lead)<\/li>\n
        • Consistent attribution and conversion windows<\/li>\n
        • Data quality checks (missing tags, duplicate events, bot filtering)<\/li>\n
        • Stable naming conventions for campaigns and content<\/li>\n<\/ul>\n\n\n\n

          Metrics and segmentation<\/h3>\n\n\n\n
            \n
          • A small set of primary KPIs and supporting diagnostics<\/li>\n
          • Benchmarks by segment (new vs returning, brand vs non-brand, mobile vs desktop)<\/li>\n
          • Funnel benchmarks at each step, not only at the final conversion<\/li>\n<\/ul>\n\n\n\n

            Process and governance<\/h3>\n\n\n\n
              \n
            • Ownership (who maintains the benchmark and approves definition changes)<\/li>\n
            • Cadence (weekly monitoring, monthly reviews, quarterly recalibration)<\/li>\n
            • Documentation (how metrics are calculated, exclusions, known limitations)<\/li>\n<\/ul>\n\n\n\n

              Reporting and decisioning<\/h3>\n\n\n\n
                \n
              • Dashboards that show benchmark lines and variance<\/li>\n
              • Threshold rules for investigation (e.g., \u201c10% below benchmark for 3 days\u201d)<\/li>\n
              • Experiment tracking to connect changes to movements against the benchmark<\/li>\n<\/ul>\n\n\n\n

                These components help ensure Analytics Benchmark<\/strong> work supports trustworthy Conversion & Measurement<\/strong>, not just more charts.<\/p>\n\n\n\n

                \n\n\n\n

                Types of Analytics Benchmark<\/h2>\n\n\n\n

                While \u201cAnalytics Benchmark\u201d isn\u2019t a single rigid methodology, there are practical categories used in Analytics<\/strong>:<\/p>\n\n\n\n

                1) Internal (historical) benchmarks<\/h3>\n\n\n\n

                Built from your own prior performance. These are often the most actionable because they reflect your audience, pricing, funnel, and constraints.<\/p>\n\n\n\n

                2) External (industry\/peer) benchmarks<\/h3>\n\n\n\n

                Based on aggregated market data. Useful for direction and context, but risky if definitions differ (conversion event, attribution window, traffic mix).<\/p>\n\n\n\n

                3) Baseline vs target benchmarks<\/h3>\n\n\n\n
                  \n
                • Baseline benchmark:<\/strong> current reality (e.g., trailing 28-day average).<\/li>\n
                • Target benchmark:<\/strong> desired performance tied to business goals (e.g., \u201cincrease trial-to-paid by 15%\u201d).<\/li>\n<\/ul>\n\n\n\n

                  4) Segment-specific benchmarks<\/h3>\n\n\n\n

                  Different baselines for different conditions, such as:\n– Paid search vs organic\n– Brand vs non-brand\n– Mobile vs desktop\n– New vs returning users\nThese are often critical in Conversion & Measurement<\/strong>, because blended averages can hide real problems.<\/p>\n\n\n\n

                  \n\n\n\n

                  Real-World Examples of Analytics Benchmark<\/h2>\n\n\n\n

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

                  A retailer uses an Analytics Benchmark<\/strong> for each funnel step: product view \u2192 add-to-cart \u2192 checkout start \u2192 purchase. They notice mobile checkout completion is 20% below benchmark while desktop is stable. In Conversion & Measurement<\/strong>, that points to a device-specific friction (payment options, form usability, page speed). In Analytics<\/strong>, they validate the issue with session segmentation and then A\/B test a streamlined checkout.<\/p>\n\n\n\n

                  Example 2: B2B lead quality and sales alignment<\/h3>\n\n\n\n

                  A SaaS company benchmarks lead-to-meeting rate and meeting-to-opportunity rate by channel. Paid social hits cost-per-lead targets but sits below the Analytics Benchmark<\/strong> for sales-qualified conversion. This reframes the discussion: the problem isn\u2019t volume, it\u2019s qualification. In Conversion & Measurement<\/strong>, they adjust targeting and the lead form to capture intent signals; in Analytics<\/strong>, they track downstream CRM stages.<\/p>\n\n\n\n

                  Example 3: Content performance benchmarking for SEO<\/h3>\n\n\n\n

                  A publisher sets an Analytics Benchmark<\/strong> for organic content: engagement rate, scroll depth, newsletter signups per 1,000 sessions, and returning visitor rate. New articles that beat the benchmark on engagement but underperform on signups trigger CTA and offer testing. This ties content work directly to Conversion & Measurement<\/strong> outcomes using Analytics<\/strong> rather than traffic-only reporting.<\/p>\n\n\n\n

                  \n\n\n\n

                  Benefits of Using Analytics Benchmark<\/h2>\n\n\n\n

                  A well-maintained Analytics Benchmark<\/strong> delivers practical upside:<\/p>\n\n\n\n

                    \n
                  • Performance improvements:<\/strong> You spot underperformance faster and focus optimizations where the benchmark gap is meaningful.<\/li>\n
                  • Cost savings:<\/strong> Benchmarks help cut spend on low-quality traffic and reduce wasted experimentation.<\/li>\n
                  • Efficiency gains:<\/strong> Teams stop rebuilding \u201cwhat good looks like\u201d in every meeting; the benchmark becomes shared language.<\/li>\n
                  • Better customer experience:<\/strong> Funnel benchmarking reveals friction points (slow pages, confusing forms, weak offers), improving user journeys and conversions.<\/li>\n<\/ul>\n\n\n\n

                    Because it strengthens prioritization, an Analytics Benchmark<\/strong> becomes a central asset in Conversion & Measurement<\/strong> strategy.<\/p>\n\n\n\n

                    \n\n\n\n

                    Challenges of Analytics Benchmark<\/h2>\n\n\n\n

                    Benchmarking fails when context or definitions are unstable. Common obstacles include:<\/p>\n\n\n\n

                      \n
                    • Inconsistent definitions:<\/strong> If \u201cconversion\u201d changes between teams or tools, your Analytics Benchmark<\/strong> becomes misleading.<\/li>\n
                    • Attribution and window differences:<\/strong> Comparing metrics across channels without aligned attribution logic can distort conclusions in Analytics<\/strong>.<\/li>\n
                    • Seasonality and promotions:<\/strong> A benchmark built in peak season may be unrealistic in off-season (and vice versa).<\/li>\n
                    • Sample size and noise:<\/strong> Small datasets can swing wildly; benchmarks need enough volume to be statistically credible.<\/li>\n
                    • Vanity benchmarks:<\/strong> Choosing easy-to-improve metrics (like clicks) rather than meaningful outcomes (like qualified revenue) weakens Conversion & Measurement<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                      The fix is not to abandon benchmarking, but to document assumptions and recalibrate responsibly.<\/p>\n\n\n\n

                      \n\n\n\n

                      Best Practices for Analytics Benchmark<\/h2>\n\n\n\n

                      To make an Analytics Benchmark<\/strong> durable and decision-ready:<\/p>\n\n\n\n

                        \n
                      1. \n

                        Benchmark outcomes first, then drivers<\/strong>\n – Start with conversions, revenue, qualified leads, retention.\n – Add diagnostic metrics (CTR, bounce, speed) to explain movement.<\/p>\n<\/li>\n

                      2. \n

                        Use segmented benchmarks, not only blended averages<\/strong>\n – Separate benchmarks by channel, intent, and device to avoid hiding problems.<\/p>\n<\/li>\n

                      3. \n

                        Define time horizons clearly<\/strong>\n – Use trailing 28 days for operational monitoring and year-over-year views for seasonality.\n – In Conversion & Measurement<\/strong>, align the horizon to buying cycles and sales velocity.<\/p>\n<\/li>\n

                      4. \n

                        Document metric definitions and exclusions<\/strong>\n – Note attribution logic, conversion windows, bot filtering, internal traffic rules, and refunds\/chargebacks handling.<\/p>\n<\/li>\n

                      5. \n

                        Treat benchmarks as living references<\/strong>\n – Recalibrate when tracking changes, pricing changes, major channel mix shifts, or product updates occur.<\/p>\n<\/li>\n

                      6. \n

                        Connect benchmarks to actions<\/strong>\n – Create threshold-based playbooks: what to investigate when a KPI is 10\u201320% off the Analytics Benchmark<\/strong>.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                        \n\n\n\n

                        Tools Used for Analytics Benchmark<\/h2>\n\n\n\n

                        An Analytics Benchmark<\/strong> is typically operationalized across a tool stack rather than a single platform. Common tool categories include:<\/p>\n\n\n\n

                          \n
                        • Analytics tools:<\/strong> Web\/app measurement platforms to define events, build funnels, segment users, and track cohorts for Conversion & Measurement<\/strong>.<\/li>\n
                        • Reporting dashboards:<\/strong> BI and visualization layers to show benchmark lines, variance, and drill-downs for stakeholders.<\/li>\n
                        • Tag management and data pipelines:<\/strong> Systems that standardize event collection and move data into warehouses for deeper Analytics<\/strong>.<\/li>\n
                        • Ad platforms:<\/strong> Channel reporting for spend, impressions, clicks, and conversion signals\u2014especially important when benchmarking efficiency.<\/li>\n
                        • CRM systems:<\/strong> Down-funnel benchmarking (lead quality, pipeline stages, win rate) that completes the Conversion & Measurement<\/strong> story.<\/li>\n
                        • SEO tools:<\/strong> Keyword, content, and visibility monitoring to support organic benchmarks beyond sessions alone.<\/li>\n<\/ul>\n\n\n\n

                          The key is consistency: benchmarks are only as trustworthy as the definitions and data flows behind them.<\/p>\n\n\n\n

                          \n\n\n\n

                          Metrics Related to Analytics Benchmark<\/h2>\n\n\n\n

                          The best metrics depend on your model, but these are common benchmarked indicators in Analytics<\/strong> and Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                          Conversion and revenue metrics<\/h3>\n\n\n\n
                            \n
                          • Conversion rate (by funnel stage)<\/li>\n
                          • Revenue per session \/ revenue per visitor<\/li>\n
                          • Average order value (AOV) and purchase frequency<\/li>\n
                          • Lead-to-MQL, MQL-to-SQL, SQL-to-win rates (for B2B funnels)<\/li>\n<\/ul>\n\n\n\n

                            Efficiency and ROI metrics<\/h3>\n\n\n\n
                              \n
                            • Cost per acquisition (CPA) or cost per lead (CPL)<\/li>\n
                            • Return on ad spend (ROAS) and marketing ROI<\/li>\n
                            • Customer acquisition cost (CAC) and CAC payback period<\/li>\n<\/ul>\n\n\n\n

                              Engagement and quality metrics<\/h3>\n\n\n\n
                                \n
                              • Engagement rate, time on site\/app, scroll depth (context-dependent)<\/li>\n
                              • Returning visitor rate and cohort retention<\/li>\n
                              • Form completion rate and checkout abandonment<\/li>\n<\/ul>\n\n\n\n

                                Experience and technical metrics<\/h3>\n\n\n\n
                                  \n
                                • Page speed and key performance timings (especially on mobile)<\/li>\n
                                • Error rates in critical flows (payment failures, form validation errors)<\/li>\n<\/ul>\n\n\n\n

                                  Benchmarking these metrics makes trend shifts immediately visible and supports faster root-cause analysis.<\/p>\n\n\n\n

                                  \n\n\n\n

                                  Future Trends of Analytics Benchmark<\/h2>\n\n\n\n

                                  Analytics Benchmark<\/strong> practices are evolving as measurement becomes more privacy-aware and automation-driven:<\/p>\n\n\n\n

                                    \n
                                  • AI-assisted benchmarking:<\/strong> Models can forecast expected performance ranges and flag anomalies beyond simple averages, improving monitoring in Conversion & Measurement<\/strong>.<\/li>\n
                                  • More emphasis on first-party data:<\/strong> As tracking becomes constrained, internal benchmarks and cohort analysis become more valuable than broad external norms.<\/li>\n
                                  • Incrementality and experimentation:<\/strong> Benchmarks will increasingly incorporate lift testing and causal methods to validate whether changes truly drive outcomes in Analytics<\/strong>.<\/li>\n
                                  • Personalization and segment explosion:<\/strong> As experiences personalize, teams need benchmark frameworks that handle many segments without drowning in dashboards.<\/li>\n
                                  • Measurement resilience:<\/strong> Benchmarks will be designed to survive tracking gaps\u2014using multiple signals (CRM outcomes, modeled conversions, server-side events) to maintain continuity.<\/li>\n<\/ul>\n\n\n\n

                                    The direction is clear: Analytics Benchmark<\/strong> will become more predictive, more segmented, and more integrated with decision automation.<\/p>\n\n\n\n

                                    \n\n\n\n

                                    Analytics Benchmark vs Related Terms<\/h2>\n\n\n\n

                                    Analytics Benchmark vs KPI<\/h3>\n\n\n\n

                                    A KPI is what you measure; an Analytics Benchmark<\/strong> is the reference you compare it to. For example, \u201ctrial-to-paid conversion rate\u201d is a KPI; \u201clast quarter\u2019s median trial-to-paid rate by channel\u201d is a benchmark.<\/p>\n\n\n\n

                                    Analytics Benchmark vs Baseline<\/h3>\n\n\n\n

                                    A baseline is often the starting point before a change (e.g., pre-test performance). An Analytics Benchmark<\/strong> can be a baseline, but it can also be an industry norm, a target threshold, or a segmented standard used continuously in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                    Analytics Benchmark vs Goal\/Target<\/h3>\n\n\n\n

                                    A goal is the desired outcome; an Analytics Benchmark<\/strong> is the comparison point used to judge progress. Strong Analytics<\/strong> teams use both: benchmarks for context and targets for direction.<\/p>\n\n\n\n

                                    \n\n\n\n

                                    Who Should Learn Analytics Benchmark<\/h2>\n\n\n\n
                                      \n
                                    • Marketers:<\/strong> To set realistic targets, evaluate channel performance, and justify budget shifts using Conversion & Measurement<\/strong> evidence.<\/li>\n
                                    • Analysts:<\/strong> To create trustworthy comparisons, detect anomalies, and communicate insights clearly within Analytics<\/strong> reporting.<\/li>\n
                                    • Agencies:<\/strong> To standardize client performance reviews, avoid misleading comparisons, and show improvement against consistent baselines.<\/li>\n
                                    • Business owners and founders:<\/strong> To understand whether growth is healthy, scalable, and efficient\u2014not just increasing in raw volume.<\/li>\n
                                    • Developers and data teams:<\/strong> To implement consistent event tracking, maintain data quality, and support scalable benchmarking pipelines in Analytics<\/strong>.<\/li>\n<\/ul>\n\n\n\n
                                      \n\n\n\n

                                      Summary of Analytics Benchmark<\/h2>\n\n\n\n

                                      An Analytics Benchmark<\/strong> is a defined reference point used to evaluate performance meaningfully. It matters because Conversion & Measurement<\/strong> without benchmarks becomes guesswork\u2014numbers lack context, and teams struggle to prioritize. Within Analytics<\/strong>, benchmarking connects data to decisions by standardizing comparisons across time, segments, and channels. When built on consistent definitions and strong data quality, an Analytics Benchmark<\/strong> improves performance management, reduces wasted spend, and accelerates optimization.<\/p>\n\n\n\n

                                      \n\n\n\n

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

                                      1) What is an Analytics Benchmark in simple terms?<\/h3>\n\n\n\n

                                      An Analytics Benchmark<\/strong> is a comparison standard\u2014like your past performance, a target threshold, or an industry reference\u2014that helps you interpret whether a metric is doing well or poorly.<\/p>\n\n\n\n

                                      2) Should I use internal or external benchmarks?<\/h3>\n\n\n\n

                                      Start with internal benchmarks because they reflect your audience and funnel. Use external benchmarks cautiously for directional context, and only when definitions (conversion event, attribution, time window) are comparable in Analytics<\/strong>.<\/p>\n\n\n\n

                                      3) How often should I update an Analytics Benchmark?<\/h3>\n\n\n\n

                                      Operational benchmarks are often reviewed monthly and recalibrated quarterly, but update sooner if tracking, pricing, channel mix, or the product changes significantly\u2014especially in Conversion & Measurement<\/strong> programs.<\/p>\n\n\n\n

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

                                      Comparing apples to oranges: mixing channels, devices, or attribution rules and treating the result as a single truth. Segmentation and documentation prevent misleading benchmark conclusions.<\/p>\n\n\n\n

                                      5) How do I benchmark when I don\u2019t have much data?<\/h3>\n\n\n\n

                                      Use longer time windows, focus on higher-volume leading indicators (like step-level funnel conversion), and avoid overreacting to small swings. In Analytics<\/strong>, you can also group similar campaigns or pages to reach sufficient sample size.<\/p>\n\n\n\n

                                      6) How does Analytics benchmarking help improve conversions?<\/h3>\n\n\n\n

                                      Benchmarking highlights where the funnel deviates from expected performance (for example, checkout completion below benchmark). That directs testing and fixes to the highest-impact friction points in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                      7) Which metrics should I benchmark first?<\/h3>\n\n\n\n

                                      Begin with business outcomes: conversions, qualified leads, revenue, CAC\/CPA, and retention. Then benchmark driver metrics (CTR, landing-page conversion, speed) to diagnose why<\/em> outcomes changed in Analytics<\/strong>.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                      An **Analytics Benchmark** is a reference point you use to judge whether performance is strong, weak, improving, or declining. In **Conversion & Measurement**, it turns raw numbers into decisions by answering a practical question: *\u201cCompared to what?\u201d* Without a benchmark, metrics like conversion rate, cost per lead, or retention can look \u201cgood\u201d or \u201cbad\u201d based on gut feel rather than evidence.<\/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-6992","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\/6992","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=6992"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/6992\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=6992"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=6992"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=6992"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}