{"id":9208,"date":"2026-03-27T12:53:09","date_gmt":"2026-03-27T12:53:09","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/engagement-benchmark\/"},"modified":"2026-03-27T12:53:09","modified_gmt":"2026-03-27T12:53:09","slug":"engagement-benchmark","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/engagement-benchmark\/","title":{"rendered":"Engagement Benchmark: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Influencer Marketing"},"content":{"rendered":"\n

An Engagement Benchmark<\/strong> is a reference point that helps you judge whether your content performance is strong, average, or weak compared with a relevant standard. In Organic Marketing<\/strong>, it\u2019s the difference between \u201cthis post got 500 likes\u201d and \u201cthis post performed 35% above our typical results for this platform, audience size, and content type.\u201d In Influencer Marketing<\/strong>, it becomes even more important because you\u2019re evaluating creators who have different audiences, formats, and posting habits\u2014so raw engagement counts can mislead.<\/p>\n\n\n\n

Modern Organic Marketing<\/strong> strategies depend on repeatable measurement. Algorithms change, audiences shift, and attention is scarce. A well-defined Engagement Benchmark<\/strong> gives teams a consistent way to set expectations, evaluate campaigns, prioritize channels, and improve creative\u2014without overreacting to one-off spikes or dips.<\/p>\n\n\n\n

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

An Engagement Benchmark<\/strong> is a baseline or comparative standard used to evaluate engagement metrics (such as likes, comments, shares, saves, clicks, replies, or watch time) for a specific context. The \u201ccontext\u201d part matters: a benchmark should reflect the platform, format, audience size, niche, and timeframe you\u2019re measuring.<\/p>\n\n\n\n

At its core, the concept answers three practical questions:<\/p>\n\n\n\n

    \n
  • What does \u201cgood engagement\u201d look like for us (or for this creator) right now?<\/strong><\/li>\n
  • How does this post\/campaign compare to a relevant peer set?<\/strong><\/li>\n
  • What should we change if performance is below the benchmark\u2014or scale if it\u2019s above?<\/strong><\/li>\n<\/ul>\n\n\n\n

    From a business perspective, an Engagement Benchmark<\/strong> turns engagement from a vanity metric into a management tool. It supports better decisions about content planning, influencer selection, budget allocation (even in mostly non-paid programs), and how to report outcomes to stakeholders.<\/p>\n\n\n\n

    In Organic Marketing<\/strong>, it helps you standardize evaluation across your owned channels (brand social, community, blog distribution, email engagement patterns, etc.). In Influencer Marketing<\/strong>, it helps you compare creators fairly by normalizing performance to audience size and typical behavior, rather than being impressed by follower count alone.<\/p>\n\n\n\n

    Why Engagement Benchmark Matters in Organic Marketing<\/h2>\n\n\n\n

    In Organic Marketing<\/strong>, performance is rarely linear. One week\u2019s reach can drop due to platform volatility; another week a post can take off due to timing or community dynamics. An Engagement Benchmark<\/strong> adds stability by anchoring your analysis to something consistent and relevant.<\/p>\n\n\n\n

    Strategically, it matters because it:<\/p>\n\n\n\n

      \n
    • Improves goal setting:<\/strong> Teams can define realistic targets (e.g., \u201cbeat our benchmark by 10% for saves per impression\u201d) instead of vague goals like \u201cincrease engagement.\u201d<\/li>\n
    • Enables prioritization:<\/strong> If short-form video consistently outperforms the benchmark while static posts underperform, you have evidence to shift effort.<\/li>\n
    • Creates accountability:<\/strong> A benchmark makes it clear whether execution improved, not just whether a single piece of content went viral.<\/li>\n
    • Strengthens competitive advantage:<\/strong> When you benchmark against peers or industry segments, you spot gaps and opportunities earlier.<\/li>\n<\/ul>\n\n\n\n

      For Influencer Marketing<\/strong>, benchmarks help protect your spend and your brand. They reveal whether a creator\u2019s results are typical, inflated by one-off virality, or potentially driven by low-quality engagement. In short, the Engagement Benchmark<\/strong> becomes a risk-control mechanism and a performance compass for your organic growth engine.<\/p>\n\n\n\n

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

      An Engagement Benchmark<\/strong> is conceptual, but it becomes practical through a repeatable workflow:<\/p>\n\n\n\n

        \n
      1. \n

        Input (what you measure)<\/strong>\n – Collect engagement data for the platform(s) and content types you care about.\n – Define the cohort: your brand posts, a set of influencers, a competitor set, or an industry segment.\n – Choose a timeframe that reflects current reality (often the last 30\u201390 days, with longer windows for slower cycles).<\/p>\n<\/li>\n

      2. \n

        Analysis (how you normalize and compare)<\/strong>\n – Normalize engagement so comparisons are fair (e.g., engagement rate per impressions, per reach, or per follower).\n – Segment results by variables that strongly affect engagement: format (video vs carousel), topic, posting time, creator size, or campaign phase.\n – Calculate the benchmark as a median (often more stable than an average), plus ranges (e.g., 25th\u201375th percentile) to show variability.<\/p>\n<\/li>\n

      3. \n

        Execution (how you apply it)<\/strong>\n – Use the benchmark to set content targets, influencer briefs, and acceptance criteria.\n – Identify \u201cbenchmark beaters\u201d to replicate (hooks, formats, creative angles, creator styles).\n – Adjust underperforming areas with structured tests (creative iterations, stronger calls-to-action, different distribution tactics).<\/p>\n<\/li>\n

      4. \n

        Output (what decisions it enables)<\/strong>\n – Clear performance labels: below benchmark \/ at benchmark \/ above benchmark.\n – Better reporting for Organic Marketing<\/strong> and Influencer Marketing<\/strong> programs.\n – A prioritized action plan tied to measurable outcomes\u2014not opinions.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

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

        A credible Engagement Benchmark<\/strong> depends on more than a formula. The strongest programs include:<\/p>\n\n\n\n

        Data inputs<\/h3>\n\n\n\n
          \n
        • Platform-native engagement signals (likes, comments, shares, saves, replies, watch time)<\/li>\n
        • Distribution measures (impressions, reach)<\/li>\n
        • Click behavior (link clicks, profile visits, swipe-ups where applicable)<\/li>\n
        • Audience attributes (follower count, audience growth rate, geography, niche)<\/li>\n<\/ul>\n\n\n\n

          Metrics and definitions<\/h3>\n\n\n\n
            \n
          • A consistent engagement rate definition (e.g., engagements \u00f7 impressions vs engagements \u00f7 reach)<\/li>\n
          • Clear handling of video metrics (watch time vs views vs completion rate)<\/li>\n
          • Rules for what counts as an \u201cengagement\u201d across platforms<\/li>\n<\/ul>\n\n\n\n

            Process and governance<\/h3>\n\n\n\n
              \n
            • Ownership: who maintains the benchmark (growth lead, analyst, social manager)<\/li>\n
            • Update cadence: monthly or quarterly refresh for Organic Marketing<\/strong><\/li>\n
            • Documentation: a shared definition so teams don\u2019t benchmark differently<\/li>\n<\/ul>\n\n\n\n

              Reporting system<\/h3>\n\n\n\n
                \n
              • Dashboards that show trends, segments, and variance\u2014not just totals<\/li>\n
              • Annotations for campaign launches, product releases, seasonality, or algorithm shifts<\/li>\n<\/ul>\n\n\n\n

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

                There aren\u2019t universally \u201cformal\u201d types, but in practice, teams use several distinct benchmark approaches depending on the question.<\/p>\n\n\n\n

                1) Internal vs external benchmarks<\/h3>\n\n\n\n
                  \n
                • Internal benchmark:<\/strong> based on your historical performance (best for continuous improvement in Organic Marketing<\/strong>).<\/li>\n
                • External benchmark:<\/strong> based on competitors, industry norms, or creator peer groups (useful for positioning and Influencer Marketing<\/strong> selection).<\/li>\n<\/ul>\n\n\n\n

                  2) Post-level vs campaign-level benchmarks<\/h3>\n\n\n\n
                    \n
                  • Post-level:<\/strong> evaluates single pieces of content; great for creative testing.<\/li>\n
                  • Campaign-level:<\/strong> aggregates across posts and creators; better for stakeholder reporting.<\/li>\n<\/ul>\n\n\n\n

                    3) Platform- and format-specific benchmarks<\/h3>\n\n\n\n

                    A meaningful Engagement Benchmark<\/strong> is rarely \u201cone number.\u201d Short-form video, long-form video, Stories-style content, and static posts each behave differently, and Organic Marketing<\/strong> results can swing if you ignore format.<\/p>\n\n\n\n

                    4) Audience-size benchmarks (especially for influencers)<\/h3>\n\n\n\n

                    Creators with 20k followers and 2M followers tend to have different engagement dynamics. In Influencer Marketing<\/strong>, benchmarks often segment by creator tier to avoid unfair comparisons.<\/p>\n\n\n\n

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

                    Example 1: Organic content planning for a SaaS brand<\/h3>\n\n\n\n

                    A SaaS company tracks an Engagement Benchmark<\/strong> for LinkedIn posts using median engagement rate per impression, segmented by content type (product tips, customer stories, hiring\/brand, industry commentary). They discover customer stories beat the benchmark consistently, while product tips underperform unless posted as carousels. Result: the Organic Marketing<\/strong> calendar shifts toward story-led carousels, and the team sets a target to exceed the benchmark by 15% for that segment.<\/p>\n\n\n\n

                    Example 2: Influencer short-form video evaluation<\/h3>\n\n\n\n

                    A consumer brand runs Influencer Marketing<\/strong> campaigns with 30 creators. Instead of comparing raw likes, they build an Engagement Benchmark<\/strong> by creator tier and by format (15\u201330s vs 45\u201360s). A creator with a smaller audience repeatedly beats the benchmark on saves and comments per 1,000 views, signaling strong intent. The brand renews that partnership, increases product seeding, and adapts the creator\u2019s hook style for its own Organic Marketing<\/strong> channels.<\/p>\n\n\n\n

                    Example 3: Community-first brand measuring \u201cquality engagement\u201d<\/h3>\n\n\n\n

                    A lifestyle brand finds that likes alone correlate poorly with sales. They redefine their Engagement Benchmark<\/strong> to emphasize \u201chigh-intent\u201d actions: saves, shares, DMs, and link clicks. Over two quarters, this benchmark becomes the north star for content decisions and influencer briefs. The Influencer Marketing<\/strong> team stops overpaying for creators with high like counts but low saves, and overall content becomes more practical and shareable.<\/p>\n\n\n\n

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

                    Using an Engagement Benchmark<\/strong> well can create measurable advantages:<\/p>\n\n\n\n

                      \n
                    • Performance improvements:<\/strong> You identify repeatable patterns behind above-benchmark posts and scale them across Organic Marketing<\/strong> and creator programs.<\/li>\n
                    • Cost savings:<\/strong> In Influencer Marketing<\/strong>, benchmarks help avoid overspending on creators whose engagement is weak for their tier or niche.<\/li>\n
                    • Efficiency gains:<\/strong> Teams reduce debate and subjective decisions; creative review becomes data-informed.<\/li>\n
                    • Better audience experience:<\/strong> Benchmark-driven iteration often leads to clearer messaging, stronger storytelling, and content that better matches what the audience values.<\/li>\n
                    • More credible reporting:<\/strong> Stakeholders understand results in context\u2014performance vs benchmark\u2014rather than isolated metrics.<\/li>\n<\/ul>\n\n\n\n

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

                      An Engagement Benchmark<\/strong> can mislead if it\u2019s built or applied carelessly. Common challenges include:<\/p>\n\n\n\n

                        \n
                      • Inconsistent metric definitions:<\/strong> Engagement rate varies widely depending on whether you divide by reach, impressions, or followers.<\/li>\n
                      • Platform differences:<\/strong> A \u201csave\u201d may matter more on one platform than another; comparing across platforms without context is risky for Organic Marketing<\/strong>.<\/li>\n
                      • Data access limitations:<\/strong> Some influencer data is partial or delayed, especially if creators don\u2019t share full analytics.<\/li>\n
                      • Seasonality and trend shocks:<\/strong> Holidays, news cycles, and algorithm changes can temporarily shift engagement patterns.<\/li>\n
                      • Bot or low-quality engagement risk:<\/strong> In Influencer Marketing<\/strong>, inflated engagement can distort benchmarks unless you apply quality checks.<\/li>\n
                      • Over-optimization:<\/strong> Chasing benchmark numbers can lead to repetitive content that harms brand differentiation long-term.<\/li>\n<\/ul>\n\n\n\n

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

                        To build a benchmark that stays useful as your Organic Marketing<\/strong> program evolves:<\/p>\n\n\n\n

                          \n
                        1. \n

                          Define engagement precisely<\/strong>\n – Document which actions count as engagement per platform and why.\n – Separate \u201clight\u201d engagement (likes) from \u201chigh-intent\u201d engagement (saves, shares, comments, clicks) when relevant.<\/p>\n<\/li>\n

                        2. \n

                          Use medians and percentiles<\/strong>\n – Medians reduce the influence of viral outliers.\n – Percentile bands help teams understand normal variance.<\/p>\n<\/li>\n

                        3. \n

                          Segment before you conclude<\/strong>\n – Segment by format, topic, creator tier, and campaign phase.\n – In Influencer Marketing<\/strong>, segment by niche and audience size so comparisons are fair.<\/p>\n<\/li>\n

                        4. \n

                          Refresh on a realistic cadence<\/strong>\n – Monthly updates for fast-moving social programs.\n – Quarterly updates for more stable content ecosystems.<\/p>\n<\/li>\n

                        5. \n

                          Treat benchmarks as decision tools, not scorecards<\/strong>\n – A below-benchmark post can still be valuable if it drives the right downstream action (email signups, trials, brand trust).<\/p>\n<\/li>\n

                        6. \n

                          Pair benchmarking with testing<\/strong>\n – Use the benchmark to define hypotheses (e.g., \u201cAdd a clearer hook to raise comments per impression by 20% vs benchmark\u201d).<\/p>\n<\/li>\n<\/ol>\n\n\n\n

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

                          An Engagement Benchmark<\/strong> is usually assembled from multiple tool categories rather than a single platform:<\/p>\n\n\n\n

                            \n
                          • Analytics tools:<\/strong> Measure engagement, reach, impressions, and audience growth across channels; export data for deeper analysis.<\/li>\n
                          • Platform-native insights:<\/strong> The most direct source for post-level engagement details and video retention patterns.<\/li>\n
                          • Social listening tools:<\/strong> Add context about sentiment, share of voice, and community response beyond simple counts\u2014useful for Organic Marketing<\/strong> brand health.<\/li>\n
                          • Reporting dashboards \/ BI tools:<\/strong> Combine data sources, segment benchmarks, and track percentile bands over time.<\/li>\n
                          • CRM systems:<\/strong> Connect engagement to leads, pipeline stages, and retention when your Organic Marketing<\/strong> and Influencer Marketing<\/strong> efforts aim to drive revenue outcomes.<\/li>\n
                          • Automation and workflow tools:<\/strong> Standardize tagging, campaign naming, and content metadata so benchmarking doesn\u2019t become manual chaos.<\/li>\n
                          • SEO tools (supporting role):<\/strong> Helpful when engagement benchmarking includes organic distribution of content that supports search demand, brand discovery, or creator-driven content amplification.<\/li>\n<\/ul>\n\n\n\n

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

                            The right benchmark depends on your objective. Common metrics that feed an Engagement Benchmark<\/strong> include:<\/p>\n\n\n\n

                            Core engagement metrics<\/h3>\n\n\n\n
                              \n
                            • Likes, reactions<\/li>\n
                            • Comments and comment rate<\/li>\n
                            • Shares\/reposts<\/li>\n
                            • Saves\/bookmarks<\/li>\n
                            • Replies (for conversational platforms)<\/li>\n<\/ul>\n\n\n\n

                              Normalized performance metrics<\/h3>\n\n\n\n
                                \n
                              • Engagement rate per impression<\/li>\n
                              • Engagement rate per reach<\/li>\n
                              • Engagements per follower (use carefully; follower count is not always a distribution driver)<\/li>\n
                              • Comments per 1,000 impressions (quality-focused)<\/li>\n<\/ul>\n\n\n\n

                                Video and attention metrics<\/h3>\n\n\n\n
                                  \n
                                • Average watch time<\/li>\n
                                • Completion rate<\/li>\n
                                • 3-second \/ 5-second hold rate (where available)<\/li>\n
                                • Rewatches (if reported)<\/li>\n<\/ul>\n\n\n\n

                                  Traffic and intent metrics (often most business-relevant)<\/h3>\n\n\n\n
                                    \n
                                  • Link clicks and click-through rate<\/li>\n
                                  • Profile visits<\/li>\n
                                  • Signups attributed to organic posts or creator content (when measurement is feasible)<\/li>\n<\/ul>\n\n\n\n

                                    Quality and brand metrics<\/h3>\n\n\n\n
                                      \n
                                    • Sentiment signals (positive\/neutral\/negative patterns)<\/li>\n
                                    • Share of voice changes during campaigns<\/li>\n
                                    • Brand search lift (directional, not always perfectly attributable)<\/li>\n<\/ul>\n\n\n\n

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

                                      Several trends are reshaping how an Engagement Benchmark<\/strong> is built and used in Organic Marketing<\/strong>:<\/p>\n\n\n\n

                                        \n
                                      • AI-assisted analysis:<\/strong> Teams increasingly use automation to detect patterns behind above-benchmark content (topic clusters, hook language, creative structure) and to surface anomalies.<\/li>\n
                                      • Shift toward \u201cquality engagement\u201d:<\/strong> Saves, shares, meaningful comments, and watch time are becoming more central than likes alone, especially in Influencer Marketing<\/strong> evaluation.<\/li>\n
                                      • Privacy and measurement constraints:<\/strong> Reduced tracking granularity pushes teams to rely more on platform-native signals and aggregated reporting.<\/li>\n
                                      • Personalization and micro-audiences:<\/strong> Benchmarks will become more segmented as content targets narrower communities and intent states.<\/li>\n
                                      • Creator-brand hybrid models:<\/strong> As brands build long-term creator partnerships, benchmarks will measure consistency and audience trust over time\u2014not just campaign spikes.<\/li>\n<\/ul>\n\n\n\n

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

                                        Engagement Benchmark vs engagement rate<\/h3>\n\n\n\n
                                          \n
                                        • Engagement rate<\/strong> is a metric (a calculation).<\/li>\n
                                        • Engagement Benchmark<\/strong> is a reference standard used to interpret that metric in context (historical, peer, or industry).<\/li>\n<\/ul>\n\n\n\n

                                          Engagement Benchmark vs KPIs<\/h3>\n\n\n\n
                                            \n
                                          • KPIs<\/strong> are chosen targets tied to business outcomes (e.g., \u201cincrease high-intent engagement by 20%\u201d).<\/li>\n
                                          • An Engagement Benchmark<\/strong> helps set realistic KPI targets and evaluate whether KPI movement is meaningful in Organic Marketing<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                                            Engagement Benchmark vs influencer vetting<\/h3>\n\n\n\n
                                              \n
                                            • Influencer vetting<\/strong> is the process of selecting creators based on fit and risk (brand alignment, audience quality, content style).<\/li>\n
                                            • An Engagement Benchmark<\/strong> supports vetting with performance context, but it doesn\u2019t replace qualitative review\u2014especially in Influencer Marketing<\/strong>, where brand safety and creative fit matter.<\/li>\n<\/ul>\n\n\n\n

                                              Who Should Learn Engagement Benchmark<\/h2>\n\n\n\n
                                                \n
                                              • Marketers:<\/strong> To set smarter content targets, improve creative iteration, and report Organic Marketing<\/strong> results credibly.<\/li>\n
                                              • Analysts:<\/strong> To build reliable measurement frameworks, reduce bias from outliers, and improve segmentation.<\/li>\n
                                              • Agencies:<\/strong> To justify recommendations, compare creators fairly, and standardize reporting across clients and verticals.<\/li>\n
                                              • Business owners and founders:<\/strong> To make confident decisions about where to invest time\u2014brand channels, community, or Influencer Marketing<\/strong> partnerships.<\/li>\n
                                              • Developers and data teams:<\/strong> To help implement clean data pipelines, consistent tagging, and dashboards that keep benchmarking trustworthy.<\/li>\n<\/ul>\n\n\n\n

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

                                                An Engagement Benchmark<\/strong> is a contextual standard for evaluating engagement performance. It matters because it turns raw engagement into insight, helping teams improve results, allocate effort, and communicate impact. In Organic Marketing<\/strong>, it anchors content strategy in consistent measurement despite shifting algorithms and audience behavior. In Influencer Marketing<\/strong>, it enables fair comparisons across creators and formats, improving partner selection and creative direction.<\/p>\n\n\n\n

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

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

                                                An Engagement Benchmark<\/strong> is a \u201cnormal\u201d or \u201cexpected\u201d engagement level for a specific situation\u2014platform, format, and audience\u2014used to judge whether performance is above or below that standard.<\/p>\n\n\n\n

                                                2) Should my Engagement Benchmark be based on reach, impressions, or followers?<\/h3>\n\n\n\n

                                                Use the denominator that best represents distribution. For most Organic Marketing<\/strong> analysis, engagement per impression or per reach is more reliable than per follower, because follower count doesn\u2019t equal visibility.<\/p>\n\n\n\n

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

                                                For fast-moving social channels, refresh monthly. For more stable programs, quarterly can work. Update sooner if your content mix changes significantly or a platform shift impacts distribution.<\/p>\n\n\n\n

                                                4) What\u2019s a good benchmark for Influencer Marketing campaigns?<\/h3>\n\n\n\n

                                                There isn\u2019t one universal number. In Influencer Marketing<\/strong>, the best approach is to benchmark by creator tier, niche, and format, and focus on medians and percentile ranges rather than single averages.<\/p>\n\n\n\n

                                                5) Can an Engagement Benchmark help with ROI measurement?<\/h3>\n\n\n\n

                                                Yes\u2014indirectly. A benchmark makes engagement trends interpretable, and you can then connect above-benchmark engagement to downstream actions like signups, trials, or sales where attribution is feasible.<\/p>\n\n\n\n

                                                6) What\u2019s the biggest mistake teams make with engagement benchmarks?<\/h3>\n\n\n\n

                                                Comparing unlike with unlike\u2014different platforms, formats, creator sizes, or timeframes\u2014and then making big strategic decisions. A useful Engagement Benchmark<\/strong> is segmented, documented, and updated regularly.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                                An **Engagement Benchmark** is a reference point that helps you judge whether your content performance is strong, average, or weak compared with a relevant standard. In **Organic Marketing**, it\u2019s the difference between \u201cthis post got 500 likes\u201d and \u201cthis post performed 35% above our typical results for this platform, audience size, and content type.\u201d In **Influencer Marketing**, it becomes even more important because you\u2019re evaluating creators who have different audiences, formats, and posting habits\u2014so raw engagement counts can mislead.<\/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":[],"tags":[],"class_list":["post-9208","post","type-post","status-publish","format-standard","hentry"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/9208","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=9208"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/9208\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=9208"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=9208"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=9208"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}