{"id":7717,"date":"2026-03-24T23:43:43","date_gmt":"2026-03-24T23:43:43","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/fit-score\/"},"modified":"2026-03-24T23:43:43","modified_gmt":"2026-03-24T23:43:43","slug":"fit-score","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/fit-score\/","title":{"rendered":"Fit Score: What It Is, Key Features, Benefits, Use Cases, and How It Fits in CRM Marketing"},"content":{"rendered":"\n

Fit Score is a structured way to quantify how well a person, account, or customer matches your ideal target for a specific offer, product, or lifecycle stage. In Direct & Retention Marketing<\/strong>, it helps teams decide who should receive which message, through which channel, and with what urgency. In CRM Marketing<\/strong>, it becomes the connective tissue between customer data, segmentation, and automated journeys\u2014turning \u201cwe think this audience is right\u201d into a measurable, repeatable decision.<\/p>\n\n\n\n

Modern inboxes, push notifications, and ad feeds are crowded, and privacy changes reduce easy targeting. Fit Score matters because it improves relevance when attention is scarce. When you operationalize Fit Score inside CRM Marketing<\/strong>, you can prioritize high-likelihood audiences, avoid over-messaging low-fit contacts, and allocate budget and creative effort where it has the best chance to perform in Direct & Retention Marketing<\/strong>.<\/p>\n\n\n\n

\n\n\n\n

What Is Fit Score?<\/h2>\n\n\n\n

Fit Score<\/strong> is a numeric or categorical rating that represents how strongly a contact aligns with your \u201cbest customer\u201d profile or campaign eligibility rules. It typically combines who someone is<\/strong> (profile and firmographic traits) with what they do<\/strong> (behavior and engagement signals) to estimate suitability for a message or next step.<\/p>\n\n\n\n

At its core, Fit Score answers a practical business question: \u201cIs this the right person for this action right now?\u201d<\/em> That action could be a trial invitation, a win-back email, a loyalty offer, an upsell prompt, or a service message. In Direct & Retention Marketing<\/strong>, the score supports decisions about targeting, sequencing, frequency, and channel selection.<\/p>\n\n\n\n

Within CRM Marketing<\/strong>, Fit Score usually lives alongside segmentation, lifecycle stages, and suppression rules. It can be calculated in your data layer, stored in the CRM, and referenced by automation logic\u2014so campaigns are driven by consistent criteria instead of ad-hoc list pulls.<\/p>\n\n\n\n

\n\n\n\n

Why Fit Score Matters in Direct & Retention Marketing<\/h2>\n\n\n\n

In Direct & Retention Marketing<\/strong>, every send has an opportunity cost: sending the wrong message can reduce trust, increase unsubscribes, and weaken deliverability. Fit Score improves strategic focus by ensuring campaigns are built around relevance, not just reach.<\/p>\n\n\n\n

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

    \n
  • Higher conversion efficiency:<\/strong> High-fit audiences respond more often and require fewer touches to move.<\/li>\n
  • Better retention outcomes:<\/strong> Fit Score can highlight customers who are likely to renew, expand, or churn\u2014so interventions are timely.<\/li>\n
  • Smarter resource allocation:<\/strong> Creative, incentives, and personalization can be reserved for segments where uplift is plausible.<\/li>\n
  • Competitive advantage:<\/strong> Teams that operationalize Fit Score in CRM Marketing<\/strong> learn faster, target more precisely, and waste less budget in Direct & Retention Marketing<\/strong>.<\/li>\n<\/ul>\n\n\n\n

    Fit Score also enables consistency across teams. When paid, lifecycle, and sales-adjacent motions use shared scoring logic, your customer experience becomes more coherent instead of channel-by-channel guesswork.<\/p>\n\n\n\n

    \n\n\n\n

    How Fit Score Works<\/h2>\n\n\n\n

    Fit Score can be simple rules-based scoring or a model-driven score, but the practical workflow in CRM Marketing<\/strong> is usually similar.<\/p>\n\n\n\n

      \n
    1. \n

      Inputs (signals and attributes)<\/strong>\n You collect profile data (e.g., geography, plan type, industry), behavioral data (e.g., product usage, site visits), and engagement data (e.g., email clicks, SMS replies). In Direct & Retention Marketing<\/strong>, channel data like send history and fatigue indicators also matter.<\/p>\n<\/li>\n

    2. \n

      Scoring (logic or model)<\/strong>\n You convert those inputs into a Fit Score using:\n – Rules (point systems, thresholds, inclusion\/exclusion criteria), or\n – Predictive methods (propensity or classification models), when you have sufficient data and governance.<\/p>\n<\/li>\n

    3. \n

      Activation (campaign decisions)<\/strong>\n The Fit Score is used to:\n – Qualify entry into a journey,\n – Select variants (offer A vs. B),\n – Set frequency caps (protect low-fit contacts from over-targeting),\n – Trigger escalation (e.g., add in-app + email when fit is high).<\/p>\n<\/li>\n

    4. \n

      Outcomes and learning<\/strong>\n You measure results (conversion, retention, churn reduction), then recalibrate the Fit Score. In mature Direct & Retention Marketing<\/strong>, scoring is treated as a living system that evolves with products, pricing, and customer behavior.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

      \n\n\n\n

      Key Components of Fit Score<\/h2>\n\n\n\n

      A dependable Fit Score requires more than a formula. It\u2019s an operating capability across data, process, and governance\u2014especially in CRM Marketing<\/strong> where scores directly drive automation.<\/p>\n\n\n\n

      Data inputs<\/h3>\n\n\n\n

      Common inputs used to compute Fit Score include:<\/p>\n\n\n\n

        \n
      • Profile\/identity:<\/strong> location, language, account type, acquisition source, preference center selections<\/li>\n
      • Customer status:<\/strong> lifecycle stage, tenure, plan tier, contract dates, renewal window<\/li>\n
      • Behavioral signals:<\/strong> product usage frequency, feature adoption, browsing depth, cart activity<\/li>\n
      • Engagement signals:<\/strong> opens\/clicks, push engagement, SMS responses, website return rate<\/li>\n
      • Value indicators:<\/strong> AOV, LTV-to-date, refund rate, support load, margin band (when appropriate)<\/li>\n<\/ul>\n\n\n\n

        Systems and processes<\/h3>\n\n\n\n

        To apply Fit Score consistently in Direct & Retention Marketing<\/strong>, teams typically need:<\/p>\n\n\n\n

          \n
        • A reliable customer data layer (clean identity resolution and event tracking)<\/li>\n
        • A CRM or customer profile store that can persist the Fit Score<\/li>\n
        • Marketing automation that can reference the Fit Score for branching logic<\/li>\n
        • A measurement plan tying score bands to outcomes (not just engagement)<\/li>\n<\/ul>\n\n\n\n

          Governance and ownership<\/h3>\n\n\n\n

          Fit Score needs clear responsibility:\n– Marketing<\/strong> defines use cases, thresholds, and messaging policy.\n– Analytics<\/strong> validates scoring performance and monitors drift.\n– Data\/engineering<\/strong> ensures data quality, pipelines, and availability.\n– Legal\/privacy<\/strong> reviews sensitive attribute use and consent alignment\u2014critical in CRM Marketing<\/strong>.<\/p>\n\n\n\n

          \n\n\n\n

          Types of Fit Score<\/h2>\n\n\n\n

          Fit Score doesn\u2019t have one universal standard, but several practical variants are common in Direct & Retention Marketing<\/strong> and CRM Marketing<\/strong>.<\/p>\n\n\n\n

          Profile Fit Score vs. Behavioral Fit Score<\/h3>\n\n\n\n
            \n
          • Profile Fit Score<\/strong> emphasizes who the customer is (e.g., ideal industry, plan size, region).<\/li>\n
          • Behavioral Fit Score<\/strong> emphasizes what the customer is doing (e.g., high usage, recent intent signals).<\/li>\n<\/ul>\n\n\n\n

            Many teams blend both to reduce false positives (great profile, no intent) and false negatives (strong intent, weak profile).<\/p>\n\n\n\n

            Prospect Fit Score vs. Customer Fit Score<\/h3>\n\n\n\n
              \n
            • Prospect Fit Score<\/strong> supports acquisition-adjacent lifecycle messaging (trial, demo, first purchase).<\/li>\n
            • Customer Fit Score<\/strong> supports retention, expansion, and win-back\u2014core to Direct & Retention Marketing<\/strong>.<\/li>\n<\/ul>\n\n\n\n

              Offer Fit Score (fit-to-offer)<\/h3>\n\n\n\n

              Instead of one global score, you can compute fit per offer category (e.g., \u201cupgrade fit,\u201d \u201ccross-sell fit,\u201d \u201crenewal risk fit\u201d). This is often more actionable in CRM Marketing<\/strong> because it maps directly to journeys and templates.<\/p>\n\n\n\n

              Channel Fit Score<\/h3>\n\n\n\n

              Some customers respond best by email, others via push, in-app, or SMS. A channel-oriented Fit Score can reduce fatigue and improve engagement quality across Direct & Retention Marketing<\/strong>.<\/p>\n\n\n\n

              \n\n\n\n

              Real-World Examples of Fit Score<\/h2>\n\n\n\n

              Example 1: Subscription renewal save in CRM Marketing<\/h3>\n\n\n\n

              A subscription business assigns a Fit Score for renewal intervention based on:\n– Renewal window (30 days out),\n– Declining product usage,\n– Past support issues,\n– History of discount responsiveness.<\/p>\n\n\n\n

              High Fit Score customers get a proactive education sequence and an option to downgrade (retention-first). Medium Fit Score customers get value reminders. Low Fit Score customers are suppressed from discounts to protect margin. This is classic Direct & Retention Marketing<\/strong> driven by CRM Marketing<\/strong> logic.<\/p>\n\n\n\n

              Example 2: Ecommerce cross-sell targeting<\/h3>\n\n\n\n

              An ecommerce brand computes Fit Score for a cross-sell campaign using:\n– Category affinity (browsing + purchases),\n– Price sensitivity proxy (use of coupons),\n– Recency\/frequency (RFM),\n– Return rate.<\/p>\n\n\n\n

              High Fit Score customers see personalized bundles and recommendations; low Fit Score customers receive broader content instead of hard-selling. The result is higher conversion with fewer messages\u2014an efficiency win in Direct & Retention Marketing<\/strong>.<\/p>\n\n\n\n

              Example 3: B2B product-led expansion<\/h3>\n\n\n\n

              A product-led SaaS team builds Fit Score for expansion based on:\n– Team account size,\n– Feature adoption depth,\n– Admin actions (adding users, creating projects),\n– Engagement with onboarding content.<\/p>\n\n\n\n

              CRM Marketing<\/strong> uses the score to trigger in-app guidance, lifecycle emails, and targeted webinars. The Fit Score ensures expansion messaging hits accounts with both capability and intent\u2014key to sustainable Direct & Retention Marketing<\/strong>.<\/p>\n\n\n\n

              \n\n\n\n

              Benefits of Using Fit Score<\/h2>\n\n\n\n

              When implemented carefully, Fit Score improves both performance and customer experience:<\/p>\n\n\n\n

                \n
              • Higher relevance and engagement:<\/strong> Messages align with needs and timing, reducing \u201cnoise.\u201d<\/li>\n
              • Better conversion and retention:<\/strong> High-fit audiences tend to respond faster and churn less.<\/li>\n
              • Lower cost per outcome:<\/strong> Fewer sends and less incentive spend per conversion.<\/li>\n
              • Reduced fatigue and complaints:<\/strong> Suppression of low-fit contacts protects deliverability and brand trust.<\/li>\n
              • Faster learning loops:<\/strong> In CRM Marketing<\/strong>, score bands create clean cohorts to test creative, cadence, and offers in Direct & Retention Marketing<\/strong>.<\/li>\n<\/ul>\n\n\n\n
                \n\n\n\n

                Challenges of Fit Score<\/h2>\n\n\n\n

                Fit Score can underperform\u2014or cause harm\u2014when the foundation is weak.<\/p>\n\n\n\n

                  \n
                • Data quality gaps:<\/strong> Missing events, broken identity stitching, or inconsistent attributes lead to unreliable scores.<\/li>\n
                • Overfitting to past behavior:<\/strong> A Fit Score based only on historical winners can ignore new segments or new products.<\/li>\n
                • Misaligned incentives:<\/strong> Optimizing Fit Score for clicks can damage long-term retention or margin.<\/li>\n
                • Bias and fairness risks:<\/strong> Using sensitive attributes (directly or indirectly) can create inequitable targeting outcomes; governance matters in CRM Marketing<\/strong>.<\/li>\n
                • Operational complexity:<\/strong> Keeping the Fit Score consistent across tools, teams, and channels is non-trivial in Direct & Retention Marketing<\/strong>.<\/li>\n
                • Score drift:<\/strong> Customer behavior changes, and models degrade unless monitored and recalibrated.<\/li>\n<\/ul>\n\n\n\n
                  \n\n\n\n

                  Best Practices for Fit Score<\/h2>\n\n\n\n

                  To make Fit Score durable and useful across Direct & Retention Marketing<\/strong> and CRM Marketing<\/strong>, prioritize operational clarity over fancy math.<\/p>\n\n\n\n

                    \n
                  1. \n

                    Start with a single use case<\/strong>\n Pick one outcome (e.g., \u201ctrial-to-paid,\u201d \u201crenewal save,\u201d \u201csecond purchase\u201d) and build a Fit Score that supports it.<\/p>\n<\/li>\n

                  2. \n

                    Define score bands with actions<\/strong>\n A Fit Score is only valuable if it changes what you do. Example:\n – High: premium personalization + multi-channel\n – Medium: standard journey\n – Low: suppress or send content-only<\/p>\n<\/li>\n

                  3. \n

                    Separate eligibility from ranking<\/strong>\n Use hard rules for compliance and feasibility (consent, region, inventory), then use Fit Score to rank or prioritize.<\/p>\n<\/li>\n

                  4. \n

                    Validate against business outcomes<\/strong>\n Measure lift in conversion, retention, margin, and complaints\u2014not only open\/click rates. This keeps CRM Marketing<\/strong> aligned to business impact.<\/p>\n<\/li>\n

                  5. \n

                    Monitor drift and recalibrate on schedule<\/strong>\n Set reviews monthly or quarterly. In Direct & Retention Marketing<\/strong>, seasonality alone can distort scoring.<\/p>\n<\/li>\n

                  6. \n

                    Document inputs and ownership<\/strong>\n Maintain a simple scoring spec: fields used, refresh cadence, definitions, and who approves changes.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                    \n\n\n\n

                    Tools Used for Fit Score<\/h2>\n\n\n\n

                    Fit Score is typically operationalized through a stack of systems rather than one tool. In CRM Marketing<\/strong>, integration and data consistency matter more than brand names.<\/p>\n\n\n\n

                      \n
                    • CRM systems \/ customer profile stores:<\/strong> Persist Fit Score per contact\/account and expose it to segmentation.<\/li>\n
                    • Customer data platforms or event pipelines:<\/strong> Collect behavioral events and unify identities needed for accurate Fit Score computation.<\/li>\n
                    • Marketing automation platforms:<\/strong> Use Fit Score to branch journeys, set cadence, and trigger Direct & Retention Marketing<\/strong> campaigns.<\/li>\n
                    • Analytics tools:<\/strong> Evaluate whether Fit Score predicts desired outcomes; run cohort and funnel analyses.<\/li>\n
                    • Reporting dashboards \/ BI:<\/strong> Track score distribution, performance by band, drift, and campaign results over time.<\/li>\n
                    • Experimentation frameworks:<\/strong> Test whether different Fit Score thresholds or features improve outcomes.<\/li>\n<\/ul>\n\n\n\n
                      \n\n\n\n

                      Metrics Related to Fit Score<\/h2>\n\n\n\n

                      To judge whether Fit Score is working, measure both predictive quality and business impact.<\/p>\n\n\n\n

                      Outcome metrics (primary)<\/h3>\n\n\n\n
                        \n
                      • Conversion rate by Fit Score band (purchase, upgrade, renewal)<\/li>\n
                      • Retention rate \/ churn rate by band<\/li>\n
                      • Revenue per recipient, margin per recipient (when applicable)<\/li>\n
                      • LTV uplift for high-fit targeting vs. baseline<\/li>\n<\/ul>\n\n\n\n

                        Efficiency and risk metrics (guardrails)<\/h3>\n\n\n\n
                          \n
                        • Cost per conversion (including incentive cost)<\/li>\n
                        • Unsubscribe rate, complaint rate, opt-out rate<\/li>\n
                        • Deliverability indicators (bounce rate, spam placement signals)<\/li>\n
                        • Frequency and fatigue metrics (touches per user per week)<\/li>\n<\/ul>\n\n\n\n

                          Score health metrics (model\/logic quality)<\/h3>\n\n\n\n
                            \n
                          • Score distribution stability over time (drift)<\/li>\n
                          • Coverage (percentage of contacts with a usable Fit Score)<\/li>\n
                          • Data freshness (time since last update)<\/li>\n
                          • Calibration checks (do \u201chigh\u201d scores consistently outperform \u201clow\u201d?)<\/li>\n<\/ul>\n\n\n\n

                            These metrics keep CRM Marketing<\/strong> accountable for real outcomes in Direct & Retention Marketing<\/strong>.<\/p>\n\n\n\n

                            \n\n\n\n

                            Future Trends of Fit Score<\/h2>\n\n\n\n

                            Fit Score is evolving as customer expectations and measurement constraints change.<\/p>\n\n\n\n

                              \n
                            • More automation, more governance:<\/strong> AI-assisted scoring can be powerful, but CRM Marketing<\/strong> teams will need stricter documentation, approvals, and monitoring.<\/li>\n
                            • Shift toward first-party signals:<\/strong> As third-party data becomes less reliable, Fit Score will lean more on product usage, on-site behavior, and declared preferences\u2014core inputs for Direct & Retention Marketing<\/strong>.<\/li>\n
                            • Real-time and contextual scoring:<\/strong> Instead of weekly batch updates, more teams will compute Fit Score closer to the moment of action (e.g., immediately after key behaviors).<\/li>\n
                            • Multi-objective scoring:<\/strong> Businesses will optimize not just for conversion likelihood, but for profitability, retention, and customer experience simultaneously.<\/li>\n
                            • Privacy-aware feature selection:<\/strong> Expect more emphasis on consent, minimization, and explainability\u2014especially when Fit Score drives messaging frequency in CRM Marketing<\/strong>.<\/li>\n<\/ul>\n\n\n\n
                              \n\n\n\n

                              Fit Score vs Related Terms<\/h2>\n\n\n\n

                              Fit Score vs Lead Score<\/h3>\n\n\n\n

                              A lead score<\/strong> often focuses on sales readiness (especially in B2B), while Fit Score<\/strong> is broader: it can apply to prospects and<\/em> customers, and it\u2019s frequently used to power Direct & Retention Marketing<\/strong> journeys inside CRM Marketing<\/strong>.<\/p>\n\n\n\n

                              Fit Score vs Propensity Score<\/h3>\n\n\n\n

                              A propensity score<\/strong> usually predicts the probability of a specific event (buy, churn, upgrade). Fit Score may include propensity modeling, but it often also includes eligibility and \u201cright customer\u201d criteria that are not purely predictive.<\/p>\n\n\n\n

                              Fit Score vs Engagement Score<\/h3>\n\n\n\n

                              An engagement score<\/strong> measures interaction intensity (opens, clicks, sessions). Fit Score may include engagement, but adds suitability (profile, value, constraints) so highly engaged but wrong-fit audiences don\u2019t get over-prioritized in Direct & Retention Marketing<\/strong>.<\/p>\n\n\n\n

                              \n\n\n\n

                              Who Should Learn Fit Score<\/h2>\n\n\n\n
                                \n
                              • Marketers:<\/strong> Fit Score improves segmentation, personalization, and send strategy across Direct & Retention Marketing<\/strong>.<\/li>\n
                              • Analysts:<\/strong> It\u2019s a practical framework for turning data into repeatable targeting decisions and measurable lift in CRM Marketing<\/strong>.<\/li>\n
                              • Agencies:<\/strong> Fit Score provides a defensible way to recommend audience strategies and prove incremental value beyond creative.<\/li>\n
                              • Business owners and founders:<\/strong> It ties messaging and offers to unit economics\u2014helping scale growth without burning trust.<\/li>\n
                              • Developers and marketing ops:<\/strong> Implementing Fit Score requires clean events, reliable pipelines, and automation-friendly data structures inside CRM Marketing<\/strong>.<\/li>\n<\/ul>\n\n\n\n
                                \n\n\n\n

                                Summary of Fit Score<\/h2>\n\n\n\n

                                Fit Score is a structured rating that quantifies how well a contact or customer matches an ideal target for a specific action. It matters because Direct & Retention Marketing<\/strong> succeeds on relevance, timing, and efficient use of attention. Implemented well, Fit Score becomes a practical decision engine inside CRM Marketing<\/strong>\u2014powering segmentation, journey branching, channel choice, and measurement in a consistent, scalable way.<\/p>\n\n\n\n

                                \n\n\n\n

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

                                1) What is a Fit Score and what should it include?<\/h3>\n\n\n\n

                                A Fit Score is a numeric or banded rating of suitability for a specific message or action. It typically includes profile traits (who they are), behavioral intent (what they do), and operational constraints (eligibility, consent, fatigue) so Direct & Retention Marketing<\/strong> decisions are consistent.<\/p>\n\n\n\n

                                2) How is Fit Score different from basic segmentation?<\/h3>\n\n\n\n

                                Segmentation groups customers by shared attributes; Fit Score ranks or qualifies customers by likelihood or suitability. In CRM Marketing<\/strong>, segmentation often defines the audience universe, while Fit Score prioritizes who gets the strongest offers or highest frequency.<\/p>\n\n\n\n

                                3) Can CRM Marketing teams build Fit Score without machine learning?<\/h3>\n\n\n\n

                                Yes. Many high-performing CRM Marketing<\/strong> programs start with simple rules and thresholds, then refine using outcome data. In Direct & Retention Marketing<\/strong>, a transparent rules-based Fit Score is often easier to govern and iterate.<\/p>\n\n\n\n

                                4) How often should Fit Score be updated?<\/h3>\n\n\n\n

                                It depends on how quickly signals change. For many CRM Marketing<\/strong> use cases, daily or weekly refreshes work. For intent-heavy scenarios in Direct & Retention Marketing<\/strong> (cart activity, onboarding), near-real-time updates can perform better.<\/p>\n\n\n\n

                                5) What data is most important for a reliable Fit Score?<\/h3>\n\n\n\n

                                First-party behavioral events, lifecycle status, and preference\/consent data are usually the highest value. Without clean inputs and identity matching, Fit Score can become noisy and reduce performance in CRM Marketing<\/strong>.<\/p>\n\n\n\n

                                6) Should Fit Score be one global number or different scores per use case?<\/h3>\n\n\n\n

                                If you have multiple goals (renewal, cross-sell, win-back), separate fit-to-offer scores are often more actionable. A single global Fit Score can still work as a starting point, but it may be less precise for Direct & Retention Marketing<\/strong> journeys.<\/p>\n\n\n\n

                                7) How do you know if Fit Score is actually improving results?<\/h3>\n\n\n\n

                                Compare conversion, retention, revenue per recipient, and unsubscribe\/complaint rates by Fit Score band versus a baseline approach. In CRM Marketing<\/strong>, a good Fit Score creates consistent separation: high-fit cohorts should outperform low-fit cohorts on business outcomes, not just engagement.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                Fit Score is a structured way to quantify how well a person, account, or customer matches your ideal target for a specific offer, product, or lifecycle stage. In **Direct & Retention Marketing**, it helps teams decide who should receive which message, through which channel, and with what urgency. In **CRM Marketing**, it becomes the connective tissue between customer data, segmentation, and automated journeys\u2014turning \u201cwe think this audience is right\u201d into a measurable, repeatable decision.<\/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":[1893],"tags":[],"class_list":["post-7717","post","type-post","status-publish","format-standard","hentry","category-crm-marketing"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7717","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=7717"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7717\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7717"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7717"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7717"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}