{"id":6917,"date":"2026-03-23T17:32:36","date_gmt":"2026-03-23T17:32:36","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/purchase-probability\/"},"modified":"2026-03-23T17:32:36","modified_gmt":"2026-03-23T17:32:36","slug":"purchase-probability","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/purchase-probability\/","title":{"rendered":"Purchase Probability: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Analytics"},"content":{"rendered":"\n

Purchase Probability is the estimated likelihood that a person (or account) will complete a purchase within a defined period and context. In Conversion & Measurement<\/strong>, it helps teams move from simply counting conversions to predicting which audiences, sessions, or leads are most likely to convert next. In Analytics<\/strong>, it sits at the intersection of behavioral data, customer intent signals, and statistical modeling\u2014turning messy, multi-touch interactions into an actionable probability score.<\/p>\n\n\n\n

Why it matters now: modern customer journeys are fragmented across devices, channels, and time. With rising acquisition costs and tighter privacy controls, smart Conversion & Measurement<\/strong> strategies increasingly depend on prioritization\u2014deciding where to spend, who to nurture, and what to personalize. Purchase Probability is one of the most practical ways to create that prioritization using Analytics<\/strong> rather than guesswork.<\/p>\n\n\n\n

\n\n\n\n

What Is Purchase Probability?<\/h2>\n\n\n\n

Purchase Probability<\/strong> is a probability score (often expressed as a percentage from 0\u2013100% or a 0\u20131 value) that represents how likely a user, lead, or account is to buy. It can be calculated for a single event window (e.g., \u201cwithin 7 days\u201d) or a lifecycle stage (e.g., \u201cbefore trial expires\u201d). The core concept is simple: some people show stronger purchase intent than others, and you can quantify that intent using data.<\/p>\n\n\n\n

From a business perspective, Purchase Probability is a decision-support metric. Instead of treating all traffic, leads, or subscribers the same, you allocate budget and effort based on expected conversion outcomes. In Conversion & Measurement<\/strong>, it complements conversion rate by adding forward-looking<\/em> insight\u2014helping you predict conversions, not just report them. In Analytics<\/strong>, it is typically produced by a model or scoring system that learns patterns from historical purchase behavior and current user signals.<\/p>\n\n\n\n

\n\n\n\n

Why Purchase Probability Matters in Conversion & Measurement<\/h2>\n\n\n\n

Purchase Probability drives strategic and financial impact because it improves how you deploy resources across the funnel. In Conversion & Measurement<\/strong>, teams often face competing priorities: scale acquisition, lift conversion rate, reduce CAC, improve retention, and increase LTV. A probability-driven approach helps resolve these trade-offs by identifying the segments most likely to buy and the moments where nudges matter.<\/p>\n\n\n\n

Key ways it creates value:<\/p>\n\n\n\n

    \n
  • Better budget allocation:<\/strong> Spend more where incremental conversions are more likely, and reduce waste on low-intent audiences.<\/li>\n
  • Improved funnel management:<\/strong> Prioritize nurturing and sales outreach based on likelihood to purchase, not just lead volume.<\/li>\n
  • More effective experimentation:<\/strong> Use probability segments to design A\/B tests that target high-impact cohorts and interpret results with more context.<\/li>\n
  • Competitive advantage:<\/strong> Faster decision-making and more precise personalization can outperform competitors who rely only on lagging metrics.<\/li>\n<\/ul>\n\n\n\n

    Ultimately, Purchase Probability strengthens Conversion & Measurement<\/strong> maturity by connecting activity metrics (clicks, sessions, open rates) with predicted business outcomes using Analytics<\/strong>.<\/p>\n\n\n\n

    \n\n\n\n

    How Purchase Probability Works<\/h2>\n\n\n\n

    Purchase Probability can be implemented in multiple ways\u2014from simple scoring rules to machine-learning models\u2014but the practical workflow is consistent.<\/p>\n\n\n\n

    1) Inputs (signals and context)<\/h3>\n\n\n\n

    Common inputs include:\n– On-site behavior: product views, add-to-cart, checkout starts, search queries\n– Engagement: email opens\/clicks, push notifications, return frequency\n– Customer attributes: geography, device, plan type, prior purchases, tenure\n– Source\/medium: paid search intent vs. social discovery traffic\n– B2B signals: firmographics, account engagement, demo attendance<\/p>\n\n\n\n

    2) Processing (scoring or modeling)<\/h3>\n\n\n\n

    Your Analytics<\/strong> approach may be:\n– Rule-based scoring:<\/strong> \u201c+10 points for add-to-cart, +5 for repeat visit,\u201d then map to probability bands.\n– Statistical models:<\/strong> logistic regression or survival models to predict purchase likelihood within a window.\n– Machine learning:<\/strong> gradient boosting, random forests, or similar methods trained on historical data.<\/p>\n\n\n\n

    The model learns relationships between signals and actual purchases, producing a calibrated estimate (e.g., \u201cthis user has a 0.42 probability of purchasing within 14 days\u201d).<\/p>\n\n\n\n

    3) Execution (using the score)<\/h3>\n\n\n\n

    In Conversion & Measurement<\/strong>, the score is operationalized through:\n– audience segmentation for ads\n– personalization rules onsite\n– lifecycle messaging (email\/SMS\/push)\n– sales prioritization (B2B lead routing)\n– experimentation targeting and analysis<\/p>\n\n\n\n

    4) Output (measurable outcomes)<\/h3>\n\n\n\n

    Outputs can include:\n– a per-user or per-account probability score\n– segments (high\/medium\/low intent)\n– expected conversions and revenue forecasts\n– more efficient spend and improved conversion outcomes tracked via Analytics<\/strong><\/p>\n\n\n\n

    \n\n\n\n

    Key Components of Purchase Probability<\/h2>\n\n\n\n

    A reliable Purchase Probability system is more than a model\u2014it is data, process, and governance working together.<\/p>\n\n\n\n

    Data inputs and tracking<\/h3>\n\n\n\n
      \n
    • Event tracking:<\/strong> consistent definitions for view, add-to-cart, checkout, purchase, and key micro-conversions<\/li>\n
    • Identity and attribution:<\/strong> user stitching (where permitted), clear session\/user logic, and channel tagging<\/li>\n
    • Data quality checks:<\/strong> missing events, duplicates, and bot filtering<\/li>\n<\/ul>\n\n\n\n

      Modeling and scoring approach<\/h3>\n\n\n\n
        \n
      • Clear prediction target (e.g., \u201cpurchase within 7 days\u201d)<\/li>\n
      • Feature engineering (intent signals, recency, frequency, monetary value)<\/li>\n
      • Model evaluation and calibration so probabilities reflect reality<\/li>\n<\/ul>\n\n\n\n

        Activation systems<\/h3>\n\n\n\n
          \n
        • CRM and marketing automation to trigger journeys<\/li>\n
        • Ad platforms for audience building and exclusions<\/li>\n
        • Website\/app personalization and experimentation tools<\/li>\n<\/ul>\n\n\n\n

          Governance and responsibilities<\/h3>\n\n\n\n
            \n
          • Marketing and growth teams define use cases and success criteria in Conversion & Measurement<\/strong><\/li>\n
          • Analysts and data teams build and validate the scoring in Analytics<\/strong><\/li>\n
          • Legal\/privacy stakeholders ensure compliant data use and retention policies<\/li>\n<\/ul>\n\n\n\n
            \n\n\n\n

            Types of Purchase Probability<\/h2>\n\n\n\n

            Purchase Probability doesn\u2019t have one universal taxonomy, but in practice it appears in a few common \u201ctypes\u201d based on what you\u2019re predicting and how it\u2019s used.<\/p>\n\n\n\n

            Time-window probability<\/h3>\n\n\n\n

            Predicts likelihood to buy within a defined window:\n– \u201cwithin 24 hours\u201d (high-intent eCommerce)\n– \u201cwithin 14 days\u201d (consideration-heavy categories)\n– \u201cbefore trial ends\u201d (SaaS)<\/p>\n\n\n\n

            Stage-based probability<\/h3>\n\n\n\n

            Predicts purchase likelihood conditioned on funnel stage:\n– visitor-to-buyer probability\n– cart-to-purchase probability\n– lead-to-customer probability (B2B)<\/p>\n\n\n\n

            Entity level<\/h3>\n\n\n\n
              \n
            • User-level<\/strong> probability (consumer and self-serve)<\/li>\n
            • Account-level<\/strong> probability (B2B and ABM)<\/li>\n
            • Session-level<\/strong> probability (real-time personalization and bidding)<\/li>\n<\/ul>\n\n\n\n

              Model complexity<\/h3>\n\n\n\n
                \n
              • Heuristic scoring<\/strong> (fast to implement, less precise)<\/li>\n
              • Predictive modeling<\/strong> (more accurate, requires stronger Analytics<\/strong> capability)<\/li>\n<\/ul>\n\n\n\n
                \n\n\n\n

                Real-World Examples of Purchase Probability<\/h2>\n\n\n\n

                Example 1: eCommerce cart recovery and offer strategy<\/h3>\n\n\n\n

                A retailer uses Purchase Probability to segment cart abandoners:\n– High probability: send a reminder email without discount (protect margin)\n– Medium probability: send social proof and free shipping threshold\n– Low probability: delay discount until a second signal appears (e.g., revisit)<\/p>\n\n\n\n

                In Conversion & Measurement<\/strong>, success is tracked via incremental conversions and margin impact, while Analytics<\/strong> monitors calibration and segment performance.<\/p>\n\n\n\n

                Example 2: SaaS trial conversion and in-app guidance<\/h3>\n\n\n\n

                A SaaS company predicts Purchase Probability during a 14-day trial using:\n– product activation events (key feature usage)\n– team invites\n– integration setup<\/p>\n\n\n\n

                High-probability trials get sales outreach or upgrade prompts; low-probability trials get guided onboarding and educational content. Conversion & Measurement<\/strong> evaluates lift in trial-to-paid rate; Analytics<\/strong> validates whether the score generalizes across cohorts.<\/p>\n\n\n\n

                Example 3: B2B lead routing and pipeline efficiency<\/h3>\n\n\n\n

                A B2B firm assigns Purchase Probability at the account level using:\n– firmographics (industry, company size)\n– intent activity (webinar attendance, pricing page visits)\n– engagement across stakeholders<\/p>\n\n\n\n

                Sales prioritizes high-probability accounts, while marketing suppresses low-probability accounts from expensive retargeting until new intent signals appear. This ties Conversion & Measurement<\/strong> to pipeline outcomes and uses Analytics<\/strong> to reduce wasted touches.<\/p>\n\n\n\n

                \n\n\n\n

                Benefits of Using Purchase Probability<\/h2>\n\n\n\n

                Purchase Probability delivers gains when it\u2019s used to change decisions, not just generate scores.<\/p>\n\n\n\n

                  \n
                • Higher conversion efficiency:<\/strong> Focus on audiences and journeys most likely to convert.<\/li>\n
                • Lower acquisition waste:<\/strong> Reduce spend on low-intent segments and improve targeting.<\/li>\n
                • Improved personalization:<\/strong> Serve content, offers, and messages aligned to intent level.<\/li>\n
                • Faster sales cycles (B2B):<\/strong> Route high-likelihood accounts to the right reps sooner.<\/li>\n
                • Better forecasting:<\/strong> Use probability-weighted pipelines or expected revenue models for planning.<\/li>\n
                • Stronger experimentation:<\/strong> More precise cohort analysis improves Conversion & Measurement<\/strong> learnings, supported by Analytics<\/strong> rigor.<\/li>\n<\/ul>\n\n\n\n
                  \n\n\n\n

                  Challenges of Purchase Probability<\/h2>\n\n\n\n

                  Purchase Probability can fail when teams overtrust the score or underinvest in measurement fundamentals.<\/p>\n\n\n\n

                  Data and tracking limitations<\/h3>\n\n\n\n
                    \n
                  • incomplete event coverage across devices and channels<\/li>\n
                  • inconsistent definitions of \u201cconversion\u201d and \u201cpurchase\u201d<\/li>\n
                  • identity gaps due to privacy constraints and consent choices<\/li>\n<\/ul>\n\n\n\n

                    Modeling risks<\/h3>\n\n\n\n
                      \n
                    • bias and leakage:<\/strong> using features that indirectly encode outcomes or unfairly skew segments<\/li>\n
                    • stale models:<\/strong> behavior changes after pricing updates, seasonality, or channel shifts<\/li>\n
                    • poor calibration:<\/strong> scores that look good in rank-ordering but misstate true likelihood<\/li>\n<\/ul>\n\n\n\n

                      Organizational and activation barriers<\/h3>\n\n\n\n
                        \n
                      • difficulty integrating scoring into CRM, ad platforms, and lifecycle automation<\/li>\n
                      • unclear ownership between marketing, data, and product teams<\/li>\n
                      • misalignment on what \u201cgood\u201d means in Conversion & Measurement<\/strong> (e.g., revenue vs. volume)<\/li>\n<\/ul>\n\n\n\n
                        \n\n\n\n

                        Best Practices for Purchase Probability<\/h2>\n\n\n\n

                        Define the prediction target precisely<\/h3>\n\n\n\n

                        Specify:\n– who is being scored (user\/account\/session)\n– what counts as purchase\n– the time horizon (7 days, 30 days, etc.)\nThis clarity improves both Analytics<\/strong> quality and Conversion & Measurement<\/strong> decision-making.<\/p>\n\n\n\n

                        Start simple, then iterate<\/h3>\n\n\n\n

                        A rules-based model can create quick wins. As data maturity grows, move to predictive modeling. The key is to measure incremental impact, not model sophistication.<\/p>\n\n\n\n

                        Validate and calibrate regularly<\/h3>\n\n\n\n

                        Track:\n– calibration (do \u201c30%\u201d users actually purchase ~30% of the time?)\n– performance by channel, device, geography, and cohort\n– drift after product or campaign changes<\/p>\n\n\n\n

                        Use probability as a decision threshold, not a label<\/h3>\n\n\n\n

                        Define operational thresholds:\n– \u201c>0.6: sales outreach\u201d\n– \u201c0.3\u20130.6: nurture sequence\u201d\n– \u201c<0.3: suppress from high-cost retargeting\u201d\nTie thresholds to Conversion & Measurement<\/strong> goals like CAC, ROAS, and margin.<\/p>\n\n\n\n

                        Protect against self-fulfilling outcomes<\/h3>\n\n\n\n

                        If you only market to high-probability users, the model may \u201clearn\u201d that only they convert. Keep controlled holdouts and test groups to maintain Analytics<\/strong> integrity.<\/p>\n\n\n\n

                        Document assumptions and data definitions<\/h3>\n\n\n\n

                        A shared measurement dictionary prevents teams from optimizing different versions of \u201cpurchase\u201d and undermining Conversion & Measurement<\/strong> consistency.<\/p>\n\n\n\n

                        \n\n\n\n

                        Tools Used for Purchase Probability<\/h2>\n\n\n\n

                        Purchase Probability is usually implemented across a stack rather than in one tool category.<\/p>\n\n\n\n

                          \n
                        • Analytics tools:<\/strong> collect and analyze event data, build funnels, and validate score behavior.<\/li>\n
                        • Data platforms and warehouses:<\/strong> centralize behavioral, CRM, and transaction data for modeling.<\/li>\n
                        • Marketing automation platforms:<\/strong> trigger journeys based on probability segments (nurture, reactivation, cart recovery).<\/li>\n
                        • CRM systems:<\/strong> store lead\/account scores and support routing rules for sales.<\/li>\n
                        • Ad platforms:<\/strong> build audiences, create exclusions, and adjust bidding strategies based on intent tiers.<\/li>\n
                        • Experimentation and personalization tools:<\/strong> deliver on-site\/app experiences tailored to probability bands.<\/li>\n
                        • Reporting dashboards:<\/strong> monitor drift, calibration, and Conversion & Measurement<\/strong> results over time.<\/li>\n<\/ul>\n\n\n\n

                          The right mix depends on whether you\u2019re doing real-time scoring (on-site personalization) or batch scoring (daily\/weekly updates for CRM and ads).<\/p>\n\n\n\n

                          \n\n\n\n

                          Metrics Related to Purchase Probability<\/h2>\n\n\n\n

                          Purchase Probability should be evaluated with both model quality metrics and business outcome metrics.<\/p>\n\n\n\n

                          Model and score quality (Analytics-focused)<\/h3>\n\n\n\n
                            \n
                          • Calibration:<\/strong> how closely predicted probabilities match actual outcomes<\/li>\n
                          • Discrimination:<\/strong> ability to rank high-intent above low-intent users (e.g., AUC\/ROC)<\/li>\n
                          • Lift by decile:<\/strong> conversion rate improvement from top-scored segments vs. average<\/li>\n
                          • Stability\/drift:<\/strong> score distribution changes over time, indicating behavior shifts<\/li>\n<\/ul>\n\n\n\n

                            Business and Conversion & Measurement outcomes<\/h3>\n\n\n\n
                              \n
                            • Conversion rate by probability band<\/strong><\/li>\n
                            • Incremental conversions<\/strong> from probability-based targeting vs. baseline<\/li>\n
                            • CAC \/ CPA changes<\/strong> when spend is shifted by score<\/li>\n
                            • ROAS or MER<\/strong> improvements in paid media<\/li>\n
                            • Revenue per user\/lead<\/strong> and margin impact (especially if discounting is involved)<\/li>\n
                            • Sales efficiency metrics:<\/strong> speed-to-lead, win rate, pipeline velocity (B2B)<\/li>\n<\/ul>\n\n\n\n
                              \n\n\n\n

                              Future Trends of Purchase Probability<\/h2>\n\n\n\n

                              Purchase Probability is evolving as Conversion & Measurement<\/strong> adapts to privacy, automation, and AI-driven personalization.<\/p>\n\n\n\n

                                \n
                              • More first-party data emphasis:<\/strong> stronger reliance on consented event and CRM data as third-party signals fade.<\/li>\n
                              • Real-time decisioning:<\/strong> more use cases require scoring within-session for personalization and on-site offers.<\/li>\n
                              • Causal measurement integration:<\/strong> teams will combine probability models with incrementality testing to avoid optimizing for conversions that would have happened anyway.<\/li>\n
                              • Automated audience management:<\/strong> probability segments will increasingly drive exclusions, bid modifiers, and creative sequencing.<\/li>\n
                              • AI-assisted feature discovery:<\/strong> faster identification of intent signals, while governance becomes more important to prevent leakage and bias.<\/li>\n
                              • Privacy-aware modeling:<\/strong> aggregated and cohort-based approaches will complement user-level scoring where individual tracking is limited.<\/li>\n<\/ul>\n\n\n\n

                                In short, Purchase Probability will become a core layer in Conversion & Measurement<\/strong>, with Analytics<\/strong> teams focusing more on robustness, calibration, and incremental impact.<\/p>\n\n\n\n

                                \n\n\n\n

                                Purchase Probability vs Related Terms<\/h2>\n\n\n\n

                                Purchase Probability vs Conversion Rate<\/h3>\n\n\n\n
                                  \n
                                • Conversion rate<\/strong> is a historical ratio: conversions \u00f7 visitors (or sessions).<\/li>\n
                                • Purchase Probability<\/strong> is a forward-looking estimate for an individual or segment.\nConversion rate explains what happened; Purchase Probability helps decide what to do next in Conversion & Measurement<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                                  Purchase Probability vs Lead Score<\/h3>\n\n\n\n
                                    \n
                                  • Lead scoring<\/strong> often assigns points based on actions and demographics, sometimes without probabilistic meaning.<\/li>\n
                                  • Purchase Probability<\/strong> explicitly estimates likelihood to buy and can be calibrated and validated in Analytics<\/strong>.\nLead scoring can be a proxy; Purchase Probability is the more measurable, model-driven version when implemented well.<\/li>\n<\/ul>\n\n\n\n

                                    Purchase Probability vs Purchase Intent<\/h3>\n\n\n\n
                                      \n
                                    • Purchase intent<\/strong> is a broader concept describing signals of readiness to buy.<\/li>\n
                                    • Purchase Probability<\/strong> quantifies that intent into a numerical likelihood tied to a timeframe and outcome.\nIntent is qualitative; probability is operational.<\/li>\n<\/ul>\n\n\n\n
                                      \n\n\n\n

                                      Who Should Learn Purchase Probability<\/h2>\n\n\n\n
                                        \n
                                      • Marketers and growth teams:<\/strong> to prioritize audiences, personalize journeys, and improve ROAS within Conversion & Measurement<\/strong>.<\/li>\n
                                      • Analysts and data practitioners:<\/strong> to build, validate, and monitor probability models and ensure Analytics<\/strong> rigor.<\/li>\n
                                      • Agencies:<\/strong> to demonstrate measurable lift and smarter budget allocation across clients and channels.<\/li>\n
                                      • Business owners and founders:<\/strong> to forecast demand, optimize spend, and align teams on revenue outcomes.<\/li>\n
                                      • Developers and product teams:<\/strong> to implement event tracking, real-time scoring hooks, and experimentation infrastructure that makes Purchase Probability actionable.<\/li>\n<\/ul>\n\n\n\n
                                        \n\n\n\n

                                        Summary of Purchase Probability<\/h2>\n\n\n\n

                                        Purchase Probability is an estimate of how likely a user, lead, or account is to make a purchase within a defined context and timeframe. It matters because it turns raw behavioral signals into prioritization\u2014helping teams decide where to invest, who to nurture, and how to personalize. Within Conversion & Measurement<\/strong>, it improves efficiency and forecasting by focusing on expected outcomes rather than only past results. Within Analytics<\/strong>, it is a practical application of data modeling, calibration, and continuous monitoring to support better marketing and product decisions.<\/p>\n\n\n\n

                                        \n\n\n\n

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

                                        1) What is Purchase Probability used for in marketing?<\/h3>\n\n\n\n

                                        It\u2019s used to prioritize actions\u2014such as ad targeting, lifecycle messaging, sales outreach, and personalization\u2014based on who is most likely to buy, improving Conversion & Measurement<\/strong> efficiency.<\/p>\n\n\n\n

                                        2) How do you calculate Purchase Probability?<\/h3>\n\n\n\n

                                        You can calculate it with rules-based scoring or predictive models trained on historical purchases and behavior signals. The best approach depends on data quality, volume, and how the score will be activated.<\/p>\n\n\n\n

                                        3) Is Purchase Probability the same as conversion rate?<\/h3>\n\n\n\n

                                        No. Conversion rate summarizes past performance for a population. Purchase Probability estimates the likelihood of purchase for an individual, session, or account, which is more actionable for targeting and personalization.<\/p>\n\n\n\n

                                        4) What data is most important for accurate Purchase Probability?<\/h3>\n\n\n\n

                                        High-quality first-party event data (product views, cart actions, checkout steps), reliable purchase events, and contextual attributes (source, device, recency\/frequency) typically drive the strongest results in Analytics<\/strong>.<\/p>\n\n\n\n

                                        5) How do you evaluate Purchase Probability in Analytics?<\/h3>\n\n\n\n

                                        Check calibration (predicted vs. actual), ranking ability (lift\/deciles, AUC), and business impact (incremental conversions, CAC\/ROAS). A \u201cgood\u201d model must improve outcomes, not just accuracy metrics.<\/p>\n\n\n\n

                                        6) Can Purchase Probability work with privacy restrictions and consent requirements?<\/h3>\n\n\n\n

                                        Yes, but you may need to rely more on consented first-party data, aggregated reporting, and careful governance. The Conversion & Measurement<\/strong> plan should specify what data is allowed and how it\u2019s retained.<\/p>\n\n\n\n

                                        7) What\u2019s a practical first step to implement Purchase Probability?<\/h3>\n\n\n\n

                                        Start by defining the purchase event and time window, ensure tracking is consistent, build a simple segmentation (high\/medium\/low intent) using a few strong signals, and measure incremental lift before scaling to advanced modeling.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                        Purchase Probability is the estimated likelihood that a person (or account) will complete a purchase within a defined period and context. In **Conversion & Measurement**, it helps teams move from simply counting conversions to predicting which audiences, sessions, or leads are most likely to convert next. In **Analytics**, it sits at the intersection of behavioral data, customer intent signals, and statistical modeling\u2014turning messy, multi-touch interactions into an actionable probability score.<\/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-6917","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\/6917","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=6917"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/6917\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=6917"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=6917"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=6917"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}