{"id":7422,"date":"2026-03-24T12:22:36","date_gmt":"2026-03-24T12:22:36","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/deal-risk-score\/"},"modified":"2026-03-24T12:22:36","modified_gmt":"2026-03-24T12:22:36","slug":"deal-risk-score","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/deal-risk-score\/","title":{"rendered":"Deal Risk Score: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Demand Generation & B2B Marketing"},"content":{"rendered":"\n

In Demand Generation & B2B Marketing<\/strong>, pipeline is only as valuable as its likelihood to close. A Deal Risk Score<\/strong> is a structured way to estimate how \u201cat risk\u201d a specific opportunity is\u2014based on signals from the buyer journey, sales activity, engagement patterns, and operational realities. Instead of relying on gut feel or optimistic forecasts, teams use a Deal Risk Score to spot deals that are likely to stall, slip, or churn before closing.<\/p>\n\n\n\n

This concept matters in modern Demand Generation & B2B Marketing<\/strong> because marketing and sales are increasingly measured on pipeline quality, revenue contribution, and forecast reliability\u2014not just lead volume. When you can quantify deal risk, you can prioritize the right interventions, allocate budget more intelligently, and improve the handoff from campaigns to revenue teams.<\/p>\n\n\n\n

\n\n\n\n

2) What Is Deal Risk Score?<\/h2>\n\n\n\n

A Deal Risk Score<\/strong> is a numeric or categorical rating that represents the probability that an open sales opportunity will not close as expected (or will close later, at a smaller amount, or with unfavorable terms). It\u2019s typically calculated from multiple signals\u2014behavioral, firmographic, process-based, and historical\u2014and is used to guide action.<\/p>\n\n\n\n

At its core, the Deal Risk Score is about deal health<\/strong> and deal momentum<\/strong>:<\/p>\n\n\n\n

    \n
  • Health<\/strong>: Is the opportunity aligned with the right buyer, need, budget, authority, and timeline?<\/li>\n
  • Momentum<\/strong>: Are there recent, meaningful interactions that indicate progress toward a decision?<\/li>\n<\/ul>\n\n\n\n

    From a business perspective, a Deal Risk Score helps answer: \u201cWhich deals require attention right now, and why?\u201d<\/em> In Demand Generation & B2B Marketing<\/strong>, it also clarifies which parts of the funnel are producing deals that consistently de-risk over time versus deals that look good early but later stall.<\/p>\n\n\n\n

    Within Demand Generation & B2B Marketing<\/strong>, the Deal Risk Score becomes a shared language across marketing, SDR\/BDR, sales, and revenue operations\u2014turning pipeline reviews into measurable diagnosis instead of subjective debate.<\/p>\n\n\n\n

    \n\n\n\n

    3) Why Deal Risk Score Matters in Demand Generation & B2B Marketing<\/h2>\n\n\n\n

    A Deal Risk Score creates strategic and financial leverage because it improves decision-making across the entire revenue system.<\/p>\n\n\n\n

    Strategic importance<\/strong>\n– Enables proactive pipeline management instead of reactive \u201cend-of-quarter heroics.\u201d\n– Aligns go-to-market teams on what \u201cgood pipeline\u201d truly means.<\/p>\n\n\n\n

    Business value<\/strong>\n– Improves forecast confidence by highlighting which opportunities are likely to slip.\n– Reduces wasted effort on low-probability deals while protecting high-value opportunities.<\/p>\n\n\n\n

    Marketing outcomes<\/strong>\n– Helps marketers understand which programs generate lower-risk pipeline<\/em>, not just more pipeline.\n– Improves nurture strategy by identifying where deals need education, proof, or internal alignment content.<\/p>\n\n\n\n

    Competitive advantage<\/strong>\n– Teams that operationalize a Deal Risk Score respond faster to buyer hesitation, competitor pressure, and internal buying committee dynamics\u2014often winning deals that otherwise drift to \u201cno decision.\u201d<\/p>\n\n\n\n

    In Demand Generation & B2B Marketing<\/strong>, the score acts like an early-warning system: it surfaces friction before revenue is lost.<\/p>\n\n\n\n

    \n\n\n\n

    4) How Deal Risk Score Works<\/h2>\n\n\n\n

    A Deal Risk Score can be simple (rules-based) or sophisticated (statistical\/ML), but in practice it follows a consistent workflow:<\/p>\n\n\n\n

    1) Inputs \/ triggers<\/strong>\n– Opportunity stage changes, meeting activity, email engagement, product usage (for PLG motions), proposal events, pricing requests, or inactivity periods.\n– Buyer signals such as visits to key pages, intent surges, or engagement from new stakeholders.<\/p>\n\n\n\n

    2) Analysis \/ processing<\/strong>\n– The system evaluates signals against defined risk factors (for example: long stage duration, single-threading, missing next steps, weak persona mix, or declining engagement).\n– Scores may be weighted by deal size, segment, or historical close patterns.<\/p>\n\n\n\n

    3) Execution \/ application<\/strong>\n– The score is displayed in the CRM and used in pipeline reviews.\n– Alerts or tasks are created for sales and marketing (e.g., \u201cstakeholder missing,\u201d \u201ctimeline unclear,\u201d \u201cno activity in 14 days\u201d).\n– Marketing can trigger specific nurture paths, enablement content, or executive outreach programs.<\/p>\n\n\n\n

    4) Outputs \/ outcomes<\/strong>\n– A numeric score (0\u2013100) or categories (Low\/Medium\/High risk).\n– Recommended actions (diagnostic + next best step).\n– Better prioritization and improved conversion, velocity, and retention of pipeline.<\/p>\n\n\n\n

    In Demand Generation & B2B Marketing<\/strong>, what matters most is not the number itself\u2014it\u2019s whether the score reliably drives the right interventions and improves outcomes.<\/p>\n\n\n\n

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    5) Key Components of Deal Risk Score<\/h2>\n\n\n\n

    A robust Deal Risk Score typically includes several operational components:<\/p>\n\n\n\n

    Data inputs (signals)<\/h3>\n\n\n\n
      \n
    • Engagement<\/strong>: email replies, meeting attendance, content consumption, stakeholder breadth, site\/product activity.<\/li>\n
    • Process<\/strong>: stage age, next step scheduled, proposal sent, procurement initiated, legal\/security steps.<\/li>\n
    • Fit<\/strong>: ICP match, firmographics, technographics, use case alignment, budget range.<\/li>\n
    • History<\/strong>: past win\/loss patterns by segment, source, and deal characteristics.<\/li>\n<\/ul>\n\n\n\n

      Systems and processes<\/h3>\n\n\n\n
        \n
      • CRM hygiene<\/strong>: accurate stages, close dates, amounts, stakeholders, and next steps.<\/li>\n
      • Lifecycle definitions<\/strong>: shared meaning of stages and qualification criteria.<\/li>\n
      • Feedback loop<\/strong>: win\/loss analysis to recalibrate factors and weights.<\/li>\n<\/ul>\n\n\n\n

        Governance and responsibilities<\/h3>\n\n\n\n
          \n
        • Revenue operations<\/strong>: defines scoring logic, audits data quality, monitors drift.<\/li>\n
        • Sales leadership<\/strong>: enforces consistent usage in forecasting and coaching.<\/li>\n
        • Marketing ops \/ demand gen<\/strong>: maps programs to risk reduction (nurture, ABM, retargeting, enablement content).<\/li>\n<\/ul>\n\n\n\n

          A Deal Risk Score fails most often when ownership is unclear or inputs are inconsistent.<\/p>\n\n\n\n

          \n\n\n\n

          6) Types of Deal Risk Score<\/h2>\n\n\n\n

          There isn\u2019t one universal standard, but there are practical variants that teams use:<\/p>\n\n\n\n

          Rules-based vs. predictive<\/h3>\n\n\n\n
            \n
          • Rules-based<\/strong>: if\/then scoring (e.g., \u201cNo meeting in 14 days = higher risk\u201d). Easier to implement and explain.<\/li>\n
          • Predictive<\/strong>: uses statistical models or machine learning trained on historical outcomes. Often more accurate, but requires strong data foundations and monitoring.<\/li>\n<\/ul>\n\n\n\n

            Stage-specific scoring<\/h3>\n\n\n\n
              \n
            • Early-stage opportunities may emphasize fit and stakeholder engagement<\/strong>.<\/li>\n
            • Late-stage opportunities may emphasize procurement milestones, mutual action plans, and legal\/security progress<\/strong>.<\/li>\n<\/ul>\n\n\n\n

              Segment-specific scoring<\/h3>\n\n\n\n

              Enterprise deals can carry different \u201cnormal\u201d patterns (longer cycles, more stakeholders) than SMB. Segment-aware Deal Risk Score models reduce false positives.<\/p>\n\n\n\n

              Account-level vs. opportunity-level risk<\/h3>\n\n\n\n
                \n
              • Opportunity-level<\/strong> focuses on a specific deal.<\/li>\n
              • Account-level<\/strong> adds context (expansion potential, multi-product adoption, overall relationship strength) and can explain why similar deals behave differently.<\/li>\n<\/ul>\n\n\n\n

                These distinctions are especially useful in Demand Generation & B2B Marketing<\/strong>, where program performance varies dramatically by segment and motion.<\/p>\n\n\n\n

                \n\n\n\n

                7) Real-World Examples of Deal Risk Score<\/h2>\n\n\n\n

                Example 1: ABM program flags single-threaded enterprise deals<\/h3>\n\n\n\n

                A team running account-based programs notices several large opportunities created from executive webinar attendance. The Deal Risk Score<\/strong> rises because the CRM shows only one active stakeholder and no upcoming meeting scheduled. Marketing responds by launching a persona-based nurture sequence aimed at finance and security stakeholders, while sales is prompted to multi-thread. Result: fewer late-stage stalls and better enterprise forecast stability\u2014an immediate win for Demand Generation & B2B Marketing<\/strong> pipeline quality.<\/p>\n\n\n\n

                Example 2: Mid-funnel content reduces \u201cno decision\u201d outcomes<\/h3>\n\n\n\n

                A SaaS company sees deals slipping after demos with \u201cwe\u2019ll get back to you.\u201d The Deal Risk Score identifies a pattern: high engagement before demo, then a drop in activity and no mutual action plan. Marketing adds a post-demo toolkit (ROI narrative, implementation plan, stakeholder email templates). Deals with this content touch show improved momentum and lower risk within two weeks\u2014demonstrating how Demand Generation & B2B Marketing<\/strong> can directly de-risk revenue.<\/p>\n\n\n\n

                Example 3: Paid search produces volume but higher-risk pipeline<\/h3>\n\n\n\n

                Paid search drives many opportunities, but the Deal Risk Score shows they have weaker ICP match and shorter initial engagement. Instead of turning off spend, the team tightens keyword intent, adjusts landing page qualification, and routes borderline leads into nurture. Result: fewer low-quality opps created, higher win rate, and improved cost per won deal\u2014making Demand Generation & B2B Marketing<\/strong> spend more defensible.<\/p>\n\n\n\n

                \n\n\n\n

                8) Benefits of Using Deal Risk Score<\/h2>\n\n\n\n

                Using a Deal Risk Score well produces benefits beyond forecasting:<\/p>\n\n\n\n

                  \n
                • Performance improvements<\/strong>: higher win rates, faster pipeline velocity, fewer end-of-quarter surprises.<\/li>\n
                • Cost savings<\/strong>: reduced time spent on low-probability deals; more efficient use of SDR, AE, and marketing resources.<\/li>\n
                • Operational efficiency<\/strong>: standardized deal reviews, clearer coaching priorities, and less subjective pipeline debate.<\/li>\n
                • Better buyer experience<\/strong>: interventions become more relevant\u2014addressing real concerns (security, ROI, stakeholder alignment) instead of generic follow-ups.<\/li>\n<\/ul>\n\n\n\n

                  In mature Demand Generation & B2B Marketing<\/strong> teams, the score becomes a bridge between program execution and revenue reality.<\/p>\n\n\n\n

                  \n\n\n\n

                  9) Challenges of Deal Risk Score<\/h2>\n\n\n\n

                  A Deal Risk Score can create false confidence if implemented poorly. Common challenges include:<\/p>\n\n\n\n

                    \n
                  • Data quality issues<\/strong>: missing next steps, inaccurate close dates, inconsistent stage definitions, and incomplete stakeholder records.<\/li>\n
                  • Signal noise<\/strong>: high email activity might indicate confusion rather than progress; meeting volume can mask lack of decision authority.<\/li>\n
                  • Model bias<\/strong>: historical data can embed past targeting mistakes (e.g., under-investing in certain segments).<\/li>\n
                  • Over-automation<\/strong>: teams may follow the score blindly, ignoring nuance like competitor displacement, internal politics, or economic shifts.<\/li>\n
                  • Change management<\/strong>: if sales doesn\u2019t trust the score, it won\u2019t be used\u2014no matter how good the analytics are.<\/li>\n<\/ul>\n\n\n\n

                    In Demand Generation & B2B Marketing<\/strong>, adoption is as important as accuracy.<\/p>\n\n\n\n

                    \n\n\n\n

                    10) Best Practices for Deal Risk Score<\/h2>\n\n\n\n

                    To make a Deal Risk Score reliable and actionable:<\/p>\n\n\n\n

                    1) Start with explainable factors<\/strong>\nBegin with a short list of high-signal risk drivers (stage age, inactivity, single-threading, missing next step, weak ICP match). Expand only when you can prove incremental value.<\/p>\n\n\n\n

                    2) Define \u201cgood data\u201d operationally<\/strong>\nSet minimum CRM requirements (next step required, at least X stakeholders, close date rules). Audit regularly.<\/p>\n\n\n\n

                    3) Calibrate by segment and stage<\/strong>\nA single score for all deals often mislabels healthy enterprise deals as risky. Use segment\/stage context to reduce false alarms.<\/p>\n\n\n\n

                    4) Tie scores to playbooks<\/strong>\nA Deal Risk Score should trigger actions: stakeholder mapping, executive sponsor outreach, ROI validation, proof points, or technical validation steps.<\/p>\n\n\n\n

                    5) Validate with win\/loss and holdout testing<\/strong>\nCompare scored vs. unscored cohorts. Measure whether interventions reduce risk and improve outcomes, not just whether the model \u201clooks smart.\u201d<\/p>\n\n\n\n

                    6) Use it in weekly rituals<\/strong>\nMake the score part of pipeline review, QBRs, and campaign analysis within Demand Generation & B2B Marketing<\/strong> so it becomes a habit, not a dashboard artifact.<\/p>\n\n\n\n

                    \n\n\n\n

                    11) Tools Used for Deal Risk Score<\/h2>\n\n\n\n

                    A Deal Risk Score is usually operationalized across a stack rather than a single tool:<\/p>\n\n\n\n

                      \n
                    • CRM systems<\/strong>: the system of record for stages, close dates, activity, contacts, and opportunity fields.<\/li>\n
                    • Marketing automation platforms<\/strong>: engagement signals from emails, forms, nurture, and event attendance that inform risk.<\/li>\n
                    • Sales engagement tools<\/strong>: cadence activity and reply data that indicate momentum or stalling.<\/li>\n
                    • Product analytics (for PLG\/hybrid motions)<\/strong>: usage depth, activation milestones, and adoption signals that correlate with closing.<\/li>\n
                    • Conversation intelligence<\/strong>: extracts themes like budget, timeline, competitors, and next steps from calls to refine risk factors.<\/li>\n
                    • Data warehouse\/CDP<\/strong>: unifies signals across web, product, and revenue systems for consistent scoring.<\/li>\n
                    • BI and reporting dashboards<\/strong>: score distribution, trends by source\/segment, and outcome correlations for leadership.<\/li>\n<\/ul>\n\n\n\n

                      In Demand Generation & B2B Marketing<\/strong>, the goal is not tooling complexity\u2014it\u2019s consistent inputs, transparent logic, and reliable actions.<\/p>\n\n\n\n

                      \n\n\n\n

                      12) Metrics Related to Deal Risk Score<\/h2>\n\n\n\n

                      A Deal Risk Score becomes more useful when monitored alongside outcome metrics:<\/p>\n\n\n\n

                        \n
                      • Win rate by risk band<\/strong> (Low vs. Medium vs. High): verifies the score is discriminating outcomes.<\/li>\n
                      • Forecast accuracy<\/strong>: compares predicted close timing\/amount to actuals.<\/li>\n
                      • Pipeline velocity<\/strong>: time-in-stage and overall cycle time by risk band and segment.<\/li>\n
                      • Slippage rate<\/strong>: percent of deals that push close date, especially from late stages.<\/li>\n
                      • No-decision rate<\/strong>: opportunities closed-lost to inaction; often the biggest hidden risk.<\/li>\n
                      • Conversion rates<\/strong>: MQL\u2192SQL, SQL\u2192Opportunity, Opportunity\u2192Closed-Won\u2014mapped to average risk at creation.<\/li>\n
                      • Marketing-sourced pipeline quality<\/strong>: average Deal Risk Score of opportunities created by channel\/program.<\/li>\n<\/ul>\n\n\n\n

                        These metrics help Demand Generation & B2B Marketing<\/strong> teams prove that pipeline quality improvements translate into revenue.<\/p>\n\n\n\n

                        \n\n\n\n

                        13) Future Trends of Deal Risk Score<\/h2>\n\n\n\n

                        Several shifts are shaping how Deal Risk Score models evolve within Demand Generation & B2B Marketing<\/strong>:<\/p>\n\n\n\n

                          \n
                        • AI-assisted signal extraction<\/strong>: automated identification of risk themes from calls, emails, and notes (e.g., unclear timeline, missing champion).<\/li>\n
                        • Real-time scoring<\/strong>: moving from weekly refreshes to event-driven updates (proposal view, security questionnaire, stakeholder added).<\/li>\n
                        • Personalized risk reduction<\/strong>: next-best-action recommendations tailored to segment, industry, and buying committee role.<\/li>\n
                        • Privacy-aware measurement<\/strong>: greater reliance on first-party data, modeled engagement, and aggregated signals as tracking constraints increase.<\/li>\n
                        • Unified revenue metrics<\/strong>: closer alignment of marketing and sales performance around risk-adjusted pipeline rather than raw volume.<\/li>\n<\/ul>\n\n\n\n

                          The long-term direction is clear: a Deal Risk Score will be less of a static number and more of an operational control system for revenue.<\/p>\n\n\n\n

                          \n\n\n\n

                          14) Deal Risk Score vs Related Terms<\/h2>\n\n\n\n

                          Deal Risk Score vs Lead Scoring<\/h3>\n\n\n\n
                            \n
                          • Lead scoring<\/strong> estimates how likely a lead<\/em> is to become sales-qualified or convert to an opportunity.<\/li>\n
                          • Deal Risk Score<\/strong> evaluates an open opportunity<\/em> and how likely it is to close successfully and on time.<\/li>\n<\/ul>\n\n\n\n

                            Deal Risk Score vs Opportunity Health Score<\/h3>\n\n\n\n
                              \n
                            • \u201cOpportunity health\u201d often includes positive momentum indicators (strength, progress).<\/li>\n
                            • A Deal Risk Score is typically framed around downside probability (stall, slip, loss). Many teams combine both views.<\/li>\n<\/ul>\n\n\n\n

                              Deal Risk Score vs Forecast Category<\/h3>\n\n\n\n
                                \n
                              • Forecast categories (e.g., Commit, Best Case, Pipeline) are often rep-assigned judgments.<\/li>\n
                              • A Deal Risk Score is evidence-based and consistent across reps, helping reduce optimism bias.<\/li>\n<\/ul>\n\n\n\n

                                In Demand Generation & B2B Marketing<\/strong>, these concepts complement each other: lead scoring feeds better opportunities, while Deal Risk Score protects and converts pipeline.<\/p>\n\n\n\n

                                \n\n\n\n

                                15) Who Should Learn Deal Risk Score<\/h2>\n\n\n\n
                                  \n
                                • Marketers<\/strong>: to optimize for revenue outcomes, improve pipeline quality, and design nurture that reduces risk.<\/li>\n
                                • Analysts<\/strong>: to build models, validate signals, and connect program performance to revenue impact.<\/li>\n
                                • Agencies<\/strong>: to prove pipeline quality and retention value\u2014not just traffic, leads, or vanity metrics.<\/li>\n
                                • Business owners and founders<\/strong>: to improve predictability, reduce sales-cycle surprises, and allocate resources confidently.<\/li>\n
                                • Developers and marketing ops<\/strong>: to integrate data sources, automate alerts, and build reliable reporting pipelines.<\/li>\n<\/ul>\n\n\n\n

                                  For anyone working in Demand Generation & B2B Marketing<\/strong>, understanding Deal Risk Score improves decision-making across the funnel.<\/p>\n\n\n\n

                                  \n\n\n\n

                                  16) Summary of Deal Risk Score<\/h2>\n\n\n\n

                                  A Deal Risk Score<\/strong> is a structured way to quantify the likelihood that an opportunity will stall, slip, or fail to close. It matters because it improves forecast reliability, prioritization, and the ability to intervene early with the right message, content, or sales motion. In Demand Generation & B2B Marketing<\/strong>, it connects campaign performance to pipeline quality and helps teams shift from volume-driven reporting to revenue-driven execution. Used well, Deal Risk Score supports a more predictable, efficient, and buyer-aligned growth engine.<\/p>\n\n\n\n

                                  \n\n\n\n

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

                                  1) What is a Deal Risk Score?<\/h3>\n\n\n\n

                                  A Deal Risk Score is a rating (numeric or categorical) that estimates how likely an open sales opportunity is to slip, stall, or be lost, based on signals like engagement, stage duration, stakeholder coverage, and process milestones.<\/p>\n\n\n\n

                                  2) How is Deal Risk Score calculated?<\/h3>\n\n\n\n

                                  Most teams use either rules-based scoring (weighted risk factors) or predictive models trained on historical win\/loss data. The best approach depends on data maturity, segmentation needs, and how explainable the score must be for adoption.<\/p>\n\n\n\n

                                  3) How does Deal Risk Score help Demand Generation & B2B Marketing teams?<\/h3>\n\n\n\n

                                  It helps Demand Generation & B2B Marketing<\/strong> teams identify which programs generate low-risk pipeline, trigger targeted nurture to address buyer concerns, and prove impact using downstream revenue metrics rather than only top-of-funnel conversions.<\/p>\n\n\n\n

                                  4) What are common signals that increase deal risk?<\/h3>\n\n\n\n

                                  Typical risk signals include long time-in-stage, no upcoming meeting, declining engagement, single-threaded contacts, missing champion, unclear timeline\/budget, and lack of progress on procurement or security steps.<\/p>\n\n\n\n

                                  5) Should marketing teams own the Deal Risk Score?<\/h3>\n\n\n\n

                                  Marketing rarely \u201cowns\u201d it alone. Revenue operations typically governs the model, sales uses it in forecasting and coaching, and marketing operationalizes risk-reduction plays (nurture, ABM, enablement content) to improve outcomes.<\/p>\n\n\n\n

                                  6) Can a Deal Risk Score be wrong\u2014and what should we do about it?<\/h3>\n\n\n\n

                                  Yes. Treat it as decision support, not truth. Review false positives\/negatives, refine factors, improve CRM hygiene, and validate changes against win rate, slippage, and forecast accuracy.<\/p>\n\n\n\n

                                  7) When should a company implement Deal Risk Score?<\/h3>\n\n\n\n

                                  Implement it once you have consistent opportunity stages, reliable activity tracking, and enough historical outcomes to learn from. Many teams start with an explainable rules-based Deal Risk Score and evolve toward predictive scoring over time.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                  In **Demand Generation & B2B Marketing**, pipeline is only as valuable as its likelihood to close. A **Deal Risk Score** is a structured way to estimate how \u201cat risk\u201d a specific opportunity is\u2014based on signals from the buyer journey, sales activity, engagement patterns, and operational realities. Instead of relying on gut feel or optimistic forecasts, teams use a Deal Risk Score to spot deals that are likely to stall, slip, or churn before closing.<\/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":[1891],"tags":[],"class_list":["post-7422","post","type-post","status-publish","format-standard","hentry","category-demand-generation-b2b-marketing"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7422","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=7422"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7422\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7422"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7422"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7422"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}