{"id":7448,"date":"2026-03-24T13:25:03","date_gmt":"2026-03-24T13:25:03","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/lead-score\/"},"modified":"2026-03-24T13:25:03","modified_gmt":"2026-03-24T13:25:03","slug":"lead-score","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/lead-score\/","title":{"rendered":"Lead 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>, a Lead Score<\/strong> is the mechanism that turns scattered signals\u2014profile fit, buying intent, and engagement\u2014into a consistent way to prioritize follow-up. Instead of treating every inbound form fill or webinar attendee the same, Lead Score helps teams decide who is most likely to become a qualified opportunity and who should be nurtured longer.<\/p>\n\n\n\n

This matters because modern Demand Generation & B2B Marketing<\/strong> runs across many touchpoints (paid, organic, email, events, partners, product-led motions), and sales teams can\u2019t manually interpret every interaction. A well-designed Lead Score<\/strong> creates alignment: marketing knows what \u201cquality\u201d means, sales trusts the queue, and revenue teams can scale attention where it produces pipeline.<\/p>\n\n\n\n

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

A Lead Score<\/strong> is a numeric value (or a set of values) assigned to a lead to represent how ready that lead is for a sales conversation or a specific next action. It combines two broad ideas:<\/p>\n\n\n\n

    \n
  • Fit<\/strong>: how well the lead matches your ideal customer profile (ICP) and buying committee characteristics <\/li>\n
  • Interest\/intent<\/strong>: what the lead is doing that indicates demand (content consumption, product engagement, demo requests, pricing page visits, event attendance)<\/li>\n<\/ul>\n\n\n\n

    In business terms, Lead Score is a prioritization system. It answers: \u201cWhich leads should we contact now, which should we route to a specific team, and which should go into nurture?\u201d<\/p>\n\n\n\n

    Within Demand Generation & B2B Marketing<\/strong>, Lead Score typically sits between lead capture and lifecycle routing. It influences whether a lead becomes marketing qualified, which nurture path they enter, and how quickly sales development or account executives should respond.<\/p>\n\n\n\n

    Why Lead Score Matters in Demand Generation & B2B Marketing<\/h2>\n\n\n\n

    In Demand Generation & B2B Marketing<\/strong>, the volume of leads is rarely the bottleneck\u2014attention and timing are. Lead Score matters because it improves decisions at the exact point where many funnels leak: handoff and follow-up.<\/p>\n\n\n\n

    Key reasons it\u2019s strategically important:<\/p>\n\n\n\n

      \n
    • Protects sales time<\/strong>: Sales teams spend more time on high-probability conversations and less on low-fit inquiries. <\/li>\n
    • Improves speed-to-lead<\/strong>: High-scoring leads can trigger fast routing and immediate outreach, increasing conversion rates. <\/li>\n
    • Creates a shared definition of quality<\/strong>: A consistent Lead Score reduces friction between marketing and sales about \u201clead quality.\u201d <\/li>\n
    • Enables scalable personalization<\/strong>: Scores can drive automation rules, content recommendations, and outreach sequencing. <\/li>\n
    • Increases competitive advantage<\/strong>: Teams that respond to true intent faster win more evaluations and reduce competitive displacement.<\/li>\n<\/ul>\n\n\n\n

      Ultimately, Lead Score is a control system for pipeline efficiency\u2014especially important when budgets tighten and every campaign needs to prove incremental impact in Demand Generation & B2B Marketing<\/strong>.<\/p>\n\n\n\n

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

      A Lead Score<\/strong> is conceptual, but it operates through a practical workflow that most teams can implement in stages:<\/p>\n\n\n\n

        \n
      1. \n

        Inputs (signals captured)<\/strong>\n Signals include demographic\/firmographic data, declared intent (forms), behavioral engagement (email clicks, page views), product usage (if applicable), and campaign\/source data.<\/p>\n<\/li>\n

      2. \n

        Processing (scoring logic applied)<\/strong>\n Scoring rules translate signals into points. A job title might add points for fit; repeated visits to pricing pages might add points for intent; a competitor email domain might subtract points. Many teams also apply time decay so older behaviors matter less.<\/p>\n<\/li>\n

      3. \n

        Execution (routing and actions)<\/strong>\n The Lead Score triggers actions: assign to a sales rep, create tasks, enroll in an outreach sequence, add to a segment, or move to a nurture track. It can also determine SLA expectations (e.g., respond within 15 minutes for top scores).<\/p>\n<\/li>\n

      4. \n

        Outputs (outcomes tracked and optimized)<\/strong>\n The system is validated by outcomes: conversion to qualified stages, meetings held, opportunities created, and revenue influenced. These outcomes feed back into iteration\u2014adjusting point values, thresholds, and signals.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        In Demand Generation & B2B Marketing<\/strong>, the score is only valuable when it changes behavior (routing, messaging, prioritization) and is continuously evaluated against pipeline results.<\/p>\n\n\n\n

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

        A durable Lead Score<\/strong> program typically includes these components:<\/p>\n\n\n\n

        Data inputs<\/h3>\n\n\n\n
          \n
        • Firmographics<\/strong>: company size, industry, location, revenue, tech stack indicators <\/li>\n
        • Demographics<\/strong>: role, seniority, department, use case <\/li>\n
        • Behavioral signals<\/strong>: high-intent page views, return visits, content depth, email engagement, event attendance <\/li>\n
        • Conversion signals<\/strong>: demo request, pricing inquiry, trial start, chatbot conversations <\/li>\n
        • Negative signals<\/strong>: student\/consultant tags, low-fit industries, unsubscribes, invalid contact data<\/li>\n<\/ul>\n\n\n\n

          Scoring model and thresholds<\/h3>\n\n\n\n
            \n
          • Point rules and weighting <\/li>\n
          • Thresholds (e.g., \u201croute to sales at 65+\u201d) <\/li>\n
          • Time windows and decay (e.g., last 30 days weighted higher)<\/li>\n<\/ul>\n\n\n\n

            Governance and responsibilities<\/h3>\n\n\n\n
              \n
            • Marketing operations owns logic and QA <\/li>\n
            • Sales leadership defines capacity and acceptance criteria <\/li>\n
            • Analytics validates outcomes and bias <\/li>\n
            • RevOps ensures routing, CRM hygiene, and lifecycle definitions<\/li>\n<\/ul>\n\n\n\n

              Operational integration<\/h3>\n\n\n\n
                \n
              • CRM lifecycle stages <\/li>\n
              • Marketing automation segmentation <\/li>\n
              • Reporting dashboards and attribution context<\/li>\n<\/ul>\n\n\n\n

                In Demand Generation & B2B Marketing<\/strong>, the best Lead Score designs are not \u201cset-and-forget\u201d; they\u2019re operationalized with clear ownership and feedback loops.<\/p>\n\n\n\n

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

                While teams use different terminology, most Lead Score<\/strong> approaches fall into a few practical categories:<\/p>\n\n\n\n

                Explicit vs. implicit scoring<\/h3>\n\n\n\n
                  \n
                • Explicit (fit) scoring<\/strong>: based on known attributes like title, industry, company size <\/li>\n
                • Implicit (engagement) scoring<\/strong>: based on observed behaviors like site activity and email engagement<\/li>\n<\/ul>\n\n\n\n

                  Single score vs. dual score<\/h3>\n\n\n\n
                    \n
                  • Single score<\/strong>: one number blending fit and intent\u2014simple, but can hide tradeoffs <\/li>\n
                  • Dual score<\/strong>: separate fit and engagement scores\u2014more transparent for routing (\u201chigh fit, low intent\u201d vs. \u201clow fit, high intent\u201d)<\/li>\n<\/ul>\n\n\n\n

                    Rule-based vs. predictive<\/h3>\n\n\n\n
                      \n
                    • Rule-based<\/strong>: points assigned by business logic; easier to explain and control <\/li>\n
                    • Predictive<\/strong>: statistical or machine-learning models that estimate conversion likelihood; can outperform rules but require careful monitoring and good data<\/li>\n<\/ul>\n\n\n\n

                      Lead-level vs. account-level scoring<\/h3>\n\n\n\n

                      In Demand Generation & B2B Marketing<\/strong>, buying decisions are often account-driven. Many teams complement Lead Score with account-level scoring to reflect multi-person engagement across a target company.<\/p>\n\n\n\n

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

                      Example 1: SaaS demo funnel prioritization<\/h3>\n\n\n\n

                      A mid-market SaaS company uses Lead Score to accelerate response for high-intent leads:\n– +30 for \u201cRequest a demo\u201d\n– +15 for pricing page visits (multiple sessions)\n– +10 for company size within ICP range\n– -20 for non-target industry\nAt 60+, the lead routes to sales development with a 15-minute SLA; below 60, it enters a nurture track focused on use cases and ROI.<\/p>\n\n\n\n

                      Demand Generation & B2B Marketing<\/strong> impact: fewer missed high-intent leads and higher meeting-to-opportunity conversion.<\/p>\n\n\n\n

                      Example 2: Webinar-driven pipeline with role weighting<\/h3>\n\n\n\n

                      A B2B services firm runs webinars that attract mixed audiences. Their Lead Score emphasizes seniority and follow-up behavior:\n– +20 for director+ titles\n– +10 for attending 50%+ of the webinar\n– +10 for downloading the post-event playbook\n– +5 for visiting \u201ccase studies\u201d within 7 days\nHigh scores trigger personalized outreach from the relevant practice lead; others are nurtured by industry.<\/p>\n\n\n\n

                      Demand Generation & B2B Marketing<\/strong> impact: improved sales acceptance and less time wasted on low-authority attendees.<\/p>\n\n\n\n

                      Example 3: Product-led motion with usage signals<\/h3>\n\n\n\n

                      A PLG company blends product analytics into Lead Score:\n– +25 for inviting teammates\n– +15 for hitting a key activation milestone\n– +10 for integrating with a core tool\n– +20 for visiting billing\/pricing screens\nThe score triggers \u201cassist\u201d actions: lifecycle emails, in-app prompts, and SDR outreach when usage suggests buying readiness.<\/p>\n\n\n\n

                      Demand Generation & B2B Marketing<\/strong> impact: better timing\u2014outreach aligns with real adoption, not just form fills.<\/p>\n\n\n\n

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

                      A well-managed Lead Score<\/strong> delivers benefits across revenue operations:<\/p>\n\n\n\n

                        \n
                      • Higher conversion rates<\/strong>: Better prioritization increases MQL-to-SQL, meeting rates, and opportunity creation. <\/li>\n
                      • Lower cost per opportunity<\/strong>: Paid and content budgets go further when follow-up is focused on likely buyers. <\/li>\n
                      • More efficient sales coverage<\/strong>: Reps work the right leads first; managers can allocate territories and queues more intelligently. <\/li>\n
                      • Improved buyer experience<\/strong>: High-intent prospects get quick, relevant responses; early-stage leads get helpful nurturing instead of premature sales pressure. <\/li>\n
                      • Better forecasting inputs<\/strong>: Consistent scoring creates cleaner pipeline hygiene and more reliable top-of-funnel indicators for Demand Generation & B2B Marketing<\/strong> planning.<\/li>\n<\/ul>\n\n\n\n

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

                        Lead Score can fail\u2014or create mistrust\u2014when teams overlook common pitfalls:<\/p>\n\n\n\n

                          \n
                        • Bad or incomplete data<\/strong>: Missing titles, inaccurate company matching, and poor tracking reduce score accuracy. <\/li>\n
                        • Overweighting vanity engagement<\/strong>: Email clicks and generic blog visits can inflate scores without real intent. <\/li>\n
                        • Misaligned thresholds<\/strong>: If the \u201csales-ready\u201d threshold is too low, sales gets spammed; too high, pipeline is delayed. <\/li>\n
                        • Lack of decay<\/strong>: Leads that engaged months ago can remain artificially \u201chot.\u201d <\/li>\n
                        • No closed-loop feedback<\/strong>: Without outcome-based validation, scoring becomes opinion-driven. <\/li>\n
                        • Process friction<\/strong>: Even a strong Lead Score fails if routing rules, SLAs, and CRM stages aren\u2019t enforced.<\/li>\n<\/ul>\n\n\n\n

                          In Demand Generation & B2B Marketing<\/strong>, trust is everything: sales must believe the score reflects buying readiness, not marketing activity.<\/p>\n\n\n\n

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

                          To make your Lead Score<\/strong> accurate, actionable, and scalable:<\/p>\n\n\n\n

                            \n
                          1. \n

                            Start with lifecycle definitions<\/strong>\n Define what qualifies a lead for sales follow-up (and what does not). Document stages and acceptance criteria.<\/p>\n<\/li>\n

                          2. \n

                            Separate fit from intent (even if you display one number)<\/strong>\n Internally, maintain clarity on what\u2019s driving the score so you can troubleshoot and optimize.<\/p>\n<\/li>\n

                          3. \n

                            Use outcome-based calibration<\/strong>\n Evaluate which signals correlate with meetings, opportunities, and revenue\u2014not just clicks.<\/p>\n<\/li>\n

                          4. \n

                            Apply time decay and recency windows<\/strong>\n Recent intent should matter more than older activity. Make decay rules explicit.<\/p>\n<\/li>\n

                          5. \n

                            Include negative scoring and disqualification logic<\/strong>\n Reduce scores for competitors, job seekers, students, and repeated low-quality patterns.<\/p>\n<\/li>\n

                          6. \n

                            Operationalize SLAs and routing<\/strong>\n A Lead Score should trigger specific actions: owners, queues, tasks, and timelines.<\/p>\n<\/li>\n

                          7. \n

                            Review and iterate on a schedule<\/strong>\n Monthly light checks (volume, conversion, false positives) and quarterly deeper recalibration works well for many Demand Generation & B2B Marketing<\/strong> teams.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

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

                            Lead Score is typically implemented through a stack of systems rather than a single tool:<\/p>\n\n\n\n

                              \n
                            • CRM systems<\/strong>: store lead\/account records, lifecycle stages, ownership, and sales outcomes used for validation. <\/li>\n
                            • Marketing automation platforms<\/strong>: capture engagement, run nurture programs, and execute scoring rules and routing workflows. <\/li>\n
                            • Analytics tools<\/strong>: analyze conversion paths, cohort performance, and signal-to-outcome relationships. <\/li>\n
                            • Product analytics (if applicable)<\/strong>: provides activation and usage signals that improve scoring accuracy in PLG motions. <\/li>\n
                            • Data enrichment and identity resolution<\/strong>: improves firmographics, reduces unknowns, and supports account matching. <\/li>\n
                            • Reporting dashboards<\/strong>: unify score distributions with pipeline KPIs so leaders can monitor health.<\/li>\n<\/ul>\n\n\n\n

                              In Demand Generation & B2B Marketing<\/strong>, the best tooling setup is the one that keeps scoring transparent, auditable, and easy to change without breaking downstream routing.<\/p>\n\n\n\n

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

                              To evaluate whether your Lead Score<\/strong> is working, track metrics that connect scoring to revenue outcomes:<\/p>\n\n\n\n

                                \n
                              • Score distribution<\/strong>: how many leads fall into each score band; watch for sudden shifts after changes. <\/li>\n
                              • MQL-to-SQL rate by score band<\/strong>: higher scores should convert at higher rates. <\/li>\n
                              • Meeting set rate and meeting held rate<\/strong>: validates whether \u201csales-ready\u201d leads are genuinely ready. <\/li>\n
                              • Opportunity creation rate and pipeline per lead<\/strong>: key indicators for Demand Generation & B2B Marketing<\/strong> ROI. <\/li>\n
                              • Sales acceptance rate \/ rejection reasons<\/strong>: reveals misalignment or data issues. <\/li>\n
                              • Time-to-first-touch and SLA compliance<\/strong>: confirms operational execution. <\/li>\n
                              • False positives and false negatives<\/strong>: leads scored high that go nowhere vs. leads scored low that later convert.<\/li>\n<\/ul>\n\n\n\n

                                Good scoring raises conversion at the same or lower volume\u2014rather than simply pushing more leads to sales.<\/p>\n\n\n\n

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

                                Lead Score is evolving as data availability, privacy expectations, and automation capabilities change:<\/p>\n\n\n\n

                                  \n
                                • More account-centric scoring<\/strong>: In complex Demand Generation & B2B Marketing<\/strong>, account engagement often predicts pipeline better than single-lead behavior. <\/li>\n
                                • AI-assisted scoring with governance<\/strong>: Machine learning can surface patterns humans miss, but teams will demand explainability, bias checks, and version control. <\/li>\n
                                • First-party data emphasis<\/strong>: As tracking constraints increase, first-party engagement and product signals become more valuable than third-party cues. <\/li>\n
                                • Real-time routing and personalization<\/strong>: Scoring will increasingly trigger immediate next-best actions\u2014tailored content, SDR alerts, or in-app nudges. <\/li>\n
                                • Richer intent interpretation<\/strong>: Teams will weight \u201cdepth\u201d of evaluation (pricing + comparisons + security docs) more than raw visit counts.<\/li>\n<\/ul>\n\n\n\n

                                  In Demand Generation & B2B Marketing<\/strong>, the winning approach will combine automation with transparency: fast decisions that humans can audit.<\/p>\n\n\n\n

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

                                  Lead Score vs lead qualification<\/h3>\n\n\n\n
                                    \n
                                  • Lead Score<\/strong> is a system for ranking and routing using points or model outputs. <\/li>\n
                                  • Lead qualification<\/strong> is the broader process of determining if a lead meets criteria (often including human review, discovery, and validation).<\/li>\n<\/ul>\n\n\n\n

                                    Lead Score vs MQL (Marketing Qualified Lead)<\/h3>\n\n\n\n
                                      \n
                                    • A Lead Score<\/strong> is the measurement that can help decide readiness. <\/li>\n
                                    • MQL<\/strong> is a lifecycle status indicating a lead met a defined threshold (which may be score-based or criteria-based).<\/li>\n<\/ul>\n\n\n\n

                                      Lead Score vs lead grading<\/h3>\n\n\n\n
                                        \n
                                      • Lead grading<\/strong> usually focuses on fit\/ICP alignment (often letter grades). <\/li>\n
                                      • Lead Score<\/strong> often blends fit plus behavioral intent; many teams use both to keep decisions clear.<\/li>\n<\/ul>\n\n\n\n

                                        Who Should Learn Lead Score<\/h2>\n\n\n\n
                                          \n
                                        • Marketers<\/strong>: to align programs to pipeline quality, not just lead volume, and to improve Demand Generation & B2B Marketing<\/strong> performance. <\/li>\n
                                        • Analysts<\/strong>: to validate scoring models, quantify lift, and connect signals to revenue outcomes. <\/li>\n
                                        • Agencies<\/strong>: to design funnels and reporting that drive qualified demand, not just traffic or form fills. <\/li>\n
                                        • Business owners and founders<\/strong>: to understand why sales capacity feels constrained and how to prioritize the right prospects. <\/li>\n
                                        • Developers and technical teams<\/strong>: to implement reliable tracking, data pipelines, identity matching, and event schemas that make Lead Score trustworthy.<\/li>\n<\/ul>\n\n\n\n

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

                                          A Lead Score<\/strong> is a structured way to rank leads by fit and buying intent so teams can route, prioritize, and personalize follow-up. In Demand Generation & B2B Marketing<\/strong>, it improves alignment between marketing and sales, raises conversion efficiency, and ensures fast response to real demand. When tied to outcomes and maintained with governance, Lead Score becomes a scalable operating system that supports predictable pipeline in Demand Generation & B2B Marketing<\/strong>.<\/p>\n\n\n\n

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

                                          1) What is a Lead Score used for?<\/h3>\n\n\n\n

                                          A Lead Score is used to prioritize leads for follow-up, route them to the right team, and trigger nurture or outreach actions based on readiness and fit.<\/p>\n\n\n\n

                                          2) How do I choose a threshold for when a lead becomes sales-ready?<\/h3>\n\n\n\n

                                          Start by analyzing historical conversions (to meetings and opportunities) and set an initial threshold where conversion meaningfully increases. Then adjust based on sales capacity and acceptance rates.<\/p>\n\n\n\n

                                          3) What signals should matter most in Demand Generation & B2B Marketing?<\/h3>\n\n\n\n

                                          High-intent behaviors (demo requests, pricing\/comparison views, security documentation interest), strong ICP fit (industry, size, role), and recent engagement typically matter more than low-intent clicks or generic blog traffic.<\/p>\n\n\n\n

                                          4) Should Lead Score be based more on fit or behavior?<\/h3>\n\n\n\n

                                          Both. Fit prevents wasted effort on the wrong audience, while behavior captures timing. Many teams use a dual approach (fit score + intent score) to avoid confusing \u201chigh activity but low fit\u201d leads with true opportunities.<\/p>\n\n\n\n

                                          5) How often should we update our scoring model?<\/h3>\n\n\n\n

                                          Review monthly for obvious issues (routing errors, score inflation, drops in sales acceptance). Recalibrate quarterly using pipeline outcomes and updated go-to-market priorities.<\/p>\n\n\n\n

                                          6) Can Lead Score work without perfect data?<\/h3>\n\n\n\n

                                          Yes, but it must be designed around reliable signals and improved over time. Start with a small set of high-confidence inputs, add enrichment, and validate using closed-loop reporting to reduce false positives.<\/p>\n\n\n\n

                                          7) What\u2019s the biggest reason Lead Score fails?<\/h3>\n\n\n\n

                                          Lack of alignment and feedback loops. If sales doesn\u2019t trust the score\u2014or if scoring changes aren\u2019t validated against opportunity outcomes\u2014teams stop using it and routing becomes inconsistent.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                          In **Demand Generation & B2B Marketing**, a **Lead Score** is the mechanism that turns scattered signals\u2014profile fit, buying intent, and engagement\u2014into a consistent way to prioritize follow-up. Instead of treating every inbound form fill or webinar attendee the same, Lead Score helps teams decide who is most likely to become a qualified opportunity and who should be nurtured longer.<\/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-7448","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\/7448","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=7448"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7448\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7448"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7448"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7448"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}