{"id":7011,"date":"2026-03-23T21:04:58","date_gmt":"2026-03-23T21:04:58","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/analytics-roadmap\/"},"modified":"2026-03-23T21:04:58","modified_gmt":"2026-03-23T21:04:58","slug":"analytics-roadmap","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/analytics-roadmap\/","title":{"rendered":"Analytics Roadmap: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Analytics"},"content":{"rendered":"\n

An Analytics Roadmap<\/strong> is a structured plan that turns measurement needs into a sequence of prioritized actions\u2014what you will track, how you will track it, who owns it, and when it will be delivered. In Conversion & Measurement<\/strong>, it is the difference between \u201cwe have data\u201d and \u201cwe can prove what drives outcomes.\u201d<\/p>\n\n\n\n

Modern marketing spans multiple channels, devices, and touchpoints, while privacy rules and platform changes make tracking harder. An Analytics Roadmap<\/strong> helps teams maintain reliable Analytics<\/strong>, align stakeholders on what \u201csuccess\u201d means, and continuously improve measurement so decisions are based on evidence instead of assumptions.<\/p>\n\n\n\n

What Is Analytics Roadmap?<\/h2>\n\n\n\n

An Analytics Roadmap<\/strong> is a time-phased plan that defines how an organization will build, improve, and govern its measurement capabilities. It connects business objectives to metrics, data collection, reporting, experimentation, and decision workflows.<\/p>\n\n\n\n

At its core, the concept is simple: map the current state of Analytics<\/strong>, define the desired future state, then plan the projects needed to close the gaps\u2014sequenced by impact, effort, and risk.<\/p>\n\n\n\n

From a business perspective, an Analytics Roadmap<\/strong> answers practical questions:<\/p>\n\n\n\n

    \n
  • Which conversions matter most (leads, trials, purchases, renewals), and how are they defined?<\/li>\n
  • What data is required to measure them accurately across channels?<\/li>\n
  • Which reports and insights are needed to make better decisions?<\/li>\n
  • What improvements should be delivered first to increase confidence in Conversion & Measurement<\/strong>?<\/li>\n<\/ul>\n\n\n\n

    Within Conversion & Measurement<\/strong>, the roadmap is the operating plan for measurement. Within Analytics<\/strong>, it is the blueprint that aligns instrumentation (events\/tags), data pipelines, reporting, and governance with real business goals.<\/p>\n\n\n\n

    Why Analytics Roadmap Matters in Conversion & Measurement<\/h2>\n\n\n\n

    An Analytics Roadmap<\/strong> matters because measurement is a system, not a single dashboard. Without a plan, teams often build tracking in an ad hoc way\u2014resulting in duplicated events, inconsistent definitions, and reporting that different stakeholders don\u2019t trust.<\/p>\n\n\n\n

    Strategically, an Analytics Roadmap<\/strong> creates alignment between marketing, product, sales, and leadership. It forces clarity on what a conversion is, which funnel stages matter, and how attribution and performance evaluation will be handled in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

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

      \n
    • Faster decision-making because reporting reflects agreed definitions and reliable data.<\/li>\n
    • Better budget allocation because channels are judged on comparable outcomes.<\/li>\n
    • Lower operational cost because fixes are planned and standardized rather than reactive.<\/li>\n
    • Stronger competitive advantage because teams learn faster through better Analytics<\/strong> and testing.<\/li>\n<\/ul>\n\n\n\n

      In short, an Analytics Roadmap<\/strong> is a force multiplier: it makes every optimization effort in Conversion & Measurement<\/strong> more effective because you can measure results with confidence.<\/p>\n\n\n\n

      How Analytics Roadmap Works<\/h2>\n\n\n\n

      An Analytics Roadmap<\/strong> is more practical than theoretical. It typically works as a cycle that turns business needs into measurable delivery.<\/p>\n\n\n\n

        \n
      1. \n

        Inputs (goals and constraints)<\/strong>\n The process begins with business objectives (growth, retention, profitability), conversion definitions, stakeholder questions, and constraints like privacy requirements, consent rules, and technical limitations.<\/p>\n<\/li>\n

      2. \n

        Analysis (gap and priority assessment)<\/strong>\n Teams assess the current measurement state: what is tracked, data quality, identity resolution approach, reporting reliability, and where decisions are currently blocked. This is where Analytics<\/strong> maturity is evaluated and gaps are documented.<\/p>\n<\/li>\n

      3. \n

        Execution (a sequenced plan of initiatives)<\/strong>\n The roadmap turns gaps into initiatives with owners, timelines, and acceptance criteria\u2014such as implementing missing funnel events, standardizing UTM governance, improving consent handling, or building an experimentation framework for Conversion & Measurement<\/strong>.<\/p>\n<\/li>\n

      4. \n

        Outputs (capabilities and outcomes)<\/strong>\n The result is not only \u201cmore tracking.\u201d The output is improved decision capability: trustworthy dashboards, stable KPIs, clearer attribution inputs, better funnel visibility, and measurable improvements in conversion performance.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        A strong Analytics Roadmap<\/strong> is revisited regularly (often quarterly) because channels, tracking rules, and business priorities change.<\/p>\n\n\n\n

        Key Components of Analytics Roadmap<\/h2>\n\n\n\n

        While every organization\u2019s Analytics Roadmap<\/strong> looks different, high-performing roadmaps typically include these components:<\/p>\n\n\n\n

        1) Business goals and measurement questions<\/h3>\n\n\n\n

        Clear objectives and the decisions the business must make, translated into measurable questions (e.g., \u201cWhich landing page changes increase qualified leads?\u201d).<\/p>\n\n\n\n

        2) KPI and conversion definitions<\/h3>\n\n\n\n

        Documented definitions for conversions, micro-conversions, funnel stages, and qualification logic. This is foundational to Conversion & Measurement<\/strong> consistency.<\/p>\n\n\n\n

        3) Data collection and instrumentation plan<\/h3>\n\n\n\n

        A plan for events, parameters, tags, server-side signals where applicable, and validation steps. It also includes naming conventions and versioning to reduce confusion in Analytics<\/strong>.<\/p>\n\n\n\n

        4) Data quality and governance<\/h3>\n\n\n\n

        Rules for ownership, change management, data QA, consent compliance, and documentation. Governance is what keeps measurement stable as teams scale.<\/p>\n\n\n\n

        5) Reporting and insight delivery<\/h3>\n\n\n\n

        Dashboards, stakeholder reporting cadence, alerting for anomalies, and self-serve analysis workflows.<\/p>\n\n\n\n

        6) Experimentation and learning loop<\/h3>\n\n\n\n

        A process to test hypotheses, measure lift, and incorporate learnings back into marketing and product changes\u2014central to modern Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

        7) Resourcing and timeline<\/h3>\n\n\n\n

        Who will do the work (marketing ops, analysts, engineers), dependencies, and realistic timeframes.<\/p>\n\n\n\n

        Types of Analytics Roadmap<\/h2>\n\n\n\n

        There aren\u2019t \u201cofficial\u201d global categories, but in practice an Analytics Roadmap<\/strong> varies by scope, time horizon, and maturity. Common distinctions include:<\/p>\n\n\n\n

        Strategic vs. tactical roadmaps<\/h3>\n\n\n\n
          \n
        • Strategic<\/strong> roadmaps focus on multi-quarter capability building: governance, data models, experimentation, and cross-channel measurement.<\/li>\n
        • Tactical<\/strong> roadmaps focus on near-term delivery: fixing broken tracking, adding missing events, building priority dashboards.<\/li>\n<\/ul>\n\n\n\n

          Marketing-focused vs. product-focused roadmaps<\/h3>\n\n\n\n
            \n
          • Marketing measurement<\/strong> emphasizes acquisition performance, lead quality, attribution inputs, and channel efficiency in Conversion & Measurement<\/strong>.<\/li>\n
          • Product analytics<\/strong> emphasizes onboarding funnels, activation, retention, and feature engagement. Many organizations need both, coordinated under a shared Analytics Roadmap<\/strong>.<\/li>\n<\/ul>\n\n\n\n

            Maturity-based roadmaps<\/h3>\n\n\n\n
              \n
            • Foundation stage<\/strong>: consistent tagging, conversion definitions, basic reporting, QA routines.<\/li>\n
            • Optimization stage<\/strong>: cohorting, funnel segmentation, experimentation processes, better identity and deduplication.<\/li>\n
            • Advanced stage<\/strong>: incrementality testing, forecasting, stronger privacy-safe modeling, and tighter integration between spend and outcomes.<\/li>\n<\/ul>\n\n\n\n

              Real-World Examples of Analytics Roadmap<\/h2>\n\n\n\n

              Example 1: Lead generation website (B2B services)<\/h3>\n\n\n\n

              A B2B firm sees inconsistent lead counts across systems. Their Analytics Roadmap<\/strong> prioritizes:\n– Standardizing \u201clead\u201d vs. \u201cqualified lead\u201d definitions across forms and CRM stages.\n– Implementing consistent source\/medium capture and governance for campaign tagging.\n– Building a funnel dashboard from visit \u2192 form start \u2192 submit \u2192 qualified \u2192 booked meeting.\nOutcome: leadership trusts Conversion & Measurement<\/strong> reporting, and marketing optimizes toward qualified outcomes, not just form fills.<\/p>\n\n\n\n

              Example 2: Ecommerce brand improving purchase conversion<\/h3>\n\n\n\n

              An ecommerce team has traffic growth but flat revenue. The Analytics Roadmap<\/strong> focuses on:\n– Validating purchase event accuracy and refund\/cancel handling.\n– Improving cart and checkout funnel tracking, including error states.\n– Setting up anomaly alerts for conversion rate drops and payment failures.\nOutcome: faster detection of checkout issues and clearer insights into which campaigns drive profitable purchases, improving Analytics<\/strong> reliability.<\/p>\n\n\n\n

              Example 3: Mobile app subscription funnel<\/h3>\n\n\n\n

              A subscription app wants to reduce trial churn. Their Analytics Roadmap<\/strong> includes:\n– Defining activation events (first key action) and retention cohorts.\n– Instrumenting paywall views, trial starts, cancellations, and renewal outcomes with consistent parameters.\n– Establishing an experimentation workflow to test onboarding and paywall messaging.\nOutcome: better Conversion & Measurement<\/strong> visibility across the subscription lifecycle and a repeatable testing system backed by trustworthy Analytics<\/strong>.<\/p>\n\n\n\n

              Benefits of Using Analytics Roadmap<\/h2>\n\n\n\n

              A well-run Analytics Roadmap<\/strong> delivers benefits that go beyond reporting:<\/p>\n\n\n\n

                \n
              • Performance improvements<\/strong>: clearer funnels, better segmentation, and reliable experiment measurement lead to higher conversion rates over time.<\/li>\n
              • Cost savings<\/strong>: fewer duplicated tools and fewer emergency fixes caused by broken tracking or unclear definitions.<\/li>\n
              • Efficiency gains<\/strong>: teams spend less time reconciling numbers and more time acting on insights.<\/li>\n
              • Better customer experience<\/strong>: by measuring friction points (errors, slow steps, drop-offs), teams can prioritize UX fixes that improve Conversion & Measurement<\/strong> outcomes.<\/li>\n
              • Organizational alignment<\/strong>: consistent metrics reduce stakeholder conflict and create shared accountability.<\/li>\n<\/ul>\n\n\n\n

                Challenges of Analytics Roadmap<\/h2>\n\n\n\n

                An Analytics Roadmap<\/strong> also faces real obstacles. Planning alone doesn\u2019t fix measurement; execution and governance do.<\/p>\n\n\n\n

                  \n
                • Technical complexity<\/strong>: multiple sites\/apps, cross-domain journeys, and backend systems can make attribution inputs and deduplication difficult.<\/li>\n
                • Privacy and consent limitations<\/strong>: consent requirements reduce available data and require privacy-safe design choices.<\/li>\n
                • Metric ambiguity<\/strong>: teams may disagree on what constitutes a conversion, qualification, or valid attribution touch.<\/li>\n
                • Resource constraints<\/strong>: analysts and engineers are often shared across projects; roadmap timelines must reflect reality.<\/li>\n
                • Tool sprawl and inconsistency<\/strong>: different teams may use different definitions, dashboards, or collection methods, weakening Analytics<\/strong> trust.<\/li>\n
                • Change management risk<\/strong>: updating events or dashboards without versioning can break reports and confuse stakeholders in Conversion & Measurement<\/strong> reviews.<\/li>\n<\/ul>\n\n\n\n

                  Best Practices for Analytics Roadmap<\/h2>\n\n\n\n

                  Use these practices to make an Analytics Roadmap<\/strong> actionable and durable:<\/p>\n\n\n\n

                    \n
                  1. \n

                    Start with decisions, not dashboards<\/strong>\n Define the decisions stakeholders need to make (budget shifts, landing page changes, lifecycle messaging). Then map the required measurement.<\/p>\n<\/li>\n

                  2. \n

                    Document conversion definitions in plain language<\/strong>\n Include edge cases (duplicates, spam, refunds, internal traffic). This is essential for stable Conversion & Measurement<\/strong>.<\/p>\n<\/li>\n

                  3. \n

                    Prioritize by impact and confidence<\/strong>\n Fix measurement issues that block trust first (broken conversions, inconsistent attribution inputs). \u201cBetter data\u201d often beats \u201cmore data.\u201d<\/p>\n<\/li>\n

                  4. \n

                    Treat QA as a deliverable<\/strong>\n Include validation steps, test cases, and ongoing monitoring. An Analytics Roadmap<\/strong> without QA becomes a backlog of unverified assumptions.<\/p>\n<\/li>\n

                  5. \n

                    Create ownership and change control<\/strong>\n Assign owners for events, dashboards, and governance. Use a lightweight change process so updates don\u2019t silently break Analytics<\/strong>.<\/p>\n<\/li>\n

                  6. \n

                    Build an iteration rhythm<\/strong>\n Revisit the roadmap quarterly. Align it to campaign calendars, product releases, and reporting cycles in Conversion & Measurement<\/strong>.<\/p>\n<\/li>\n

                  7. \n

                    Make room for experimentation<\/strong>\n Reserve capacity for testing and learning, not just implementation. Measurement should enable improvements, not merely record them.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                    Tools Used for Analytics Roadmap<\/h2>\n\n\n\n

                    An Analytics Roadmap<\/strong> is tool-enabled but not tool-dependent. Common tool categories include:<\/p>\n\n\n\n

                      \n
                    • Analytics tools<\/strong> for traffic, events, funnels, and cohort analysis.<\/li>\n
                    • Tag management systems<\/strong> to deploy and manage tracking with governance and versioning.<\/li>\n
                    • Consent and preference management tools<\/strong> to support compliant data collection.<\/li>\n
                    • Data pipelines and warehouses<\/strong> to unify datasets from web, app, CRM, and ad platforms for more reliable Analytics<\/strong>.<\/li>\n
                    • Reporting dashboards and BI tools<\/strong> for stakeholder views, self-serve exploration, and standardized KPI reporting.<\/li>\n
                    • Automation tools<\/strong> for alerts, scheduled reporting, and workflow triggers when anomalies occur.<\/li>\n
                    • Ad platforms and campaign managers<\/strong> as inputs for spend, impressions, clicks, and campaign metadata used in Conversion & Measurement<\/strong>.<\/li>\n
                    • CRM systems<\/strong> to connect marketing actions to pipeline, revenue, retention, and lifecycle outcomes.<\/li>\n
                    • SEO tools<\/strong> to monitor organic performance and tie content strategy to measurable conversions.<\/li>\n<\/ul>\n\n\n\n

                      The best roadmaps define how tools work together, how data flows between them, and who is responsible for maintaining each layer.<\/p>\n\n\n\n

                      Metrics Related to Analytics Roadmap<\/h2>\n\n\n\n

                      You measure the success of an Analytics Roadmap<\/strong> with both performance metrics and measurement-quality metrics.<\/p>\n\n\n\n

                      Conversion & Measurement performance metrics<\/h3>\n\n\n\n
                        \n
                      • Conversion rate by funnel stage (visit \u2192 lead \u2192 sale, or view \u2192 cart \u2192 checkout \u2192 purchase)<\/li>\n
                      • Cost per acquisition (CPA) or cost per qualified lead<\/li>\n
                      • Revenue per visitor \/ average order value (where applicable)<\/li>\n
                      • Retention, churn, and lifetime value (for subscription or repeat purchase models)<\/li>\n
                      • Experiment lift and statistical confidence (when testing)<\/li>\n<\/ul>\n\n\n\n

                        Analytics quality and efficiency metrics<\/h3>\n\n\n\n
                          \n
                        • Tracking coverage (percentage of key events implemented vs. planned)<\/li>\n
                        • Data accuracy and reconciliation rate across systems (site\/app vs. CRM vs. payments)<\/li>\n
                        • Tag\/event stability (breakage rate after releases)<\/li>\n
                        • Dashboard adoption (active users, decision usage)<\/li>\n
                        • Time-to-insight (how long it takes to answer common questions)<\/li>\n
                        • Data freshness and latency (how quickly reports reflect reality)<\/li>\n<\/ul>\n\n\n\n

                          A mature Analytics Roadmap<\/strong> treats data quality as measurable work, not a vague aspiration.<\/p>\n\n\n\n

                          Future Trends of Analytics Roadmap<\/h2>\n\n\n\n

                          The Analytics Roadmap<\/strong> is evolving as measurement becomes more privacy-constrained and more automated.<\/p>\n\n\n\n

                            \n
                          • Privacy-first measurement design<\/strong>: more emphasis on consent-aware tracking, data minimization, and clear retention policies.<\/li>\n
                          • First-party data strategies<\/strong>: deeper integration between owned channels, CRM, and onsite behavior to strengthen Conversion & Measurement<\/strong> insights.<\/li>\n
                          • Server-side and resilient collection patterns<\/strong>: greater focus on data reliability and controlled instrumentation where appropriate.<\/li>\n
                          • Modeling and incrementality<\/strong>: increased use of experiments, holdouts, and statistical approaches to understand true lift when deterministic tracking is incomplete.<\/li>\n
                          • AI-assisted analysis<\/strong>: automation for anomaly detection, narrative reporting, and faster exploration\u2014paired with stronger governance to prevent misleading conclusions in Analytics<\/strong>.<\/li>\n
                          • Personalization with accountability<\/strong>: personalization efforts will increasingly require rigorous measurement frameworks so changes can be evaluated credibly.<\/li>\n<\/ul>\n\n\n\n

                            The roadmap of the future is less about \u201ctracking everything\u201d and more about building trustworthy, privacy-safe Conversion & Measurement<\/strong> systems that support decision-making.<\/p>\n\n\n\n

                            Analytics Roadmap vs Related Terms<\/h2>\n\n\n\n

                            Analytics Roadmap vs Measurement plan<\/h3>\n\n\n\n

                            A measurement plan<\/strong> defines what you will measure (KPIs, events, definitions). An Analytics Roadmap<\/strong> goes further: it sequences the work, assigns ownership, and includes the systems, governance, and timeline needed to deliver measurement improvements.<\/p>\n\n\n\n

                            Analytics Roadmap vs KPI framework<\/h3>\n\n\n\n

                            A KPI framework<\/strong> organizes metrics and relationships (north star metrics, leading\/lagging indicators). An Analytics Roadmap<\/strong> operationalizes how those KPIs will be captured, validated, and reported within Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                            Analytics Roadmap vs Data strategy<\/h3>\n\n\n\n

                            A data strategy<\/strong> is broader, often covering enterprise data architecture, security, and governance beyond marketing. An Analytics Roadmap<\/strong> is more focused on measurement capabilities and the practical delivery needed for Analytics<\/strong> and performance optimization.<\/p>\n\n\n\n

                            Who Should Learn Analytics Roadmap<\/h2>\n\n\n\n
                              \n
                            • Marketers<\/strong> need an Analytics Roadmap<\/strong> to connect campaigns to outcomes, reduce reporting disputes, and improve Conversion & Measurement<\/strong> ROI.<\/li>\n
                            • Analysts<\/strong> use it to prioritize analysis enablement, standardize definitions, and keep Analytics<\/strong> trustworthy as the business evolves.<\/li>\n
                            • Agencies<\/strong> benefit by aligning clients on measurement scope, deliverables, timelines, and governance\u2014especially when multiple channels are involved.<\/li>\n
                            • Business owners and founders<\/strong> gain clarity on what is measurable now, what is not, and which investments will unlock better decisions.<\/li>\n
                            • Developers and technical teams<\/strong> use the roadmap to plan instrumentation, QA, and data flow changes without breaking reporting.<\/li>\n<\/ul>\n\n\n\n

                              Summary of Analytics Roadmap<\/h2>\n\n\n\n

                              An Analytics Roadmap<\/strong> is a prioritized, time-bound plan for building and improving measurement capabilities. It matters because strong Conversion & Measurement<\/strong> depends on consistent definitions, reliable data collection, and reporting that teams trust. By aligning goals, instrumentation, governance, and delivery, an Analytics Roadmap<\/strong> turns Analytics<\/strong> from passive reporting into an active decision system that supports growth.<\/p>\n\n\n\n

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

                              1) What should an Analytics Roadmap include at minimum?<\/h3>\n\n\n\n

                              At minimum: business goals, conversion definitions, a prioritized backlog of tracking\/reporting initiatives, owners, timelines, and a QA process to validate data accuracy.<\/p>\n\n\n\n

                              2) How long does it take to build an Analytics Roadmap?<\/h3>\n\n\n\n

                              A first version can be created in 1\u20133 weeks if stakeholders are available. Execution is ongoing; most teams plan in quarterly cycles and refine as Conversion & Measurement<\/strong> needs evolve.<\/p>\n\n\n\n

                              3) How do I prioritize items in an Analytics Roadmap?<\/h3>\n\n\n\n

                              Prioritize by (1) business impact, (2) severity of measurement risk (trust blockers first), (3) effort and dependencies, and (4) how directly the work improves decisions in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                              4) How does an Analytics Roadmap improve Analytics accuracy?<\/h3>\n\n\n\n

                              It enforces consistent definitions, structured instrumentation, validation routines, and change management. That reduces broken events, mismatched counts, and contradictory dashboards.<\/p>\n\n\n\n

                              5) Do small businesses need an Analytics Roadmap?<\/h3>\n\n\n\n

                              Yes, but it can be lightweight. A simple roadmap that clarifies conversions, campaign tagging rules, and a few critical reports often produces outsized gains in Analytics<\/strong> reliability.<\/p>\n\n\n\n

                              6) Who should own the Analytics Roadmap?<\/h3>\n\n\n\n

                              Ownership typically sits with a marketing ops lead, analytics lead, or growth operations role, with shared accountability from engineering (instrumentation) and stakeholders who rely on Conversion & Measurement<\/strong> reporting.<\/p>\n\n\n\n

                              7) How often should the roadmap be updated?<\/h3>\n\n\n\n

                              Review it monthly for delivery status and update it quarterly for priorities. Major site changes, new products, or shifts in privacy requirements are also triggers to revisit the Analytics Roadmap<\/strong>.<\/p>\n","protected":false},"excerpt":{"rendered":"

                              An **Analytics Roadmap** is a structured plan that turns measurement needs into a sequence of prioritized actions\u2014what you will track, how you will track it, who owns it, and when it will be delivered. In **Conversion & Measurement**, it is the difference between \u201cwe have data\u201d and \u201cwe can prove what drives outcomes.\u201d<\/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-7011","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\/7011","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=7011"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7011\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7011"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7011"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7011"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}