{"id":6999,"date":"2026-03-23T20:39:25","date_gmt":"2026-03-23T20:39:25","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/analytics-experiment\/"},"modified":"2026-03-23T20:39:25","modified_gmt":"2026-03-23T20:39:25","slug":"analytics-experiment","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/analytics-experiment\/","title":{"rendered":"Analytics Experiment: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Analytics"},"content":{"rendered":"\n

An Analytics Experiment<\/strong> is a structured way to test a change\u2014on a website, in a funnel, inside a campaign, or across a customer journey\u2014and measure whether it truly improves outcomes. In Conversion & Measurement<\/strong>, it\u2019s the discipline that turns opinions (\u201cthis new landing page feels better\u201d) into evidence (\u201cit increased qualified leads by 8% without harming sales\u201d).<\/p>\n\n\n\n

In modern Analytics<\/strong>, an Analytics Experiment matters because marketing has become faster, more personalized, and more complex. Attribution is imperfect, channels interact, and user behavior shifts quickly. A well-designed Analytics Experiment helps you isolate cause and effect, reduce waste, and scale what works with confidence.<\/p>\n\n\n\n

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

An Analytics Experiment<\/strong> is a planned measurement approach that evaluates the impact of a defined change (the \u201ctreatment\u201d) against a baseline (the \u201ccontrol\u201d), using data to determine whether the change caused a meaningful difference.<\/p>\n\n\n\n

At its core, an Analytics Experiment combines three ideas:<\/p>\n\n\n\n

    \n
  • A hypothesis<\/strong> about what will improve performance (conversion rate, revenue, retention, lead quality, etc.).<\/li>\n
  • A measurement design<\/strong> that makes results interpretable (controls, comparisons, time windows, segmentation, and guardrails).<\/li>\n
  • A decision rule<\/strong> for what happens next (ship, iterate, roll back, or research further).<\/li>\n<\/ul>\n\n\n\n

    The business meaning is straightforward: an Analytics Experiment reduces uncertainty in decision-making. Instead of relying on averages, anecdotes, or last-click stories, you use Conversion & Measurement<\/strong> methods to understand incremental impact.<\/p>\n\n\n\n

    Where it fits in Conversion & Measurement<\/strong>: it sits between tracking (collecting reliable events) and optimization (changing experiences and budgets). It ensures your optimization efforts are measurable and credible.<\/p>\n\n\n\n

    Its role inside Analytics<\/strong>: it\u2019s one of the most practical applications of analytics\u2014moving from descriptive reporting (\u201cwhat happened?\u201d) to causal learning (\u201cwhat caused it?\u201d).<\/p>\n\n\n\n

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

    In Conversion & Measurement<\/strong>, the goal isn\u2019t just to report numbers\u2014it\u2019s to improve them responsibly. An Analytics Experiment<\/strong> matters because it:<\/p>\n\n\n\n

      \n
    • Protects you from false wins.<\/strong> Seasonality, campaign mix shifts, and returning-user behavior can make changes look better (or worse) than they are.<\/li>\n
    • Improves marketing ROI.<\/strong> By validating which actions actually move key metrics, you allocate spend and effort to proven drivers.<\/li>\n
    • Creates a competitive advantage.<\/strong> Teams that run consistent Analytics Experiment cycles learn faster, avoid churn-inducing changes, and compound gains over time.<\/li>\n
    • Aligns stakeholders.<\/strong> A shared experimental method reduces debates driven by titles or preferences and replaces them with agreed-upon evidence.<\/li>\n<\/ul>\n\n\n\n

      Most importantly, Analytics Experiment thinking upgrades your Analytics<\/strong> practice from \u201cdashboarding\u201d to \u201cdecisioning,\u201d which is where measurement starts generating real business value.<\/p>\n\n\n\n

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

      An Analytics Experiment<\/strong> is often run as a controlled test, but the broader idea is a repeatable learning workflow. In practice, it typically follows this sequence:<\/p>\n\n\n\n

        \n
      1. \n

        Input \/ Trigger (the question)<\/strong>\n – A performance problem (e.g., high checkout drop-off).\n – A growth idea (e.g., new message for a paid campaign).\n – A risk event (e.g., tracking changes, consent shifts).\n – A strategic bet (e.g., new pricing page layout).<\/p>\n<\/li>\n

      2. \n

        Analysis \/ Planning (the design)<\/strong>\n – Define a clear hypothesis and success metric.\n – Choose the comparison method (randomized test when possible; otherwise quasi-experimental methods).\n – Decide the unit of analysis (user, session, account, region).\n – Establish guardrail metrics to prevent harmful trade-offs.<\/p>\n<\/li>\n

      3. \n

        Execution \/ Application (the run)<\/strong>\n – Implement the change (variant) and maintain a baseline (control).\n – Ensure instrumentation is correct (events, attribution windows, identity rules).\n – Monitor data quality during the run.<\/p>\n<\/li>\n

      4. \n

        Output \/ Outcome (the decision)<\/strong>\n – Evaluate effect size and uncertainty (not just \u201csignificant or not\u201d).\n – Assess segment differences and downstream quality (lead-to-sale, refunds, churn).\n – Document learnings and decide: roll out, iterate, or abandon.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        This is the \u201cengine\u201d that makes Conversion & Measurement<\/strong> trustworthy inside everyday Analytics<\/strong> operations.<\/p>\n\n\n\n

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

        A strong Analytics Experiment<\/strong> relies on several building blocks:<\/p>\n\n\n\n

        Hypothesis and scope<\/h3>\n\n\n\n

        A good hypothesis includes:\n– The change (what you\u2019ll do)\n– The audience (who it affects)\n– The expected impact (what metric should move, and why)<\/p>\n\n\n\n

        Measurement model<\/h3>\n\n\n\n

        You need clarity on:\n– Primary KPI (the metric you optimize)\n– Secondary metrics (supporting signals)\n– Guardrails (metrics you must not damage, like refund rate or unsubscribe rate)<\/p>\n\n\n\n

        Data instrumentation<\/h3>\n\n\n\n

        In Analytics<\/strong>, experiment results are only as good as the tracking:\n– Consistent event definitions (e.g., \u201clead_submitted\u201d)\n– Correct identity handling (user vs device vs account)\n– Clean source\/medium rules and campaign tagging where relevant<\/p>\n\n\n\n

        Experimental design choices<\/h3>\n\n\n\n

        Key decisions include:\n– Randomization approach (if possible)\n– Sample size and runtime expectations\n– Segmentation plan (predefined, not retrofitted)<\/p>\n\n\n\n

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

        In Conversion & Measurement<\/strong>, experiments work best with clear ownership:\n– Marketer or PM: hypothesis and business context\n– Analyst: design, validation, interpretation\n– Developer: implementation and QA\n– Stakeholders: decision-making and rollout rules<\/p>\n\n\n\n

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

        \u201cAnalytics Experiment\u201d doesn\u2019t refer to only one formal method. In real-world Analytics<\/strong>, it usually falls into a few practical approaches:<\/p>\n\n\n\n

        Controlled online experiments (randomized)<\/h3>\n\n\n\n
          \n
        • User-level splits (A\/B or multivariate)<\/li>\n
        • Holdouts (a portion of traffic sees no change)\nBest when you can randomize and instrument reliably.<\/li>\n<\/ul>\n\n\n\n

          Geo or time-based experiments<\/h3>\n\n\n\n
            \n
          • Region-based holdouts (market A vs market B)<\/li>\n
          • Interrupted time series (before vs after, with controls)\nUseful when user-level randomization is difficult (e.g., TV, out-of-home, broad pricing changes).<\/li>\n<\/ul>\n\n\n\n

            Incrementality tests for marketing channels<\/h3>\n\n\n\n
              \n
            • Conversion lift via holdout audiences<\/li>\n
            • Budget on\/off tests with careful controls\nCommon in Conversion & Measurement<\/strong> when attribution alone is insufficient.<\/li>\n<\/ul>\n\n\n\n

              Exploratory vs hypothesis-driven<\/h3>\n\n\n\n
                \n
              • Exploratory: identify patterns worth testing (still needs measurement discipline).<\/li>\n
              • Hypothesis-driven: test a precise claim with pre-defined metrics and decision criteria.<\/li>\n<\/ul>\n\n\n\n

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

                Example 1: Landing page message test for lead quality<\/h3>\n\n\n\n

                A B2B company suspects a new headline will increase demo requests.<\/p>\n\n\n\n

                  \n
                • Analytics Experiment design:<\/strong> Split traffic 50\/50 between two page variants.<\/li>\n
                • Conversion & Measurement focus:<\/strong> Primary KPI is qualified demo requests; guardrail is lead-to-opportunity rate.<\/li>\n
                • Analytics execution:<\/strong> Track form submits, qualification signals, and CRM outcomes.<\/li>\n
                • Result interpretation:<\/strong> Even if form fills rise, you only \u201cwin\u201d if downstream quality holds.<\/li>\n<\/ul>\n\n\n\n

                  Example 2: Paid media incrementality holdout<\/h3>\n\n\n\n

                  A brand wants to know if retargeting ads add incremental conversions.<\/p>\n\n\n\n

                    \n
                  • Analytics Experiment design:<\/strong> Create a holdout group that doesn\u2019t receive retargeting.<\/li>\n
                  • Conversion & Measurement focus:<\/strong> Incremental conversion rate and incremental revenue, not attributed conversions.<\/li>\n
                  • Analytics execution:<\/strong> Ensure audience assignment is stable; validate that holdout users aren\u2019t exposed through other paths.<\/li>\n
                  • Outcome:<\/strong> Budget shifts to the segments with real lift, not just high last-click volume.<\/li>\n<\/ul>\n\n\n\n

                    Example 3: Checkout friction reduction with guardrails<\/h3>\n\n\n\n

                    An ecommerce team removes a step from checkout.<\/p>\n\n\n\n

                      \n
                    • Analytics Experiment design:<\/strong> Test the new flow against the existing flow.<\/li>\n
                    • Conversion & Measurement focus:<\/strong> Primary KPI is purchase conversion rate; guardrails include refund rate, support tickets, and payment failures.<\/li>\n
                    • Analytics execution:<\/strong> Track funnel steps, errors, and post-purchase outcomes.<\/li>\n
                    • Outcome:<\/strong> Roll out only if gains persist without creating costly downstream problems.<\/li>\n<\/ul>\n\n\n\n

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

                      A consistent Analytics Experiment<\/strong> program delivers benefits that go beyond \u201cwinning tests\u201d:<\/p>\n\n\n\n

                        \n
                      • Performance improvements:<\/strong> Higher conversion rates, improved average order value, stronger retention, better lead quality.<\/li>\n
                      • Cost savings:<\/strong> Reduced spend on ineffective channels and fewer engineering hours shipped on unproven ideas.<\/li>\n
                      • Operational efficiency:<\/strong> Clear priorities, faster iteration cycles, and reusable experiment templates inside Analytics<\/strong> workflows.<\/li>\n
                      • Better customer experience:<\/strong> Changes are validated against user outcomes, reducing the chance of friction, confusion, or trust loss.<\/li>\n<\/ul>\n\n\n\n

                        In Conversion & Measurement<\/strong>, these benefits compound: each experiment improves both outcomes and your organization\u2019s ability to measure truthfully.<\/p>\n\n\n\n

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

                        An Analytics Experiment<\/strong> can fail for reasons that have nothing to do with the idea being tested:<\/p>\n\n\n\n

                          \n
                        • Data quality issues:<\/strong> Missing events, duplicated events, broken attribution, or inconsistent identity resolution can invalidate conclusions in Analytics<\/strong>.<\/li>\n
                        • Insufficient sample size:<\/strong> Small traffic or low conversion rates make results noisy; \u201cno result\u201d may simply mean \u201cnot enough data.\u201d<\/li>\n
                        • Contamination and interference:<\/strong> Users switching devices, overlapping campaigns, or exposure outside the test can blur control vs treatment.<\/li>\n
                        • Misaligned KPIs:<\/strong> Optimizing for clicks or form fills can harm revenue, retention, or brand trust\u2014especially without guardrails.<\/li>\n
                        • Organizational friction:<\/strong> Lack of governance, unclear ownership, or \u201ccherry-picking\u201d results undermines the credibility of Conversion & Measurement<\/strong>.<\/li>\n<\/ul>\n\n\n\n

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

                          Use these practices to make an Analytics Experiment<\/strong> both credible and actionable:<\/p>\n\n\n\n

                            \n
                          1. \n

                            Pre-register the essentials<\/strong>\n – Hypothesis, primary KPI, guardrails, target audience, and runtime expectations.\n – Decide what \u201cship\u201d means before you see results.<\/p>\n<\/li>\n

                          2. \n

                            Design for decision-making, not just significance<\/strong>\n – Focus on effect size and business impact (e.g., incremental revenue).\n – Use confidence intervals or credible ranges to express uncertainty.<\/p>\n<\/li>\n

                          3. \n

                            Instrument and QA like it\u2019s production<\/strong>\n – Validate event firing, funnel counts, and segment splits early.\n – Monitor data drift during the run (tracking changes mid-test are a common failure mode in Analytics<\/strong>).<\/p>\n<\/li>\n

                          4. \n

                            Use guardrails to prevent costly trade-offs<\/strong>\n – Include quality metrics (lead-to-sale rate, churn, refunds, complaint rate).\n – In Conversion & Measurement<\/strong>, guardrails are often what separate \u201cgrowth\u201d from \u201cgrowth at any cost.\u201d<\/p>\n<\/li>\n

                          5. \n

                            Document learnings and build a library<\/strong>\n – Record what was tested, why, what happened, and what you\u2019d do next.\n – Over time, this becomes a strategic asset for marketing and product teams.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

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

                            An Analytics Experiment<\/strong> is rarely a single tool\u2014it\u2019s a workflow across systems. Common tool categories in Conversion & Measurement<\/strong> and Analytics<\/strong> include:<\/p>\n\n\n\n

                              \n
                            • Analytics tools:<\/strong> Event and session analysis, funnel reporting, cohort analysis, segmentation, and experiment result views.<\/li>\n
                            • Tag management and instrumentation:<\/strong> Consistent event collection, version control for tracking, and deployment governance.<\/li>\n
                            • Experimentation platforms:<\/strong> Traffic splitting, feature flags, holdouts, and result aggregation.<\/li>\n
                            • Data warehouses and transformation pipelines:<\/strong> Reliable storage, modeling, and repeatable metric definitions.<\/li>\n
                            • BI and reporting dashboards:<\/strong> Executive-ready views, anomaly detection, and self-serve exploration.<\/li>\n
                            • CRM and marketing automation:<\/strong> Lead quality, pipeline impact, lifecycle stages, and downstream outcomes.<\/li>\n
                            • Privacy and consent systems:<\/strong> Consent-aware tracking, retention rules, and compliance-aligned measurement.<\/li>\n<\/ul>\n\n\n\n

                              The best stack is the one that supports trustworthy measurement, stable definitions, and scalable experimentation\u2014not just more reports.<\/p>\n\n\n\n

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

                              The right metrics depend on the business model, but most Analytics Experiment<\/strong> programs use a mix of:<\/p>\n\n\n\n

                              Core performance metrics<\/h3>\n\n\n\n
                                \n
                              • Conversion rate (by funnel step and overall)<\/li>\n
                              • Revenue per visitor \/ revenue per session<\/li>\n
                              • Average order value<\/li>\n
                              • Lead submission rate and qualified lead rate<\/li>\n<\/ul>\n\n\n\n

                                Efficiency and ROI metrics<\/h3>\n\n\n\n
                                  \n
                                • Cost per acquisition (CPA) and customer acquisition cost (CAC)<\/li>\n
                                • Return on ad spend (ROAS) at an incrementality-aware level<\/li>\n
                                • Payback period (especially for subscription businesses)<\/li>\n<\/ul>\n\n\n\n

                                  Quality and guardrail metrics<\/h3>\n\n\n\n
                                    \n
                                  • Refund\/chargeback rate<\/li>\n
                                  • Churn and retention (D7\/D30, monthly retention)<\/li>\n
                                  • Support contacts, complaint rate, unsubscribe rate<\/li>\n
                                  • Page performance and error rates (when UX or technical changes are tested)<\/li>\n<\/ul>\n\n\n\n

                                    Experiment interpretation metrics<\/h3>\n\n\n\n
                                      \n
                                    • Effect size (absolute and relative lift)<\/li>\n
                                    • Uncertainty estimates (intervals, not just binary outcomes)<\/li>\n
                                    • Sample size, runtime, and exposure balance (control vs variant)<\/li>\n<\/ul>\n\n\n\n

                                      In Conversion & Measurement<\/strong>, the \u201cbest\u201d metric is the one that reflects durable business value and can be measured reliably.<\/p>\n\n\n\n

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

                                      Analytics Experiment<\/strong> practice is evolving quickly within Conversion & Measurement<\/strong> due to several trends:<\/p>\n\n\n\n

                                        \n
                                      • AI-assisted experimentation:<\/strong> Faster hypothesis generation, automated QA checks, smarter segmentation, and improved anomaly detection\u2014while humans still define goals and guardrails.<\/li>\n
                                      • More emphasis on incrementality:<\/strong> As attribution becomes less dependable, marketers increasingly rely on holdouts and lift studies to understand true impact.<\/li>\n
                                      • Privacy-driven measurement changes:<\/strong> Consent requirements and reduced identifier availability push teams toward server-side measurement, modeled conversions, and carefully designed experiments.<\/li>\n
                                      • Better decision frameworks:<\/strong> Growth teams are moving beyond \u201cwin\/loss\u201d to portfolio thinking\u2014balancing risk, expected value, and learning velocity.<\/li>\n
                                      • Personalization with controls:<\/strong> As experiences personalize, experiments increasingly test policies (how to personalize) rather than single static variants.<\/li>\n<\/ul>\n\n\n\n

                                        The direction is clear: Analytics<\/strong> teams that can run trustworthy experiments will lead strategy, not just report on it.<\/p>\n\n\n\n

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

                                        Analytics Experiment vs A\/B testing<\/h3>\n\n\n\n

                                        A\/B testing is a common type<\/strong> of Analytics Experiment, usually user-randomized with two variants. Analytics Experiment is broader: it includes holdouts, geo tests, time-based designs, and incrementality studies across channels.<\/p>\n\n\n\n

                                        Analytics Experiment vs Conversion Rate Optimization (CRO)<\/h3>\n\n\n\n

                                        CRO is the practice of improving conversion performance through research, UX improvements, and testing. An Analytics Experiment<\/strong> is one of the primary methods used in CRO, but CRO also includes qualitative research, usability testing, and heuristic reviews that may not be experimental.<\/p>\n\n\n\n

                                        Analytics Experiment vs Attribution modeling<\/h3>\n\n\n\n

                                        Attribution modeling assigns credit to touchpoints; it often answers \u201cwhich channels were involved?\u201d An Analytics Experiment aims to answer \u201cwhat caused incremental change?\u201d In Conversion & Measurement<\/strong>, experiments are typically more credible for causality, while attribution is useful for directional optimization and planning.<\/p>\n\n\n\n

                                        Who Should Learn Analytics Experiment<\/h2>\n\n\n\n
                                          \n
                                        • Marketers:<\/strong> To validate channel strategies, creative changes, landing pages, and lifecycle messaging with credible Conversion & Measurement<\/strong>.<\/li>\n
                                        • Analysts:<\/strong> To move from reporting to causal inference, improve stakeholder trust, and build repeatable Analytics<\/strong> processes.<\/li>\n
                                        • Agencies:<\/strong> To prove incremental value, reduce client churn, and create a measurable optimization roadmap.<\/li>\n
                                        • Business owners and founders:<\/strong> To make high-stakes decisions (pricing, positioning, budget shifts) with less risk and clearer expected outcomes.<\/li>\n
                                        • Developers and product teams:<\/strong> To ship changes safely, measure impact accurately, and avoid \u201cinvisible regressions\u201d that hurt conversion.<\/li>\n<\/ul>\n\n\n\n

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

                                          An Analytics Experiment<\/strong> is a structured method for testing changes and measuring whether they cause meaningful improvements. It matters because modern Conversion & Measurement<\/strong> requires causal clarity, not just dashboards. Done well, it strengthens Analytics<\/strong> by improving data discipline, decision-making quality, and sustainable performance gains across marketing and product.<\/p>\n\n\n\n

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

                                          1) What is an Analytics Experiment in simple terms?<\/h3>\n\n\n\n

                                          An Analytics Experiment<\/strong> is a planned test where you compare a change against a baseline to learn whether the change caused better outcomes, such as higher conversion rate or revenue.<\/p>\n\n\n\n

                                          2) How long should an Analytics Experiment run?<\/h3>\n\n\n\n

                                          Long enough to reach a reliable sample size and cover normal variability (day-of-week effects, campaign cycles). Many teams set a minimum runtime and stop only when both sample size and data quality checks are satisfied.<\/p>\n\n\n\n

                                          3) Do I always need randomization for an Analytics Experiment?<\/h3>\n\n\n\n

                                          Randomization is ideal, but not always possible. In Conversion & Measurement<\/strong>, geo tests, time-based designs with controls, and holdouts can still produce useful causal insights if carefully planned.<\/p>\n\n\n\n

                                          4) What\u2019s the biggest mistake teams make with Analytics experiments?<\/h3>\n\n\n\n

                                          Optimizing for an easy metric (clicks, form fills) without guardrails. This can \u201cimprove\u201d conversions while lowering quality, increasing refunds, or damaging retention.<\/p>\n\n\n\n

                                          5) How does Analytics help interpret experiment results?<\/h3>\n\n\n\n

                                          Analytics<\/strong> helps validate tracking, segment results, quantify uncertainty, and connect top-funnel changes to downstream outcomes like revenue, retention, and customer value.<\/p>\n\n\n\n

                                          6) What metrics should I choose as primary and guardrail metrics?<\/h3>\n\n\n\n

                                          Pick one primary metric that matches the business objective (e.g., qualified leads, purchases, revenue per visitor). Choose guardrails that protect the business (e.g., churn, refund rate, unsubscribe rate, error rate).<\/p>\n\n\n\n

                                          7) Can an Analytics Experiment be \u201csuccessful\u201d even if it doesn\u2019t win?<\/h3>\n\n\n\n

                                          Yes. A \u201cno lift\u201d result can prevent wasted spend or risky rollouts and often reveals what to test next. In strong Conversion & Measurement<\/strong> programs, learning velocity is a form of success.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                          An **Analytics Experiment** is a structured way to test a change\u2014on a website, in a funnel, inside a campaign, or across a customer journey\u2014and measure whether it truly improves outcomes. In **Conversion & Measurement**, it\u2019s the discipline that turns opinions (\u201cthis new landing page feels better\u201d) into evidence (\u201cit increased qualified leads by 8% without harming sales\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-6999","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\/6999","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=6999"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/6999\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=6999"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=6999"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=6999"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}