{"id":7107,"date":"2026-03-24T00:34:45","date_gmt":"2026-03-24T00:34:45","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/a-b-test\/"},"modified":"2026-03-24T00:34:45","modified_gmt":"2026-03-24T00:34:45","slug":"a-b-test","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/a-b-test\/","title":{"rendered":"A\/B Test: What It Is, Key Features, Benefits, Use Cases, and How It Fits in CRO"},"content":{"rendered":"\n

An A\/B Test<\/strong> is one of the most reliable ways to make better marketing and product decisions using evidence instead of opinions. In Conversion & Measurement<\/strong>, it provides a structured method to compare two versions of an experience\u2014such as a landing page, ad message, email, or checkout step\u2014and quantify which one drives better outcomes.<\/p>\n\n\n\n

In the context of CRO<\/strong> (conversion rate optimization), an A\/B Test<\/strong> turns optimization from a guessing game into a repeatable discipline. It helps teams learn what actually influences user behavior, attribute performance changes to specific changes, and prioritize improvements with measurable impact. Done well, it becomes a cornerstone of modern Conversion & Measurement<\/strong> strategy: test, learn, iterate, and scale.<\/p>\n\n\n\n

What Is A\/B Test?<\/h2>\n\n\n\n

An A\/B Test<\/strong> (often shortened to A\/B<\/strong>) is an experiment where traffic or users are split into two groups:<\/p>\n\n\n\n

    \n
  • Version A (control):<\/strong> the current experience <\/li>\n
  • Version B (variant):<\/strong> a modified experience intended to improve a metric<\/li>\n<\/ul>\n\n\n\n

    The core concept is simple: change one meaningful element, hold everything else as constant as possible, and measure the difference in outcomes. The business meaning is even more important: an A\/B Test<\/strong> helps you decide which option creates more value\u2014more sign-ups, purchases, qualified leads, or retained users\u2014based on observed behavior.<\/p>\n\n\n\n

    Within Conversion & Measurement<\/strong>, an A\/B Test<\/strong> is a measurement method (it produces causal evidence, not just correlation). Within CRO<\/strong>, it\u2019s a decision framework for improving conversion funnels, user journeys, and messaging with controlled experimentation.<\/p>\n\n\n\n

    Why A\/B Test Matters in Conversion & Measurement<\/h2>\n\n\n\n

    Conversion & Measurement<\/strong> is not only about reporting what happened; it\u2019s about understanding why<\/em> it happened and what to do next. An A\/B Test<\/strong> matters because it reduces uncertainty when you\u2019re making changes that affect revenue, lead volume, or customer experience.<\/p>\n\n\n\n

    Key ways an A\/B Test<\/strong> creates business value:<\/p>\n\n\n\n

      \n
    • Improves marketing outcomes:<\/strong> Better landing pages, emails, ad creatives, and offers can increase conversions without increasing spend\u2014classic CRO<\/strong> leverage.<\/li>\n
    • Protects performance:<\/strong> Testing helps avoid rolling out \u201cimprovements\u201d that actually hurt conversion rates, average order value, or retention.<\/li>\n
    • Creates competitive advantage:<\/strong> Organizations that run high-quality experiments learn faster than competitors and compound incremental gains.<\/li>\n
    • Aligns teams on evidence:<\/strong> Product, design, growth, and leadership can rally around measured results instead of subjective debates.<\/li>\n<\/ul>\n\n\n\n

      In mature Conversion & Measurement<\/strong> programs, experimentation becomes a roadmap input\u2014what to build, what to message, and what to prioritize next.<\/p>\n\n\n\n

      How A\/B Test Works<\/h2>\n\n\n\n

      Although an A\/B Test<\/strong> is a concept, it follows a practical workflow that keeps experiments trustworthy.<\/p>\n\n\n\n

        \n
      1. \n

        Input (hypothesis and goal)<\/strong>\n You start with a hypothesis such as: \u201cIf we reduce form friction by removing one field, more users will submit.\u201d Define the primary metric (for example, completed sign-ups) and decide what success looks like for CRO<\/strong>.<\/p>\n<\/li>\n

      2. \n

        Processing (experiment design and audience split)<\/strong>\n Users are randomly assigned to control (A) or variant (B). Randomization is essential in Conversion & Measurement<\/strong> because it reduces bias and helps ensure differences are caused by the change\u2014not by audience differences.<\/p>\n<\/li>\n

      3. \n

        Execution (serve experiences and track events)<\/strong>\n Each group sees its version, and instrumentation records exposures and outcomes (page views, clicks, form submits, purchases). Good tracking ensures you know who saw what and what they did afterward.<\/p>\n<\/li>\n

      4. \n

        Output (analysis and decision)<\/strong>\n After enough data is collected, you evaluate performance, statistical confidence (or an alternative decision rule), and practical impact. Then you decide to ship, iterate, or stop\u2014feeding learning back into your CRO<\/strong> backlog.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        Key Components of A\/B Test<\/h2>\n\n\n\n

        A dependable A\/B Test<\/strong> program in Conversion & Measurement<\/strong> typically includes the following components:<\/p>\n\n\n\n

        Experiment design<\/h3>\n\n\n\n
          \n
        • Hypothesis:<\/strong> a clear statement connecting change \u2192 expected behavior \u2192 metric impact <\/li>\n
        • Primary metric:<\/strong> the main decision metric (avoid choosing after seeing results) <\/li>\n
        • Guardrail metrics:<\/strong> metrics that must not degrade (refund rate, unsubscribe rate, error rate)<\/li>\n<\/ul>\n\n\n\n

          Data and instrumentation<\/h3>\n\n\n\n
            \n
          • Event tracking:<\/strong> consistent definitions for \u201cconversion,\u201d \u201cadd to cart,\u201d \u201cqualified lead,\u201d etc. <\/li>\n
          • Exposure logging:<\/strong> record who was eligible and which version they saw <\/li>\n
          • Data quality checks:<\/strong> bot filtering, duplicate events, broken tags, and missing attribution<\/li>\n<\/ul>\n\n\n\n

            Governance and responsibilities<\/h3>\n\n\n\n
              \n
            • Ownership:<\/strong> who approves tests, reviews results, and decides whether to ship <\/li>\n
            • Experiment calendar:<\/strong> prevents overlapping tests that interfere with each other <\/li>\n
            • Documentation:<\/strong> test rationale, screenshots, targeting, dates, and outcomes\u2014critical for scaling CRO<\/strong><\/li>\n<\/ul>\n\n\n\n

              Metrics and evaluation methods<\/h3>\n\n\n\n
                \n
              • Sample size planning:<\/strong> approximate traffic needed to detect a meaningful change <\/li>\n
              • Significance or decision thresholds:<\/strong> rules for calling winners and avoiding false positives <\/li>\n
              • Segmentation:<\/strong> interpret results by device, channel, region, or user type without \u201ccherry-picking\u201d<\/li>\n<\/ul>\n\n\n\n

                Types of A\/B Test<\/h2>\n\n\n\n

                In practice, \u201ctypes\u201d of A\/B Test<\/strong> usually refer to how and where the experiment is executed.<\/p>\n\n\n\n

                Client-side vs server-side A\/B Test<\/h3>\n\n\n\n
                  \n
                • Client-side:<\/strong> changes applied in the browser (often easier to launch, but can be impacted by flicker, ad blockers, or tracking restrictions). <\/li>\n
                • Server-side:<\/strong> changes applied before the page\/app renders (often more robust, better for performance and complex logic).<\/li>\n<\/ul>\n\n\n\n

                  Website, product, and marketing A\/B Test contexts<\/h3>\n\n\n\n
                    \n
                  • Landing page tests:<\/strong> headlines, social proof, page layout, forms <\/li>\n
                  • Product tests:<\/strong> onboarding flows, feature discoverability, pricing presentation <\/li>\n
                  • Lifecycle tests:<\/strong> email subject lines, send times, in-app prompts<\/li>\n<\/ul>\n\n\n\n

                    Targeted vs broad A\/B Test<\/h3>\n\n\n\n
                      \n
                    • Broad tests:<\/strong> apply to most traffic for maximum learning speed. <\/li>\n
                    • Targeted tests:<\/strong> apply to specific segments (e.g., paid traffic, returning users) to improve relevance\u2014common in CRO<\/strong> when intent varies.<\/li>\n<\/ul>\n\n\n\n

                      Real-World Examples of A\/B Test<\/h2>\n\n\n\n

                      1) Lead generation landing page optimization<\/h3>\n\n\n\n

                      A B2B company runs an A\/B Test<\/strong> on its demo request page. Version B reduces the form from 8 fields to 5 and adds reassurance text about response time.\n– Primary metric:<\/strong> completed demo requests\n– Guardrails:<\/strong> lead quality score, spam rate\n– Conversion & Measurement tie-in:<\/strong> use consistent UTM\/channel attribution and track downstream pipeline to avoid optimizing for low-quality leads\nThis is classic CRO<\/strong>: reduce friction while protecting quality.<\/p>\n\n\n\n

                      2) Ecommerce checkout improvement<\/h3>\n\n\n\n

                      An online store runs an A\/B Test<\/strong> that adds express payment options earlier in checkout and simplifies error messaging.\n– Primary metric:<\/strong> purchase completion rate\n– Guardrails:<\/strong> average order value, refund rate, payment failures\n– Conversion & Measurement tie-in:<\/strong> ensure events are deduplicated and that \u201cpurchase\u201d is captured reliably across devices\nThe win may be small in percent terms but large in revenue impact.<\/p>\n\n\n\n

                      3) Email campaign performance experiment<\/h3>\n\n\n\n

                      A subscription business runs an A\/B Test<\/strong> on onboarding emails: Version B changes the subject line and the first call-to-action.\n– Primary metric:<\/strong> activation event (first key action in product)\n– Guardrails:<\/strong> unsubscribe rate, complaint rate\n– Conversion & Measurement tie-in:<\/strong> connect email click data to on-site\/product activation events for true CRO<\/strong> outcomes, not just opens<\/p>\n\n\n\n

                      Benefits of Using A\/B Test<\/h2>\n\n\n\n

                      A well-run A\/B Test<\/strong> program delivers benefits that compound over time:<\/p>\n\n\n\n

                        \n
                      • Performance improvements:<\/strong> systematic uplifts to conversion rate, activation rate, or revenue per visitor <\/li>\n
                      • Cost efficiency:<\/strong> higher conversion at the same spend lowers customer acquisition costs <\/li>\n
                      • Faster learning:<\/strong> teams discover what messaging, UX, and offers work for real users <\/li>\n
                      • Better customer experience:<\/strong> fewer confusing steps, clearer value propositions, and reduced friction <\/li>\n
                      • Risk reduction:<\/strong> changes are validated before full rollout, strengthening Conversion & Measurement<\/strong> confidence<\/li>\n<\/ul>\n\n\n\n

                        In CRO<\/strong>, the biggest benefit is not a single \u201cwinner,\u201d but a growing library of validated insights you can reuse across channels and pages.<\/p>\n\n\n\n

                        Challenges of A\/B Test<\/h2>\n\n\n\n

                        An A\/B Test<\/strong> can mislead if measurement or execution is weak. Common challenges include:<\/p>\n\n\n\n

                          \n
                        • Insufficient sample size:<\/strong> small tests often produce noisy results and false confidence. <\/li>\n
                        • Peeking and early stopping:<\/strong> checking results too often increases false positives unless you use appropriate sequential methods. <\/li>\n
                        • Overlapping experiments:<\/strong> multiple tests on the same audience can interact and distort outcomes\u2014an ongoing Conversion & Measurement<\/strong> governance issue. <\/li>\n
                        • Instrumentation gaps:<\/strong> missing exposure logs, broken events, or inconsistent definitions undermine trust. <\/li>\n
                        • External factors:<\/strong> seasonality, promotions, outages, or channel mix shifts can contaminate results. <\/li>\n
                        • Local maxima:<\/strong> teams may optimize button colors while bigger CRO<\/strong> opportunities exist in pricing, positioning, or onboarding.<\/li>\n<\/ul>\n\n\n\n

                          Best Practices for A\/B Test<\/h2>\n\n\n\n

                          These practices make A\/B Test<\/strong> results more reliable and more actionable in Conversion & Measurement<\/strong> and CRO<\/strong>.<\/p>\n\n\n\n

                          Design better hypotheses<\/h3>\n\n\n\n
                            \n
                          • Tie each test to a specific user problem (confusion, friction, uncertainty). <\/li>\n
                          • Predict directionality and mechanism (\u201cwhy it should work\u201d), not just \u201ctry this.\u201d<\/li>\n<\/ul>\n\n\n\n

                            Choose metrics intentionally<\/h3>\n\n\n\n
                              \n
                            • Define one primary metric and a small set of guardrails. <\/li>\n
                            • Avoid switching the success metric after viewing results.<\/li>\n<\/ul>\n\n\n\n

                              Plan duration and sample size<\/h3>\n\n\n\n
                                \n
                              • Estimate how long it will take to capture enough conversions. <\/li>\n
                              • Run tests across full business cycles when possible (weekday\/weekend effects matter).<\/li>\n<\/ul>\n\n\n\n

                                Maintain experiment hygiene<\/h3>\n\n\n\n
                                  \n
                                • Randomize assignment and keep allocation stable (avoid users switching between A and B). <\/li>\n
                                • Log exposure and ensure users counted in analysis were actually eligible.<\/li>\n<\/ul>\n\n\n\n

                                  Analyze with discipline<\/h3>\n\n\n\n
                                    \n
                                  • Segment carefully (device, channel, new vs returning) to understand impact, but avoid \u201cfinding a winner\u201d only in a tiny slice without a plan. <\/li>\n
                                  • Look at practical significance (impact size), not just statistical significance.<\/li>\n<\/ul>\n\n\n\n

                                    Scale through a system<\/h3>\n\n\n\n
                                      \n
                                    • Keep a testing backlog prioritized by potential impact and effort. <\/li>\n
                                    • Document results and learnings so CRO<\/strong> doesn\u2019t repeat the same experiments.<\/li>\n<\/ul>\n\n\n\n

                                      Tools Used for A\/B Test<\/h2>\n\n\n\n

                                      An A\/B Test<\/strong> program typically involves a stack of systems rather than a single tool. In Conversion & Measurement<\/strong>, these tool categories commonly work together:<\/p>\n\n\n\n

                                        \n
                                      • Experimentation platforms:<\/strong> manage traffic splitting, targeting, and variant delivery (client-side or server-side). <\/li>\n
                                      • Web and product analytics tools:<\/strong> measure conversion funnels, cohort behavior, and attribution; validate that events fire correctly. <\/li>\n
                                      • Tag management systems:<\/strong> deploy and manage tracking tags and event schemas with version control. <\/li>\n
                                      • Data warehouses and BI dashboards:<\/strong> centralize experiment and outcome data for consistent reporting across teams. <\/li>\n
                                      • CRM and marketing automation:<\/strong> connect test exposure to lead quality, pipeline, retention, and lifecycle outcomes\u2014critical for end-to-end CRO<\/strong> impact. <\/li>\n
                                      • Error monitoring and performance tools:<\/strong> ensure variants do not degrade page speed, app stability, or checkout reliability.<\/li>\n<\/ul>\n\n\n\n

                                        Tooling matters, but process and measurement discipline matter more.<\/p>\n\n\n\n

                                        Metrics Related to A\/B Test<\/h2>\n\n\n\n

                                        The best metrics depend on the funnel stage, but these are the most common in Conversion & Measurement<\/strong> and CRO<\/strong>:<\/p>\n\n\n\n

                                        Primary conversion metrics<\/h3>\n\n\n\n
                                          \n
                                        • Conversion rate (CR):<\/strong> conversions \u00f7 eligible users\/sessions <\/li>\n
                                        • Revenue per visitor (RPV):<\/strong> revenue \u00f7 visitors (often better than CR alone for ecommerce) <\/li>\n
                                        • Lead conversion rate:<\/strong> qualified leads \u00f7 landing page visitors <\/li>\n
                                        • Activation rate:<\/strong> users completing a key \u201caha\u201d action<\/li>\n<\/ul>\n\n\n\n

                                          Efficiency and ROI metrics<\/h3>\n\n\n\n
                                            \n
                                          • Cost per acquisition (CPA):<\/strong> spend \u00f7 conversions <\/li>\n
                                          • Return on ad spend (ROAS):<\/strong> revenue \u00f7 ad spend (when tests affect paid landing experiences)<\/li>\n<\/ul>\n\n\n\n

                                            Quality and risk metrics (guardrails)<\/h3>\n\n\n\n
                                              \n
                                            • Refund\/chargeback rate<\/strong> <\/li>\n
                                            • Unsubscribe\/complaint rate<\/strong> <\/li>\n
                                            • Bounce rate and engagement depth<\/strong> (interpreted carefully) <\/li>\n
                                            • Error rate, latency, page load time<\/strong> (performance regressions can erase CRO<\/strong> gains)<\/li>\n<\/ul>\n\n\n\n

                                              Future Trends of A\/B Test<\/h2>\n\n\n\n

                                              The A\/B Test<\/strong> is evolving as privacy, platforms, and automation change how Conversion & Measurement<\/strong> works.<\/p>\n\n\n\n

                                                \n
                                              • AI-assisted experimentation:<\/strong> AI can propose hypotheses, generate variants, and detect anomalies faster, but teams still need strong governance to avoid misleading \u201cauto-wins.\u201d <\/li>\n
                                              • Personalization with safeguards:<\/strong> more tests will move from one-size-fits-all to segment-aware experiences, with guardrails to prevent unfair or inconsistent outcomes. <\/li>\n
                                              • Privacy-driven measurement changes:<\/strong> reduced third-party tracking and stricter consent requirements will increase reliance on first-party data, server-side tracking, and robust identity resolution where appropriate. <\/li>\n
                                              • Experimentation beyond web pages:<\/strong> more A\/B Test<\/strong> programs will expand into product features, pricing presentation, support flows, and omnichannel journeys. <\/li>\n
                                              • Better decision frameworks:<\/strong> teams will adopt sequential testing, Bayesian approaches, and always-on experimentation pipelines to support continuous CRO<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                                                A\/B Test vs Related Terms<\/h2>\n\n\n\n

                                                Understanding nearby concepts helps teams choose the right method in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                                A\/B Test vs Multivariate Test (MVT)<\/h3>\n\n\n\n
                                                  \n
                                                • A\/B Test:<\/strong> compares two (or a few) full experiences; simpler, faster, and often the default for CRO<\/strong>. <\/li>\n
                                                • Multivariate test:<\/strong> tests combinations of multiple element changes; requires much more traffic and is easier to misinterpret.<\/li>\n<\/ul>\n\n\n\n

                                                  A\/B Test vs Split URL Test<\/h3>\n\n\n\n
                                                    \n
                                                  • A\/B Test:<\/strong> can be run on the same URL with dynamic content changes. <\/li>\n
                                                  • Split URL test:<\/strong> sends users to different URLs (useful when experiences are radically different or when server-side routing is preferred).<\/li>\n<\/ul>\n\n\n\n

                                                    A\/B Test vs Incrementality Test<\/h3>\n\n\n\n
                                                      \n
                                                    • A\/B Test:<\/strong> usually compares experience variants (page, message, flow). <\/li>\n
                                                    • Incrementality test:<\/strong> isolates the true incremental effect of a channel or campaign (e.g., ads vs no ads). Both belong in Conversion & Measurement<\/strong>, but answer different questions.<\/li>\n<\/ul>\n\n\n\n

                                                      Who Should Learn A\/B Test<\/h2>\n\n\n\n

                                                      An A\/B Test<\/strong> is a foundational skill across roles:<\/p>\n\n\n\n

                                                        \n
                                                      • Marketers:<\/strong> improve landing pages, email programs, and offers with measurable CRO<\/strong> gains. <\/li>\n
                                                      • Analysts:<\/strong> design experiments, validate instrumentation, and communicate results credibly in Conversion & Measurement<\/strong>. <\/li>\n
                                                      • Agencies:<\/strong> prove impact, prioritize recommendations, and avoid subjective \u201cbest practices\u201d claims. <\/li>\n
                                                      • Business owners and founders:<\/strong> make higher-confidence decisions on pricing, positioning, and funnel changes. <\/li>\n
                                                      • Developers and product teams:<\/strong> implement server-side testing, ensure performance, and build experimentation into release workflows.<\/li>\n<\/ul>\n\n\n\n

                                                        Summary of A\/B Test<\/h2>\n\n\n\n

                                                        An A\/B Test<\/strong> (or A\/B<\/strong>) is a controlled experiment that compares a control and a variant to measure which performs better on a defined outcome. It matters because it produces causal insight, reduces risk, and enables systematic improvement. Within Conversion & Measurement<\/strong>, it\u2019s a rigorous method for evaluating change; within CRO<\/strong>, it\u2019s a practical engine for increasing conversions, revenue, and user satisfaction through repeatable learning.<\/p>\n\n\n\n

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

                                                        1) What is an A\/B Test and when should I use it?<\/h3>\n\n\n\n

                                                        An A\/B Test<\/strong> compares two versions of an experience to determine which drives better results on a specific metric. Use it when you can control the user experience, track outcomes reliably, and have enough traffic or conversions to measure a meaningful difference.<\/p>\n\n\n\n

                                                        2) How long should an A\/B Test run?<\/h3>\n\n\n\n

                                                        Run it long enough to reach an adequate sample size and cover normal variability (often at least a full business cycle, such as one to two weeks). In Conversion & Measurement<\/strong>, duration should be driven by conversions and stability, not by impatience.<\/p>\n\n\n\n

                                                        3) What\u2019s the difference between statistical significance and practical impact?<\/h3>\n\n\n\n

                                                        Statistical significance addresses confidence that the difference isn\u2019t random noise. Practical impact asks whether the improvement is large enough to matter for the business (revenue, pipeline, retention). Strong CRO<\/strong> decisions require both.<\/p>\n\n\n\n

                                                        4) Can I run multiple A\/B tests at the same time?<\/h3>\n\n\n\n

                                                        Yes, but only if you manage interactions. Avoid overlapping tests on the same pages or the same conversion events unless you have a clear experimentation plan. Proper governance is a major part of scalable Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                                        5) Which metric should be the primary metric for CRO?<\/h3>\n\n\n\n

                                                        Choose the metric that best represents the goal of the page or flow (purchase completion, qualified lead, activation). Pair it with guardrails like refund rate, unsubscribe rate, or performance metrics so CRO<\/strong> improvements don\u2019t create downstream harm.<\/p>\n\n\n\n

                                                        6) Why do A\/B Test results sometimes \u201cdisappear\u201d after launch?<\/h3>\n\n\n\n

                                                        Common reasons include seasonality, novelty effects, changes in traffic mix, tracking differences between test and production, or regression to the mean. A disciplined Conversion & Measurement<\/strong> approach includes validation after rollout and monitoring over time.<\/p>\n\n\n\n

                                                        7) Is an A\/B Test useful with low traffic?<\/h3>\n\n\n\n

                                                        It can be, but you may need bigger changes, longer run times, or alternative methods (e.g., qualitative research, usability testing, or aggregating learnings across similar pages). Low traffic increases uncertainty, so CRO<\/strong> teams should prioritize high-impact hypotheses and strong measurement.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                                        An **A\/B Test** is one of the most reliable ways to make better marketing and product decisions using evidence instead of opinions. In **Conversion & Measurement**, it provides a structured method to compare two versions of an experience\u2014such as a landing page, ad message, email, or checkout step\u2014and quantify which one drives better outcomes.<\/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":[1889],"tags":[],"class_list":["post-7107","post","type-post","status-publish","format-standard","hentry","category-cro"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7107","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=7107"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7107\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7107"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7107"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7107"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}