{"id":8668,"date":"2026-03-26T14:27:01","date_gmt":"2026-03-26T14:27:01","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/mobile-app-experiment\/"},"modified":"2026-03-26T14:27:01","modified_gmt":"2026-03-26T14:27:01","slug":"mobile-app-experiment","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/mobile-app-experiment\/","title":{"rendered":"Mobile App Experiment: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Mobile & App Marketing"},"content":{"rendered":"\n

A Mobile App Experiment<\/strong> is a structured test you run inside a mobile app to learn what changes improve user behavior and business outcomes. In Mobile & App Marketing<\/strong>, it\u2019s how teams move from opinions (\u201cthis onboarding screen feels better\u201d) to evidence (\u201cthis onboarding increased activation by 6% without hurting retention\u201d). <\/p>\n\n\n\n

Because mobile apps blend product, marketing, and analytics, a Mobile App Experiment is rarely \u201cjust a marketing test.\u201d It\u2019s often the fastest, safest way to improve acquisition performance, activation, engagement, and revenue while protecting user experience. In modern Mobile & App Marketing<\/strong>, experimentation is also how teams adapt to privacy constraints, rising acquisition costs, and rapidly changing user expectations.<\/p>\n\n\n\n

What Is Mobile App Experiment?<\/h2>\n\n\n\n

A Mobile App Experiment<\/strong> is a planned comparison between two or more app experiences (or strategies) designed to measure causal impact on defined metrics. You intentionally change one thing\u2014such as a paywall layout, push notification timing, referral incentive, or onboarding sequence\u2014then evaluate whether the change improves outcomes compared to a control group.<\/p>\n\n\n\n

The core concept<\/h3>\n\n\n\n

At its core, a Mobile App Experiment follows scientific thinking:\n– Form a hypothesis (what will change and why)\n– Expose comparable user groups to different variants\n– Measure outcomes with clear success criteria\n– Decide to ship, iterate, or stop based on data<\/p>\n\n\n\n

The business meaning<\/h3>\n\n\n\n

Business-wise, a Mobile App Experiment reduces risk. Instead of shipping a big change to everyone, you validate whether it improves retention, conversion, or revenue. In Mobile & App Marketing<\/strong>, it turns growth into a repeatable system\u2014one where learning compounds.<\/p>\n\n\n\n

Where it fits in Mobile & App Marketing<\/h3>\n\n\n\n

A Mobile App Experiment sits at the intersection of:\n– acquisition strategy (creative, targeting, store listing quality)\n– product experience (activation, habit formation, monetization)\n– lifecycle messaging (push, in-app, email)\n– measurement and attribution (incrementality, cohorts, LTV)<\/p>\n\n\n\n

In other words, it\u2019s an engine for continuous optimization in Mobile & App Marketing<\/strong>.<\/p>\n\n\n\n

Why Mobile App Experiment Matters in Mobile & App Marketing<\/h2>\n\n\n\n

A well-run Mobile App Experiment<\/strong> creates advantages that are hard to copy because they come from accumulated learning about your users.<\/p>\n\n\n\n

Strategic importance<\/h3>\n\n\n\n
    \n
  • Aligns teams around evidence:<\/strong> product, design, marketing, and engineering share a measurable definition of success.<\/li>\n
  • Builds a learning roadmap:<\/strong> experiments reveal what matters most (pricing, onboarding clarity, trust signals, content discovery, etc.).<\/li>\n
  • Improves decision quality:<\/strong> reduces \u201cHIPPO\u201d decisions (highest-paid person\u2019s opinion).<\/li>\n<\/ul>\n\n\n\n

    Business value<\/h3>\n\n\n\n
      \n
    • Higher LTV:<\/strong> improving activation and retention often beats simply buying more installs.<\/li>\n
    • Lower CAC pressure:<\/strong> better conversion and retention make paid acquisition more sustainable.<\/li>\n
    • Faster iteration:<\/strong> small tests help you move quickly without breaking the app experience.<\/li>\n<\/ul>\n\n\n\n

      Marketing outcomes<\/h3>\n\n\n\n

      In Mobile & App Marketing<\/strong>, experimentation can lift:\n– app store conversion (views \u2192 installs)\n– onboarding completion and time-to-value\n– opt-in rates (push permissions, tracking consent where applicable)\n– purchase conversion and subscription starts\n– churn reduction via better engagement loops<\/p>\n\n\n\n

      Competitive advantage<\/h3>\n\n\n\n

      Competitors can copy features; they can\u2019t easily copy your experimentation culture, your user insights, or your validated playbooks.<\/p>\n\n\n\n

      How Mobile App Experiment Works<\/h2>\n\n\n\n

      A Mobile App Experiment<\/strong> is most effective when treated as a workflow rather than a one-off test.<\/p>\n\n\n\n

        \n
      1. \n

        Input \/ trigger: identify an opportunity<\/strong>\n Signals include funnel drop-offs, poor cohort retention, rising cancellation, low trial-to-paid conversion, or an underperforming campaign in Mobile & App Marketing<\/strong>.<\/p>\n<\/li>\n

      2. \n

        Analysis \/ processing: form a testable hypothesis<\/strong>\n Example: \u201cIf we shorten onboarding to two steps and show social proof, activation will increase because users reach the core value faster.\u201d<\/p>\n<\/li>\n

      3. \n

        Execution \/ application: implement variants and assignment<\/strong>\n Users are randomly assigned (or segmented intentionally) into:\n – Control (current experience)\n – Variant A (new experience)\n – Sometimes Variant B (alternate approach)<\/p>\n<\/li>\n

      4. \n

        Output \/ outcome: measure, conclude, and act<\/strong>\n You evaluate results against primary metrics (e.g., activation rate) and guardrails (e.g., crash rate, refunds). Then you decide to:\n – roll out\n – iterate and re-test\n – stop and document learnings<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        In Mobile & App Marketing<\/strong>, the \u201cact\u201d step is critical\u2014experiments only matter if they lead to product changes, messaging changes, or budget shifts.<\/p>\n\n\n\n

        Key Components of Mobile App Experiment<\/h2>\n\n\n\n

        A reliable Mobile App Experiment<\/strong> depends on several building blocks:<\/p>\n\n\n\n

        Experiment design<\/h3>\n\n\n\n
          \n
        • Hypothesis, variants, and success criteria<\/li>\n
        • Eligibility rules (new users only, lapsed users, specific regions)<\/li>\n
        • Sample size and duration planning to avoid premature conclusions<\/li>\n<\/ul>\n\n\n\n

          Instrumentation and data inputs<\/h3>\n\n\n\n
            \n
          • Event tracking for funnel steps (install \u2192 open \u2192 signup \u2192 key action)<\/li>\n
          • Revenue events (trial start, subscription conversion, renewals, refunds)<\/li>\n
          • Engagement events (sessions, content views, shares, saves)<\/li>\n<\/ul>\n\n\n\n

            Systems and processes<\/h3>\n\n\n\n
              \n
            • Experiment assignment (randomization, exposure control)<\/li>\n
            • Feature delivery mechanism (release-based or remote configuration)<\/li>\n
            • QA plan and rollback plan<\/li>\n<\/ul>\n\n\n\n

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

              Clear ownership prevents broken tests:\n– Marketer\/growth lead: hypothesis, priorities, interpretation\n– Analyst\/data scientist: methodology, metrics, validity checks\n– Engineer: implementation, performance, safeguards\n– Designer\/research: UX quality, qualitative insight to explain \u201cwhy\u201d<\/p>\n\n\n\n

              This cross-functional structure is especially important in Mobile & App Marketing<\/strong>, where small UX changes can heavily influence revenue.<\/p>\n\n\n\n

              Types of Mobile App Experiment<\/h2>\n\n\n\n

              \u201cTypes\u201d of Mobile App Experiment<\/strong> are best understood by context and test surface:<\/p>\n\n\n\n

              1) Product experience experiments<\/h3>\n\n\n\n

              Tests inside the app experience:\n– onboarding flows\n– navigation\/discovery layouts\n– personalization modules\n– paywall design and pricing presentation<\/p>\n\n\n\n

              2) Lifecycle and messaging experiments<\/h3>\n\n\n\n

              Tests that shape how you communicate:\n– push notification copy and send time\n– in-app messages and interstitials\n– email sequences tied to app behavior (when applicable)<\/p>\n\n\n\n

              3) Monetization experiments<\/h3>\n\n\n\n

              Tests focused on revenue mechanics:\n– trial length and trial messaging\n– subscription tiers and value framing\n– discount offers or win-back flows<\/p>\n\n\n\n

              4) Acquisition-adjacent experiments<\/h3>\n\n\n\n

              Often managed by marketing but measured through app behavior:\n– deep link landing experiences\n– referral prompts and incentives\n– post-install flows for users from specific campaigns<\/p>\n\n\n\n

              In Mobile & App Marketing<\/strong>, the most valuable tests often connect acquisition source to in-app outcomes (not just installs).<\/p>\n\n\n\n

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

              Example 1: Onboarding simplification for activation lift<\/h3>\n\n\n\n

              A subscription app notices a drop between \u201cinstall\u201d and \u201cfirst key action.\u201d They run a Mobile App Experiment<\/strong>:\n– Control: 5-step onboarding with multiple permissions requests\n– Variant: 2-step onboarding + permission request deferred until value is demonstrated\nOutcome:<\/strong> activation rate increases, and day-7 retention stays flat (a good sign). The team rolls out the new flow and updates lifecycle messaging\u2014classic Mobile & App Marketing<\/strong> optimization.<\/p>\n\n\n\n

              Example 2: Paywall messaging and price anchoring<\/h3>\n\n\n\n

              A media app tests whether value framing improves trial starts:\n– Control: paywall with feature list only\n– Variant: adds \u201cmost popular\u201d plan badge, clearer renewal terms, and a value comparison\nOutcome:<\/strong> trial starts rise, but refunds also increase slightly. The team iterates with clearer expectations and a stronger guardrail metric, showing how a Mobile App Experiment can uncover tradeoffs.<\/p>\n\n\n\n

              Example 3: Push notification timing by behavior segment<\/h3>\n\n\n\n

              An ecommerce app tests push timing for cart abandoners:\n– Control: send at 1 hour after abandon\n– Variant: send at 15 minutes for high-intent users, 2 hours for low-intent users\nOutcome:<\/strong> revenue per recipient improves with no increase in opt-outs. The insight becomes a reusable rule in their Mobile & App Marketing<\/strong> playbook.<\/p>\n\n\n\n

              Benefits of Using Mobile App Experiment<\/h2>\n\n\n\n

              A consistent Mobile App Experiment<\/strong> program delivers compounding gains:<\/p>\n\n\n\n

                \n
              • Performance improvements:<\/strong> higher activation, retention, and conversion rates through validated UX and messaging changes.<\/li>\n
              • Cost savings:<\/strong> less wasted engineering effort and fewer broad rollouts that don\u2019t move metrics.<\/li>\n
              • Efficiency gains:<\/strong> faster decision-making with clear success criteria and documented learnings.<\/li>\n
              • Better customer experience:<\/strong> fewer intrusive prompts, smarter personalization, and more relevant lifecycle messaging\u2014key to sustainable Mobile & App Marketing<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                Challenges of Mobile App Experiment<\/h2>\n\n\n\n

                Running a trustworthy Mobile App Experiment<\/strong> has real constraints:<\/p>\n\n\n\n

                Technical challenges<\/h3>\n\n\n\n
                  \n
                • event tracking gaps or inconsistent schemas<\/li>\n
                • experiment \u201cleakage\u201d (users seeing multiple variants across devices)<\/li>\n
                • performance issues (slow app, crashes) that bias results<\/li>\n<\/ul>\n\n\n\n

                  Strategic risks<\/h3>\n\n\n\n
                    \n
                  • optimizing for short-term conversion while harming long-term retention or brand trust<\/li>\n
                  • over-testing UI changes without a clear strategy (lots of motion, little progress)<\/li>\n<\/ul>\n\n\n\n

                    Implementation barriers<\/h3>\n\n\n\n
                      \n
                    • limited engineering bandwidth for experimentation hooks<\/li>\n
                    • slow release cycles, especially for native apps without remote config<\/li>\n<\/ul>\n\n\n\n

                      Data and measurement limitations<\/h3>\n\n\n\n
                        \n
                      • attribution uncertainty and privacy changes affecting user-level analysis<\/li>\n
                      • seasonality and external factors (promotions, holidays) confusing results<\/li>\n
                      • insufficient sample size for small segments<\/li>\n<\/ul>\n\n\n\n

                        In Mobile & App Marketing<\/strong>, the hardest part is often deciding what not<\/em> to test and ensuring results are genuinely causal.<\/p>\n\n\n\n

                        Best Practices for Mobile App Experiment<\/h2>\n\n\n\n

                        To make each Mobile App Experiment<\/strong> more reliable and impactful:<\/p>\n\n\n\n

                          \n
                        1. \n

                          Write hypotheses that include a reason<\/strong>\n \u201cChanging X will improve Y because Z.\u201d This helps interpret outcomes and plan follow-ups.<\/p>\n<\/li>\n

                        2. \n

                          Define one primary metric and 2\u20134 guardrails<\/strong>\n Example: Primary = trial start rate; Guardrails = refund rate, day-7 retention, crash-free sessions, support tickets.<\/p>\n<\/li>\n

                        3. \n

                          Plan sample size and duration before launch<\/strong>\n Avoid ending tests early just because results \u201clook good.\u201d Pre-commit to a decision rule.<\/p>\n<\/li>\n

                        4. \n

                          Segment thoughtfully, but start simple<\/strong>\n New vs returning users often behave differently. Don\u2019t over-segment until you can support it statistically.<\/p>\n<\/li>\n

                        5. \n

                          Document results and learnings<\/strong>\n Keep an experiment log: hypothesis, screenshots, targeting, results, decision, and next steps. This is how Mobile & App Marketing<\/strong> teams avoid repeating the same tests.<\/p>\n<\/li>\n

                        6. \n

                          Scale winners with rollout controls<\/strong>\n Use staged rollouts (e.g., 10% \u2192 50% \u2192 100%) and monitor guardrails continuously.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                          Tools Used for Mobile App Experiment<\/h2>\n\n\n\n

                          A Mobile App Experiment<\/strong> is enabled by a stack of systems rather than one \u201cmagic tool.\u201d Common tool categories in Mobile & App Marketing<\/strong> include:<\/p>\n\n\n\n

                            \n
                          • Product analytics tools:<\/strong> event tracking, funnels, cohorts, retention, pathing, and experiment result analysis.<\/li>\n
                          • Experimentation and feature management systems:<\/strong> remote configuration, feature flags, variant assignment, and phased rollouts.<\/li>\n
                          • Attribution and measurement tools:<\/strong> campaign source data, cohort-level ROAS, and post-install performance insights.<\/li>\n
                          • CRM and lifecycle messaging platforms:<\/strong> push notifications, in-app messaging, email orchestration, and audience segmentation.<\/li>\n
                          • Reporting dashboards and BI:<\/strong> centralized metrics, data modeling, and executive-ready reporting.<\/li>\n
                          • SEO tools (app discovery support):<\/strong> for teams also optimizing app landing pages or content that drives installs; not required for every experiment, but often part of broader Mobile & App Marketing<\/strong> efforts.<\/li>\n<\/ul>\n\n\n\n

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

                            Metrics Related to Mobile App Experiment<\/h2>\n\n\n\n

                            A strong Mobile App Experiment<\/strong> uses metrics that reflect both growth and user value:<\/p>\n\n\n\n

                            Performance and engagement metrics<\/h3>\n\n\n\n
                              \n
                            • activation rate (first key action completion)<\/li>\n
                            • onboarding completion rate<\/li>\n
                            • session frequency and session length (use cautiously; \u201cmore time\u201d isn\u2019t always better)<\/li>\n
                            • push opt-in rate and notification open rate<\/li>\n
                            • feature adoption rate<\/li>\n<\/ul>\n\n\n\n

                              Revenue and ROI metrics<\/h3>\n\n\n\n
                                \n
                              • trial start rate and trial-to-paid conversion<\/li>\n
                              • average revenue per user (ARPU) and revenue per active user<\/li>\n
                              • cohort LTV (prefer cohort-based over single-session views)<\/li>\n
                              • retention-adjusted ROAS for paid acquisition<\/li>\n<\/ul>\n\n\n\n

                                Efficiency and quality metrics (guardrails)<\/h3>\n\n\n\n
                                  \n
                                • crash-free sessions \/ stability rate<\/li>\n
                                • app start time and latency<\/li>\n
                                • uninstall rate<\/li>\n
                                • refund rate and chargebacks<\/li>\n
                                • support tickets or negative reviews (when measurable)<\/li>\n<\/ul>\n\n\n\n

                                  In Mobile & App Marketing<\/strong>, the most mature teams choose metrics that align short-term conversion with long-term retention and trust.<\/p>\n\n\n\n

                                  Future Trends of Mobile App Experiment<\/h2>\n\n\n\n

                                  Several forces are reshaping how Mobile App Experiment<\/strong> programs run inside Mobile & App Marketing<\/strong>:<\/p>\n\n\n\n

                                    \n
                                  • AI-assisted experimentation:<\/strong> faster idea generation, automated audience insights, and anomaly detection for guardrails (with human oversight).<\/li>\n
                                  • More personalization, more complexity:<\/strong> experiments will increasingly test tailored experiences by intent or lifecycle stage, requiring careful governance to avoid fragmentation.<\/li>\n
                                  • Privacy-driven measurement shifts:<\/strong> greater reliance on aggregated reporting, cohort analysis, and incrementality thinking as user-level signals become less reliable.<\/li>\n
                                  • Automation of rollouts:<\/strong> continuous delivery patterns and remote config will make it easier to ship and iterate, but will increase the need for strong experiment review processes.<\/li>\n
                                  • Experimentation beyond UI:<\/strong> more tests on pricing strategy, bundling, content recommendation logic, and lifecycle orchestration\u2014core levers in Mobile & App Marketing<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                                    Mobile App Experiment vs Related Terms<\/h2>\n\n\n\n

                                    Mobile App Experiment vs A\/B Testing<\/h3>\n\n\n\n

                                    A\/B testing is a method<\/strong> (compare A vs B). A Mobile App Experiment<\/strong> is broader: it includes the hypothesis, targeting rules, implementation approach, measurement plan, and decision-making. Many Mobile App Experiment programs use A\/B testing, but not all experiments are simple A\/B tests.<\/p>\n\n\n\n

                                    Mobile App Experiment vs Feature Flagging<\/h3>\n\n\n\n

                                    Feature flags are a delivery and control mechanism<\/strong>\u2014turn features on\/off or expose them to segments. A Mobile App Experiment may use feature flags to run variants safely, but feature flags alone don\u2019t guarantee randomization, valid measurement, or a clear success metric.<\/p>\n\n\n\n

                                    Mobile App Experiment vs Incrementality Testing<\/h3>\n\n\n\n

                                    Incrementality testing focuses specifically on \u201cwhat is the causal lift compared to doing nothing,\u201d often used for advertising effectiveness. A Mobile App Experiment<\/strong> can be incremental, but it may also be comparative (Variant A vs Variant B) inside the product experience. In Mobile & App Marketing<\/strong>, both approaches are useful\u2014just for different questions.<\/p>\n\n\n\n

                                    Who Should Learn Mobile App Experiment<\/h2>\n\n\n\n

                                    Understanding Mobile App Experiment<\/strong> is valuable across roles:<\/p>\n\n\n\n

                                      \n
                                    • Marketers and growth leads:<\/strong> to prioritize tests that improve acquisition-to-LTV performance, not just installs.<\/li>\n
                                    • Analysts:<\/strong> to ensure statistical validity, clean instrumentation, and trustworthy reporting.<\/li>\n
                                    • Agencies:<\/strong> to connect creative and campaign strategy to post-install outcomes and retention.<\/li>\n
                                    • Business owners and founders:<\/strong> to reduce product and pricing risk while building a growth system.<\/li>\n
                                    • Developers and product teams:<\/strong> to ship changes safely, measure impact, and avoid unnecessary rework.<\/li>\n<\/ul>\n\n\n\n

                                      In Mobile & App Marketing<\/strong>, experimentation literacy is a career multiplier because it links strategy to measurable outcomes.<\/p>\n\n\n\n

                                      Summary of Mobile App Experiment<\/h2>\n\n\n\n

                                      A Mobile App Experiment<\/strong> is a structured test in a mobile app designed to measure the causal impact of a change on user behavior and business results. It matters because it reduces risk, improves performance, and builds compounding insights. Within Mobile & App Marketing<\/strong>, it connects acquisition, onboarding, engagement, and monetization into a disciplined optimization loop. Used well, a Mobile App Experiment program becomes a repeatable system that strengthens both marketing efficiency and product experience.<\/p>\n\n\n\n

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

                                      1) What is a Mobile App Experiment?<\/h3>\n\n\n\n

                                      A Mobile App Experiment<\/strong> is a controlled test where different user groups see different experiences (or strategies) so you can measure which option improves specific metrics like activation, retention, or revenue.<\/p>\n\n\n\n

                                      2) How long should a Mobile App Experiment run?<\/h3>\n\n\n\n

                                      Long enough to reach the planned sample size and cover meaningful user behavior cycles. Many app tests need at least 1\u20132 weeks, but duration depends on traffic, conversion rates, and the metric\u2019s time-to-realize (e.g., retention requires more time).<\/p>\n\n\n\n

                                      3) What metrics should I use as the \u201cwinner\u201d criteria?<\/h3>\n\n\n\n

                                      Pick one primary metric tied to the goal (e.g., trial start rate) and add guardrails (e.g., refunds, retention, crash rate). This prevents \u201cwinning\u201d on conversion while harming long-term value.<\/p>\n\n\n\n

                                      4) How does Mobile & App Marketing benefit from experimentation?<\/h3>\n\n\n\n

                                      In Mobile & App Marketing<\/strong>, experiments reveal which messaging, onboarding flows, paywalls, and lifecycle tactics actually improve LTV and ROAS\u2014so budgets and roadmaps are guided by evidence instead of assumptions.<\/p>\n\n\n\n

                                      5) Can I run a Mobile App Experiment without a feature flag system?<\/h3>\n\n\n\n

                                      Yes, but it\u2019s harder and riskier. You can test via separate builds or phased releases, but feature management and remote config typically make experiments faster, safer, and easier to roll back.<\/p>\n\n\n\n

                                      6) What are common reasons Mobile App Experiment results are misleading?<\/h3>\n\n\n\n

                                      Frequent issues include broken tracking, small sample sizes, ending tests early, overlapping experiments affecting the same users, and ignoring guardrail metrics (like retention or refunds).<\/p>\n\n\n\n

                                      7) What\u2019s a good first Mobile App Experiment to run?<\/h3>\n\n\n\n

                                      Start with a high-impact, measurable funnel step: onboarding completion, permission prompts, paywall messaging, or a single lifecycle message. Choose something you can implement cleanly and measure reliably, then document learnings for the next iteration.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                      A **Mobile App Experiment** is a structured test you run inside a mobile app to learn what changes improve user behavior and business outcomes. In **Mobile & App Marketing**, it\u2019s how teams move from opinions (\u201cthis onboarding screen feels better\u201d) to evidence (\u201cthis onboarding increased activation by 6% without hurting retention\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":[1900],"tags":[],"class_list":["post-8668","post","type-post","status-publish","format-standard","hentry","category-mobile-app-marketing"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/8668","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=8668"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/8668\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=8668"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=8668"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=8668"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}