{"id":7194,"date":"2026-03-24T03:43:00","date_gmt":"2026-03-24T03:43:00","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/segmentation-based-test\/"},"modified":"2026-03-24T03:43:00","modified_gmt":"2026-03-24T03:43:00","slug":"segmentation-based-test","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/segmentation-based-test\/","title":{"rendered":"Segmentation-based Test: What It Is, Key Features, Benefits, Use Cases, and How It Fits in CRO"},"content":{"rendered":"\n

A Segmentation-based Test<\/strong> is an experiment designed, analyzed, or interpreted through the lens of meaningful audience segments\u2014such as device type, traffic source, geography, lifecycle stage, intent, or customer status. In Conversion & Measurement<\/strong>, this approach helps teams understand who<\/em> a change works for, not just whether it works \u201con average.\u201d In CRO<\/strong>, that distinction is often the difference between a safe incremental win and a misleading result that masks real opportunities (or risks) within specific audiences.<\/p>\n\n\n\n

Modern user journeys are fragmented across channels, devices, and contexts. When a single overall conversion rate is treated as \u201cthe truth,\u201d teams can miss important patterns: a change might help new visitors but hurt returning customers; improve mobile but degrade desktop; or lift low-intent traffic while decreasing high-intent leads. A well-planned Segmentation-based Test<\/strong> turns those patterns into actionable decisions that strengthen both Conversion & Measurement<\/strong> and long-term CRO<\/strong> strategy.<\/p>\n\n\n\n

What Is Segmentation-based Test?<\/h2>\n\n\n\n

A Segmentation-based Test<\/strong> is a testing method where you evaluate experiment performance by predefined (or carefully justified) segments, rather than relying solely on a single aggregated outcome. The core concept is simple: different audiences behave differently, so experiments should be interpreted with that variability in mind.<\/p>\n\n\n\n

In business terms, a Segmentation-based Test<\/strong> helps answer questions like:<\/p>\n\n\n\n

    \n
  • Which customer group benefits most from this change?<\/li>\n
  • Are we improving conversions by attracting the \u201cwrong\u201d users or degrading lead quality?<\/li>\n
  • Does the change create friction for high-value customers?<\/li>\n<\/ul>\n\n\n\n

    Within Conversion & Measurement<\/strong>, segmentation-based testing is a bridge between analytics and experimentation: it connects behavioral data to decisions. Inside CRO<\/strong>, it\u2019s a discipline that improves prioritization, reduces false confidence, and supports personalization and targeting strategies without guessing.<\/p>\n\n\n\n

    Why Segmentation-based Test Matters in Conversion & Measurement<\/h2>\n\n\n\n

    Averages can lie\u2014especially when your traffic mix changes or your audience is diverse. Conversion & Measurement<\/strong> programs that ignore segments often make two costly mistakes: shipping changes that harm key users, and rejecting changes that would have produced meaningful gains for the right group.<\/p>\n\n\n\n

    Strategically, a Segmentation-based Test<\/strong> matters because it:<\/p>\n\n\n\n

      \n
    • Increases decision accuracy:<\/strong> You identify where impact is real and where it\u2019s noise.<\/li>\n
    • Protects high-value segments:<\/strong> You avoid optimizing for easy wins that reduce revenue or lead quality.<\/li>\n
    • Supports smarter roadmaps:<\/strong> Segment insights reveal which audiences deserve tailored experiences.<\/li>\n
    • Improves learning velocity:<\/strong> Each test teaches more than \u201cvariant B wins\u201d; it explains why<\/em> and for whom<\/em>.<\/li>\n<\/ul>\n\n\n\n

      From a competitive standpoint, teams that master segmentation in CRO<\/strong> can outperform rivals by building experiences that fit user context\u2014without relying solely on broad redesigns or gut feeling. In mature Conversion & Measurement<\/strong> practices, segmentation-based testing is often what turns experimentation into a scalable growth system.<\/p>\n\n\n\n

      How Segmentation-based Test Works<\/h2>\n\n\n\n

      A Segmentation-based Test<\/strong> is less about a special \u201ctype of A\/B test\u201d and more about how you plan, run, and interpret experiments. In practice, it follows a workflow:<\/p>\n\n\n\n

        \n
      1. \n

        Input \/ Trigger: define the hypothesis and segments<\/strong>\n – You define the change (e.g., new messaging, layout, pricing display) and the success criteria.\n – You specify segments that are relevant to the hypothesis (e.g., mobile users, new vs returning, brand vs non-brand traffic).\n – Crucially, you decide which segment reads are planned<\/em> versus exploratory.<\/p>\n<\/li>\n

      2. \n

        Analysis \/ Processing: instrument and validate measurement<\/strong>\n – You ensure tracking is consistent across segments (events, funnels, attribution, identity).\n – You confirm sample sizes are sufficient for each segment you intend to decide on.\n – You check baseline behavior: segments should be stable enough to interpret.<\/p>\n<\/li>\n

      3. \n

        Execution \/ Application: run the experiment<\/strong>\n – Users are randomized into control and variant.\n – You monitor data quality and guardrails (errors, page speed, bounce rate, revenue per user).<\/p>\n<\/li>\n

      4. \n

        Output \/ Outcome: interpret results by segment and decide<\/strong>\n – You evaluate the primary KPI overall, then read planned segment performance.\n – You weigh practical significance (business impact) alongside statistical confidence.\n – You decide: ship broadly, ship to a segment, iterate, or stop.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        In CRO<\/strong>, the \u201cwin\u201d is not always a global rollout. Sometimes the correct decision from a Segmentation-based Test<\/strong> is to target the variant to the segment that benefits, while leaving other users on the control experience.<\/p>\n\n\n\n

        Key Components of Segmentation-based Test<\/h2>\n\n\n\n

        A reliable Segmentation-based Test<\/strong> depends on several components working together across analytics, experimentation, and governance.<\/p>\n\n\n\n

        Data inputs and segment definitions<\/h3>\n\n\n\n

        Segments can come from:\n– Behavioral data:<\/strong> pages viewed, category interest, engagement depth\n– Acquisition data:<\/strong> channel, campaign, keyword intent, referral source\n– User context:<\/strong> device, browser, geography, time of day\n– Customer attributes:<\/strong> lead status, plan tier, industry, lifecycle stage (when available and permitted)<\/p>\n\n\n\n

        Good segment definitions are stable, interpretable, and aligned with business goals in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

        Experiment design and statistical plan<\/h3>\n\n\n\n

        Key decisions include:\n– Primary KPI and guardrails\n– Planned segments and minimum detectable effect per segment\n– Multiple-comparison considerations (more segments = more chances of false positives)\n– Duration and stopping rules<\/p>\n\n\n\n

        Instrumentation and identity resolution<\/h3>\n\n\n\n

        Segmentation requires consistent tracking. That includes:\n– Clean event taxonomy (e.g., \u201cbegin_checkout,\u201d \u201csubmit_lead\u201d)\n– Consistent user identifiers across sessions\/devices where appropriate\n– Clear handling of logged-in vs anonymous users<\/p>\n\n\n\n

        Team responsibilities and governance<\/h3>\n\n\n\n

        Successful CRO<\/strong> teams define:\n– Who can create segments and how they\u2019re reviewed\n– Documentation standards (hypothesis, segments, outcomes, caveats)\n– A decision framework for when to personalize vs simplify<\/p>\n\n\n\n

        Types of Segmentation-based Test<\/h2>\n\n\n\n

        \u201cSegmentation-based Test\u201d doesn\u2019t have rigid formal types, but in practice there are common approaches that matter in Conversion & Measurement<\/strong> and CRO<\/strong>:<\/p>\n\n\n\n

        1) Planned segmentation vs exploratory segmentation<\/h3>\n\n\n\n
          \n
        • Planned segmentation:<\/strong> segments specified in advance, used for decision-making.<\/li>\n
        • Exploratory segmentation:<\/strong> segments discovered after results, used for learning and follow-up tests.<\/li>\n<\/ul>\n\n\n\n

          Planned segmentation is safer for decision-making; exploratory segmentation is valuable but must be treated carefully due to increased false discovery risk.<\/p>\n\n\n\n

          2) Audience segmentation vs context segmentation<\/h3>\n\n\n\n
            \n
          • Audience segmentation:<\/strong> who the user is (customer vs prospect, lifecycle stage).<\/li>\n
          • Context segmentation:<\/strong> the situation (mobile vs desktop, channel intent, landing page type).<\/li>\n<\/ul>\n\n\n\n

            Many strong Segmentation-based Test<\/strong> insights come from combining both (e.g., \u201cnew users on mobile from paid social\u201d).<\/p>\n\n\n\n

            3) Diagnostic segmentation vs rollout segmentation<\/h3>\n\n\n\n
              \n
            • Diagnostic segmentation:<\/strong> used to understand why results differ.<\/li>\n
            • Rollout segmentation:<\/strong> used to decide whether to ship a change only to specific segments.<\/li>\n<\/ul>\n\n\n\n

              This distinction is central to modern CRO<\/strong> where targeted experiences can outperform one-size-fits-all changes.<\/p>\n\n\n\n

              Real-World Examples of Segmentation-based Test<\/h2>\n\n\n\n

              Example 1: Ecommerce checkout messaging by device<\/h3>\n\n\n\n

              A retailer tests a simplified checkout page with fewer fields and a prominent \u201cshipping estimates\u201d module. Overall conversion improves slightly, but a Segmentation-based Test<\/strong> reveals:\n– Mobile conversion increases significantly\n– Desktop conversion is flat\n– A guardrail shows customer support chats increase on desktop due to missing details<\/p>\n\n\n\n

              Decision: ship to mobile first, then iterate on desktop. This is a classic Conversion & Measurement<\/strong> win that improves CRO<\/strong> without creating new friction.<\/p>\n\n\n\n

              Example 2: B2B lead form length by traffic intent<\/h3>\n\n\n\n

              A SaaS company tests a shorter lead form. Overall lead submissions rise, but segmentation shows:\n– Paid social leads increase sharply but have lower qualification rates\n– High-intent search traffic shows a smaller lift, but better pipeline conversion<\/p>\n\n\n\n

              Decision: keep short form for low-intent segments; maintain a slightly longer form (or add progressive profiling) for high-intent traffic. The Segmentation-based Test<\/strong> prevents optimizing for volume at the expense of revenue\u2014an essential CRO<\/strong> mindset.<\/p>\n\n\n\n

              Example 3: Pricing page layout by customer status<\/h3>\n\n\n\n

              A subscription business tests a pricing layout emphasizing annual plans. Overall revenue per visitor is unchanged, but segmentation shows:\n– New visitors are more likely to start trials\n– Returning visitors are more likely to choose annual, increasing revenue<\/p>\n\n\n\n

              Decision: tailor the experience based on returning status and revisit messaging for new users. Here, Conversion & Measurement<\/strong> segmentation directly informs a personalization roadmap grounded in CRO<\/strong> evidence.<\/p>\n\n\n\n

              Benefits of Using Segmentation-based Test<\/h2>\n\n\n\n

              A well-run Segmentation-based Test<\/strong> delivers benefits beyond a single uplift:<\/p>\n\n\n\n

                \n
              • More reliable optimization:<\/strong> You reduce the risk of shipping changes that harm key segments.<\/li>\n
              • Higher ROI from experimentation:<\/strong> Tests produce richer insights, improving future prioritization.<\/li>\n
              • Better customer experience:<\/strong> You can remove friction where it matters most (e.g., mobile, first-time users).<\/li>\n
              • Efficient resource allocation:<\/strong> Engineering and design effort goes toward changes that impact valuable audiences.<\/li>\n
              • Stronger alignment with business outcomes:<\/strong> Segment-level reads tie Conversion & Measurement<\/strong> to revenue, retention, and lead quality\u2014core goals of CRO<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                Challenges of Segmentation-based Test<\/h2>\n\n\n\n

                Segmentation is powerful, but it introduces complexity that teams must manage.<\/p>\n\n\n\n

                Statistical and decision risks<\/h3>\n\n\n\n
                  \n
                • Multiple comparisons:<\/strong> The more segments you check, the more likely you\u2019ll see a \u201cwinner\u201d by chance.<\/li>\n
                • Underpowered segments:<\/strong> Small segments can produce volatile results and false confidence.<\/li>\n
                • Post-hoc storytelling:<\/strong> It\u2019s easy to \u201cfind\u201d a segment that supports what you wanted to believe.<\/li>\n<\/ul>\n\n\n\n

                  Measurement and data limitations<\/h3>\n\n\n\n
                    \n
                  • Tracking inconsistency across devices or browsers can distort segment results.<\/li>\n
                  • Identity issues (logged-out vs logged-in) can blur user-level outcomes.<\/li>\n
                  • Attribution differences by channel can complicate interpretation in Conversion & Measurement<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                    Operational complexity<\/h3>\n\n\n\n
                      \n
                    • Segment definitions can drift over time.<\/li>\n
                    • Teams may disagree on which segments matter.<\/li>\n
                    • Personalization based on segments can increase maintenance and QA burden\u2014important for sustainable CRO<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                      Best Practices for Segmentation-based Test<\/h2>\n\n\n\n

                      Predefine what matters<\/h3>\n\n\n\n
                        \n
                      • Choose 2\u20135 planned segments<\/strong> tied to your hypothesis.<\/li>\n
                      • Document why each segment is expected to behave differently.<\/li>\n
                      • Define guardrails (e.g., refunds, cancellation rate, lead quality).<\/li>\n<\/ul>\n\n\n\n

                        Design for power and practicality<\/h3>\n\n\n\n
                          \n
                        • Estimate whether segments will reach adequate sample size.<\/li>\n
                        • Prefer fewer, more meaningful segments over many thin cuts.<\/li>\n
                        • Focus on practical significance: is the lift large enough to matter in revenue or pipeline?<\/li>\n<\/ul>\n\n\n\n

                          Treat exploratory findings as hypotheses<\/h3>\n\n\n\n
                            \n
                          • If you discover a surprising segment effect, validate it with a follow-up test or holdout.<\/li>\n
                          • Use sequential testing or confirmation windows to reduce false positives.<\/li>\n<\/ul>\n\n\n\n

                            Ensure measurement integrity<\/h3>\n\n\n\n
                              \n
                            • Audit event tracking before launch.<\/li>\n
                            • Confirm consistent KPI definitions across platforms (analytics vs experiment tool vs CRM).<\/li>\n
                            • Watch for instrumentation changes mid-test.<\/li>\n<\/ul>\n\n\n\n

                              Decide how to ship<\/h3>\n\n\n\n

                              A Segmentation-based Test<\/strong> can lead to different rollout strategies:\n– Ship to all users (global win)\n– Ship only to winning segments (targeted win)\n– Iterate and retest (inconclusive or mixed outcomes)\n– Stop and learn (negative or risky outcomes)<\/p>\n\n\n\n

                              This decision discipline is a hallmark of mature CRO<\/strong> and Conversion & Measurement<\/strong> programs.<\/p>\n\n\n\n

                              Tools Used for Segmentation-based Test<\/h2>\n\n\n\n

                              A Segmentation-based Test<\/strong> is enabled by a stack, not a single tool. Common tool groups include:<\/p>\n\n\n\n

                                \n
                              • Experimentation platforms:<\/strong> run A\/B tests, manage targeting rules, and report results by segment.<\/li>\n
                              • Analytics tools:<\/strong> build funnels, cohorts, and behavioral segments; validate tracking and trends in Conversion & Measurement<\/strong>.<\/li>\n
                              • Tag management systems:<\/strong> deploy and govern events consistently across pages and apps.<\/li>\n
                              • CDPs and data warehouses:<\/strong> unify customer data, define reusable segments, and support deeper analysis.<\/li>\n
                              • CRM and marketing automation:<\/strong> connect experiment exposure to downstream outcomes (qualified leads, pipeline, retention).<\/li>\n
                              • Reporting dashboards and BI tools:<\/strong> standardize experiment reporting and monitor CRO<\/strong> performance over time.<\/li>\n<\/ul>\n\n\n\n

                                The key is integration and consistency: segment definitions should match across analytics, experimentation, and revenue systems.<\/p>\n\n\n\n

                                Metrics Related to Segmentation-based Test<\/h2>\n\n\n\n

                                Your metrics should reflect both conversion performance and business quality. Common metrics include:<\/p>\n\n\n\n

                                Core conversion metrics<\/h3>\n\n\n\n
                                  \n
                                • Conversion rate (purchase, signup, lead submission)<\/li>\n
                                • Funnel step completion rates<\/li>\n
                                • Revenue per visitor \/ average order value (for ecommerce)<\/li>\n<\/ul>\n\n\n\n

                                  Segment-sensitive quality metrics<\/h3>\n\n\n\n
                                    \n
                                  • Lead qualification rate (e.g., MQL\/SQL rate)<\/li>\n
                                  • Trial-to-paid conversion<\/li>\n
                                  • Retention or churn (when measurable)<\/li>\n<\/ul>\n\n\n\n

                                    Efficiency and cost metrics<\/h3>\n\n\n\n
                                      \n
                                    • Cost per acquisition (CPA) by segment<\/li>\n
                                    • Return on ad spend (ROAS) by segment<\/li>\n
                                    • Time to convert (sales cycle length, time-to-purchase)<\/li>\n<\/ul>\n\n\n\n

                                      Guardrails and experience metrics<\/h3>\n\n\n\n
                                        \n
                                      • Page load time \/ performance metrics by device segment<\/li>\n
                                      • Error rates, form validation failures<\/li>\n
                                      • Refund rate, support tickets, complaint rate<\/li>\n<\/ul>\n\n\n\n

                                        A strong Conversion & Measurement<\/strong> setup ensures these metrics are attributable to experiment exposure and comparable across segments\u2014critical for trustworthy CRO<\/strong> decisions.<\/p>\n\n\n\n

                                        Future Trends of Segmentation-based Test<\/h2>\n\n\n\n

                                        Several trends are shaping how Segmentation-based Test<\/strong> evolves within Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                                          \n
                                        • AI-assisted insight generation:<\/strong> AI will help detect segment patterns and propose follow-up tests, but teams will still need governance to avoid spurious findings.<\/li>\n
                                        • Automation in targeting and rollout:<\/strong> More experimentation programs will auto-roll out variants to segments with sustained lift, using guardrails to manage risk.<\/li>\n
                                        • Privacy-driven measurement changes:<\/strong> As tracking becomes more constrained, segmentation will rely more on first-party data, modeled conversion signals, and server-side measurement approaches.<\/li>\n
                                        • Personalization with restraint:<\/strong> Teams will use segmentation to personalize where it clearly helps, while avoiding over-fragmented experiences that increase complexity and dilute learning.<\/li>\n
                                        • Unified outcome measurement:<\/strong> More organizations will connect tests to downstream revenue and retention, making CRO<\/strong> and Conversion & Measurement<\/strong> less page-centric and more lifecycle-centric.<\/li>\n<\/ul>\n\n\n\n

                                          Segmentation-based Test vs Related Terms<\/h2>\n\n\n\n

                                          Segmentation-based Test vs A\/B test<\/h3>\n\n\n\n

                                          An A\/B test is the experiment framework (control vs variant). A Segmentation-based Test<\/strong> is an approach to designing and interpreting that experiment through segments. You can run an A\/B test without segmentation; you can\u2019t do a true segmentation-based approach without segment-aware analysis and decision rules.<\/p>\n\n\n\n

                                          Segmentation-based Test vs personalization<\/h3>\n\n\n\n

                                          Personalization is delivering different experiences to different users. A Segmentation-based Test<\/strong> is how you validate<\/em> whether those differentiated experiences improve outcomes. In other words, segmentation-based testing is often the evidence layer that keeps personalization accountable in CRO<\/strong>.<\/p>\n\n\n\n

                                          Segmentation-based Test vs cohort analysis<\/h3>\n\n\n\n

                                          Cohort analysis groups users by a shared start point (e.g., signup month) and tracks behavior over time. A Segmentation-based Test<\/strong> compares control vs variant outcomes within segments during an experiment window. Cohorts are observational; segmentation-based tests are experimental and designed for causal inference within Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                          Who Should Learn Segmentation-based Test<\/h2>\n\n\n\n
                                            \n
                                          • Marketers:<\/strong> to understand which channels and messages drive not just conversions, but the right conversions\u2014core to Conversion & Measurement<\/strong>.<\/li>\n
                                          • Analysts:<\/strong> to design statistically responsible segment reads and prevent false insights that mislead CRO<\/strong> decisions.<\/li>\n
                                          • Agencies:<\/strong> to deliver higher-quality experimentation programs and clearer client recommendations rooted in segment outcomes.<\/li>\n
                                          • Business owners and founders:<\/strong> to avoid growth decisions based on misleading averages and to prioritize changes that protect revenue.<\/li>\n
                                          • Developers and product teams:<\/strong> to implement clean instrumentation, reliable segment rules, and scalable targeting for experimentation.<\/li>\n<\/ul>\n\n\n\n

                                            Summary of Segmentation-based Test<\/h2>\n\n\n\n

                                            A Segmentation-based Test<\/strong> is an experiment approach that evaluates performance across meaningful audience or context segments, not just overall averages. It matters because it improves decision accuracy, protects high-value users, and generates deeper learning\u2014strengthening both Conversion & Measurement<\/strong> and CRO<\/strong>. Done well, it helps teams decide whether to ship globally, target specific segments, or iterate, turning experimentation into a more reliable and scalable growth practice.<\/p>\n\n\n\n

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

                                            1) What is a Segmentation-based Test in simple terms?<\/h3>\n\n\n\n

                                            A Segmentation-based Test<\/strong> is an experiment where you compare results for different groups of users (like mobile vs desktop or new vs returning) to see who the change helps or hurts, not just the overall average.<\/p>\n\n\n\n

                                            2) How many segments should I analyze in a Segmentation-based Test?<\/h3>\n\n\n\n

                                            For decision-making, keep it tight\u2014often 2\u20135 planned segments tied to the hypothesis. You can explore more segments after the fact, but treat those findings as ideas to validate with another test.<\/p>\n\n\n\n

                                            3) Does segmentation make CRO results less trustworthy?<\/h3>\n\n\n\n

                                            Segmentation can make CRO<\/strong> results more<\/em> trustworthy when planned correctly, but it can also increase false positives if you slice data too many ways. Predefining segments and ensuring adequate sample size are key.<\/p>\n\n\n\n

                                            4) What metrics work best for segmentation-based testing in Conversion & Measurement?<\/h3>\n\n\n\n

                                            Use a primary conversion KPI (purchase, signup, lead) plus quality metrics (revenue per visitor, qualification rate, retention) and guardrails (refunds, errors, page speed). This keeps Conversion & Measurement<\/strong> aligned with real business outcomes.<\/p>\n\n\n\n

                                            5) When should I roll out a winning variant only to certain segments?<\/h3>\n\n\n\n

                                            When the Segmentation-based Test<\/strong> shows a meaningful lift for one segment and neutral or negative impact for others, segment-only rollout can be the best CRO<\/strong> decision\u2014especially if the segment is valuable and stable.<\/p>\n\n\n\n

                                            6) How do I avoid false discoveries when analyzing segments?<\/h3>\n\n\n\n

                                            Predefine segments, limit the number of comparisons, avoid stopping early based on a single segment spike, and validate unexpected segment wins with follow-up testing or longer confirmation periods.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                            A **Segmentation-based Test** is an experiment designed, analyzed, or interpreted through the lens of meaningful audience segments\u2014such as device type, traffic source, geography, lifecycle stage, intent, or customer status. In **Conversion & Measurement**, this approach helps teams understand *who* a change works for, not just whether it works \u201con average.\u201d In **CRO**, that distinction is often the difference between a safe incremental win and a misleading result that masks real opportunities (or risks) within specific audiences.<\/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-7194","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\/7194","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=7194"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7194\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7194"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7194"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7194"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}