{"id":7142,"date":"2026-03-24T01:49:35","date_gmt":"2026-03-24T01:49:35","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/frustration-signal\/"},"modified":"2026-03-24T01:49:35","modified_gmt":"2026-03-24T01:49:35","slug":"frustration-signal","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/frustration-signal\/","title":{"rendered":"Frustration Signal: What It Is, Key Features, Benefits, Use Cases, and How It Fits in CRO"},"content":{"rendered":"\n

A Frustration Signal<\/strong> is any measurable hint that users are struggling, confused, or blocked while trying to complete an action\u2014especially an action tied to revenue, lead generation, or activation. In Conversion & Measurement<\/strong>, these signals help you move beyond \u201cwhat happened\u201d (a drop in conversion rate) to \u201cwhy it happened\u201d (a broken field, unclear pricing, slow page, or mismatched intent). For CRO<\/strong>, a Frustration Signal is often the fastest way to find high-impact friction that traditional funnel reports hide.<\/p>\n\n\n\n

Modern Conversion & Measurement<\/strong> strategies rely on more than pageviews and conversions. As user journeys spread across devices, channels, and privacy-restricted environments, teams need stronger diagnostic indicators. A well-instrumented Frustration Signal framework turns qualitative pain into quantifiable evidence\u2014so you can prioritize fixes, validate hypotheses, and prove business impact.<\/p>\n\n\n\n

What Is Frustration Signal?<\/h2>\n\n\n\n

A Frustration Signal<\/strong> is a behavioral, technical, or feedback-based indicator that a user encountered friction during a journey. It is not a single metric; it\u2019s a category of evidence that suggests the experience is harder than it should be.<\/p>\n\n\n\n

At its core, the concept is simple:<\/p>\n\n\n\n

    \n
  • Users arrive with intent (buy, sign up, request a demo).<\/li>\n
  • Something in the experience creates resistance (confusion, delay, failure, mistrust).<\/li>\n
  • That resistance shows up in observable patterns (repeated clicks, errors, abandonment, negative feedback).<\/li>\n<\/ul>\n\n\n\n

    The business meaning is equally direct: Frustration Signals are leading indicators of lost conversions and rising acquisition costs. In Conversion & Measurement<\/strong>, they help explain sudden changes in performance, differences between segments, and why \u201chigh-traffic pages\u201d don\u2019t always produce proportional results. Inside CRO<\/strong>, a Frustration Signal is a high-quality hypothesis input\u2014often more actionable than broad engagement metrics.<\/p>\n\n\n\n

    Why Frustration Signal Matters in Conversion & Measurement<\/h2>\n\n\n\n

    A Frustration Signal matters because it connects user experience to measurable business outcomes. Many teams can detect that conversions dropped; fewer can pinpoint the specific moment users start struggling.<\/p>\n\n\n\n

    In Conversion & Measurement<\/strong>, Frustration Signals provide:<\/p>\n\n\n\n

      \n
    • Faster root-cause analysis:<\/strong> Identify whether conversion loss is due to UX friction, technical defects, or message mismatch.<\/li>\n
    • Better prioritization:<\/strong> Fix what blocks intent instead of optimizing cosmetic elements.<\/li>\n
    • More confident experimentation:<\/strong> Create test hypotheses based on observed struggle, not opinions.<\/li>\n<\/ul>\n\n\n\n

      From a business value perspective, Frustration Signals can reduce wasted ad spend, increase funnel throughput, and protect brand perception. They also create competitive advantage: organizations that detect friction early can ship improvements faster, while competitors keep debating why their funnel \u201cfeels off.\u201d<\/p>\n\n\n\n

      For CRO<\/strong>, the strategic benefit is focus. Instead of running many low-signal tests, teams can concentrate on removing proven blockers and measuring lift with clearer attribution in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

      How Frustration Signal Works<\/h2>\n\n\n\n

      A Frustration Signal is conceptual, but it becomes practical when you treat it as a workflow that turns raw behaviors into decisions:<\/p>\n\n\n\n

        \n
      1. \n

        Input (user behavior and feedback)<\/strong>\n – Click patterns (rapid repeats, clicks on non-clickable elements)\n – Form interactions (repeated validation errors, field abandonment)\n – Navigation behaviors (back-and-forth, pogo-sticking between pages)\n – Technical telemetry (slow loads, JavaScript errors, failed API calls)\n – Voice-of-customer (on-site feedback, support tickets, chat transcripts)<\/p>\n<\/li>\n

      2. \n

        Analysis (detection and interpretation)<\/strong>\n – Tag events and define thresholds (e.g., \u201crage clicks\u201d = multiple clicks in a small area within seconds)\n – Segment by device, browser, traffic source, new vs returning users\n – Pair quantitative counts with qualitative context (session replay or user comments)\n – Validate against outcomes (conversion rate, drop-off rate, revenue per session)<\/p>\n<\/li>\n

      3. \n

        Execution (actions taken)<\/strong>\n – Bug fixes and UX changes (forms, navigation, clarity improvements)\n – Content adjustments (copy, pricing explanation, trust elements)\n – Experiment design in CRO<\/strong> (A\/B or multivariate tests)\n – Monitoring rules and alerts in Conversion & Measurement<\/strong> for regressions<\/p>\n<\/li>\n

      4. \n

        Output (measurable outcomes)<\/strong>\n – Reduced Frustration Signal rates\n – Higher completion rates and conversion rate\n – Lower support volume or fewer complaint keywords\n – Improved acquisition efficiency (more conversions per same spend)<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        Key Components of Frustration Signal<\/h2>\n\n\n\n

        A strong Frustration Signal program typically includes the following components:<\/p>\n\n\n\n

        Data inputs<\/h3>\n\n\n\n
          \n
        • Interaction events:<\/strong> clicks, taps, scroll depth, form field focus\/blur, validation errors<\/li>\n
        • Performance data:<\/strong> page load timing, input latency, rendering delays<\/li>\n
        • Error tracking:<\/strong> front-end exceptions, API failures, checkout errors<\/li>\n
        • Journey context:<\/strong> traffic source, campaign, landing page, device, geography<\/li>\n
        • Customer feedback:<\/strong> micro-surveys, on-site prompts, chat logs, support tickets<\/li>\n<\/ul>\n\n\n\n

          Processes and governance<\/h3>\n\n\n\n
            \n
          • Clear definitions:<\/strong> what counts as a Frustration Signal and what doesn\u2019t<\/li>\n
          • Ownership:<\/strong> who triages (CRO\/analytics), who fixes (product\/engineering), who validates (analytics\/QA)<\/li>\n
          • Prioritization model:<\/strong> impact \u00d7 frequency \u00d7 effort, tied to revenue steps<\/li>\n
          • Documentation:<\/strong> a shared library of known issues, thresholds, and playbooks<\/li>\n<\/ul>\n\n\n\n

            Measurement integration<\/h3>\n\n\n\n

            In Conversion & Measurement<\/strong>, Frustration Signals should be linked to funnel steps and business events (lead submitted, trial started, payment completed). In CRO<\/strong>, they should feed a testing backlog with explicit hypotheses and success criteria.<\/p>\n\n\n\n

            Types of Frustration Signal<\/h2>\n\n\n\n

            \u201cTypes\u201d are best understood as practical categories rather than formal standards. Common distinctions include:<\/p>\n\n\n\n

            Behavioral Frustration Signals<\/h3>\n\n\n\n

            These show up in interaction patterns:\n– Repeated clicks\/taps in the same region\n– Dead clicks (clicks on elements that don\u2019t respond)\n– Excessive backtracking or looping through steps\n– Rapid scrolling followed by immediate exit (often \u201ccan\u2019t find it\u201d behavior)<\/p>\n\n\n\n

            Form and Checkout Frustration Signals<\/h3>\n\n\n\n

            These are high-value in CRO<\/strong> because they happen near conversion:\n– Multiple validation errors on the same field\n– Repeated attempts to submit with no success\n– Abandonment after \u201cpromo code\u201d interaction\n– Payment failures or unexpected resets<\/p>\n\n\n\n

            Technical Frustration Signals<\/h3>\n\n\n\n

            These indicate the experience is failing, not just unclear:\n– Slow pages on specific devices\/browsers\n– JavaScript exceptions during critical steps\n– Broken UI components after deploys\n– Timeouts or failed API calls<\/p>\n\n\n\n

            Expectation and Intent Mismatch Signals<\/h3>\n\n\n\n

            These happen when marketing promise and page reality diverge:\n– High ad CTR but low on-page progression\n– High bounce\/exit on landing pages paired with strong intent keywords\n– Heavy FAQ clicks around price, eligibility, or limitations<\/p>\n\n\n\n

            In Conversion & Measurement<\/strong>, the most useful approach is to track multiple types and correlate them with conversions and revenue, rather than relying on a single \u201cfrustration score.\u201d<\/p>\n\n\n\n

            Real-World Examples of Frustration Signal<\/h2>\n\n\n\n

            Example 1: Lead gen form friction on mobile<\/h3>\n\n\n\n

            A B2B site sees stable traffic but declining demo requests. Conversion & Measurement<\/strong> shows the drop is mostly mobile. Session analysis reveals a Frustration Signal: users repeatedly tap \u201cSubmit,\u201d then scroll up and down, then abandon. Error telemetry shows the phone number field rejects common formats without clearly explaining why.<\/p>\n\n\n\n

            CRO action:<\/strong> Fix validation messaging, allow flexible formats, and add inline hints.\nOutcome:<\/strong> Lower form-error rate, higher completion rate, and measurable lift in demo submissions.<\/p>\n\n\n\n

            Example 2: Checkout failure after a payment provider update<\/h3>\n\n\n\n

            An ecommerce team notices a sudden spike in cart abandonment. A technical Frustration Signal appears: increased \u201cpayment failed\u201d events and longer processing times in certain browsers. Support tickets mention \u201cit spins forever.\u201d<\/p>\n\n\n\n

            Conversion & Measurement<\/strong> ties the error increase to a specific release window.\nCRO action:<\/strong> Roll back the change, add better error states, and implement monitoring alerts for future spikes.\nOutcome:<\/strong> Abandonment returns to baseline and refunds\/support load decreases.<\/p>\n\n\n\n

            Example 3: Campaign intent mismatch on a landing page<\/h3>\n\n\n\n

            A paid search campaign targets \u201ctransparent pricing,\u201d but the landing page emphasizes features and hides pricing behind a form. The Frustration Signal shows up as high scroll depth, repeated clicks on non-clickable \u201cPricing\u201d text, and exits clustered around the pricing section.<\/p>\n\n\n\n

            CRO action:<\/strong> Test a pricing-first variant, add a clear pricing anchor, and improve navigation.\nOutcome:<\/strong> Higher lead quality and improved conversion rate from that campaign segment.<\/p>\n\n\n\n

            Benefits of Using Frustration Signal<\/h2>\n\n\n\n

            When operationalized, Frustration Signal tracking produces benefits that are both performance- and efficiency-oriented:<\/p>\n\n\n\n

              \n
            • Higher conversion rates:<\/strong> Removing friction typically improves completion on key steps.<\/li>\n
            • Lower wasted acquisition spend:<\/strong> Fewer paid clicks are lost to preventable UX or technical blockers.<\/li>\n
            • Faster troubleshooting:<\/strong> Teams can isolate issues by segment (device, browser, campaign) instead of guessing.<\/li>\n
            • Better customer experience:<\/strong> Less confusion and fewer errors improves trust and satisfaction.<\/li>\n
            • More effective CRO backlogs:<\/strong> Testing and optimization become evidence-led, improving win rates.<\/li>\n<\/ul>\n\n\n\n

              In Conversion & Measurement<\/strong>, these benefits are strongest when Frustration Signals are directly connected to business events and monitored over time.<\/p>\n\n\n\n

              Challenges of Frustration Signal<\/h2>\n\n\n\n

              Frustration Signals are powerful, but they come with real limitations:<\/p>\n\n\n\n

                \n
              • Ambiguity:<\/strong> Not every repeated click is frustration; sometimes it\u2019s impatience, curiosity, or accessibility needs.<\/li>\n
              • Instrumentation gaps:<\/strong> If events aren\u2019t tagged well (especially in single-page apps), signals can be incomplete.<\/li>\n
              • Noise and false positives:<\/strong> Thresholds that are too sensitive generate alerts without business impact.<\/li>\n
              • Privacy constraints:<\/strong> Consent requirements and reduced tracking can limit session-level visibility, impacting Conversion & Measurement<\/strong>.<\/li>\n
              • Organizational handoffs:<\/strong> CRO<\/strong> teams may identify Frustration Signals, but engineering priorities can delay fixes.<\/li>\n<\/ul>\n\n\n\n

                A mature approach balances automation with human review and focuses on signals that correlate with conversion loss.<\/p>\n\n\n\n

                Best Practices for Frustration Signal<\/h2>\n\n\n\n

                To make Frustration Signal data actionable and scalable:<\/p>\n\n\n\n

                  \n
                1. \n

                  Start with revenue-critical journeys<\/strong>\n Focus on checkout, sign-up, demo request, onboarding, and pricing pages.<\/p>\n<\/li>\n

                2. \n

                  Define a small set of standard signals<\/strong>\n Establish 5\u201310 core Frustration Signals (e.g., dead clicks, form errors, slow load on key pages) before expanding.<\/p>\n<\/li>\n

                3. \n

                  Tie signals to outcomes<\/strong>\n In Conversion & Measurement<\/strong>, always analyze Frustration Signal rates alongside conversion rate, drop-off, and revenue per session.<\/p>\n<\/li>\n

                4. \n

                  Segment aggressively<\/strong>\n Break down by device, browser, traffic source, new\/returning users, and locale. Many Frustration Signals are segment-specific.<\/p>\n<\/li>\n

                5. \n

                  Use a \u201cfrequency \u00d7 impact\u201d model<\/strong>\n Prioritize fixes that happen often and affect high-value steps. Rare issues can still matter if impact is severe (e.g., payment failures).<\/p>\n<\/li>\n

                6. \n

                  Close the loop with CRO experiments<\/strong>\n When the fix isn\u2019t obvious (copy clarity, information architecture), turn the Frustration Signal into a testable hypothesis.<\/p>\n<\/li>\n

                7. \n

                  Set monitoring and regression alerts<\/strong>\n After improvements, keep tracking the same Frustration Signals to prevent silent regressions after releases.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                  Tools Used for Frustration Signal<\/h2>\n\n\n\n

                  Frustration Signal work is usually done with a tool stack rather than one tool. Common categories include:<\/p>\n\n\n\n

                    \n
                  • Web and product analytics tools:<\/strong> event tracking, funnels, cohorts, pathing for Conversion & Measurement<\/strong><\/li>\n
                  • Session replay and heatmap tools:<\/strong> visual context for dead clicks, rage clicks, scroll patterns<\/li>\n
                  • Performance monitoring tools:<\/strong> page speed, real user monitoring, synthetic checks<\/li>\n
                  • Error tracking tools:<\/strong> front-end exceptions, API errors, crash reports<\/li>\n
                  • Experimentation platforms:<\/strong> run CRO<\/strong> tests and measure incremental impact<\/li>\n
                  • Customer feedback systems:<\/strong> on-site surveys, chat, ticketing, and VOC tagging<\/li>\n
                  • BI and reporting dashboards:<\/strong> combine Frustration Signal rates with revenue and lifecycle metrics<\/li>\n<\/ul>\n\n\n\n

                    The key is consistency: definitions, event naming, and segment logic should align across tools so Conversion & Measurement<\/strong> reporting remains trustworthy.<\/p>\n\n\n\n

                    Metrics Related to Frustration Signal<\/h2>\n\n\n\n

                    Useful metrics depend on the journey, but these are common starting points:<\/p>\n\n\n\n

                    Frustration-specific metrics<\/h3>\n\n\n\n
                      \n
                    • Dead click rate (dead clicks per session or per page)<\/li>\n
                    • Rage click rate (rapid repeated clicks in a small area)<\/li>\n
                    • Form error rate (errors per attempt; repeat errors on same field)<\/li>\n
                    • Task abandonment rate (start event without completion event)<\/li>\n
                    • Time-to-complete (median time from step start to completion)<\/li>\n<\/ul>\n\n\n\n

                      Conversion & Measurement outcomes<\/h3>\n\n\n\n
                        \n
                      • Step-to-step funnel conversion rates<\/li>\n
                      • Overall conversion rate and micro-conversions<\/li>\n
                      • Revenue per visitor\/session<\/li>\n
                      • Refund rate or failed transaction rate (where applicable)<\/li>\n<\/ul>\n\n\n\n

                        Experience and support indicators<\/h3>\n\n\n\n
                          \n
                        • Page performance percentiles (not just averages)<\/li>\n
                        • Error incidence on key steps<\/li>\n
                        • Support ticket volume and top complaint categories<\/li>\n
                        • Customer satisfaction or effort indicators (when measured)<\/li>\n<\/ul>\n\n\n\n

                          In CRO<\/strong>, the best practice is to treat Frustration Signal metrics as diagnostic leading indicators and conversion metrics as validation lagging indicators.<\/p>\n\n\n\n

                          Future Trends of Frustration Signal<\/h2>\n\n\n\n

                          Several trends are shaping how Frustration Signal detection evolves within Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                            \n
                          • AI-assisted pattern detection:<\/strong> Automated clustering of \u201cstruggle sessions\u201d and surfacing likely root causes (e.g., confusing component, broken UI state).<\/li>\n
                          • Real-time intervention:<\/strong> Triggering help prompts, chat offers, or alternate flows when Frustration Signals spike mid-session (with careful testing to avoid annoyance).<\/li>\n
                          • Privacy-aware measurement:<\/strong> More aggregation, modeled insights, and server-side event collection to maintain reliable Conversion & Measurement<\/strong> while respecting consent.<\/li>\n
                          • Personalization with safeguards:<\/strong> Using Frustration Signals to personalize guidance (not prices or sensitive content) and validating impact through CRO<\/strong> experimentation.<\/li>\n
                          • Cross-channel stitching:<\/strong> Better linking of on-site frustration to upstream promises (ads, email, SEO snippets) to diagnose intent mismatch more precisely.<\/li>\n<\/ul>\n\n\n\n

                            Frustration Signal vs Related Terms<\/h2>\n\n\n\n

                            Frustration Signal vs Friction<\/h3>\n\n\n\n

                            Friction<\/strong> is the broader concept: anything that makes a task harder. A Frustration Signal<\/strong> is the measurable evidence that friction occurred. In Conversion & Measurement<\/strong>, friction is a hypothesis; the signal is what you track to prove it.<\/p>\n\n\n\n

                            Frustration Signal vs Drop-off<\/h3>\n\n\n\n

                            Drop-off<\/strong> tells you where users leave. A Frustration Signal helps explain why<\/em> they leave\u2014errors, dead clicks, slow loads, confusing copy\u2014making it more actionable for CRO<\/strong>.<\/p>\n\n\n\n

                            Frustration Signal vs UX metrics (engagement)<\/h3>\n\n\n\n

                            Engagement metrics (time on page, pages per session) can be misleading: more time might mean confusion. A Frustration Signal focuses on struggle patterns and failure states, which are often more directly tied to conversion outcomes in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                            Who Should Learn Frustration Signal<\/h2>\n\n\n\n
                              \n
                            • Marketers:<\/strong> Improve landing page relevance, reduce campaign waste, and connect messaging to on-site behavior using Conversion & Measurement<\/strong>.<\/li>\n
                            • Analysts:<\/strong> Build better diagnostics, segmentation, and alerting that go beyond funnel counts.<\/li>\n
                            • Agencies:<\/strong> Deliver higher-impact audits and CRO<\/strong> roadmaps backed by evidence, not opinions.<\/li>\n
                            • Business owners and founders:<\/strong> Prioritize product and site investments that protect revenue and reduce churn.<\/li>\n
                            • Developers:<\/strong> Use Frustration Signals to catch regressions, improve performance, and validate fixes with measurable outcomes.<\/li>\n<\/ul>\n\n\n\n

                              Summary of Frustration Signal<\/h2>\n\n\n\n

                              A Frustration Signal<\/strong> is measurable evidence that users are struggling during a journey\u2014whether due to UX confusion, technical failures, or mismatched expectations. It matters because it turns vague \u201csomething\u2019s wrong\u201d moments into actionable insights that improve performance. In Conversion & Measurement<\/strong>, Frustration Signals connect behavior, telemetry, and feedback to revenue outcomes. In CRO<\/strong>, they power stronger hypotheses, better prioritization, and more reliable optimization wins.<\/p>\n\n\n\n

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

                              1) What is a Frustration Signal in simple terms?<\/h3>\n\n\n\n

                              A Frustration Signal is a measurable sign that users are having trouble completing a task\u2014such as repeated clicks, form errors, slow loading, or abandoning a step after multiple attempts.<\/p>\n\n\n\n

                              2) How do Frustration Signals improve CRO results?<\/h3>\n\n\n\n

                              They help CRO<\/strong> teams focus on proven blockers (errors, dead ends, confusing steps) instead of guessing. Fixing or testing around these blockers typically increases completion rates and conversion rate.<\/p>\n\n\n\n

                              3) Are Frustration Signals only tracked with session replays?<\/h3>\n\n\n\n

                              No. Session replays are helpful for context, but Frustration Signals can come from analytics events, performance monitoring, error logs, and customer feedback. Strong Conversion & Measurement<\/strong> combines multiple sources.<\/p>\n\n\n\n

                              4) What are the most common Frustration Signals on websites?<\/h3>\n\n\n\n

                              Common ones include dead clicks, rage clicks, repeated form validation errors, slow page loads on key steps, checkout\/payment failures, and back-and-forth navigation that suggests users can\u2019t find information.<\/p>\n\n\n\n

                              5) How do you prioritize which Frustration Signal to fix first?<\/h3>\n\n\n\n

                              Use a simple model: how often it occurs, how close it is to conversion, and how large the conversion impact appears in Conversion & Measurement<\/strong>. High-frequency issues near checkout or lead submission usually win.<\/p>\n\n\n\n

                              6) Can a Frustration Signal be a false alarm?<\/h3>\n\n\n\n

                              Yes. Some behaviors look like frustration but aren\u2019t. Validate by checking correlation with drop-offs, conversions, segment patterns, and qualitative feedback before committing major resources.<\/p>\n\n\n\n

                              7) How do you track Frustration Signal trends over time?<\/h3>\n\n\n\n

                              Define consistent metrics (rates per session\/page\/step), segment them (device, browser, campaign), and set baselines. Then monitor after releases and CRO<\/strong> changes to catch regressions and confirm improvements.<\/p>\n","protected":false},"excerpt":{"rendered":"

                              A **Frustration Signal** is any measurable hint that users are struggling, confused, or blocked while trying to complete an action\u2014especially an action tied to revenue, lead generation, or activation. In **Conversion & Measurement**, these signals help you move beyond \u201cwhat happened\u201d (a drop in conversion rate) to \u201cwhy it happened\u201d (a broken field, unclear pricing, slow page, or mismatched intent). For **CRO**, a Frustration Signal is often the fastest way to find high-impact friction that traditional funnel reports hide.<\/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-7142","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\/7142","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=7142"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7142\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7142"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7142"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7142"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}