{"id":8607,"date":"2026-03-26T11:53:17","date_gmt":"2026-03-26T11:53:17","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/firebase-app-analytics\/"},"modified":"2026-03-26T11:53:17","modified_gmt":"2026-03-26T11:53:17","slug":"firebase-app-analytics","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/firebase-app-analytics\/","title":{"rendered":"Firebase App Analytics: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Mobile & App Marketing"},"content":{"rendered":"\n

Firebase App Analytics is a mobile analytics approach built around event tracking: it helps teams understand how people discover, install, and use an app, and how those behaviors connect to business outcomes like sign-ups, purchases, renewals, and retention. In Mobile & App Marketing<\/strong>, it\u2019s the measurement layer that turns \u201cwe shipped a feature\u201d or \u201cwe launched a campaign\u201d into measurable impact.<\/p>\n\n\n\n

For modern Mobile & App Marketing<\/strong>, optimization is only as good as the data behind it. Firebase App Analytics matters because it creates a consistent way to collect in-app behavior data, segment users, analyze funnels and cohorts, and connect product decisions to marketing performance\u2014so you can scale what works and fix what doesn\u2019t.<\/p>\n\n\n\n

What Is Firebase App Analytics?<\/h2>\n\n\n\n

Firebase App Analytics is an app measurement system that records user interactions (events) and user attributes (properties) to help you analyze engagement and conversion inside a mobile app. Instead of relying on pageviews like traditional web analytics, it focuses on in-app actions such as onboarding steps, screen views, purchases, searches, content plays, or any custom action that represents value.<\/p>\n\n\n\n

At its core, Firebase App Analytics is about instrumentation + interpretation<\/strong>:<\/p>\n\n\n\n

    \n
  • Instrumentation<\/strong>: defining which behaviors to track and implementing those events correctly.<\/li>\n
  • Interpretation<\/strong>: using reports, funnels, cohorts, and segments to understand what drives outcomes.<\/li>\n<\/ul>\n\n\n\n

    From a business perspective, Firebase App Analytics supports decisions like:<\/p>\n\n\n\n

      \n
    • Which acquisition channels bring users that actually retain and monetize?<\/li>\n
    • Where do users drop off in onboarding or checkout?<\/li>\n
    • Which features correlate with long-term value (LTV) and lower churn?<\/li>\n<\/ul>\n\n\n\n

      In Mobile & App Marketing<\/strong>, Firebase App Analytics sits between acquisition and retention: it helps you evaluate campaign quality, improve in-app conversion rate, and power lifecycle messaging and experimentation. It also plays an important role inside Mobile & App Marketing<\/strong> operations by standardizing measurement so marketers, product managers, analysts, and developers can work from the same definitions.<\/p>\n\n\n\n

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

      In Mobile & App Marketing<\/strong>, you\u2019re competing on speed (shipping and iterating), relevance (personalized experiences), and efficiency (lower CAC, higher LTV). Firebase App Analytics contributes directly to all three.<\/p>\n\n\n\n

      Key strategic reasons it matters:<\/p>\n\n\n\n

        \n
      • Proves marketing quality, not just volume<\/strong>: installs are easy to buy; retained, high-value users are not. Firebase App Analytics helps you judge channel and campaign performance using downstream behaviors.<\/li>\n
      • Improves conversion across the lifecycle<\/strong>: from first open to activation to purchase, event-based measurement reveals where friction lives and which fixes move metrics.<\/li>\n
      • Enables faster iteration<\/strong>: when tracking is consistent, teams can run experiments, measure lift, and ship improvements confidently.<\/li>\n
      • Creates competitive advantage through insight<\/strong>: apps that understand behavioral drivers can personalize onboarding, offers, and content more effectively than apps that rely on surface-level metrics.<\/li>\n<\/ul>\n\n\n\n

        In practice, Firebase App Analytics makes Mobile & App Marketing<\/strong> more accountable: it ties spend and effort to user outcomes, not guesses.<\/p>\n\n\n\n

        How Firebase App Analytics Works<\/h2>\n\n\n\n

        Firebase App Analytics is easiest to understand as a workflow that turns user actions into decisions.<\/p>\n\n\n\n

          \n
        1. \n

          Input \/ trigger: user actions and app signals<\/strong>\n A user installs the app, opens it, navigates screens, taps buttons, searches, watches content, subscribes, or makes a purchase. The app records these as events (some automatic, some custom), often enriched with parameters like value, category, screen name, or item ID.<\/p>\n<\/li>\n

        2. \n

          Processing: collection, organization, and aggregation<\/strong>\n Events are collected and associated with a user (or device) along with timestamps and contextual data (e.g., app version, platform). User properties help segment behavior (e.g., subscriber vs. non-subscriber, region, plan type).<\/p>\n<\/li>\n

        3. \n

          Execution \/ application: analysis and activation<\/strong>\n Teams explore funnels, retention cohorts, and segments to see what drives activation and revenue. These insights feed product changes, campaign optimization, and lifecycle messaging\u2014core activities in Mobile & App Marketing<\/strong>.<\/p>\n<\/li>\n

        4. \n

          Output \/ outcome: measurable improvements<\/strong>\n Outcomes include higher onboarding completion, improved trial-to-paid conversion, reduced churn, better ROAS, and stronger LTV\u2014measured through defined KPIs rather than intuition.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

          Key Components of Firebase App Analytics<\/h2>\n\n\n\n

          Strong Firebase App Analytics depends on more than \u201cturning it on.\u201d The major components include:<\/p>\n\n\n\n

          Event taxonomy (your tracking plan)<\/h3>\n\n\n\n

          A documented list of events, parameters, and naming conventions that represent your funnel and key behaviors (e.g., sign_up<\/code>, tutorial_complete<\/code>, add_to_cart<\/code>, purchase<\/code>).<\/p>\n\n\n\n

          Automatic and custom data inputs<\/h3>\n\n\n\n
            \n
          • Automatic signals<\/strong>: baseline app usage, engagement, and device\/app context.<\/li>\n
          • Custom events<\/strong>: business-specific actions like \u201csaved search,\u201d \u201cwatched 80% of video,\u201d or \u201ccompleted KYC.\u201d<\/li>\n<\/ul>\n\n\n\n

            User properties and segmentation<\/h3>\n\n\n\n

            Stable attributes used for analysis and targeting, such as plan type, acquisition cohort, content preference, or lifecycle stage.<\/p>\n\n\n\n

            Funnels, cohorts, and retention analysis<\/h3>\n\n\n\n

            Core analytical views used to quantify drop-off, conversion rate, repeat behavior, and user longevity\u2014critical for Mobile & App Marketing<\/strong> teams managing acquisition-to-retention performance.<\/p>\n\n\n\n

            Governance and shared responsibility<\/h3>\n\n\n\n

            High-quality measurement requires clear ownership:\n– Marketing defines outcomes, attribution needs, and lifecycle KPIs.\n– Product defines behavioral milestones and feature usage signals.\n– Engineering implements instrumentation and QA.\n– Analytics ensures consistency, documentation, and reporting standards.<\/p>\n\n\n\n

            Types of Firebase App Analytics (Practical Distinctions)<\/h2>\n\n\n\n

            Firebase App Analytics isn\u2019t typically described in \u201cformal types,\u201d but in real work there are important distinctions in how teams use it:<\/p>\n\n\n\n

            1) Automatic vs. custom instrumentation<\/h3>\n\n\n\n
              \n
            • Automatic<\/strong>: good for baseline engagement and adoption, but limited for business-specific questions.<\/li>\n
            • Custom<\/strong>: essential for understanding your unique funnel and monetization model.<\/li>\n<\/ul>\n\n\n\n

              2) Product analytics vs. marketing analytics use cases<\/h3>\n\n\n\n
                \n
              • Product analytics<\/strong>: feature adoption, UX friction, onboarding progression.<\/li>\n
              • Marketing analytics<\/strong>: campaign quality, audience segmentation, lifecycle performance.\nMost mature teams unify both, since Mobile & App Marketing<\/strong> performance depends on the product experience.<\/li>\n<\/ul>\n\n\n\n

                3) Macro-conversions vs. micro-conversions<\/h3>\n\n\n\n
                  \n
                • Macro<\/strong>: purchase, subscription, lead submission.<\/li>\n
                • Micro<\/strong>: account created, content saved, trial started, add payment method.\nMicro-conversions often predict macro outcomes and are invaluable for optimization.<\/li>\n<\/ul>\n\n\n\n

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

                  Example 1: Improving onboarding completion for a subscription app<\/h3>\n\n\n\n

                  A subscription app sees high install volume but low trial starts. Using Firebase App Analytics, the team builds an onboarding funnel: first open \u2192 permissions \u2192 account creation \u2192 plan selection \u2192 trial start. They discover a major drop at permissions. After redesigning the permissions prompt timing and clarifying value, onboarding completion rises and paid conversion follows. This is a classic Mobile & App Marketing<\/strong> win: better in-app conversion improves the ROI of every campaign.<\/p>\n\n\n\n

                  Example 2: Measuring campaign quality beyond installs<\/h3>\n\n\n\n

                  A retailer runs multiple paid acquisition campaigns. Firebase App Analytics segments users by campaign identifiers (where available) and compares cohorts on early signals: product views per session, add-to-cart rate, and first-week purchase rate. One campaign drives cheaper installs but significantly worse downstream conversion. Budget shifts to the higher-quality cohort, improving blended ROAS\u2014an everyday decision in Mobile & App Marketing<\/strong>.<\/p>\n\n\n\n

                  Example 3: Feature adoption tied to retention for a fintech app<\/h3>\n\n\n\n

                  A fintech app suspects that users who set up autopay retain longer. Firebase App Analytics tracks autopay_enabled<\/code> and evaluates retention cohorts. The insight: enabling autopay within the first 48 hours strongly correlates with 30-day retention. The team then promotes autopay via in-app prompts and lifecycle messages, improving retention and stabilizing growth\u2014exactly the type of cross-functional loop Mobile & App Marketing<\/strong> needs.<\/p>\n\n\n\n

                  Benefits of Using Firebase App Analytics<\/h2>\n\n\n\n

                  Using Firebase App Analytics well can deliver tangible advantages:<\/p>\n\n\n\n

                    \n
                  • Higher conversion rates<\/strong>: funnel visibility helps remove friction from onboarding, checkout, and key journeys.<\/li>\n
                  • More efficient spend<\/strong>: by evaluating user quality, you reduce wasted acquisition budgets.<\/li>\n
                  • Faster experimentation<\/strong>: event-based measurement supports rapid A\/B testing and iteration.<\/li>\n
                  • Better customer experience<\/strong>: personalization and lifecycle improvements become data-driven rather than generic.<\/li>\n
                  • Stronger alignment<\/strong>: shared metrics reduce disagreements between marketing and product teams and speed up decisions in Mobile & App Marketing<\/strong> programs.<\/li>\n<\/ul>\n\n\n\n

                    Challenges of Firebase App Analytics<\/h2>\n\n\n\n

                    Firebase App Analytics is powerful, but common pitfalls can undermine results:<\/p>\n\n\n\n

                      \n
                    • Poor event design<\/strong>: inconsistent names, missing parameters, and unclear definitions make reporting unreliable.<\/li>\n
                    • Over-tracking<\/strong>: tracking everything increases complexity without improving decisions; it can also raise privacy and governance burdens.<\/li>\n
                    • Attribution limitations<\/strong>: app analytics alone may not fully solve cross-channel attribution, especially under privacy constraints; you often need additional measurement methods.<\/li>\n
                    • Sampling and data differences across tools<\/strong>: teams may see mismatched numbers between app analytics, ad platforms, and backend systems.<\/li>\n
                    • Implementation coordination<\/strong>: marketers often depend on engineering cycles; without a process, tracking stays incomplete and Mobile & App Marketing<\/strong> optimization stalls.<\/li>\n<\/ul>\n\n\n\n

                      Best Practices for Firebase App Analytics<\/h2>\n\n\n\n

                      Start with outcomes, then instrument backwards<\/h3>\n\n\n\n

                      Define 1\u20133 primary business outcomes (e.g., purchase, subscription, qualified lead) and map the steps that predict them. Track those steps as events.<\/p>\n\n\n\n

                      Use a documented tracking plan<\/h3>\n\n\n\n

                      Include:\n– event names and definitions\n– parameters and allowed values\n– when the event fires\n– ownership and QA steps\nThis prevents \u201cmetric drift\u201d as the app evolves.<\/p>\n\n\n\n

                      Separate diagnosis metrics from reporting metrics<\/h3>\n\n\n\n

                      Not every event should be a KPI. Maintain a small KPI set (north star + supporting metrics) and a broader diagnostic layer for analysis.<\/p>\n\n\n\n

                      Validate data quality continuously<\/h3>\n\n\n\n

                      Use checklists for:\n– duplicate events\n– missing parameters\n– changes after app updates\n– unexpected spikes\/drops\nMeasurement regression is common in app releases, and it directly harms Mobile & App Marketing<\/strong> decision-making.<\/p>\n\n\n\n

                      Segment by lifecycle stage<\/h3>\n\n\n\n

                      Analyze new vs. returning users, trial vs. paid, and high-intent vs. low-intent cohorts. Aggregates hide problems and opportunities.<\/p>\n\n\n\n

                      Pair in-app behavior with backend truth<\/h3>\n\n\n\n

                      Whenever possible, reconcile revenue and subscription states with backend systems to reduce ambiguity and improve ROI reporting.<\/p>\n\n\n\n

                      Tools Used for Firebase App Analytics<\/h2>\n\n\n\n

                      Firebase App Analytics typically lives inside a broader Mobile & App Marketing<\/strong> stack. Common tool categories include:<\/p>\n\n\n\n

                        \n
                      • Analytics and product intelligence<\/strong>: tools for funnels, cohorts, user journeys, and behavioral segmentation.<\/li>\n
                      • Mobile attribution and measurement<\/strong>: systems that help connect campaigns to installs and post-install events (often essential under modern privacy rules).<\/li>\n
                      • Marketing automation and lifecycle messaging<\/strong>: push notifications, in-app messaging, email, and journey orchestration based on events and segments.<\/li>\n
                      • CRM and customer data platforms (CDPs)<\/strong>: unify identities and sync attributes across channels for consistent targeting and personalization.<\/li>\n
                      • Experimentation platforms<\/strong>: A\/B tests and feature flags to measure causal impact of changes.<\/li>\n
                      • Data pipelines and warehouses<\/strong>: centralize event data for advanced modeling, LTV analysis, and finance-grade reporting.<\/li>\n
                      • BI dashboards and reporting<\/strong>: stakeholder-friendly dashboards that align product, marketing, and leadership around consistent KPIs.<\/li>\n
                      • Privacy, consent, and governance tools<\/strong>: manage consent states, data retention, and access control\u2014critical for compliant Mobile & App Marketing<\/strong> measurement.<\/li>\n<\/ul>\n\n\n\n

                        Metrics Related to Firebase App Analytics<\/h2>\n\n\n\n

                        Firebase App Analytics supports a wide range of metrics. The most useful sets are:<\/p>\n\n\n\n

                        Engagement metrics<\/h3>\n\n\n\n
                          \n
                        • Active users (daily\/weekly\/monthly)<\/li>\n
                        • Sessions per user and engagement time<\/li>\n
                        • Screen flow and key feature usage frequency<\/li>\n<\/ul>\n\n\n\n

                          Conversion and funnel metrics<\/h3>\n\n\n\n
                            \n
                          • Onboarding completion rate<\/li>\n
                          • Trial start rate, checkout completion rate<\/li>\n
                          • Event-to-event conversion (e.g., view_item<\/code> \u2192 add_to_cart<\/code> \u2192 purchase<\/code>)<\/li>\n<\/ul>\n\n\n\n

                            Retention and cohort metrics<\/h3>\n\n\n\n
                              \n
                            • Day 1 \/ Day 7 \/ Day 30 retention<\/li>\n
                            • Repeat purchase rate<\/li>\n
                            • Churn proxies (e.g., inactivity windows)<\/li>\n<\/ul>\n\n\n\n

                              Monetization metrics<\/h3>\n\n\n\n
                                \n
                              • ARPU \/ ARPPU<\/li>\n
                              • Revenue per user cohort<\/li>\n
                              • LTV (modeled or observed, depending on maturity and data access)<\/li>\n<\/ul>\n\n\n\n

                                Efficiency and ROI metrics (when paired with spend data)<\/h3>\n\n\n\n
                                  \n
                                • CAC by channel\/cohort<\/li>\n
                                • ROAS and payback period\nThese are central to performance-focused Mobile & App Marketing<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                                  Future Trends of Firebase App Analytics<\/h2>\n\n\n\n

                                  Firebase App Analytics is evolving with the broader measurement landscape:<\/p>\n\n\n\n

                                    \n
                                  • Privacy-driven measurement changes<\/strong>: stricter platform policies and consent requirements push teams toward aggregated reporting, modeled attribution, and better first-party data practices.<\/li>\n
                                  • AI-assisted insights<\/strong>: anomaly detection, predictive audiences, and automated insight surfacing will reduce time-to-diagnosis and help teams focus on action.<\/li>\n
                                  • Deeper personalization<\/strong>: real-time segmentation and event-driven journeys will increasingly tailor onboarding, offers, and content.<\/li>\n
                                  • Server-side and hybrid approaches<\/strong>: more teams will combine client-side events with backend events for accuracy and resilience.<\/li>\n
                                  • Measurement standardization across apps and web<\/strong>: unified taxonomies and shared lifecycle metrics will become the norm for scaled Mobile & App Marketing<\/strong> programs.<\/li>\n<\/ul>\n\n\n\n

                                    Firebase App Analytics vs Related Terms<\/h2>\n\n\n\n

                                    Firebase App Analytics vs mobile attribution<\/h3>\n\n\n\n

                                    Firebase App Analytics focuses on in-app behavior<\/strong> and engagement\/conversion analysis. Mobile attribution focuses on connecting marketing touchpoints to installs and post-install outcomes<\/strong>, often using privacy-aware methods. In practice, Mobile & App Marketing<\/strong> teams use both: attribution for acquisition performance and Firebase App Analytics for in-app optimization and retention.<\/p>\n\n\n\n

                                    Firebase App Analytics vs product analytics<\/h3>\n\n\n\n

                                    Product analytics is a broader discipline of understanding user behavior to improve the product (funnels, retention, feature adoption). Firebase App Analytics can serve as a product analytics implementation for mobile apps, but product analytics may also include qualitative research, session replays, experiments, and data science models beyond standard event reporting.<\/p>\n\n\n\n

                                    Firebase App Analytics vs event tracking (general)<\/h3>\n\n\n\n

                                    Event tracking is the concept of recording actions. Firebase App Analytics is a structured system that collects, organizes, and reports on events with app-specific contexts and integrated segmentation\u2014making event tracking operational for Mobile & App Marketing<\/strong> and product teams.<\/p>\n\n\n\n

                                    Who Should Learn Firebase App Analytics<\/h2>\n\n\n\n
                                      \n
                                    • Marketers<\/strong>: to evaluate campaign quality, build lifecycle segments, and improve conversion and retention.<\/li>\n
                                    • Analysts<\/strong>: to design event taxonomies, create reliable dashboards, and connect behavior to revenue outcomes.<\/li>\n
                                    • Agencies<\/strong>: to prove performance, diagnose funnel issues, and build measurement frameworks clients can maintain.<\/li>\n
                                    • Business owners and founders<\/strong>: to understand growth levers, prioritize roadmap investments, and reduce wasted spend.<\/li>\n
                                    • Developers<\/strong>: to implement clean instrumentation, ensure data quality, and collaborate effectively with Mobile & App Marketing<\/strong> stakeholders.<\/li>\n<\/ul>\n\n\n\n

                                      Summary of Firebase App Analytics<\/h2>\n\n\n\n

                                      Firebase App Analytics is an event-based measurement approach for understanding how users engage with a mobile app and how that engagement drives conversions, revenue, and retention. It matters because modern Mobile & App Marketing<\/strong> depends on trustworthy behavioral data to optimize acquisition quality, improve onboarding and monetization, and increase lifetime value. When implemented with a clear tracking plan, governance, and KPI alignment, Firebase App Analytics becomes a practical foundation that supports both Mobile & App Marketing<\/strong> execution and long-term growth strategy.<\/p>\n\n\n\n

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

                                      1) What is Firebase App Analytics used for?<\/h3>\n\n\n\n

                                      Firebase App Analytics is used to measure in-app user behavior (events), analyze funnels and retention, segment audiences, and connect product interactions to business outcomes like sign-ups, purchases, and subscriptions.<\/p>\n\n\n\n

                                      2) How does Firebase App Analytics help Mobile & App Marketing teams?<\/h3>\n\n\n\n

                                      It helps Mobile & App Marketing<\/strong> teams evaluate campaign quality beyond installs, identify drop-offs in onboarding or checkout, build segments for lifecycle messaging, and measure retention and LTV improvements tied to marketing and product changes.<\/p>\n\n\n\n

                                      3) What should I track first in Firebase App Analytics?<\/h3>\n\n\n\n

                                      Start with a small set: your primary conversion (purchase\/subscription\/lead), 3\u20135 key steps that lead to it (activation milestones), and a few engagement signals that predict retention. Expand only after those are stable and trusted.<\/p>\n\n\n\n

                                      4) How do I avoid messy or inconsistent data?<\/h3>\n\n\n\n

                                      Use a written event taxonomy with clear definitions, parameter standards, and QA checks every release. Assign owners (marketing\/product\/engineering\/analytics) so changes don\u2019t silently break reporting.<\/p>\n\n\n\n

                                      5) Can Firebase App Analytics replace attribution tools?<\/h3>\n\n\n\n

                                      Not entirely. Firebase App Analytics is best for in-app behavior and cohort insights. Attribution typically requires additional measurement capabilities and methodologies, especially for paid acquisition and privacy-constrained environments.<\/p>\n\n\n\n

                                      6) Which metrics matter most for app growth?<\/h3>\n\n\n\n

                                      Most teams focus on activation rate, conversion rate, retention (D1\/D7\/D30), ARPU\/LTV, and CAC\/ROAS (when spend data is available). The \u201cmost important\u201d set depends on your app\u2019s monetization model and lifecycle.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                      Firebase App Analytics is a mobile analytics approach built around event tracking: it helps teams understand how people discover, install, and use an app, and how those behaviors connect to business outcomes like sign-ups, purchases, renewals, and retention. In **Mobile & App Marketing**, it\u2019s the measurement layer that turns \u201cwe shipped a feature\u201d or \u201cwe launched a campaign\u201d into measurable impact.<\/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-8607","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\/8607","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=8607"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/8607\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=8607"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=8607"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=8607"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}