{"id":6897,"date":"2026-03-23T16:47:35","date_gmt":"2026-03-23T16:47:35","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/mixpanel\/"},"modified":"2026-03-23T16:47:35","modified_gmt":"2026-03-23T16:47:35","slug":"mixpanel","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/mixpanel\/","title":{"rendered":"Mixpanel: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Analytics"},"content":{"rendered":"\n

Mixpanel is a product analytics platform built around event-based tracking\u2014measuring what people actually do in a website, app, or digital product and tying those behaviors to business outcomes. In Conversion & Measurement<\/strong>, Mixpanel helps teams move beyond surface-level traffic numbers to understand how users progress through onboarding, activation, engagement, retention, and purchase. Within Analytics<\/strong>, it\u2019s commonly used to answer questions like \u201cWhich actions predict conversion?\u201d and \u201cWhere do users drop off in the funnel?\u201d<\/p>\n\n\n\n

Mixpanel matters in modern Conversion & Measurement<\/strong> strategy because customer journeys are no longer linear, attribution is noisier, and growth depends on improving product experiences\u2014not only buying more traffic. When you can measure user actions precisely and analyze them quickly, you can make better decisions about UX, messaging, features, lifecycle campaigns, and experiments.<\/p>\n\n\n\n

What Is Mixpanel?<\/h2>\n\n\n\n

Mixpanel is an Analytics<\/strong> tool focused on product and user behavior. Instead of primarily analyzing pageviews (as traditional web analytics often does), Mixpanel tracks events<\/strong> (such as \u201cSigned Up,\u201d \u201cAdded to Cart,\u201d \u201cCompleted Tutorial,\u201d or \u201cInvited Teammate\u201d) and associates them with users and their properties (plan type, acquisition channel, device, region, etc.).<\/p>\n\n\n\n

The core concept is simple: define meaningful events and then analyze how those events connect to outcomes like conversion, retention, and revenue. In business terms, Mixpanel helps teams identify what drives growth and what prevents it\u2014so they can prioritize changes that improve results.<\/p>\n\n\n\n

In Conversion & Measurement<\/strong>, Mixpanel sits in the \u201cbehavior and funnel diagnostics\u201d layer. It complements ad platform reporting and top-of-funnel web analytics by focusing on what happens after the click: activation steps, feature adoption, repeat usage, and purchase completion. As an Analytics<\/strong> system, it\u2019s designed to be fast for exploratory analysis, segmentation, and cohort-based insights.<\/p>\n\n\n\n

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

Mixpanel\u2019s strategic value in Conversion & Measurement<\/strong> comes from connecting user behavior to outcomes with precision. Many organizations can see overall conversion rates, but struggle to explain why<\/em> those rates move or which behaviors predict future value.<\/p>\n\n\n\n

Key business impacts include:<\/p>\n\n\n\n

    \n
  • Faster diagnosis of funnel issues:<\/strong> See exactly where users abandon signup, checkout, onboarding, or key workflows.<\/li>\n
  • Better segmentation:<\/strong> Compare conversion performance by channel, device, plan, geography, landing experience, or user intent.<\/li>\n
  • Stronger retention insights:<\/strong> Understand whether new users come back, which features correlate with repeat usage, and what drives churn risk.<\/li>\n
  • Experiment measurement:<\/strong> Evaluate A\/B tests and product changes using behavioral metrics, not just clicks or pageviews.<\/li>\n<\/ul>\n\n\n\n

    In competitive markets, this becomes an advantage: teams that run tighter Analytics<\/strong> loops can ship improvements faster, reduce waste in acquisition spend, and improve customer experience systematically.<\/p>\n\n\n\n

    How Mixpanel Works<\/h2>\n\n\n\n

    In practice, Mixpanel supports a workflow that looks like this:<\/p>\n\n\n\n

      \n
    1. \n

      Input (instrumentation and data capture)<\/strong>\n Teams define the events and properties they need for Conversion & Measurement<\/strong>\u2014for example \u201cStarted Trial,\u201d \u201cUploaded File,\u201d \u201cViewed Pricing,\u201d with properties like source, campaign, device, plan, and account type. These events are sent from the product (web, mobile, server) into Mixpanel.<\/p>\n<\/li>\n

    2. \n

      Processing (identity, organization, and governance)<\/strong>\n Mixpanel organizes events into user-level timelines and allows identity resolution patterns (for example, mapping anonymous activity to a known user after login). Teams also define consistent naming conventions, property schemas, and data quality rules so Analytics<\/strong> remains trustworthy.<\/p>\n<\/li>\n

    3. \n

      Analysis (exploration and insight generation)<\/strong>\n Analysts and marketers use funnels, cohorts, retention views, segmentation, and breakdowns to find where users succeed or fail. This is where Mixpanel shines for Conversion & Measurement<\/strong>: identifying drop-offs, \u201caha\u201d moments, and differences across user segments.<\/p>\n<\/li>\n

    4. \n

      Execution (decision-making and iteration)<\/strong>\n Insights translate into actions: UX changes, onboarding tweaks, lifecycle messaging, pricing experiments, performance fixes, or targeting improvements. The team then re-measures to validate impact, closing the Analytics<\/strong> feedback loop.<\/p>\n<\/li>\n

    5. \n

      Output (measurable outcomes)<\/strong>\n Outcomes include improved signup completion, higher trial-to-paid conversion, greater feature adoption, lower churn, and better ROI from acquisition channels\u2014grounded in observable user behavior.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

      Key Components of Mixpanel<\/h2>\n\n\n\n

      Mixpanel-based measurement typically includes these major elements:<\/p>\n\n\n\n

      Event Tracking Plan<\/h3>\n\n\n\n

      A documented plan that defines:\n– Core events (conversion steps, activation actions, retention behaviors)\n– Event properties (campaign, channel, device, plan, user type)\n– User properties (account size, lifecycle stage, industry)\nThis plan is the foundation of reliable Conversion & Measurement<\/strong> and consistent Analytics<\/strong>.<\/p>\n\n\n\n

      Instrumentation (Web, Mobile, Server)<\/h3>\n\n\n\n

      Implementation methods often include:\n– Client-side tracking for front-end actions\n– Server-side tracking for backend-confirmed events (payments, subscription changes)\n– Mobile app events for in-app behaviors\nThe right balance improves accuracy and reduces gaps.<\/p>\n\n\n\n

      Funnels and Journey Analysis<\/h3>\n\n\n\n

      Funnel reports show step-by-step conversion through critical paths like onboarding or checkout. Journey exploration helps teams see common sequences of actions leading to success or failure\u2014central to Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

      Cohorts and Retention<\/h3>\n\n\n\n

      Cohorts group users by shared characteristics (e.g., \u201cusers who completed onboarding within 1 day\u201d). Retention analysis shows who comes back and what they do when they return\u2014core to product-led growth Analytics<\/strong>.<\/p>\n\n\n\n

      Data Governance and Roles<\/h3>\n\n\n\n

      Successful teams assign responsibilities:\n– Product\/engineering owns instrumentation correctness\n– Analytics\/marketing defines KPIs and reporting logic\n– Data governance ensures naming standards, access controls, and documentation<\/p>\n\n\n\n

      Types of Mixpanel (Practical Contexts)<\/h2>\n\n\n\n

      Mixpanel doesn\u2019t have \u201ctypes\u201d in the academic sense, but it is used in distinct ways depending on the organization\u2019s Conversion & Measurement<\/strong> needs:<\/p>\n\n\n\n

      Web Product Analytics vs Mobile Product Analytics<\/h3>\n\n\n\n

      Web-focused implementations emphasize browser events and landing-to-signup flows, while mobile-focused implementations track in-app engagement, push notification impact, and app version behavior.<\/p>\n\n\n\n

      Marketing-Led vs Product-Led Measurement<\/h3>\n\n\n\n
        \n
      • Marketing-led Mixpanel use<\/strong> focuses on how acquisition sources affect activation and conversion (campaign-to-trial-to-paid).<\/li>\n
      • Product-led Mixpanel use<\/strong> focuses on feature adoption, onboarding completion, and long-term retention.<\/li>\n<\/ul>\n\n\n\n

        Client-Side vs Server-Side Emphasis<\/h3>\n\n\n\n

        Client-side data is easier to implement but can be blocked or incomplete. Server-side events are typically more reliable for revenue and subscription changes, improving Analytics<\/strong> integrity.<\/p>\n\n\n\n

        Real-World Examples of Mixpanel<\/h2>\n\n\n\n

        Example 1: Improving SaaS Trial-to-Paid Conversion<\/h3>\n\n\n\n

        A SaaS company tracks events like \u201cCreated Workspace,\u201d \u201cInvited Teammate,\u201d and \u201cConnected Integration.\u201d In Mixpanel, they build a funnel from \u201cStarted Trial\u201d \u2192 \u201cActivated Key Feature\u201d \u2192 \u201cAdded Billing Details\u201d \u2192 \u201cSubscribed.\u201d\nBy breaking down the funnel by acquisition channel, they learn that one channel produces many trials but low activation. Conversion & Measurement<\/strong> then shifts from \u201cbuy more traffic\u201d to \u201cfix activation for that segment,\u201d improving paid conversion and reducing CAC waste.<\/p>\n\n\n\n

        Example 2: Diagnosing Checkout Drop-Off in E-commerce<\/h3>\n\n\n\n

        An online retailer tracks \u201cViewed Product,\u201d \u201cAdded to Cart,\u201d \u201cStarted Checkout,\u201d \u201cEntered Shipping,\u201d \u201cPayment Submitted,\u201d \u201cPurchase Confirmed.\u201d\nMixpanel Analytics<\/strong> reveals the biggest drop at \u201cEntered Shipping\u201d on mobile devices for returning users. The team finds a UI issue and shipping form friction, fixes it, and measures a lift in completion rate\u2014directly tying UX improvements to Conversion & Measurement<\/strong> outcomes.<\/p>\n\n\n\n

        Example 3: Measuring Onboarding Quality for a Mobile App<\/h3>\n\n\n\n

        A consumer app defines a successful onboarding as completing tutorial steps and enabling permissions. Mixpanel retention views show that users who finish the tutorial are far more likely to return in week one.\nThis insight informs product changes and lifecycle messaging (reminders, guided prompts), improving retention\u2014a key pillar of Conversion & Measurement<\/strong> beyond immediate conversion.<\/p>\n\n\n\n

        Benefits of Using Mixpanel<\/h2>\n\n\n\n

        Mixpanel can deliver meaningful gains when implemented well:<\/p>\n\n\n\n

          \n
        • Performance improvements:<\/strong> Identify the actions that lead to conversion and optimize the steps that block it.<\/li>\n
        • Lower acquisition waste:<\/strong> Tie campaigns to downstream outcomes like activation and purchase, not just clicks.<\/li>\n
        • Operational efficiency:<\/strong> Self-serve exploration reduces dependency on lengthy reporting cycles, speeding decision-making in Analytics<\/strong>.<\/li>\n
        • Better customer experience:<\/strong> Focus improvements on real friction points and high-impact \u201caha\u201d moments.<\/li>\n
        • Stronger alignment:<\/strong> A shared view of user behavior helps marketing, product, and engineering collaborate on Conversion & Measurement<\/strong> goals.<\/li>\n<\/ul>\n\n\n\n

          Challenges of Mixpanel<\/h2>\n\n\n\n

          Mixpanel is powerful, but there are common pitfalls:<\/p>\n\n\n\n

            \n
          • Instrumentation complexity:<\/strong> If events are inconsistent or poorly named, Analytics<\/strong> becomes confusing and unreliable.<\/li>\n
          • Identity resolution issues:<\/strong> Anonymous-to-known user stitching can be tricky, impacting funnel accuracy and cohort analysis.<\/li>\n
          • Data quality and governance:<\/strong> Without documentation, teams create duplicate events, conflicting properties, and reports that don\u2019t match.<\/li>\n
          • Privacy and compliance constraints:<\/strong> Consent requirements and data minimization can limit what you track; measurement plans must adapt.<\/li>\n
          • Over-reliance on tool outputs:<\/strong> Mixpanel shows patterns, but interpretation still requires domain knowledge, experimentation discipline, and context.<\/li>\n<\/ul>\n\n\n\n

            Best Practices for Mixpanel<\/h2>\n\n\n\n

            Start with a Measurement Strategy, Not a Tracking Wishlist<\/h3>\n\n\n\n

            Define 1\u20133 core Conversion & Measurement<\/strong> journeys (signup, checkout, activation) and instrument those first. Expand only when the basics are reliable.<\/p>\n\n\n\n

            Build a Clean Event Taxonomy<\/h3>\n\n\n\n

            Use consistent naming and structure:\n– Verbs for events (e.g., \u201cCompleted Onboarding\u201d)\n– Stable properties (e.g., \u201cplan,\u201d \u201cchannel,\u201d \u201cdevice_type\u201d)\n– Clear definitions and owners in documentation\nThis makes Mixpanel Analytics<\/strong> scalable.<\/p>\n\n\n\n

            Prefer Server-Side for Revenue-Critical Events<\/h3>\n\n\n\n

            Track payments, subscription changes, refunds, and account status from backend systems where possible. This reduces discrepancies and improves trust in Conversion & Measurement<\/strong> reporting.<\/p>\n\n\n\n

            Validate Data Continuously<\/h3>\n\n\n\n

            Create checks for:\n– Event volume anomalies\n– Missing properties\n– Sudden changes after releases\nData quality monitoring prevents teams from optimizing based on broken Analytics<\/strong>.<\/p>\n\n\n\n

            Operationalize Insights<\/h3>\n\n\n\n

            Turn insights into a loop:\n1) identify drop-off, 2) form a hypothesis, 3) ship a change, 4) measure lift, 5) document learnings. Mixpanel works best when it supports iteration, not just dashboards.<\/p>\n\n\n\n

            Tools Used for Mixpanel<\/h2>\n\n\n\n

            Mixpanel rarely lives alone. In a mature Conversion & Measurement<\/strong> stack, teams commonly pair it with:<\/p>\n\n\n\n

              \n
            • Tag management systems:<\/strong> To manage web tracking and deployment workflows.<\/li>\n
            • Data warehouses\/lakes:<\/strong> For long-term storage, joining product behavior with billing, support, and finance data.<\/li>\n
            • ETL\/ELT and reverse ETL tools:<\/strong> To move data between systems and activate cohorts in other tools.<\/li>\n
            • CRM systems:<\/strong> To align lifecycle stages, sales activity, and account health with behavioral Analytics<\/strong>.<\/li>\n
            • Marketing automation platforms:<\/strong> For triggered messaging based on user actions (activation nudges, re-engagement).<\/li>\n
            • Experimentation platforms:<\/strong> To run A\/B tests and measure results across funnels and cohorts.<\/li>\n
            • Reporting dashboards\/BI tools:<\/strong> For executive rollups and blended reporting alongside other Analytics<\/strong> sources.<\/li>\n<\/ul>\n\n\n\n

              Metrics Related to Mixpanel<\/h2>\n\n\n\n

              Mixpanel is particularly useful for behavioral and lifecycle metrics tied to Conversion & Measurement<\/strong>, including:<\/p>\n\n\n\n

                \n
              • Funnel conversion rate:<\/strong> Percent of users completing a defined sequence (e.g., trial \u2192 activation \u2192 paid).<\/li>\n
              • Step-to-step drop-off:<\/strong> Where users abandon a process and how that varies by segment.<\/li>\n
              • Time to convert \/ time to activate:<\/strong> How long it takes users to reach key milestones.<\/li>\n
              • Activation rate:<\/strong> Percent of new users completing the \u201caha\u201d behavior (defined per product).<\/li>\n
              • Retention rate:<\/strong> Users returning over time (day 1, week 1, month 1), often by cohort.<\/li>\n
              • Feature adoption:<\/strong> Percent of users using a specific feature within a timeframe.<\/li>\n
              • Stickiness ratios:<\/strong> How frequently users engage (e.g., daily vs monthly active usage patterns).<\/li>\n
              • Customer value proxies:<\/strong> Behaviors correlated with upgrades, renewals, or expansion.<\/li>\n<\/ul>\n\n\n\n

                Future Trends of Mixpanel<\/h2>\n\n\n\n

                Several trends are shaping how Mixpanel and product Analytics<\/strong> evolve within Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                  \n
                • AI-assisted insight discovery:<\/strong> More automated detection of anomalies, drivers of conversion, and meaningful segments\u2014reducing time from data to decision.<\/li>\n
                • Privacy-first measurement:<\/strong> Stronger consent management, data minimization, and regional compliance requirements will push teams toward smarter event design and more server-side tracking.<\/li>\n
                • Warehouse-centric architectures:<\/strong> Many organizations will blend product analytics with warehouse data to unify revenue, support, and behavioral signals\u2014raising the bar for governance.<\/li>\n
                • Real-time personalization:<\/strong> Faster segmentation and cohort activation will increase the demand for near-real-time Conversion & Measurement<\/strong> loops.<\/li>\n
                • Cross-platform journeys:<\/strong> As users move across web, mobile, and connected devices, identity and attribution remain central challenges for Analytics<\/strong> accuracy.<\/li>\n<\/ul>\n\n\n\n

                  Mixpanel vs Related Terms<\/h2>\n\n\n\n

                  Mixpanel vs Google Analytics (Traditional Web Analytics)<\/h3>\n\n\n\n

                  Traditional web analytics tools are often optimized for traffic, sessions, and marketing channel reporting. Mixpanel is optimized for event-level product behavior, funnels, cohorts, and retention. In Conversion & Measurement<\/strong>, many teams use both: one to understand acquisition and web journeys, and Mixpanel to understand in-product behavior and lifecycle outcomes.<\/p>\n\n\n\n

                  Mixpanel vs Amplitude (Product Analytics Category)<\/h3>\n\n\n\n

                  Both are product Analytics<\/strong> platforms built around events, funnels, cohorts, and retention. The practical difference is usually less about concepts and more about implementation preferences, data modeling choices, usability, governance workflows, and ecosystem fit. The key is picking a tool and executing a disciplined Conversion & Measurement<\/strong> plan.<\/p>\n\n\n\n

                  Mixpanel vs Data Warehouse + BI<\/h3>\n\n\n\n

                  A warehouse plus BI can be more flexible and \u201csingle source of truth,\u201d but often requires more engineering and longer iteration cycles. Mixpanel is typically faster for exploratory product Analytics<\/strong> and self-serve funnel\/retention analysis. Many mature teams combine them: Mixpanel for speed and workflows, warehouse\/BI for completeness and finance-grade reporting.<\/p>\n\n\n\n

                  Who Should Learn Mixpanel<\/h2>\n\n\n\n
                    \n
                  • Marketers:<\/strong> To connect campaigns to activation and downstream conversion, improving Conversion & Measurement<\/strong> beyond click-based reporting.<\/li>\n
                  • Analysts:<\/strong> To explore funnels, cohorts, and retention quickly and translate behaviors into hypotheses and tests.<\/li>\n
                  • Agencies:<\/strong> To deliver measurable CRO and lifecycle improvements using robust Analytics<\/strong> rather than opinion-led recommendations.<\/li>\n
                  • Business owners and founders:<\/strong> To understand what drives growth, reduce churn, and prioritize product work with evidence.<\/li>\n
                  • Developers and product teams:<\/strong> To implement clean tracking, validate releases, and measure feature impact in Conversion & Measurement<\/strong> terms.<\/li>\n<\/ul>\n\n\n\n

                    Summary of Mixpanel<\/h2>\n\n\n\n

                    Mixpanel is an event-based product Analytics<\/strong> platform that helps teams measure user behavior and connect it to outcomes like conversion and retention. In Conversion & Measurement<\/strong>, it\u2019s especially valuable for diagnosing funnel drop-offs, understanding activation drivers, and improving lifecycle performance across segments. When implemented with strong instrumentation and governance, Mixpanel supports faster learning cycles and more confident decisions grounded in real user actions.<\/p>\n\n\n\n

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

                    1) What is Mixpanel used for?<\/h3>\n\n\n\n

                    Mixpanel is used for product Analytics<\/strong>: tracking user actions as events, analyzing funnels and retention, and understanding which behaviors lead to conversion or long-term engagement\u2014core to Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                    2) Is Mixpanel only for mobile apps?<\/h3>\n\n\n\n

                    No. Mixpanel is commonly used for web products, SaaS platforms, and mobile apps. The key is event tracking tied to user identity, which applies to most digital products and Conversion & Measurement<\/strong> programs.<\/p>\n\n\n\n

                    3) How does Mixpanel support Conversion & Measurement better than pageview analytics?<\/h3>\n\n\n\n

                    Pageviews show content consumption, but many conversions depend on multi-step actions inside the product. Mixpanel focuses on those actions (events), making it easier to measure onboarding, activation, checkout steps, and retention with higher clarity in Analytics<\/strong>.<\/p>\n\n\n\n

                    4) What should I track first in Mixpanel?<\/h3>\n\n\n\n

                    Start with one critical journey (signup\/onboarding, checkout, or trial-to-paid). Track each step as an event, add the minimum useful properties (channel, device, plan), and validate data quality before expanding your Conversion & Measurement<\/strong> scope.<\/p>\n\n\n\n

                    5) How do I avoid messy Analytics in Mixpanel?<\/h3>\n\n\n\n

                    Use a tracking plan, consistent event naming, documented definitions, and regular audits. Assign ownership for instrumentation changes so releases don\u2019t silently break Analytics<\/strong> and reporting.<\/p>\n\n\n\n

                    6) Can Mixpanel measure ROI from marketing campaigns?<\/h3>\n\n\n\n

                    Mixpanel can connect acquisition source properties to downstream behaviors like activation and purchase, which improves Conversion & Measurement<\/strong>. True ROI often requires joining ad spend and revenue data from other systems, but Mixpanel can provide the behavioral link between marketing and outcomes.<\/p>\n\n\n\n

                    7) What\u2019s the biggest limitation teams run into with Mixpanel?<\/h3>\n\n\n\n

                    Most issues come from implementation and governance: missing events, inconsistent properties, identity stitching problems, or unclear KPI definitions. The tool is only as reliable as the measurement design behind it, especially for Analytics<\/strong> you plan to operationalize.<\/p>\n","protected":false},"excerpt":{"rendered":"

                    Mixpanel is a product analytics platform built around event-based tracking\u2014measuring what people actually do in a website, app, or digital product and tying those behaviors to business outcomes. In **Conversion & Measurement**, Mixpanel helps teams move beyond surface-level traffic numbers to understand how users progress through onboarding, activation, engagement, retention, and purchase. Within **Analytics**, it\u2019s commonly used to answer questions like \u201cWhich actions predict conversion?\u201d and \u201cWhere do users drop off in the funnel?\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":[1887],"tags":[],"class_list":["post-6897","post","type-post","status-publish","format-standard","hentry","category-analytics"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/6897","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=6897"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/6897\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=6897"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=6897"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=6897"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}