{"id":6793,"date":"2026-03-23T12:47:05","date_gmt":"2026-03-23T12:47:05","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/monthly-active-users\/"},"modified":"2026-03-23T12:47:05","modified_gmt":"2026-03-23T12:47:05","slug":"monthly-active-users","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/monthly-active-users\/","title":{"rendered":"Monthly Active Users: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Analytics"},"content":{"rendered":"\n

Monthly Active Users (MAU) is one of the most widely used growth and engagement metrics in modern Conversion & Measurement<\/strong>. It answers a deceptively simple question: \u201cHow many unique people actively used our product, website, or app in the last 30 days?\u201d In Analytics<\/strong>, MAU becomes a high-level \u201cheartbeat\u201d metric that helps teams track adoption, retention, and the real size of an engaged audience\u2014not just traffic or installs.<\/p>\n\n\n\n

MAU matters because it connects marketing activity to ongoing product usage. A campaign might drive clicks and sign-ups, but Conversion & Measurement<\/strong> is incomplete if those users never return or never perform meaningful actions. When tracked well, Monthly Active Users provides a durable view of whether growth is sustainable, whether engagement is improving, and whether your acquisition channels are delivering users who actually stick.<\/p>\n\n\n\n

What Is Monthly Active Users?<\/h2>\n\n\n\n

Monthly Active Users (MAU)<\/strong> is the count of unique users<\/strong> who perform at least one qualifying activity<\/strong> within a defined monthly period (often the last 30 days or a calendar month). The acronym MAU<\/strong> is commonly used in dashboards, stakeholder updates, and investor reporting.<\/p>\n\n\n\n

The core concept is \u201cactive\u201d plus \u201cunique\u201d plus \u201cmonthly window\u201d:<\/p>\n\n\n\n

    \n
  • Active<\/strong>: the user did something you consider meaningful (not just a page load, unless that\u2019s your definition).<\/li>\n
  • Unique<\/strong>: each user is counted once, even if they act multiple times.<\/li>\n
  • Monthly<\/strong>: activity is measured within a monthly time frame.<\/li>\n<\/ul>\n\n\n\n

    From a business standpoint, Monthly Active Users represents the size of your engaged user base<\/em>. It\u2019s a key indicator for subscription products, marketplaces, content platforms, and even B2B SaaS\u2014anywhere repeat usage matters.<\/p>\n\n\n\n

    In Conversion & Measurement<\/strong>, MAU often sits between acquisition and revenue: it helps you understand whether new users become returning users, and whether existing users remain engaged. In Analytics<\/strong>, MAU is typically tracked alongside retention, cohort performance, and conversion funnels to diagnose why growth is accelerating or stalling.<\/p>\n\n\n\n

    Why Monthly Active Users Matters in Conversion & Measurement<\/h2>\n\n\n\n

    Monthly Active Users is strategically important because it turns \u201cattention\u201d into \u201cbehavior.\u201d Many marketing metrics measure exposure or intent; MAU measures continued participation.<\/p>\n\n\n\n

    Key ways MAU creates business value in Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

      \n
    • Quality control for acquisition<\/strong>: If paid campaigns increase sign-ups but MAU stays flat, you may be acquiring low-intent users or setting poor expectations in messaging.<\/li>\n
    • Early signal of retention health<\/strong>: MAU trends can indicate improving stickiness before revenue catches up, especially for freemium models.<\/li>\n
    • Better forecasting<\/strong>: MAU is a practical input for predicting trial-to-paid conversions, upsell opportunities, and support load.<\/li>\n
    • Competitive advantage<\/strong>: Teams that manage Monthly Active Users well can optimize onboarding, lifecycle messaging, and product experience\u2014making growth harder to copy.<\/li>\n<\/ul>\n\n\n\n

      In Analytics<\/strong>, MAU is also a communication tool: it\u2019s simple enough for executives, but powerful enough for analysts when broken down by channel, cohort, and segment.<\/p>\n\n\n\n

      How Monthly Active Users Works<\/h2>\n\n\n\n

      Monthly Active Users is a metric, but it only becomes reliable when you define and operationalize it. In practice, MAU \u201cworks\u201d through a clear measurement workflow:<\/p>\n\n\n\n

        \n
      1. \n

        Define the qualifying activity (\u201cactive\u201d definition)<\/strong>\n Decide what actions count. For a SaaS product, \u201cactive\u201d might mean logging in and using a core feature. For a content site, it might mean reading at least two articles or spending a minimum time engaged. The definition should reflect value, not just presence.<\/p>\n<\/li>\n

      2. \n

        Identify users consistently (\u201cunique\u201d definition)<\/strong>\n You need a durable identifier: authenticated user ID, hashed email, device ID, or a first-party cookie\u2014often a combination. The more your users switch devices, the more identity resolution matters in Analytics<\/strong>.<\/p>\n<\/li>\n

      3. \n

        Collect events and attribute context<\/strong>\n Track events (page views, feature usage, purchases) with metadata such as source\/medium, campaign, device, geography, and plan type. This is where Conversion & Measurement<\/strong> links MAU to channel performance.<\/p>\n<\/li>\n

      4. \n

        Deduplicate and aggregate in a monthly window<\/strong>\n Count distinct users who meet the \u201cactive\u201d rule within the monthly period. Decide whether you use rolling 30 days or calendar months, and keep it consistent for trend analysis.<\/p>\n<\/li>\n

      5. \n

        Use MAU to drive decisions<\/strong>\n MAU becomes actionable when you segment it (new vs returning, paid vs organic, activated vs not activated) and connect it to funnels, retention, and revenue outcomes.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        Key Components of Monthly Active Users<\/h2>\n\n\n\n

        Reliable Monthly Active Users depends on more than a single dashboard tile. The major components include:<\/p>\n\n\n\n

          \n
        • Tracking plan and event taxonomy<\/strong>: Clear definitions for \u201cactive\u201d events, naming conventions, and required properties. This is foundational to Analytics<\/strong> quality.<\/li>\n
        • Identity and user stitching<\/strong>: A strategy for matching activity across devices and sessions (authenticated IDs, consented identifiers, or probabilistic methods where appropriate).<\/li>\n
        • Data pipeline<\/strong>: Collection (client-side and\/or server-side), storage (warehouse or analytics store), and transformation (deduplication, filtering, joins).<\/li>\n
        • Governance and ownership<\/strong>: A named owner for MAU definitions, change management, and documentation to prevent \u201cmetric drift.\u201d<\/li>\n
        • Reporting and segmentation<\/strong>: Dashboards that break MAU down by acquisition source, cohort, geography, product area, and lifecycle stage.<\/li>\n
        • Quality controls<\/strong>: Bot filtering, internal traffic exclusions, anomaly detection, and event validation to keep MAU trustworthy for Conversion & Measurement<\/strong> decisions.<\/li>\n<\/ul>\n\n\n\n

          Types of Monthly Active Users<\/h2>\n\n\n\n

          Monthly Active Users doesn\u2019t have rigid \u201cofficial\u201d types, but in real-world Analytics<\/strong> you\u2019ll see important variants and distinctions:<\/p>\n\n\n\n

          1) Rolling MAU vs Calendar MAU<\/h3>\n\n\n\n
            \n
          • Rolling MAU (last 30 days)<\/strong>: Better for day-to-day monitoring and smoother trend lines.<\/li>\n
          • Calendar MAU (by month)<\/strong>: Better for monthly reporting, finance alignment, and period-over-period comparisons.<\/li>\n<\/ul>\n\n\n\n

            2) Product MAU vs Marketing MAU<\/h3>\n\n\n\n
              \n
            • Product MAU<\/strong>: Users who perform core in-product actions (feature usage).<\/li>\n
            • Marketing MAU<\/strong>: Users who engage with owned channels (site visits, content consumption, email clicks). Useful, but it can overstate \u201ctrue\u201d product engagement.<\/li>\n<\/ul>\n\n\n\n

              3) Verified MAU vs Anonymous MAU<\/h3>\n\n\n\n
                \n
              • Verified (logged-in) MAU<\/strong>: More reliable and deduplicated; best for SaaS and apps.<\/li>\n
              • Anonymous MAU<\/strong>: Common for publishers; harder to deduplicate and more sensitive to privacy and cookie changes.<\/li>\n<\/ul>\n\n\n\n

                4) Total MAU vs Segment MAU<\/h3>\n\n\n\n

                Segment MAU (by channel, plan, cohort, region) is often where Conversion & Measurement<\/strong> insights appear\u2014like identifying which campaigns produce high-retention users.<\/p>\n\n\n\n

                Real-World Examples of Monthly Active Users<\/h2>\n\n\n\n

                Example 1: SaaS onboarding and activation<\/h3>\n\n\n\n

                A B2B SaaS company defines Monthly Active Users as \u201clogged in and used Feature X at least once in the last 30 days.\u201d Marketing improves top-of-funnel leads, but MAU lags. Analytics<\/strong> reveals many new sign-ups never reach the setup step. The team updates onboarding emails, adds in-app guidance, and adjusts ad messaging to set clearer expectations. MAU rises, and downstream trial-to-paid conversion improves\u2014tightening Conversion & Measurement<\/strong> across the funnel.<\/p>\n\n\n\n

                Example 2: Ecommerce retention and lifecycle campaigns<\/h3>\n\n\n\n

                An ecommerce brand tracks Monthly Active Users as \u201cvisited site and viewed at least 3 product pages or placed an order within 30 days.\u201d They segment MAU by acquisition channel and find that affiliate traffic produces high first-purchase volume but low returning MAU. They shift budget toward channels with stronger repeat engagement and introduce post-purchase sequences to lift returning MAU\u2014improving marketing efficiency and retention-focused Analytics<\/strong>.<\/p>\n\n\n\n

                Example 3: Content platform and subscription growth<\/h3>\n\n\n\n

                A content publisher defines Monthly Active Users as \u201cread 2+ articles and spent 60+ seconds engaged.\u201d They test a new newsletter strategy. MAU increases, but paid subscriptions don\u2019t. By tying MAU segments to conversion funnels in Conversion & Measurement<\/strong>, they discover the newsletter boosts casual readers, not high-intent topics. They refine newsletter content toward subscription-driving categories and track MAU by content cluster.<\/p>\n\n\n\n

                Benefits of Using Monthly Active Users<\/h2>\n\n\n\n

                When defined and used well, Monthly Active Users delivers practical benefits:<\/p>\n\n\n\n

                  \n
                • Sharper performance diagnosis<\/strong>: MAU helps separate \u201ctraffic spikes\u201d from genuine engagement growth in Analytics<\/strong>.<\/li>\n
                • Improved marketing efficiency<\/strong>: Teams can optimize spend toward channels that grow sustainable users, not just one-time visitors\u2014strengthening Conversion & Measurement<\/strong>.<\/li>\n
                • Better retention and lifecycle strategy<\/strong>: MAU trends highlight churn risk and reactivation opportunities.<\/li>\n
                • Clearer product-market signals<\/strong>: Rising MAU often indicates improving product value delivery, especially when paired with activation and retention metrics.<\/li>\n
                • More consistent stakeholder reporting<\/strong>: MAU is easy to communicate and can unify marketing, product, and leadership around one engagement baseline.<\/li>\n<\/ul>\n\n\n\n

                  Challenges of Monthly Active Users<\/h2>\n\n\n\n

                  Monthly Active Users can mislead if the measurement foundation is weak. Common challenges include:<\/p>\n\n\n\n

                    \n
                  • Ambiguous \u201cactive\u201d definitions<\/strong>: Counting logins or page views may inflate MAU without reflecting real value.<\/li>\n
                  • Identity fragmentation<\/strong>: Users switching devices, clearing cookies, or using private browsing can cause double-counting\u2014especially in web-focused Analytics<\/strong>.<\/li>\n
                  • Privacy and consent constraints<\/strong>: Reduced identifier availability can lower observed MAU, even if actual usage is stable, complicating Conversion & Measurement<\/strong> comparisons over time.<\/li>\n
                  • Bot and internal traffic pollution<\/strong>: Unfiltered traffic can distort MAU, particularly for content sites.<\/li>\n
                  • Metric drift<\/strong>: Teams change event instrumentation or definitions without updating dashboards and documentation, making trend lines unreliable.<\/li>\n
                  • Seasonality and campaign effects<\/strong>: Promotions can temporarily lift MAU; without cohort views, it\u2019s hard to tell if you\u2019ve increased lasting engagement.<\/li>\n<\/ul>\n\n\n\n

                    Best Practices for Monthly Active Users<\/h2>\n\n\n\n

                    To make Monthly Active Users a metric you can trust and act on:<\/p>\n\n\n\n

                      \n
                    1. \n

                      Define \u201cactive\u201d around value, not convenience<\/strong>\n Choose actions that represent meaningful engagement (core feature usage, repeat consumption, purchase intent). Revisit the definition annually, not weekly.<\/p>\n<\/li>\n

                    2. \n

                      Document MAU like a product requirement<\/strong>\n Include: qualifying events, identifier logic, exclusions, time window, and known limitations. This reduces confusion across Conversion & Measurement<\/strong> stakeholders.<\/p>\n<\/li>\n

                    3. \n

                      Use rolling MAU for monitoring, calendar MAU for reporting<\/strong>\n Keep both if needed, but label them clearly to avoid misinterpretation in Analytics<\/strong>.<\/p>\n<\/li>\n

                    4. \n

                      Always segment MAU<\/strong>\n Minimum useful segments:\n – new vs returning\n – by acquisition source (paid, organic, referral, email)\n – by cohort (signup month)\n – by plan tier or customer type (B2B vs B2C)<\/p>\n<\/li>\n

                    5. \n

                      Pair MAU with retention and activation<\/strong>\n MAU alone is a volume metric. Combine it with:\n – activation rate (reached \u201caha\u201d moment)\n – retention curves (week\/month retention)\n – engagement depth (events per active user)<\/p>\n<\/li>\n

                    6. \n

                      Implement data quality checks<\/strong>\n Validate event volume, watch for breaks after releases, filter bots, and exclude employees. This keeps MAU stable enough for Conversion & Measurement<\/strong> decisions.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                      Tools Used for Monthly Active Users<\/h2>\n\n\n\n

                      Monthly Active Users is usually produced by a measurement stack rather than a single tool. Common tool categories include:<\/p>\n\n\n\n

                        \n
                      • Analytics tools<\/strong>: Web\/app event tracking, user identification, segmentation, funnels, and cohort analysis to compute MAU and diagnose drivers.<\/li>\n
                      • Product analytics platforms<\/strong>: Strong for event-based MAU definitions (feature usage), retention curves, and behavioral cohorts.<\/li>\n
                      • Tag management systems<\/strong>: Help standardize event collection and reduce deployment friction, improving Analytics<\/strong> consistency.<\/li>\n
                      • Data warehouses and transformation layers<\/strong>: Centralize raw events, enable consistent MAU logic, and support governance for Conversion & Measurement<\/strong> reporting.<\/li>\n
                      • BI and reporting dashboards<\/strong>: Turn MAU into role-specific views (exec summaries, growth dashboards, channel reports).<\/li>\n
                      • CRM systems and marketing automation<\/strong>: Activate MAU insights with lifecycle campaigns (onboarding, reactivation, upsell).<\/li>\n
                      • SEO and content tools<\/strong>: Indirectly support MAU growth by improving discoverability and aligning content with engaged user segments.<\/li>\n<\/ul>\n\n\n\n

                        Metrics Related to Monthly Active Users<\/h2>\n\n\n\n

                        Monthly Active Users is most useful when connected to adjacent indicators:<\/p>\n\n\n\n

                          \n
                        • DAU\/MAU ratio (stickiness)<\/strong>: Indicates how frequently monthly users return. Higher ratios usually imply stronger habit formation.<\/li>\n
                        • WAU (Weekly Active Users)<\/strong>: Helpful middle ground for products with weekly usage cycles.<\/li>\n
                        • Activation rate<\/strong>: Percent of new users who reach a defined \u201caha\u201d milestone; often predicts future MAU growth.<\/li>\n
                        • Retention rate (cohort retention)<\/strong>: The share of a signup cohort active in later weeks\/months\u2014core to Analytics<\/strong> maturity.<\/li>\n
                        • Churn (user churn or customer churn)<\/strong>: Declines in MAU can foreshadow churn; customer churn ties directly to revenue.<\/li>\n
                        • Engagement depth<\/strong>: Events per active user, sessions per user, time spent, feature adoption\u2014prevents \u201cshallow MAU.\u201d<\/li>\n
                        • Conversion rate per active user<\/strong>: Purchases, upgrades, leads, or renewals divided by MAU; strengthens Conversion & Measurement<\/strong> alignment.<\/li>\n
                        • CAC payback and LTV<\/strong>: MAU quality influences downstream monetization, impacting unit economics.<\/li>\n<\/ul>\n\n\n\n

                          Future Trends of Monthly Active Users<\/h2>\n\n\n\n

                          Monthly Active Users is evolving as measurement norms and user expectations change:<\/p>\n\n\n\n

                            \n
                          • AI-assisted segmentation and anomaly detection<\/strong>: Analytics<\/strong> teams increasingly rely on automated insights to explain MAU changes (release impacts, channel mix shifts, seasonality).<\/li>\n
                          • Privacy-driven measurement redesign<\/strong>: As identifiers become less available, organizations move toward first-party data, consent-aware tracking, and modeled measurement. MAU reporting will increasingly include known limitations and confidence ranges.<\/li>\n
                          • Server-side and event standardization<\/strong>: More teams implement server-side event capture for reliability, reducing client-side loss and improving Conversion & Measurement<\/strong> consistency.<\/li>\n
                          • Personalization tied to engagement<\/strong>: MAU growth strategies will lean on personalized onboarding, recommendations, and lifecycle messaging\u2014using MAU segments as inputs.<\/li>\n
                          • From \u201ccounts\u201d to \u201cquality\u201d<\/strong>: Stakeholders will ask not only \u201cHow many Monthly Active Users?\u201d but \u201cHow many high-intent<\/em> active users?\u201d Expect more emphasis on qualified MAU definitions.<\/li>\n<\/ul>\n\n\n\n

                            Monthly Active Users vs Related Terms<\/h2>\n\n\n\n

                            Understanding MAU is easier when contrasted with adjacent metrics:<\/p>\n\n\n\n

                            Monthly Active Users vs Daily Active Users (DAU)<\/h3>\n\n\n\n
                              \n
                            • DAU<\/strong> counts unique active users per day; it\u2019s sensitive to day-to-day fluctuations.<\/li>\n
                            • Monthly Active Users<\/strong> smooths variability and captures broader engagement. DAU is better for daily habits; MAU is better for overall reach and retention health.<\/li>\n<\/ul>\n\n\n\n

                              Monthly Active Users vs Weekly Active Users (WAU)<\/h3>\n\n\n\n
                                \n
                              • WAU<\/strong> fits products used weekly (planning tools, B2B workflows).<\/li>\n
                              • MAU<\/strong> is better for month-level reporting and longer engagement cycles. In Conversion & Measurement<\/strong>, WAU can reveal short-term retention changes faster than MAU.<\/li>\n<\/ul>\n\n\n\n

                                Monthly Active Users vs Sessions \/ Pageviews<\/h3>\n\n\n\n
                                  \n
                                • Sessions\/pageviews<\/strong> measure activity volume, not unique engaged people.<\/li>\n
                                • Monthly Active Users<\/strong> focuses on unique users meeting an \u201cactive\u201d threshold, making it more comparable over time in Analytics<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                                  Who Should Learn Monthly Active Users<\/h2>\n\n\n\n

                                  Monthly Active Users is worth learning because it connects marketing actions to sustained engagement:<\/p>\n\n\n\n

                                    \n
                                  • Marketers<\/strong>: Use MAU to evaluate channel quality, lifecycle performance, and true growth beyond clicks\u2014critical for Conversion & Measurement<\/strong>.<\/li>\n
                                  • Analysts<\/strong>: Need strong MAU definitions, segmentation, and cohort work to diagnose retention and engagement issues in Analytics<\/strong>.<\/li>\n
                                  • Agencies<\/strong>: MAU helps prove impact past acquisition, especially for content, SEO, and lifecycle marketing engagements.<\/li>\n
                                  • Business owners and founders<\/strong>: MAU is a practical indicator of traction and product-market fit signals.<\/li>\n
                                  • Developers and product teams<\/strong>: Implement event tracking, identity logic, and data quality controls that make Monthly Active Users reliable.<\/li>\n<\/ul>\n\n\n\n

                                    Summary of Monthly Active Users<\/h2>\n\n\n\n

                                    Monthly Active Users (MAU)<\/strong> measures how many unique users perform a meaningful action within a month. It matters because it reflects sustained engagement, not just exposure or one-time visits. In Conversion & Measurement<\/strong>, Monthly Active Users bridges acquisition, activation, retention, and monetization\u2014helping teams optimize for long-term outcomes. In Analytics<\/strong>, MAU becomes a foundational metric that supports segmentation, cohorts, and strategy decisions when it\u2019s defined carefully and governed consistently.<\/p>\n\n\n\n

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

                                    1) What\u2019s the best definition of Monthly Active Users for my business?<\/h3>\n\n\n\n

                                    Use a definition tied to delivered value. For a SaaS product, it\u2019s often a core feature action; for content, it may be engaged reading. Avoid definitions that count accidental or low-intent activity unless that truly represents value.<\/p>\n\n\n\n

                                    2) Should I use rolling 30-day MAU or calendar-month MAU?<\/h3>\n\n\n\n

                                    Use rolling MAU for operational monitoring and faster signal detection. Use calendar MAU for reporting periods and executive summaries. If you track both, label them clearly in Analytics<\/strong> dashboards.<\/p>\n\n\n\n

                                    3) How does MAU support Conversion & Measurement decisions?<\/h3>\n\n\n\n

                                    MAU helps you see whether acquisition channels produce users who return and engage. It improves budget allocation, lifecycle optimization, and funnel analysis by connecting campaigns to ongoing behavior in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                    4) What causes Monthly Active Users to spike or drop unexpectedly?<\/h3>\n\n\n\n

                                    Common causes include campaign bursts, seasonality, tracking changes, consent rate shifts, bot traffic, product releases, and outages. Pair MAU with cohort retention and event volume checks to isolate the driver.<\/p>\n\n\n\n

                                    5) How is MAU different from sessions or website traffic?<\/h3>\n\n\n\n

                                    Traffic metrics count visits and activity volume; MAU counts unique users meeting an \u201cactive\u201d rule. In Analytics<\/strong>, MAU is often better for understanding the size of your engaged audience, while sessions help measure intensity.<\/p>\n\n\n\n

                                    6) What is a good DAU\/MAU ratio?<\/h3>\n\n\n\n

                                    It depends on the usage pattern. Daily habit products tend to have higher DAU\/MAU, while monthly workflows naturally have lower ratios. Use it as a trend metric and segment it by cohort and channel for more meaningful Conversion & Measurement<\/strong> insight.<\/p>\n\n\n\n

                                    7) How can I improve MAU without increasing ad spend?<\/h3>\n\n\n\n

                                    Focus on activation and retention: better onboarding, clearer messaging alignment, improved core workflows, reactivation campaigns, and personalization. Increasing the percentage of users who return often lifts Monthly Active Users more sustainably than acquiring more one-time visitors.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                    Monthly Active Users (MAU) is one of the most widely used growth and engagement metrics in modern **Conversion & Measurement**. It answers a deceptively simple question: \u201cHow many unique people actively used our product, website, or app in the last 30 days?\u201d In **Analytics**, MAU becomes a high-level \u201cheartbeat\u201d metric that helps teams track adoption, retention, and the real size of an engaged audience\u2014not just traffic or installs.<\/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-6793","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\/6793","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=6793"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/6793\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=6793"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=6793"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=6793"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}