{"id":11480,"date":"2026-04-01T23:46:18","date_gmt":"2026-04-01T23:46:18","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/video-ads-revenue-attribution\/"},"modified":"2026-04-01T23:46:18","modified_gmt":"2026-04-01T23:46:18","slug":"video-ads-revenue-attribution","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/video-ads-revenue-attribution\/","title":{"rendered":"Video Ads Revenue Attribution: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Video Ads"},"content":{"rendered":"\n

Video Ads Revenue Attribution is the discipline of connecting revenue outcomes (purchases, subscriptions, pipeline, renewals) back to the specific Video Ads<\/strong> and touchpoints that influenced a customer\u2019s decision. In Paid Marketing<\/strong>, it answers a deceptively simple question: Which video campaigns and creatives are truly generating revenue\u2014not just views or clicks?<\/em><\/p>\n\n\n\n

This matters because modern buying journeys are multi-session and multi-device, and video often influences decisions before a user is ready to convert. Without Video Ads Revenue Attribution, teams may optimize to easy-to-measure engagement metrics while underinvesting in the video efforts that actually create profit. With it, Paid Marketing<\/strong> budgets can be allocated with confidence, and Video Ads<\/strong> can be improved based on business outcomes\u2014not guesswork.<\/p>\n\n\n\n

What Is Video Ads Revenue Attribution?<\/h2>\n\n\n\n

Video Ads Revenue Attribution is the process of assigning credit for revenue to one or more Video Ads<\/strong> interactions along a customer journey. A \u201cvideo interaction\u201d can include an impression, a view (often with a defined threshold like 2 seconds or 50% watched), a click, a visit after exposure, or an assisted touchpoint that occurs before a conversion.<\/p>\n\n\n\n

At its core, Video Ads Revenue Attribution is about causal accountability<\/em>: understanding whether video exposure contributed meaningfully to revenue, how much it contributed, and where it sits among other touchpoints such as search ads, social, email, affiliates, and organic channels.<\/p>\n\n\n\n

From a business standpoint, it bridges the gap between upper-funnel influence and bottom-line performance. In Paid Marketing<\/strong>, it helps answer questions like:<\/p>\n\n\n\n

    \n
  • Which Video Ads<\/strong> campaigns drive the highest revenue per dollar spent?<\/li>\n
  • Are video views translating into incremental purchases or just awareness?<\/li>\n
  • Which audiences respond with high lifetime value (LTV), not just low CPA?<\/li>\n<\/ul>\n\n\n\n

    Within Video Ads<\/strong>, attribution is what turns \u201ccreative performance\u201d into \u201ccommercial performance,\u201d enabling smarter testing, targeting, and scaling.<\/p>\n\n\n\n

    Why Video Ads Revenue Attribution Matters in Paid Marketing<\/h2>\n\n\n\n

    Video often plays an assistive role: it creates demand, improves recall, and shortens the time-to-purchase\u2014yet it may not be the last click. Video Ads Revenue Attribution is critical in Paid Marketing<\/strong> because it:<\/p>\n\n\n\n

      \n
    • Protects high-impact spend from being cut.<\/strong> Without attribution, video can look inefficient compared to bottom-funnel channels.<\/li>\n
    • Improves budget allocation.<\/strong> It helps shift investment to campaigns that produce revenue, margin, or qualified pipeline.<\/li>\n
    • Aligns stakeholders.<\/strong> Finance, leadership, and performance teams can share a consistent view of ROI for Video Ads<\/strong>.<\/li>\n
    • Creates competitive advantage.<\/strong> Teams that measure incrementality and revenue contribution can out-optimize competitors who only optimize for cheap views or clicks.<\/li>\n
    • Enables full-funnel strategy.<\/strong> It ties awareness and consideration activity to downstream revenue, making Paid Marketing<\/strong> more strategic and less tactical.<\/li>\n<\/ul>\n\n\n\n

      How Video Ads Revenue Attribution Works<\/h2>\n\n\n\n

      In practice, Video Ads Revenue Attribution works through a measurable chain of events that connects ad exposure to revenue. A realistic workflow looks like this:<\/p>\n\n\n\n

        \n
      1. \n

        Inputs (tracking signals and events)<\/strong>\n Your systems capture video-related touchpoints (impressions, views, clicks) alongside on-site or in-app actions (product views, add-to-cart, form submits, purchases). For lead-gen, revenue may be captured later in a CRM or billing system.<\/p>\n<\/li>\n

      2. \n

        Processing (identity, matching, and model logic)<\/strong>\n Data is stitched together using identifiers (click IDs, cookies where available, device IDs, login IDs, CRM IDs). Then an attribution approach is applied\u2014single-touch (like last-click) or multi-touch (splitting credit across steps), sometimes combined with experiments to estimate incrementality.<\/p>\n<\/li>\n

      3. \n

        Application (reporting and decision-making)<\/strong>\n Marketers analyze revenue by campaign, audience, placement, creative, and funnel stage. Insights are used to change bidding, budgets, targeting, frequency caps, landing pages, and creative iterations for Video Ads<\/strong>.<\/p>\n<\/li>\n

      4. \n

        Outputs (actionable business outcomes)<\/strong>\n The result is a defensible view of ROI: revenue, profit, or pipeline attributable to video exposure\u2014used to scale winners, fix underperformers, and communicate impact to stakeholders within Paid Marketing<\/strong>.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        Because privacy, cross-device behavior, and platform differences create gaps, Video Ads Revenue Attribution is best treated as an estimation system with known uncertainty\u2014not a perfect ledger.<\/p>\n\n\n\n

        Key Components of Video Ads Revenue Attribution<\/h2>\n\n\n\n

        Strong Video Ads Revenue Attribution depends on multiple components working together:<\/p>\n\n\n\n

        Data inputs<\/h3>\n\n\n\n
          \n
        • Ad platform data:<\/strong> impressions, views, clicks, spend, placement, creative IDs<\/li>\n
        • Website\/app analytics:<\/strong> sessions, events, ecommerce transactions, engagement<\/li>\n
        • Conversion data:<\/strong> orders, subscriptions, revenue, refunds, margin (if available)<\/li>\n
        • CRM\/billing data (B2B or subscription):<\/strong> opportunities, closed-won revenue, renewal revenue<\/li>\n<\/ul>\n\n\n\n

          Tracking and identity<\/h3>\n\n\n\n
            \n
          • UTM parameters and campaign taxonomy<\/strong> for consistent naming and grouping<\/li>\n
          • Click identifiers<\/strong> to connect ad clicks to sessions and conversions<\/li>\n
          • First-party identifiers<\/strong> (logins, hashed emails, customer IDs) where appropriate<\/li>\n
          • Consent and preference management<\/strong> to stay compliant and preserve data quality<\/li>\n<\/ul>\n\n\n\n

            Attribution logic and governance<\/h3>\n\n\n\n
              \n
            • Attribution model definitions<\/strong> (what counts as a touch, how credit is assigned)<\/li>\n
            • Lookback windows<\/strong> (how long after an exposure you still credit the video touch)<\/li>\n
            • Cross-channel rules<\/strong> (how Video Ads<\/strong> interact with search, social, email, etc.)<\/li>\n
            • Ownership:<\/strong> clear responsibilities across performance marketing, analytics, and revenue ops<\/li>\n<\/ul>\n\n\n\n

              Reporting and activation<\/h3>\n\n\n\n
                \n
              • Dashboards that show revenue contribution by campaign\/creative<\/li>\n
              • Testing frameworks for creative and landing pages<\/li>\n
              • Budgeting processes that incorporate attribution outputs into Paid Marketing<\/strong> planning<\/li>\n<\/ul>\n\n\n\n

                Types of Video Ads Revenue Attribution<\/h2>\n\n\n\n

                Video Ads Revenue Attribution doesn\u2019t have one universal \u201cbest\u201d model. The right approach depends on sales cycle, conversion volume, and data access. Common approaches include:<\/p>\n\n\n\n

                Single-touch attribution<\/h3>\n\n\n\n
                  \n
                • Last-click:<\/strong> Gives full credit to the last clicked touchpoint before purchase. Often under-credits Video Ads<\/strong> that influence without clicks.<\/li>\n
                • First-click:<\/strong> Gives full credit to the first touch. Useful for measuring acquisition, but can over-credit early exposures.<\/li>\n<\/ul>\n\n\n\n

                  Multi-touch attribution (MTA)<\/h3>\n\n\n\n
                    \n
                  • Linear:<\/strong> Splits credit equally across touches.<\/li>\n
                  • Time-decay:<\/strong> Gives more credit to touches closer to conversion while still crediting earlier Video Ads<\/strong> influence.<\/li>\n
                  • Position-based:<\/strong> Assigns more credit to first and last touches and less to the middle.<\/li>\n
                  • Data-driven\/algorithmic:<\/strong> Uses observed patterns to assign credit. Quality depends on data completeness and model assumptions.<\/li>\n<\/ul>\n\n\n\n

                    Incrementality-focused approaches<\/h3>\n\n\n\n
                      \n
                    • Holdout tests \/ lift studies:<\/strong> Compare exposed vs. unexposed groups to estimate incremental revenue from Video Ads<\/strong>.<\/li>\n
                    • Geo experiments:<\/strong> Turn video on\/off by region to measure causal impact.<\/li>\n
                    • Media mix modeling (MMM):<\/strong> Uses aggregated data over time to estimate channel-level contribution, often helpful when user-level tracking is limited.<\/li>\n<\/ul>\n\n\n\n

                      In advanced Paid Marketing<\/strong> programs, teams often combine MTA (directional optimization) with incrementality testing (causal validation).<\/p>\n\n\n\n

                      Real-World Examples of Video Ads Revenue Attribution<\/h2>\n\n\n\n

                      Example 1: Ecommerce prospecting video that assists search conversions<\/h3>\n\n\n\n

                      A retail brand runs Video Ads<\/strong> to cold audiences. Last-click reporting shows low ROAS because many buyers later search the brand and convert via paid search. With Video Ads Revenue Attribution using multi-touch and view-through analysis, the team finds that users who watched 50%+ of the video convert at higher rates within 7 days. They keep prospecting video funded, tighten frequency, and shift creative toward product benefits that correlate with higher order value.<\/p>\n\n\n\n

                      Example 2: B2B product demo video driving qualified pipeline<\/h3>\n\n\n\n

                      A SaaS company runs Video Ads<\/strong> promoting a short demo teaser and retargets viewers with a \u201cbook a demo\u201d offer. The purchase happens weeks later via sales. By integrating ad touchpoints with CRM opportunity data, Video Ads Revenue Attribution shows certain industries have fewer demos but higher close rates and larger contract values. Paid Marketing<\/strong> reallocates spend toward those segments and updates creative to address industry-specific pain points.<\/p>\n\n\n\n

                      Example 3: Subscription app using creative-level revenue attribution<\/h3>\n\n\n\n

                      A subscription app tests multiple video creatives: lifestyle storytelling vs. feature-driven. Installs are similar, but cohort revenue differs significantly after 30 days. Video Ads Revenue Attribution tied to post-install revenue reveals the feature-driven creative brings higher LTV users. The team scales that creative, adjusts onboarding to reinforce the promise, and reduces churn\u2014improving payback period.<\/p>\n\n\n\n

                      Benefits of Using Video Ads Revenue Attribution<\/h2>\n\n\n\n

                      When implemented well, Video Ads Revenue Attribution delivers benefits that go beyond reporting:<\/p>\n\n\n\n

                        \n
                      • Better optimization decisions:<\/strong> Improve bidding, targeting, and creative based on revenue contribution, not just cheap traffic.<\/li>\n
                      • Higher efficiency in Paid Marketing:<\/strong> Reduce wasted spend on placements and audiences that look good on engagement but don\u2019t convert profitably.<\/li>\n
                      • Improved forecasting:<\/strong> More reliable revenue expectations from Video Ads<\/strong> campaigns, especially when combined with cohort tracking.<\/li>\n
                      • Stronger creative strategy:<\/strong> Identify which messages drive high-value customers, not merely high view rates.<\/li>\n
                      • Healthier customer experience:<\/strong> Better sequencing and frequency management reduces ad fatigue and improves relevance.<\/li>\n<\/ul>\n\n\n\n

                        Challenges of Video Ads Revenue Attribution<\/h2>\n\n\n\n

                        Video Ads Revenue Attribution is valuable, but it\u2019s not easy. Common challenges include:<\/p>\n\n\n\n

                          \n
                        • View-through ambiguity:<\/strong> A view doesn\u2019t always imply attention or influence, especially with autoplay placements.<\/li>\n
                        • Cross-device and identity gaps:<\/strong> Users may watch on one device and convert on another, reducing deterministic matching.<\/li>\n
                        • Privacy and consent constraints:<\/strong> Limits on tracking can reduce user-level visibility and increase reliance on modeled results.<\/li>\n
                        • Platform inconsistencies:<\/strong> \u201cView,\u201d \u201cengagement,\u201d and attribution windows can vary across Video Ads<\/strong> environments.<\/li>\n
                        • Misaligned incentives:<\/strong> Teams may optimize to platform-reported ROAS that doesn\u2019t match finance-reported revenue.<\/li>\n
                        • Attribution bias:<\/strong> Last-click bias can underfund video; overly generous view-through rules can over-credit it.<\/li>\n<\/ul>\n\n\n\n

                          The goal in Paid Marketing<\/strong> should be decision-grade measurement<\/em>, not perfection.<\/p>\n\n\n\n

                          Best Practices for Video Ads Revenue Attribution<\/h2>\n\n\n\n

                          Define what \u201crevenue\u201d means<\/h3>\n\n\n\n

                          Use consistent definitions across analytics and finance:\n– Gross revenue vs. net revenue\n– Refunds\/chargebacks handling\n– Subscription: first payment vs. LTV estimates<\/p>\n\n\n\n

                          Use a clean campaign taxonomy<\/h3>\n\n\n\n

                          Standardize naming for campaigns, ad sets, creatives, and audiences so attribution outputs can be compared over time.<\/p>\n\n\n\n

                          Choose attribution windows deliberately<\/h3>\n\n\n\n

                          Set view-through and click-through windows based on your cycle:\n– Short purchase cycles may warrant tighter windows\n– Longer cycles may require longer lookbacks, validated with experiments<\/p>\n\n\n\n

                          Combine methods<\/h3>\n\n\n\n

                          Use multi-touch attribution for day-to-day optimization, and incrementality tests to validate whether Video Ads<\/strong> are creating incremental revenue.<\/p>\n\n\n\n

                          Track cohort quality, not just immediate ROAS<\/h3>\n\n\n\n

                          For subscriptions and apps, tie Video Ads Revenue Attribution to:\n– 7\/30\/90-day revenue\n– churn and retention\n– payback period<\/p>\n\n\n\n

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

                          Create checks for:\n– missing UTMs or inconsistent naming\n– conversion tracking breaks\n– sudden attribution shifts after site releases or tag changes<\/p>\n\n\n\n

                          Tools Used for Video Ads Revenue Attribution<\/h2>\n\n\n\n

                          Video Ads Revenue Attribution typically uses a stack rather than a single tool. Common tool categories include:<\/p>\n\n\n\n

                            \n
                          • Ad platforms:<\/strong> Provide delivery, cost, and platform-attributed conversions for Video Ads<\/strong>.<\/li>\n
                          • Web\/app analytics tools:<\/strong> Capture on-site behavior, funnels, and ecommerce events; help compare Paid Marketing<\/strong> performance across channels.<\/li>\n
                          • Tag management systems:<\/strong> Centralize and control tracking pixels\/events and reduce deployment errors.<\/li>\n
                          • CRM systems:<\/strong> Essential for B2B revenue attribution when conversions happen through sales pipelines.<\/li>\n
                          • Data warehouses and ETL pipelines:<\/strong> Unify ad data, analytics events, and revenue tables for consistent reporting.<\/li>\n
                          • BI\/reporting dashboards:<\/strong> Make Video Ads Revenue Attribution visible to marketers and executives with shared definitions.<\/li>\n
                          • Experimentation frameworks:<\/strong> Support holdouts, geo tests, and lift measurement to validate incremental impact.<\/li>\n<\/ul>\n\n\n\n

                            The \u201cbest\u201d tooling depends on whether you need user-level stitching, offline revenue matching, or aggregated modeling.<\/p>\n\n\n\n

                            Metrics Related to Video Ads Revenue Attribution<\/h2>\n\n\n\n

                            To make Video Ads Revenue Attribution actionable, track a mix of revenue, efficiency, and video-specific engagement metrics:<\/p>\n\n\n\n

                            Revenue and ROI metrics<\/h3>\n\n\n\n
                              \n
                            • Attributed revenue<\/strong> (by campaign\/creative\/audience)<\/li>\n
                            • ROAS<\/strong> (return on ad spend), ideally with consistent revenue definitions<\/li>\n
                            • Contribution margin<\/strong> (when COGS and variable costs are available)<\/li>\n
                            • CAC and payback period<\/strong> (especially for subscription businesses)<\/li>\n
                            • Pipeline and revenue (B2B):<\/strong> opportunity value, closed-won revenue, win rate<\/li>\n<\/ul>\n\n\n\n

                              Efficiency and funnel metrics<\/h3>\n\n\n\n
                                \n
                              • CPA \/ cost per acquisition<\/strong><\/li>\n
                              • CVR<\/strong> (conversion rate) by audience and landing page<\/li>\n
                              • Assisted conversions<\/strong> involving Video Ads<\/strong><\/li>\n
                              • Incremental lift<\/strong> (from tests) where available<\/li>\n<\/ul>\n\n\n\n

                                Video-specific metrics (diagnostic, not final goals)<\/h3>\n\n\n\n
                                  \n
                                • View rate<\/strong> and completion rate<\/strong><\/li>\n
                                • Watch time<\/strong> or percent watched<\/li>\n
                                • Frequency<\/strong> and reach<\/li>\n
                                • Engaged-view conversions<\/strong> (where a platform defines them) as directional signals<\/li>\n<\/ul>\n\n\n\n

                                  The key is to treat video engagement metrics as inputs for creative iteration, while revenue metrics govern Paid Marketing<\/strong> investment decisions.<\/p>\n\n\n\n

                                  Future Trends of Video Ads Revenue Attribution<\/h2>\n\n\n\n

                                  Several shifts are reshaping Video Ads Revenue Attribution within Paid Marketing<\/strong>:<\/p>\n\n\n\n

                                    \n
                                  • More modeling, less direct tracking:<\/strong> Privacy changes are pushing teams toward aggregated measurement, MMM, and conversion modeling.<\/li>\n
                                  • AI-assisted insights:<\/strong> Automated anomaly detection, creative performance clustering, and predictive LTV modeling will make attribution more proactive.<\/li>\n
                                  • Better first-party data strategies:<\/strong> More companies will use login-based journeys, server-side event collection, and consented data to improve match rates.<\/li>\n
                                  • Creative-level accountability:<\/strong> As targeting options tighten, creative becomes a primary lever; attribution will focus more on which Video Ads<\/strong> narratives drive profit.<\/li>\n
                                  • Incrementality as a standard:<\/strong> Mature teams will routinely validate platform-reported attribution with experiments to avoid \u201cmeasurement inflation.\u201d<\/li>\n<\/ul>\n\n\n\n

                                    Video Ads Revenue Attribution vs Related Terms<\/h2>\n\n\n\n

                                    Video Ads Revenue Attribution vs ROAS reporting<\/h3>\n\n\n\n

                                    ROAS reporting is a ratio (revenue divided by spend) based on a specific attribution method. Video Ads Revenue Attribution is broader: it defines how<\/em> revenue is credited and how video touchpoints are counted, including assisted impact and incrementality considerations.<\/p>\n\n\n\n

                                    Video Ads Revenue Attribution vs Conversion tracking<\/h3>\n\n\n\n

                                    Conversion tracking records that a conversion happened and may pass it back to an ad platform. Video Ads Revenue Attribution determines which<\/em> Video Ads<\/strong> interactions get credit for that conversion and how much credit they receive.<\/p>\n\n\n\n

                                    Video Ads Revenue Attribution vs Media mix modeling (MMM)<\/h3>\n\n\n\n

                                    MMM estimates channel contribution using aggregated data over time. Video Ads Revenue Attribution is often user-level or event-level (when possible), but can incorporate MMM when user-level signals are limited. Many Paid Marketing<\/strong> teams use both: MMM for strategic budgeting and event-level attribution for tactical optimization.<\/p>\n\n\n\n

                                    Who Should Learn Video Ads Revenue Attribution<\/h2>\n\n\n\n
                                      \n
                                    • Marketers:<\/strong> To justify video spend, choose the right optimization goals, and build full-funnel Paid Marketing<\/strong> strategies.<\/li>\n
                                    • Analysts:<\/strong> To design attribution models, validate assumptions, and build trustworthy reporting for Video Ads<\/strong> impact.<\/li>\n
                                    • Agencies:<\/strong> To prove business outcomes, retain clients, and improve campaign decision-making beyond vanity metrics.<\/li>\n
                                    • Business owners and founders:<\/strong> To understand which growth investments are truly generating revenue and to avoid cutting effective video.<\/li>\n
                                    • Developers and data teams:<\/strong> To implement event tracking, data pipelines, identity resolution, and experimentation needed for reliable Video Ads Revenue Attribution.<\/li>\n<\/ul>\n\n\n\n

                                      Summary of Video Ads Revenue Attribution<\/h2>\n\n\n\n

                                      Video Ads Revenue Attribution is the practice of connecting revenue outcomes to Video Ads<\/strong> exposures and interactions across a customer journey. It matters because video often influences conversions without being the final click, and Paid Marketing<\/strong> decisions based only on last-click or engagement metrics can misallocate budget. Implemented with sound tracking, consistent definitions, and a mix of attribution models plus incrementality testing, Video Ads Revenue Attribution helps teams invest in what drives real business growth and improve video strategy with confidence.<\/p>\n\n\n\n

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

                                      1) What is Video Ads Revenue Attribution in simple terms?<\/h3>\n\n\n\n

                                      Video Ads Revenue Attribution is how you determine which Video Ads<\/strong> contributed to revenue and how much credit they should receive for purchases, subscriptions, or pipeline.<\/p>\n\n\n\n

                                      2) Should I use view-through conversions for Video Ads?<\/h3>\n\n\n\n

                                      Use them carefully. View-through can capture real influence, but it can also over-credit Video Ads<\/strong> if the window is too long or views are low-quality. Validate with incrementality tests when possible.<\/p>\n\n\n\n

                                      3) What attribution model is best for Paid Marketing video campaigns?<\/h3>\n\n\n\n

                                      There isn\u2019t one best model. Many teams use multi-touch attribution for optimization and confirm major budget decisions with lift tests or MMM\u2014especially when video is upper-funnel.<\/p>\n\n\n\n

                                      4) How do I attribute revenue from Video Ads in B2B when the sale happens offline?<\/h3>\n\n\n\n

                                      Connect ad touchpoints to leads and opportunities in your CRM using consistent identifiers and lifecycle stages. Then attribute closed-won revenue back to the Paid Marketing<\/strong> touches, including video assists.<\/p>\n\n\n\n

                                      5) Why do my Video Ads look unprofitable in last-click reports?<\/h3>\n\n\n\n

                                      Because video often creates awareness and intent that gets converted later through other channels (like search or email). Video Ads Revenue Attribution using multi-touch or incrementality measurement can reveal assisted revenue.<\/p>\n\n\n\n

                                      6) What metrics should I prioritize besides ROAS for Video Ads?<\/h3>\n\n\n\n

                                      Prioritize attributed revenue, CAC, payback period, and cohort LTV (if applicable). Use view rate and completion rate primarily to diagnose creative effectiveness, not as final success metrics.<\/p>\n\n\n\n

                                      7) How often should I review Video Ads Revenue Attribution results?<\/h3>\n\n\n\n

                                      Review weekly for optimization signals (creative, audience, placement) and monthly or quarterly for budget allocation decisions in Paid Marketing<\/strong>, especially when combining attribution with experiments.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                      Video Ads Revenue Attribution is the discipline of connecting revenue outcomes (purchases, subscriptions, pipeline, renewals) back to the specific **Video Ads** and touchpoints that influenced a customer\u2019s decision. In **Paid Marketing**, it answers a deceptively simple question: *Which video campaigns and creatives are truly generating revenue\u2014not just views or clicks?*<\/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":[1915],"tags":[],"class_list":["post-11480","post","type-post","status-publish","format-standard","hentry","category-video-ads"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/11480","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=11480"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/11480\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=11480"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=11480"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=11480"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}