{"id":10698,"date":"2026-03-29T19:39:43","date_gmt":"2026-03-29T19:39:43","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/completion-lift\/"},"modified":"2026-03-29T19:39:43","modified_gmt":"2026-03-29T19:39:43","slug":"completion-lift","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/completion-lift\/","title":{"rendered":"Completion Lift: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Programmatic Advertising"},"content":{"rendered":"\n

Completion Lift is a measurement concept used in Paid Marketing<\/strong>\u2014especially in Programmatic Advertising<\/strong>\u2014to quantify how much a paid exposure increases the likelihood that a user completes a desired action<\/em> compared with a reasonable baseline. Depending on the campaign, that \u201ccompletion\u201d might be finishing a video ad, completing a lead form, completing an onboarding flow, or completing a purchase funnel step.<\/p>\n\n\n\n

Modern Paid Marketing<\/strong> is optimized for outcomes, not just impressions. Completion Lift matters because it shifts evaluation from \u201cDid we get views?\u201d to \u201cDid the ad cause more people to finish<\/em> what we wanted them to finish?\u201d In Programmatic Advertising<\/strong>, where targeting, bidding, and creative rotation happen at scale, Completion Lift gives teams a practical way to judge incremental impact and to allocate budget toward placements and audiences that actually move users to completion.<\/p>\n\n\n\n

\n\n\n\n

What Is Completion Lift?<\/h2>\n\n\n\n

Completion Lift<\/strong> is the incremental increase in completion rate attributable to a marketing exposure, compared with a baseline such as an unexposed control group, a pre-campaign period, or a modeled counterfactual. In simple terms:<\/p>\n\n\n\n

    \n
  • If 10% of unexposed users complete an action, and <\/li>\n
  • 12% of exposed users complete the same action, <\/li>\n
  • then the Completion Lift is the incremental improvement (often expressed as a percentage or percentage points).<\/li>\n<\/ul>\n\n\n\n

    The core concept is incrementality<\/em>: separating what would have happened anyway from what happened because of the ad. This is especially important in Paid Marketing<\/strong>, where last-click attribution can over-credit ads that merely appear late in the journey.<\/p>\n\n\n\n

    From a business perspective, Completion Lift answers a practical question: \u201cDid this spend increase completed outcomes?\u201d<\/strong> In Programmatic Advertising<\/strong>, it fits alongside metrics like viewability, completion rate, and conversion rate, but it focuses specifically on the incremental change driven by exposure\u2014helping teams defend budget, optimize toward quality, and reduce wasted spend.<\/p>\n\n\n\n

    \n\n\n\n

    Why Completion Lift Matters in Paid Marketing<\/h2>\n\n\n\n

    Completion Lift is strategically important because it connects paid activity to measurable behavioral improvement. In Paid Marketing<\/strong>, many campaigns generate engagement signals that look good on reports but don\u2019t translate into finished actions that drive revenue or qualified pipeline.<\/p>\n\n\n\n

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

      \n
    • Budget efficiency:<\/strong> It helps prioritize spend toward tactics that increase completions<\/em>, not just interactions.<\/li>\n
    • Outcome clarity:<\/strong> It frames performance in terms stakeholders understand\u2014completed views, completed forms, completed purchases.<\/li>\n
    • Optimization signal quality:<\/strong> In Programmatic Advertising<\/strong>, optimization can chase cheap impressions or easy clicks. Completion Lift pushes optimization toward meaningful progress through the funnel.<\/li>\n
    • Competitive advantage:<\/strong> Teams that measure incrementality make better decisions about targeting, creative, and frequency\u2014often outperforming competitors who rely on surface-level metrics.<\/li>\n<\/ul>\n\n\n\n

      In short, Completion Lift elevates Paid Marketing<\/strong> from \u201cbuying media\u201d to \u201cengineering outcomes.\u201d<\/p>\n\n\n\n

      \n\n\n\n

      How Completion Lift Works<\/h2>\n\n\n\n

      Completion Lift is more of a measurement and decision framework than a single button you press. In practice, it follows a workflow that turns exposure data into incremental insight.<\/p>\n\n\n\n

        \n
      1. \n

        Input \/ Trigger: define the completion event and exposure<\/strong>\n – Choose a clear completion event (e.g., 100% video completion, form submit, checkout completion, app signup completion).\n – Define what counts as \u201cexposed\u201d (e.g., served impression, viewable impression, completed view, click).<\/p>\n<\/li>\n

      2. \n

        Analysis \/ Processing: build a baseline<\/strong>\n Common baselines include:\n – Control vs exposed tests:<\/strong> Randomly hold out a portion of the audience from ads.\n – Matched groups:<\/strong> Pair exposed users with similar unexposed users (by geography, device, propensity, or behavior).\n – Pre\/post with safeguards:<\/strong> Compare performance before and after launch, adjusting for seasonality and other campaigns.<\/p>\n<\/li>\n

      3. \n

        Execution \/ Application: calculate lift and interpret<\/strong>\n – Measure completion rate for exposed vs baseline.\n – Compute incremental difference and, when possible, statistical confidence.\n – Segment results by supply source, creative, audience, and frequency\u2014common levers in Programmatic Advertising<\/strong>.<\/p>\n<\/li>\n

      4. \n

        Output \/ Outcome: optimize and reallocate<\/strong>\n – Shift spend to placements, audiences, or creative variants that show higher Completion Lift.\n – Cap frequency where lift declines.\n – Adjust bidding strategies to favor environments driving incremental completions.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        The result is a tighter loop between measurement and action inside Paid Marketing<\/strong> operations.<\/p>\n\n\n\n

        \n\n\n\n

        Key Components of Completion Lift<\/h2>\n\n\n\n

        Effective Completion Lift measurement depends on the right foundations across data, process, and governance.<\/p>\n\n\n\n

        Data inputs<\/h3>\n\n\n\n
          \n
        • Ad exposure logs:<\/strong> impression timestamps, placement, device, geography, frequency.<\/li>\n
        • Event tracking:<\/strong> completion events recorded in analytics or server-side logs.<\/li>\n
        • Identity and matching:<\/strong> cookies, device IDs, hashed identifiers, or contextual proxies (privacy-dependent).<\/li>\n
        • Metadata:<\/strong> creative ID, format, duration, viewability signals, content category.<\/li>\n<\/ul>\n\n\n\n

          Systems and processes<\/h3>\n\n\n\n
            \n
          • Experiment design:<\/strong> holdouts, geo tests, or matched-market methods.<\/li>\n
          • Attribution and incrementality logic:<\/strong> rules to avoid double-counting and bias.<\/li>\n
          • Segmentation:<\/strong> breakdowns by audience and inventory to make lift actionable.<\/li>\n<\/ul>\n\n\n\n

            Governance and responsibilities<\/h3>\n\n\n\n
              \n
            • Marketing owners:<\/strong> define what \u201ccompletion\u201d means for the business.<\/li>\n
            • Analysts \/ measurement:<\/strong> ensure valid baselines and bias controls.<\/li>\n
            • Ad ops \/ programmatic traders:<\/strong> implement holdouts, frequency caps, and supply controls.<\/li>\n
            • Privacy\/legal:<\/strong> confirm compliant data usage, consent handling, and retention policies.<\/li>\n<\/ul>\n\n\n\n

              In Programmatic Advertising<\/strong>, these components determine whether Completion Lift becomes a reliable decision signal or a misleading number.<\/p>\n\n\n\n

              \n\n\n\n

              Types of Completion Lift<\/h2>\n\n\n\n

              Completion Lift doesn\u2019t have a single universal taxonomy, but there are common and useful distinctions based on what\u2019s being \u201ccompleted\u201d and how lift is measured.<\/p>\n\n\n\n

              By completion event<\/h3>\n\n\n\n
                \n
              • Video Completion Lift:<\/strong> incremental change in video completion rate (e.g., 100% completion).<\/li>\n
              • Funnel Step Completion Lift:<\/strong> lift in completing a key step (e.g., product page \u2192 checkout, signup \u2192 onboarding step 3).<\/li>\n
              • Conversion Completion Lift:<\/strong> lift in completed purchases or completed lead submissions.<\/li>\n<\/ul>\n\n\n\n

                By measurement approach<\/h3>\n\n\n\n
                  \n
                • Randomized holdout lift:<\/strong> strongest for causality, often used in Paid Marketing<\/strong> experiments.<\/li>\n
                • Matched-control lift:<\/strong> practical when holdouts are difficult; requires careful bias checks.<\/li>\n
                • Geo lift \/ market-level lift:<\/strong> compares regions with and without exposure; common when user-level identity is limited.<\/li>\n<\/ul>\n\n\n\n

                  By time horizon<\/h3>\n\n\n\n
                    \n
                  • Immediate lift:<\/strong> same-session or same-day completion changes.<\/li>\n
                  • Lagged lift:<\/strong> completions occurring days later (important for consideration cycles in Programmatic Advertising<\/strong>).<\/li>\n<\/ul>\n\n\n\n
                    \n\n\n\n

                    Real-World Examples of Completion Lift<\/h2>\n\n\n\n

                    Example 1: Video ads optimized beyond completion rate<\/h3>\n\n\n\n

                    A streaming brand runs programmatic video across multiple supply sources. Raw completion rate looks highest on low-cost inventory, but an incrementality test shows those completions would have occurred anyway among high-intent users. Another supply source has a slightly lower completion rate but produces higher Completion Lift<\/strong> among new prospects. The team reallocates budget and tightens frequency caps\u2014improving incremental completed signups while keeping CPM stable.<\/p>\n\n\n\n

                    Example 2: Lead form completion for B2B<\/h3>\n\n\n\n

                    A B2B SaaS company runs display and native ads to a lead form. Click-through rate increases after a creative refresh, but form completion doesn\u2019t. By measuring Completion Lift on form completion<\/em>, the team finds that one message attracts low-quality clicks that abandon the form. They shift toward creatives that pre-qualify (pricing, integration requirements), resulting in fewer clicks but higher incremental completed forms\u2014more efficient Paid Marketing<\/strong> spend.<\/p>\n\n\n\n

                    Example 3: Retail checkout completion in Programmatic Advertising<\/h3>\n\n\n\n

                    A retailer uses retargeting for cart abandoners. Last-click reports show strong ROAS, but a holdout reveals many users would return organically. Completion Lift is modest at high frequency and strongest at low frequency within 24 hours of abandonment. The team reduces frequency, shortens retargeting windows, and expands prospecting\u2014maintaining revenue while lowering wasted impressions in Programmatic Advertising<\/strong>.<\/p>\n\n\n\n

                    \n\n\n\n

                    Benefits of Using Completion Lift<\/h2>\n\n\n\n

                    When applied correctly, Completion Lift improves both performance and decision quality.<\/p>\n\n\n\n

                      \n
                    • Better performance optimization:<\/strong> You optimize toward incremental completions rather than vanity engagement.<\/li>\n
                    • Cost savings:<\/strong> Spend shifts away from inventory that looks good but doesn\u2019t change outcomes.<\/li>\n
                    • More efficient funnels:<\/strong> Identifies where paid exposure helps users finish steps (or where it doesn\u2019t).<\/li>\n
                    • Improved audience experience:<\/strong> Reduces overexposure and irrelevant retargeting when lift flattens.<\/li>\n
                    • Stronger stakeholder confidence:<\/strong> Provides a credible causal narrative for Paid Marketing<\/strong> impact.<\/li>\n<\/ul>\n\n\n\n
                      \n\n\n\n

                      Challenges of Completion Lift<\/h2>\n\n\n\n

                      Completion Lift is powerful, but it\u2019s easy to get wrong without methodological discipline.<\/p>\n\n\n\n

                        \n
                      • Selection bias:<\/strong> Exposed users can be inherently more likely to complete, inflating lift if controls aren\u2019t comparable.<\/li>\n
                      • Identity and privacy constraints:<\/strong> Cookie loss, OS restrictions, and consent limitations reduce match rates and measurement precision.<\/li>\n
                      • Cross-channel interference:<\/strong> Email, search, or offline factors can influence completions during the test window.<\/li>\n
                      • Small sample sizes:<\/strong> Lift is often subtle; insufficient volume leads to noisy results.<\/li>\n
                      • Definition drift:<\/strong> \u201cCompletion\u201d can be ambiguous (video completion vs viewability vs attention), leading to inconsistent reporting.<\/li>\n
                      • Operational friction:<\/strong> Implementing holdouts or geo tests in Programmatic Advertising<\/strong> can be complex and requires coordination.<\/li>\n<\/ul>\n\n\n\n

                        These challenges don\u2019t negate Completion Lift\u2014they just require thoughtful design and transparent interpretation.<\/p>\n\n\n\n

                        \n\n\n\n

                        Best Practices for Completion Lift<\/h2>\n\n\n\n

                        To make Completion Lift actionable and trustworthy in Paid Marketing<\/strong>, focus on measurement rigor and optimization discipline.<\/p>\n\n\n\n

                          \n
                        1. \n

                          Define completion events precisely<\/strong>\n – Use unambiguous tracking rules and consistent attribution windows.\n – Align \u201ccompletion\u201d with business value (e.g., qualified lead submit vs any submit).<\/p>\n<\/li>\n

                        2. \n

                          Prefer randomized holdouts when possible<\/strong>\n – Even small holdouts can dramatically improve causal confidence.<\/p>\n<\/li>\n

                        3. \n

                          Segment early, but decide late<\/strong>\n – Explore lift by creative, audience, and placement\u2014but avoid overreacting to tiny segments with high variance.<\/p>\n<\/li>\n

                        4. \n

                          Control frequency intentionally<\/strong>\n – Measure lift by frequency bucket; many campaigns show diminishing returns after a threshold.<\/p>\n<\/li>\n

                        5. \n

                          Account for time-to-complete<\/strong>\n – Use appropriate lookback windows; don\u2019t judge lift too early for longer consideration cycles.<\/p>\n<\/li>\n

                        6. \n

                          Validate tracking and data quality<\/strong>\n – Confirm event deduplication, consistent timestamps, and stable tagging across devices and browsers.<\/p>\n<\/li>\n

                        7. \n

                          Connect lift to financial outcomes<\/strong>\n – Translate incremental completions into incremental revenue, margin, or pipeline to guide scaling decisions in Programmatic Advertising<\/strong>.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                          \n\n\n\n

                          Tools Used for Completion Lift<\/h2>\n\n\n\n

                          Completion Lift is typically enabled by a stack of measurement and activation tools rather than a single product.<\/p>\n\n\n\n

                            \n
                          • Ad platforms and programmatic systems:<\/strong> DSPs, ad servers, and supply-path tools to manage exposure, frequency, and audience controls in Programmatic Advertising<\/strong>.<\/li>\n
                          • Analytics tools:<\/strong> web\/app analytics to track completion events, funnels, and cohorts.<\/li>\n
                          • Experimentation frameworks:<\/strong> systems to manage holdouts, geo tests, and statistical evaluation.<\/li>\n
                          • Data warehouses and ETL pipelines:<\/strong> unify exposure logs and completion events at scale.<\/li>\n
                          • Attribution and measurement platforms:<\/strong> to reconcile multi-touch paths and support incrementality analysis.<\/li>\n
                          • Reporting dashboards:<\/strong> operationalize Completion Lift with segment views and alerts for changes over time.<\/li>\n
                          • CRM systems (when relevant):<\/strong> to tie completed leads to downstream quality (SQLs, opportunities, revenue) in Paid Marketing<\/strong> reporting.<\/li>\n<\/ul>\n\n\n\n

                            The key is interoperability: you need consistent IDs or matching logic to connect exposures to completions in a privacy-compliant way.<\/p>\n\n\n\n

                            \n\n\n\n

                            Metrics Related to Completion Lift<\/h2>\n\n\n\n

                            Completion Lift is best understood alongside supporting metrics that explain why<\/em> lift rises or falls.<\/p>\n\n\n\n

                            Completion and funnel metrics<\/h3>\n\n\n\n
                              \n
                            • Completion rate:<\/strong> percentage who complete the defined action.<\/li>\n
                            • Step-to-step conversion rate:<\/strong> drop-off between funnel stages.<\/li>\n
                            • Time to completion:<\/strong> how long it takes users to finish after exposure.<\/li>\n<\/ul>\n\n\n\n

                              Media quality and delivery metrics<\/h3>\n\n\n\n
                                \n
                              • Viewability rate:<\/strong> whether ads were actually viewable (critical for video-related Completion Lift).<\/li>\n
                              • Video completion rate (VCR):<\/strong> percent who finish the video (distinct from lift).<\/li>\n
                              • Frequency and reach:<\/strong> used to interpret diminishing lift.<\/li>\n<\/ul>\n\n\n\n

                                Incrementality and efficiency metrics<\/h3>\n\n\n\n
                                  \n
                                • Incremental completions:<\/strong> exposed completions minus expected baseline completions.<\/li>\n
                                • Cost per incremental completion:<\/strong> spend divided by incremental completed actions.<\/li>\n
                                • Incremental ROAS \/ incremental CPA:<\/strong> ties lift to financial performance.<\/li>\n<\/ul>\n\n\n\n

                                  Quality and brand-adjacent signals (context-dependent)<\/h3>\n\n\n\n
                                    \n
                                  • Attention proxies:<\/strong> on-screen time, audibility, or engagement (interpret carefully).<\/li>\n
                                  • Post-completion quality:<\/strong> lead quality, cancellation rates, or refund rates for \u201ccompleted purchase\u201d actions.<\/li>\n<\/ul>\n\n\n\n
                                    \n\n\n\n

                                    Future Trends of Completion Lift<\/h2>\n\n\n\n

                                    Completion Lift is evolving as Paid Marketing<\/strong> measurement adapts to automation, AI, and privacy change.<\/p>\n\n\n\n

                                      \n
                                    • More modeled incrementality:<\/strong> As user-level tracking becomes harder, probabilistic and aggregated methods will play a bigger role\u2014especially in Programmatic Advertising<\/strong>.<\/li>\n
                                    • AI-assisted experimentation:<\/strong> Automated test design, anomaly detection, and faster readouts will reduce the operational burden of lift studies.<\/li>\n
                                    • Creative personalization tied to completion:<\/strong> Dynamic creative will increasingly optimize not just clicks, but downstream completed actions (e.g., completed onboarding).<\/li>\n
                                    • Shift from single-event success to journey completion:<\/strong> More brands will define completion as \u201ccompleted a meaningful sequence,\u201d not a single conversion.<\/li>\n
                                    • Privacy-first measurement:<\/strong> Consent-driven data, clean-room-like workflows, and aggregate reporting will influence how Completion Lift is computed and communicated.<\/li>\n<\/ul>\n\n\n\n

                                      Teams that build robust incrementality habits now will be better positioned as Paid Marketing<\/strong> becomes less reliant on deterministic user tracking.<\/p>\n\n\n\n

                                      \n\n\n\n

                                      Completion Lift vs Related Terms<\/h2>\n\n\n\n

                                      Completion Lift vs Completion Rate<\/h3>\n\n\n\n
                                        \n
                                      • Completion rate<\/strong> is a raw metric (e.g., 35% completed the video).<\/li>\n
                                      • Completion Lift<\/strong> measures the incremental change<\/em> attributable to exposure. A placement can have a high completion rate but low lift if those users would have completed anyway.<\/li>\n<\/ul>\n\n\n\n

                                        Completion Lift vs Conversion Lift<\/h3>\n\n\n\n
                                          \n
                                        • Conversion Lift<\/strong> typically refers to incremental conversions (often purchases or leads).<\/li>\n
                                        • Completion Lift<\/strong> is broader and can apply to non-revenue completions (video completion, funnel-step completion, onboarding completion). In Paid Marketing<\/strong>, both can be used together to connect upper-funnel progress to lower-funnel outcomes.<\/li>\n<\/ul>\n\n\n\n

                                          Completion Lift vs Brand Lift<\/h3>\n\n\n\n
                                            \n
                                          • Brand lift<\/strong> measures changes in awareness, recall, or preference (usually via surveys).<\/li>\n
                                          • Completion Lift<\/strong> measures behavioral completion outcomes. In Programmatic Advertising<\/strong>, brand lift can explain perception change while Completion Lift shows whether people actually finished an action.<\/li>\n<\/ul>\n\n\n\n
                                            \n\n\n\n

                                            Who Should Learn Completion Lift<\/h2>\n\n\n\n
                                              \n
                                            • Marketers:<\/strong> to evaluate whether campaigns are driving real progress, not just cheap engagement.<\/li>\n
                                            • Analysts and data teams:<\/strong> to design valid baselines, quantify incrementality, and prevent biased conclusions.<\/li>\n
                                            • Agencies:<\/strong> to justify strategy changes, defend budgets, and show clients measurable incremental impact in Programmatic Advertising<\/strong>.<\/li>\n
                                            • Business owners and founders:<\/strong> to understand whether Paid Marketing<\/strong> spend is producing additional completed outcomes or merely capturing existing demand.<\/li>\n
                                            • Developers and marketing engineers:<\/strong> to implement event tracking, exposure-to-event pipelines, and experimentation infrastructure needed for Completion Lift.<\/li>\n<\/ul>\n\n\n\n
                                              \n\n\n\n

                                              Summary of Completion Lift<\/h2>\n\n\n\n

                                              Completion Lift<\/strong> measures the incremental improvement in a defined completion outcome attributable to ad exposure. It matters because it focuses Paid Marketing<\/strong> decisions on causality and business impact\u2014helping teams reduce waste, optimize frequency and targeting, and scale what genuinely drives completed actions. In Programmatic Advertising<\/strong>, where automation and scale can amplify both good and bad spend, Completion Lift is a practical framework for proving and improving incremental results.<\/p>\n\n\n\n

                                              \n\n\n\n

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

                                              1) What does Completion Lift measure exactly?<\/h3>\n\n\n\n

                                              Completion Lift measures how much more often users complete a defined action after being exposed to ads, compared with a baseline that represents what would have happened without that exposure.<\/p>\n\n\n\n

                                              2) Is Completion Lift only for video ads?<\/h3>\n\n\n\n

                                              No. While it\u2019s common to discuss Completion Lift alongside video completion, the same concept applies to completed forms, completed checkouts, completed onboarding steps, and other funnel completions in Paid Marketing<\/strong>.<\/p>\n\n\n\n

                                              3) How do you calculate Completion Lift in a simple way?<\/h3>\n\n\n\n

                                              A basic approach is:\n(Completion rate of exposed group) \u2212 (Completion rate of control\/baseline group)<\/strong>.\nMany teams also express it as a relative percentage increase, but the key is using a defensible baseline.<\/p>\n\n\n\n

                                              4) What\u2019s the best way to measure Completion Lift in Programmatic Advertising?<\/h3>\n\n\n\n

                                              The most reliable method is a randomized holdout (control vs exposed) when feasible. If not, use matched controls or geo-based tests, and document assumptions, timing, and potential confounders.<\/p>\n\n\n\n

                                              5) Can Completion Lift replace attribution?<\/h3>\n\n\n\n

                                              Not entirely. Attribution helps with path analysis and channel reporting, while Completion Lift is designed to answer an incrementality question: \u201cDid this spend cause more completions?\u201d Many mature Paid Marketing<\/strong> programs use both.<\/p>\n\n\n\n

                                              6) What causes Completion Lift to look high but not be real?<\/h3>\n\n\n\n

                                              Common causes include selection bias (high-intent users get targeted), weak control groups, short measurement windows, broken tracking, or overlapping campaigns that influence completions outside the ad being evaluated.<\/p>\n\n\n\n

                                              7) Which optimization actions typically improve Completion Lift?<\/h3>\n\n\n\n

                                              Actions that often help include refining audiences, improving creative-message match, reducing excessive frequency, excluding poor-quality inventory, and optimizing landing experiences so users can complete the intended action more easily.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                              Completion Lift is a measurement concept used in **Paid Marketing**\u2014especially in **Programmatic Advertising**\u2014to quantify how much a paid exposure increases the likelihood that a user *completes a desired action* compared with a reasonable baseline. Depending on the campaign, that \u201ccompletion\u201d might be finishing a video ad, completing a lead form, completing an onboarding flow, or completing a purchase funnel step.<\/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":[1911],"tags":[],"class_list":["post-10698","post","type-post","status-publish","format-standard","hentry","category-programmatic-advertising"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/10698","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=10698"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/10698\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=10698"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=10698"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=10698"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}