{"id":6983,"date":"2026-03-23T20:04:35","date_gmt":"2026-03-23T20:04:35","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/view-promotion\/"},"modified":"2026-03-23T20:04:35","modified_gmt":"2026-03-23T20:04:35","slug":"view-promotion","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/view-promotion\/","title":{"rendered":"View_promotion: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Analytics"},"content":{"rendered":"\n

View_promotion is a measurement concept used in Conversion & Measurement<\/strong> and Analytics<\/strong> to record when a user is exposed to a promotion\u2014such as a banner, homepage hero, product badge, or in-app offer. It captures visibility<\/em> (the promotional impression) rather than interaction<\/em> (like a click) or outcome<\/em> (like a purchase).<\/p>\n\n\n\n

Why it matters: modern marketing optimization depends on understanding the full funnel. If you only measure clicks and purchases, you miss critical context\u2014what people actually saw<\/em> before they acted. View_promotion helps teams evaluate promotional reach, diagnose weak creative or placement, and connect exposure to downstream behavior. In short, View_promotion turns \u201cwe ran a promo\u201d into measurable evidence inside your Conversion & Measurement<\/strong> strategy and your Analytics<\/strong> reporting.<\/p>\n\n\n\n

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

View_promotion is an event (or recorded data point) that indicates a promotion was displayed to a user in a measurable way. \u201cDisplayed\u201d can mean different things depending on the implementation: it may be logged when the promotion renders on a page, when it becomes visible in the viewport, or when an app screen containing the promotion is viewed.<\/p>\n\n\n\n

At its core, View_promotion answers: Which promotions were seen, by whom, and in what context?<\/strong><\/p>\n\n\n\n

From a business perspective, View_promotion provides the missing denominator for performance analysis. Without it, click-through rate (CTR) or promotion-to-cart impact can\u2019t be interpreted reliably because you don\u2019t know how many users had the chance<\/em> to click or respond.<\/p>\n\n\n\n

Where it fits in Conversion & Measurement<\/strong>: it is an upper-funnel exposure signal that complements mid-funnel interactions (like select\/click) and lower-funnel conversions (add-to-cart, sign-up, purchase). Inside Analytics<\/strong>, View_promotion becomes a foundational event for attribution analysis, funnel reporting, and experimentation.<\/p>\n\n\n\n

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

View_promotion is strategically important because promotions are often one of the highest-leverage levers on digital experiences\u2014homepages, category pages, in-app modals, emails landing on promo pages, and seasonal campaigns. Measuring only results without exposure data can lead to wrong conclusions.<\/p>\n\n\n\n

Key reasons View_promotion matters in Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

    \n
  • Diagnoses performance issues faster<\/strong>: If conversion drops, View_promotion can reveal whether fewer users actually saw the promotion (placement issue) versus seeing it but not responding (creative\/offer issue).<\/li>\n
  • Improves marketing efficiency<\/strong>: When you know which promotions earn views but fail to drive interaction, you can reallocate space and budget to higher-performing offers.<\/li>\n
  • Supports reliable experimentation<\/strong>: In A\/B tests, View_promotion provides exposure counts so results aren\u2019t biased by differences in visibility.<\/li>\n
  • Creates competitive advantage<\/strong>: Teams that measure exposure can optimize layout, personalization, and merchandising more precisely than teams relying on clicks alone.<\/li>\n<\/ul>\n\n\n\n

    This is the practical bridge between creative decisions and measurable outcomes\u2014exactly what Analytics<\/strong> and Conversion & Measurement<\/strong> are meant to enable.<\/p>\n\n\n\n

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

    View_promotion can be implemented in several ways, but in practice it follows a simple measurement workflow:<\/p>\n\n\n\n

      \n
    1. \n

      Trigger (what causes the event)<\/strong>\n – A promotion is rendered on a page or app screen, or it becomes visible in the user\u2019s viewport.\n – The trigger definition matters: \u201crendered\u201d is easier to track, while \u201cviewable\u201d is more accurate for true exposure.<\/p>\n<\/li>\n

    2. \n

      Processing (what data gets collected)<\/strong>\n – Your instrumentation captures details such as:<\/p>\n

        \n
      • promotion identifier (ID or name)<\/li>\n
      • creative name or variant<\/li>\n
      • placement location (homepage hero, PDP banner, cart upsell)<\/li>\n
      • context (page type, category, device, locale)<\/li>\n
      • timestamp and user\/session identifiers (in line with privacy rules)<\/li>\n<\/ul>\n<\/li>\n
      • \n

        Application (how it\u2019s used in Analytics)<\/strong>\n – In Analytics<\/strong>, View_promotion events are grouped by promotion, placement, audience segment, and time period.\n – Analysts pair View_promotion with interaction events (e.g., \u201cselect promotion\u201d) and conversion events (purchase, lead) to understand influence.<\/p>\n<\/li>\n

      • \n

        Outcome (what you learn)<\/strong>\n – Exposure volume, viewability patterns, and downstream performance (CTR, conversion lift, revenue per view) become measurable.\n – Stakeholders can decide which promotions to scale, redesign, move, or retire.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        If View_promotion is implemented thoughtfully, it becomes a dependable \u201cpromo impression\u201d layer for your Conversion & Measurement<\/strong> model.<\/p>\n\n\n\n

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

        A strong View_promotion setup typically includes the following components:<\/p>\n\n\n\n

        Data definition and tracking plan<\/h3>\n\n\n\n
          \n
        • Clear definition of what counts as a \u201cview\u201d (render vs viewport visibility vs minimum time visible).<\/li>\n
        • A consistent naming convention for promotions and placements.<\/li>\n
        • Documentation for teams across marketing, product, and engineering.<\/li>\n<\/ul>\n\n\n\n

          Instrumentation and event collection<\/h3>\n\n\n\n
            \n
          • Website and\/or app tagging to fire View_promotion events.<\/li>\n
          • Rules to avoid duplicate events (e.g., re-renders, SPA route changes, infinite scroll).<\/li>\n
          • Quality checks to ensure events match actual UI states.<\/li>\n<\/ul>\n\n\n\n

            Promotion metadata<\/h3>\n\n\n\n

            To make View_promotion actionable in Analytics<\/strong>, capture fields like:\n– promotion_id \/ promotion_name\n– creative_name \/ creative_slot\n– placement (slot) and page context\n– campaign or initiative tag (seasonal sale, onboarding, clearance)<\/p>\n\n\n\n

            Governance and responsibilities<\/h3>\n\n\n\n
              \n
            • Marketing owns promotion taxonomy and business naming.<\/li>\n
            • Product\/engineering owns event triggers and data quality.<\/li>\n
            • Analytics or data teams own validation, reporting logic, and guardrails.<\/li>\n<\/ul>\n\n\n\n

              Measurement and reporting layer<\/h3>\n\n\n\n
                \n
              • Dashboards, funnel reports, and experiment readouts using View_promotion as the exposure baseline.<\/li>\n
              • Alerts for sudden drops in View_promotion volume (often a tracking break or UI change).<\/li>\n<\/ul>\n\n\n\n

                Types of View_promotion (Practical Distinctions)<\/h2>\n\n\n\n

                View_promotion isn\u2019t always categorized into formal \u201ctypes,\u201d but in real Conversion & Measurement<\/strong> work, the most useful distinctions are:<\/p>\n\n\n\n

                Render-based vs viewable-based<\/h3>\n\n\n\n
                  \n
                • Render-based<\/strong>: event fires when the promo loads. Easier to implement, but can overcount if the promo loads below the fold.<\/li>\n
                • Viewable-based<\/strong>: event fires when the promo is actually visible (often using viewport detection). More accurate, but requires careful engineering.<\/li>\n<\/ul>\n\n\n\n

                  On-site vs in-app vs cross-channel landing experiences<\/h3>\n\n\n\n
                    \n
                  • On-site View_promotion<\/strong>: homepage banners, category tiles, PDP badges.<\/li>\n
                  • In-app View_promotion<\/strong>: in-app messages, interstitials, offer cards.<\/li>\n
                  • Landing experience View_promotion<\/strong>: promo modules on pages driven by email, paid, or social campaigns.<\/li>\n<\/ul>\n\n\n\n

                    Single-slot vs multi-slot placements<\/h3>\n\n\n\n
                      \n
                    • Single-slot<\/strong>: one dominant hero area.<\/li>\n
                    • Multi-slot<\/strong>: grids, carousels, recommendation modules where multiple promotions compete for attention.<\/li>\n<\/ul>\n\n\n\n

                      These distinctions matter because they directly affect how you interpret exposure and how you design Analytics<\/strong> reporting.<\/p>\n\n\n\n

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

                      Example 1: Ecommerce homepage hero rotation<\/h3>\n\n\n\n

                      An ecommerce brand rotates three hero banners per day. They implement View_promotion when each banner becomes visible in the viewport.<\/p>\n\n\n\n

                        \n
                      • Conversion & Measurement question<\/strong>: Do hero views correlate with add-to-cart and purchase, or is the hero mostly decorative?<\/li>\n
                      • Analytics use<\/strong>: Compare revenue per View_promotion across the three creatives, controlling for device and traffic source.<\/li>\n
                      • Outcome<\/strong>: The brand learns a \u201cfree shipping\u201d hero has lower CTR but higher downstream conversion, so they keep it for new visitors and use a product-focused hero for returning users.<\/li>\n<\/ul>\n\n\n\n

                        Example 2: SaaS in-app upgrade prompt<\/h3>\n\n\n\n

                        A SaaS product shows an upgrade promotion inside the settings page. View_promotion fires when the prompt is displayed.<\/p>\n\n\n\n

                          \n
                        • Conversion & Measurement question<\/strong>: Are users seeing the prompt, and does it lead to upgrades?<\/li>\n
                        • Analytics use<\/strong>: Create a funnel: View_promotion \u2192 prompt click \u2192 pricing page view \u2192 upgrade.<\/li>\n
                        • Outcome<\/strong>: They discover high View_promotion volume but low click. They redesign copy and placement, improving upgrades without increasing exposure.<\/li>\n<\/ul>\n\n\n\n

                          Example 3: Retail cart upsell module<\/h3>\n\n\n\n

                          A retailer adds a cart module promoting add-on items. View_promotion fires when the module enters the viewport in the cart.<\/p>\n\n\n\n

                            \n
                          • Conversion & Measurement question<\/strong>: Does exposure to the upsell module increase average order value?<\/li>\n
                          • Analytics use<\/strong>: Segment by \u201cmodule viewed\u201d vs \u201cnot viewed\u201d while accounting for cart size and device type.<\/li>\n
                          • Outcome<\/strong>: The module boosts AOV on desktop but not mobile, leading to a mobile redesign and better space utilization.<\/li>\n<\/ul>\n\n\n\n

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

                            When implemented well, View_promotion provides measurable advantages:<\/p>\n\n\n\n

                              \n
                            • Better performance optimization<\/strong>: You can optimize promotions based on exposure-to-outcome relationships, not guesswork.<\/li>\n
                            • More accurate efficiency metrics<\/strong>: \u201cRevenue per promo view\u201d and \u201cconversion rate after view\u201d enable fair comparisons between placements.<\/li>\n
                            • Cost savings and smarter prioritization<\/strong>: Less time spent iterating on promos that aren\u2019t being seen, or that are seen but ineffective.<\/li>\n
                            • Improved customer experience<\/strong>: Measuring what users see helps reduce clutter, avoid repetitive promos, and improve relevance through personalization.<\/li>\n
                            • Stronger cross-team alignment<\/strong>: View_promotion creates a shared language between marketing, product, and Analytics teams within Conversion & Measurement<\/strong>.<\/li>\n<\/ul>\n\n\n\n

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

                              View_promotion also has real pitfalls. Understanding them is essential for trustworthy Analytics<\/strong>.<\/p>\n\n\n\n

                              Technical challenges<\/h3>\n\n\n\n
                                \n
                              • Duplicate firing<\/strong> in single-page apps or components that re-render.<\/li>\n
                              • Viewport detection complexity<\/strong> for carousels, sticky headers, modals, and infinite scroll.<\/li>\n
                              • Latency and offline behavior<\/strong> in apps that queue events.<\/li>\n<\/ul>\n\n\n\n

                                Strategic and measurement risks<\/h3>\n\n\n\n
                                  \n
                                • Counting \u201crendered\u201d as \u201cviewed\u201d<\/strong> can inflate exposure and depress CTR artificially.<\/li>\n
                                • Attribution ambiguity<\/strong>: seeing a promotion doesn\u2019t prove it caused a conversion; it\u2019s an influence signal, not a guarantee.<\/li>\n
                                • Over-instrumentation<\/strong>: too many promotional events can create noisy datasets and complicate reporting.<\/li>\n<\/ul>\n\n\n\n

                                  Data and governance limitations<\/h3>\n\n\n\n
                                    \n
                                  • Inconsistent naming or missing promotion IDs reduces the value of View_promotion.<\/li>\n
                                  • Privacy and consent requirements may limit user-level tracking, requiring aggregated or modeled approaches in Conversion & Measurement<\/strong>.<\/li>\n<\/ul>\n\n\n\n

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

                                    Define \u201cview\u201d precisely and document it<\/h3>\n\n\n\n
                                      \n
                                    • Choose render-based or viewable-based tracking intentionally.<\/li>\n
                                    • Document thresholds (e.g., \u201c50% visible for 1 second\u201d) if using viewability logic.<\/li>\n
                                    • Keep definitions stable so trend analysis remains meaningful.<\/li>\n<\/ul>\n\n\n\n

                                      Use consistent promotion taxonomy<\/h3>\n\n\n\n
                                        \n
                                      • Standardize: promotion_id, creative, placement, campaign tag.<\/li>\n
                                      • Avoid embedding changing details (like dates) in IDs; store dates in separate properties.<\/li>\n<\/ul>\n\n\n\n

                                        Prevent duplicates and ensure data quality<\/h3>\n\n\n\n
                                          \n
                                        • Implement deduplication per session\/page-view\/promo instance where appropriate.<\/li>\n
                                        • Validate with QA: does View_promotion fire when expected, and only once per view?<\/li>\n
                                        • Monitor event volume after releases\u2014promo tracking breaks often coincide with UI changes.<\/li>\n<\/ul>\n\n\n\n

                                          Connect View_promotion to downstream events<\/h3>\n\n\n\n

                                          To make View_promotion actionable in Analytics<\/strong>, pair it with:\n– interaction event(s) (promotion click\/select)\n– conversion events (add_to_cart, purchase, sign_up)\n– contextual events (page_view, screen_view)<\/p>\n\n\n\n

                                          Use segmentation and experiments<\/h3>\n\n\n\n
                                            \n
                                          • Break down results by audience (new vs returning), device, placement, and traffic source.<\/li>\n
                                          • In A\/B tests, ensure both variants measure View_promotion consistently; otherwise Conversion & Measurement<\/strong> comparisons become biased.<\/li>\n<\/ul>\n\n\n\n

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

                                            View_promotion is less about a single tool and more about an ecosystem that supports reliable Analytics<\/strong> and Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                                              \n
                                            • Analytics tools<\/strong>: collect events, build funnels, segment users, and analyze cohorts around View_promotion exposure.<\/li>\n
                                            • Tag management systems<\/strong>: deploy and version website tracking, manage triggers, and reduce reliance on code releases.<\/li>\n
                                            • Mobile measurement and app instrumentation<\/strong>: capture in-app View_promotion consistently across devices and OS versions.<\/li>\n
                                            • Data pipelines and warehouses<\/strong>: store event logs for deeper analysis, joining View_promotion to orders, subscriptions, or CRM outcomes.<\/li>\n
                                            • Experimentation platforms<\/strong>: measure lift with correct exposure baselines.<\/li>\n
                                            • Reporting dashboards<\/strong>: operationalize KPIs like views, CTR, and revenue per view for weekly decision-making.<\/li>\n
                                            • CRM systems<\/strong> (where applicable): connect View_promotion exposure segments to lifecycle campaigns, while respecting consent and privacy.<\/li>\n<\/ul>\n\n\n\n

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

                                              View_promotion enables a set of metrics that are often impossible\u2014or misleading\u2014without exposure counts:<\/p>\n\n\n\n

                                              Exposure and reach metrics<\/h3>\n\n\n\n
                                                \n
                                              • Promotion views<\/strong> (count of View_promotion)<\/li>\n
                                              • Unique viewers<\/strong> (distinct users who had View_promotion)<\/li>\n
                                              • Frequency<\/strong> (views per user\/session)<\/li>\n<\/ul>\n\n\n\n

                                                Engagement metrics<\/h3>\n\n\n\n
                                                  \n
                                                • Promotion CTR<\/strong> = clicks\/selects \u00f7 View_promotion<\/li>\n
                                                • Interaction rate by placement<\/strong> (hero vs cart vs category tile)<\/li>\n<\/ul>\n\n\n\n

                                                  Conversion and revenue metrics<\/h3>\n\n\n\n
                                                    \n
                                                  • Conversion rate after view<\/strong> (e.g., purchase rate among viewers within a defined window)<\/li>\n
                                                  • Revenue per view<\/strong> (or gross margin per view for merchandising decisions)<\/li>\n
                                                  • Assisted conversion rate<\/strong> (views appearing in journeys that convert, with careful attribution rules)<\/li>\n<\/ul>\n\n\n\n

                                                    Efficiency and quality metrics<\/h3>\n\n\n\n
                                                      \n
                                                    • Viewability rate<\/strong> (if tracking viewable impressions)<\/li>\n
                                                    • Incremental lift<\/strong> (from experiments): difference in conversions when View_promotion exposure changes<\/li>\n<\/ul>\n\n\n\n

                                                      The strongest Conversion & Measurement<\/strong> approach uses a balanced scorecard: exposure, interaction, and outcome\u2014interpreted together in Analytics<\/strong>.<\/p>\n\n\n\n

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

                                                      Several shifts are shaping how View_promotion evolves within Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                                                        \n
                                                      • AI-driven personalization<\/strong>: Promotions will be dynamically selected per user. View_promotion will need richer metadata (decision reasons, eligibility, variant IDs) to make Analytics<\/strong> interpretable.<\/li>\n
                                                      • Automation and real-time decisioning<\/strong>: Faster iteration will increase the importance of automated data quality checks for View_promotion.<\/li>\n
                                                      • Privacy changes and consent-first measurement<\/strong>: As user-level tracking becomes more restricted, teams will rely more on aggregated reporting, modeled conversions, and on-device processing\u2014while still needing trustworthy exposure signals.<\/li>\n
                                                      • Attention and viewability standards<\/strong>: More teams will move from render-based to viewable-based approaches to better match \u201cseen\u201d with reality.<\/li>\n
                                                      • Unified measurement across surfaces<\/strong>: Brands will aim for consistent View_promotion semantics across web, app, and embedded experiences to reduce reporting fragmentation in Analytics<\/strong>.<\/li>\n<\/ul>\n\n\n\n

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

                                                        View_promotion vs promotion click (select promotion)<\/h3>\n\n\n\n
                                                          \n
                                                        • View_promotion<\/strong> measures exposure: the user saw the promotion.<\/li>\n
                                                        • Promotion click\/select<\/strong> measures interaction: the user engaged with it.\nYou need both to understand performance. A low click rate could mean weak creative\u2014or it could mean low-quality exposure if View_promotion is overcounted.<\/li>\n<\/ul>\n\n\n\n

                                                          View_promotion vs impression (generic)<\/h3>\n\n\n\n

                                                          \u201cImpression\u201d is a broad term used across ads and content. View_promotion is typically more specific to on-site or in-app promotions and is often implemented as an event in Analytics<\/strong> for Conversion & Measurement<\/strong> analysis.<\/p>\n\n\n\n

                                                          View_promotion vs page_view \/ screen_view<\/h3>\n\n\n\n
                                                            \n
                                                          • page_view\/screen_view<\/strong> measures that a page or screen loaded.<\/li>\n
                                                          • View_promotion<\/strong> measures that a specific promotional element on that page\/screen was displayed or viewed.\nA page can load without a promo being seen (below the fold), so separating these improves measurement precision.<\/li>\n<\/ul>\n\n\n\n

                                                            Who Should Learn View_promotion<\/h2>\n\n\n\n
                                                              \n
                                                            • Marketers and growth teams<\/strong>: to evaluate creative, offers, and placements using reliable Conversion & Measurement<\/strong> signals.<\/li>\n
                                                            • Analysts<\/strong>: to build funnel analyses, attribution views, and experiments that incorporate exposure in Analytics<\/strong>.<\/li>\n
                                                            • Agencies<\/strong>: to prove promotional impact and improve client reporting beyond vanity clicks.<\/li>\n
                                                            • Business owners and founders<\/strong>: to understand which promotional levers drive revenue and which only consume attention.<\/li>\n
                                                            • Developers and product teams<\/strong>: to implement accurate triggers, manage duplicates, and ensure View_promotion data integrity.<\/li>\n<\/ul>\n\n\n\n

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

                                                              View_promotion is the measurement of promotional exposure\u2014recording when users see a promotion so teams can analyze performance with context. It sits at the top of the funnel in Conversion & Measurement<\/strong>, enabling more accurate evaluation of creative and placement. In Analytics<\/strong>, View_promotion becomes the baseline for CTR, conversion-after-view, revenue-per-view, and experiment analysis. Done well, it improves decision quality, efficiency, and the customer experience.<\/p>\n\n\n\n

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

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

                                                              View_promotion measures exposure to a specific promotion (banner, tile, module, in-app offer). Depending on implementation, it may represent \u201crendered\u201d exposure or \u201cviewable\u201d exposure when the promo enters the viewport.<\/p>\n\n\n\n

                                                              2) How is View_promotion different from a click event?<\/h3>\n\n\n\n

                                                              View_promotion indicates the promotion was seen; a click\/select event indicates the user interacted with it. In Conversion & Measurement<\/strong>, you typically use View_promotion as the denominator for CTR and then connect clicks to conversion events.<\/p>\n\n\n\n

                                                              3) Do I need View_promotion if I already track purchases?<\/h3>\n\n\n\n

                                                              Yes, if you want to optimize promotions rather than just report outcomes. Purchases alone don\u2019t tell you which promotions were actually seen, so Analytics<\/strong> can\u2019t fairly compare creative or placements without View_promotion.<\/p>\n\n\n\n

                                                              4) Should View_promotion fire when a promo loads or when it becomes visible?<\/h3>\n\n\n\n

                                                              If accuracy is the priority, viewable-based tracking is better. If simplicity and coverage are the priority, render-based can be acceptable\u2014just be consistent and interpret CTR and lift accordingly in your Analytics<\/strong>.<\/p>\n\n\n\n

                                                              5) What metrics should I pair with View_promotion?<\/h3>\n\n\n\n

                                                              Common pairings include promotion CTR (clicks \u00f7 views), conversion rate after view, revenue per view, and experiment lift. These create a full Conversion & Measurement<\/strong> chain from exposure to outcome.<\/p>\n\n\n\n

                                                              6) How can View_promotion improve Analytics reporting for promotions?<\/h3>\n\n\n\n

                                                              It provides a reliable exposure baseline, enabling segmentation by placement and audience, identifying underperforming creative, and preventing misleading conclusions drawn from clicks alone.<\/p>\n\n\n\n

                                                              7) What are the most common implementation mistakes with View_promotion?<\/h3>\n\n\n\n

                                                              The biggest issues are duplicate event firing, inconsistent promotion IDs, counting below-the-fold renders as \u201cviews,\u201d and failing to link View_promotion to downstream interactions and conversions for complete Conversion & Measurement<\/strong> analysis.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                                              View_promotion is a measurement concept used in **Conversion & Measurement** and **Analytics** to record when a user is exposed to a promotion\u2014such as a banner, homepage hero, product badge, or in-app offer. It captures *visibility* (the promotional impression) rather than *interaction* (like a click) or *outcome* (like a purchase).<\/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-6983","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\/6983","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=6983"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/6983\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=6983"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=6983"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=6983"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}