{"id":11057,"date":"2026-04-01T07:13:39","date_gmt":"2026-04-01T07:13:39","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/select-promotion\/"},"modified":"2026-04-01T07:13:39","modified_gmt":"2026-04-01T07:13:39","slug":"select-promotion","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/select-promotion\/","title":{"rendered":"Select_promotion: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Analytics"},"content":{"rendered":"\n

Select_promotion is a practical measurement concept used in Conversion & Measurement<\/strong> to capture when a user actively chooses a promotion\u2014such as clicking a homepage banner, tapping an in-app offer tile, or selecting a discount module in a checkout flow. In modern Analytics<\/strong>, this is typically implemented as an event that represents promotion selection<\/em> (not just exposure).<\/p>\n\n\n\n

Why does Select_promotion matter? Because promotions often drive meaningful user actions, but they\u2019re also easy to mis-measure. Strong Conversion & Measurement<\/strong> requires separating \u201ca user saw a promotion\u201d from \u201ca user selected it,\u201d then tying those interactions to downstream outcomes like product views, add-to-cart, purchases, leads, or subscriptions. Select_promotion creates that crucial bridge between promotional creative and measurable business impact inside your Analytics<\/strong> stack.<\/p>\n\n\n\n

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

Select_promotion<\/strong> is the measurement of a user\u2019s intentional interaction with a promotion, most commonly recorded when the user clicks or taps a promotional element.<\/p>\n\n\n\n

At a beginner level, think of it as: \u201cThe user chose this promo.\u201d<\/em> That choice is important because it indicates interest and creates a measurable step in the journey before conversion.<\/p>\n\n\n\n

At a core concept level, Select_promotion is about:<\/p>\n\n\n\n

    \n
  • Tracking intent<\/em> (selection) rather than only visibility<\/em> (impression)<\/li>\n
  • Enabling attribution from a promotion to the next actions in a session or user journey<\/li>\n
  • Improving promotional optimization using behavioral data, not assumptions<\/li>\n<\/ul>\n\n\n\n

    From a business perspective, Select_promotion helps answer questions like:<\/p>\n\n\n\n

      \n
    • Which banner or offer drives the most qualified traffic?<\/li>\n
    • Do promotions increase revenue or just shift customers to discounted purchases?<\/li>\n
    • Are users selecting promotions that lead to high-value conversions?<\/li>\n<\/ul>\n\n\n\n

      In Conversion & Measurement<\/strong>, Select_promotion sits between promotional exposure and conversion outcomes. In Analytics<\/strong>, it becomes an event you can segment, analyze, and connect to revenue, funnel steps, audience cohorts, and experiments.<\/p>\n\n\n\n

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

      Promotions are everywhere\u2014homepages, category pages, email landers, app dashboards, checkout, and even post-purchase screens. Without Select_promotion, teams often rely on weak proxies like pageviews or last-click channel reporting that can\u2019t isolate which creative or placement actually influenced behavior.<\/p>\n\n\n\n

      Key reasons Select_promotion is strategically important in Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

        \n
      • It improves decision quality.<\/strong> You can stop guessing which promotion works and use Analytics<\/strong> to compare performance by message, placement, audience, and device.<\/li>\n
      • It clarifies funnel contribution.<\/strong> Promotion selection can be a meaningful micro-conversion that predicts purchases or leads.<\/li>\n
      • It reduces wasted spend and opportunity cost.<\/strong> If a promo is heavily viewed but rarely selected, it may be cluttering the experience or underperforming.<\/li>\n
      • It supports personalization.<\/strong> Understanding what users select helps you tailor future offers and experiences.<\/li>\n
      • It creates competitive advantage.<\/strong> Many organizations track campaign traffic but fail to measure on-site\/in-app promotional mechanics. Select_promotion helps you optimize where competitors stay blind.<\/li>\n<\/ul>\n\n\n\n

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

        In practice, Select_promotion is implemented as an event-driven measurement pattern within Analytics<\/strong> and your broader Conversion & Measurement<\/strong> framework. A realistic workflow looks like this:<\/p>\n\n\n\n

          \n
        1. \n

          Input \/ trigger (user action)<\/strong>\n – A user clicks a hero banner, taps a carousel card, selects a promotional tile, or interacts with a discount module.\n – The interaction is tied to a specific promotion identity (for example: campaign name, creative name, placement).<\/p>\n<\/li>\n

        2. \n

          Processing (capturing context)<\/strong>\n – Your tracking layer collects metadata such as promotion ID\/name, creative details, placement location, and sometimes the destination URL or related product\/category.\n – Good instrumentation ensures the event fires once per intentional action and is resilient across devices and browsers.<\/p>\n<\/li>\n

        3. \n

          Execution (sending to measurement)<\/strong>\n – The event is sent to your Analytics<\/strong> system via a tag manager, SDK, server-side endpoint, or data pipeline.\n – The event is associated with session\/user identifiers and any relevant consent state.<\/p>\n<\/li>\n

        4. \n

          Output \/ outcome (analysis and optimization)<\/strong>\n – You analyze Select_promotion alongside downstream events (product views, add-to-cart, purchase, lead submit).\n – You use it to refine creative, placements, targeting, and the promotional calendar\u2014feeding back into Conversion & Measurement<\/strong> planning.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

          When implemented well, Select_promotion becomes a reliable signal that connects promotional UX decisions to measurable business results.<\/p>\n\n\n\n

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

          Strong Select_promotion measurement typically includes these elements:<\/p>\n\n\n\n

            \n
          • Promotion taxonomy:<\/strong> A consistent naming convention for promotions (IDs, names, creative variants, placements). This is essential for scalable Analytics<\/strong>.<\/li>\n
          • Event schema:<\/strong> A defined set of fields that describe the promotion selection (e.g., promotion identifier, creative, placement, and context).<\/li>\n
          • Instrumentation layer:<\/strong> A data layer, mobile event bus, or UI tracking framework that can identify which promotion was selected.<\/li>\n
          • Governance and ownership:<\/strong> Clear responsibility across marketing, product, and engineering for definitions, QA, and change management.<\/li>\n
          • Quality assurance process:<\/strong> Testing across devices, ensuring duplicate events aren\u2019t fired, and validating that promotion metadata is populated correctly.<\/li>\n
          • Reporting model:<\/strong> Dashboards and analysis views that connect Select_promotion to Conversion & Measurement<\/strong> outcomes like revenue, lead quality, or retention.<\/li>\n<\/ul>\n\n\n\n

            Types of Select_promotion<\/h2>\n\n\n\n

            Select_promotion doesn\u2019t have universal \u201cofficial types,\u201d but in real-world Analytics<\/strong> implementations, it commonly varies by context. Useful distinctions include:<\/p>\n\n\n\n

            1. On-site vs in-app Select_promotion<\/h3>\n\n\n\n
              \n
            • On-site:<\/strong> Web banners, category modules, popups, embedded offers.<\/li>\n
            • In-app:<\/strong> Home tiles, interstitial offers, upgrade prompts, feature callouts.<\/li>\n<\/ul>\n\n\n\n

              2. Merchandising vs lifecycle promotions<\/h3>\n\n\n\n
                \n
              • Merchandising promotions:<\/strong> Product discounts, seasonal sales, bundles.<\/li>\n
              • Lifecycle promotions:<\/strong> Free trials, upgrades, reactivation offers, referral prompts.<\/li>\n<\/ul>\n\n\n\n

                3. Paid-campaign landing promos vs organic experience promos<\/h3>\n\n\n\n
                  \n
                • Landing promos:<\/strong> Modules tailored to traffic from ads or email.<\/li>\n
                • Always-on promos:<\/strong> Persistent site\/app placements for broad audiences.<\/li>\n<\/ul>\n\n\n\n

                  These distinctions matter because the same Select_promotion event can represent very different intent and conversion likelihood depending on where and why the promotion appears\u2014an important nuance for Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

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

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

                  A retailer runs three rotating hero banners: \u201cSpring Sale,\u201d \u201cNew Arrivals,\u201d and \u201cFree Shipping.\u201d Each banner click triggers Select_promotion with banner ID and placement = homepage_hero.<\/p>\n\n\n\n

                  In Analytics<\/strong>, the team compares:\n– Select_promotion rate by banner\n– Revenue per Select_promotion\n– Discount usage and margin impact<\/p>\n\n\n\n

                  This supports smarter Conversion & Measurement<\/strong> decisions than relying on homepage clicks alone.<\/p>\n\n\n\n

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

                  A freemium SaaS product displays an upgrade offer in the dashboard: \u201cUnlock Pro features.\u201d When the user clicks, Select_promotion fires with creative variant and placement = dashboard_upgrade_tile.<\/p>\n\n\n\n

                  The growth team uses Analytics<\/strong> to track:\n– Select_promotion \u2192 pricing page view rate\n– Select_promotion \u2192 trial start rate\n– Select_promotion \u2192 paid conversion rate by user segment<\/p>\n\n\n\n

                  This helps quantify which messaging drives qualified upgrades, not just clicks.<\/p>\n\n\n\n

                  Example 3: Checkout cross-sell module<\/h3>\n\n\n\n

                  During checkout, a store shows \u201cAdd a warranty\u201d and \u201cAdd accessories\u201d promotions. Selecting either triggers Select_promotion and connects to cart value.<\/p>\n\n\n\n

                  In Conversion & Measurement<\/strong>, this enables:\n– Incremental revenue analysis from cross-sells\n– Funnel impact evaluation (does it cause drop-off?)\n– Better placement strategy for high-performing add-ons<\/p>\n\n\n\n

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

                  Implemented thoughtfully, Select_promotion provides benefits that compound over time:<\/p>\n\n\n\n

                    \n
                  • More accurate promotion performance measurement:<\/strong> You can evaluate creative and placement effectiveness with a reliable selection signal.<\/li>\n
                  • Better funnel optimization:<\/strong> Select_promotion can be used as a diagnostic step to understand where users show intent but fail to convert.<\/li>\n
                  • Improved budget and merchandising decisions:<\/strong> When paired with downstream conversion value in Analytics<\/strong>, you can prioritize promotions that drive profit\u2014not just engagement.<\/li>\n
                  • Higher operational efficiency:<\/strong> Clear measurement reduces internal debate and speeds up iteration cycles.<\/li>\n
                  • Better customer experience:<\/strong> By identifying irrelevant or distracting promotions (high views, low Select_promotion), you can reduce clutter and improve usability\u2014supporting Conversion & Measurement<\/strong> goals.<\/li>\n<\/ul>\n\n\n\n

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

                    Select_promotion is simple in theory, but real implementations face common issues:<\/p>\n\n\n\n

                      \n
                    • Ambiguous promotion definitions:<\/strong> Teams disagree on what counts as a promotion (banner vs navigation link vs product card). This weakens Analytics<\/strong> consistency.<\/li>\n
                    • Missing or inconsistent metadata:<\/strong> If promotion IDs or placements are not standardized, reporting becomes fragmented and hard to trust.<\/li>\n
                    • Duplicate event firing:<\/strong> UI components may trigger multiple clicks or fire on both click and navigation, inflating Select_promotion counts.<\/li>\n
                    • Attribution limitations:<\/strong> Select_promotion shows interaction, not causation. A user may click a promo and still purchase something else.<\/li>\n
                    • Privacy and consent constraints:<\/strong> In some contexts, tracking may be limited by consent requirements, impacting Conversion & Measurement<\/strong> completeness.<\/li>\n
                    • Cross-device and cross-session complexity:<\/strong> A promotion selected on mobile might convert later on desktop; your Analytics<\/strong> approach determines how well you can connect those dots.<\/li>\n<\/ul>\n\n\n\n

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

                      To make Select_promotion trustworthy and actionable:<\/p>\n\n\n\n

                        \n
                      • Define \u201cpromotion\u201d and document it.<\/strong> Include inclusion\/exclusion rules (e.g., exclude standard navigation links; include dedicated promotional modules).<\/li>\n
                      • Create a promotion naming taxonomy.<\/strong> Standardize promotion_id, promotion_name, creative, placement, and campaign mapping so Analytics<\/strong> stays clean.<\/li>\n
                      • Track impressions separately when possible.<\/strong> Pair Select_promotion with a \u201cpromotion view\u201d concept to compute selection rate (CTR) accurately in Conversion & Measurement<\/strong>.<\/li>\n
                      • Capture placement and context.<\/strong> The same creative can perform differently on homepage vs cart. Placement is essential for analysis.<\/li>\n
                      • QA every release.<\/strong> Verify event firing, metadata population, and counts versus expected behavior, especially after UI changes.<\/li>\n
                      • Connect to outcomes.<\/strong> Build reporting that ties Select_promotion to downstream conversions (purchases, leads, upgrades) and value metrics, not just clicks.<\/li>\n
                      • Use experiments where feasible.<\/strong> A\/B testing helps determine incremental impact beyond correlation\u2014strengthening Conversion & Measurement<\/strong> rigor.<\/li>\n<\/ul>\n\n\n\n

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

                        Select_promotion is less about a single tool and more about a measurement workflow across systems. Common tool categories include:<\/p>\n\n\n\n

                          \n
                        • Analytics tools:<\/strong> Event-based Analytics<\/strong> platforms that ingest promotion selection events, support segmentation, and connect events to conversions.<\/li>\n
                        • Tag management systems:<\/strong> Web containers that deploy event tags, manage triggers, and pass promotion metadata from the page.<\/li>\n
                        • Mobile SDK instrumentation:<\/strong> App analytics SDKs or internal event frameworks that capture taps and UI interactions.<\/li>\n
                        • Server-side measurement and pipelines:<\/strong> Server endpoints or event routers that improve reliability, reduce client-side loss, and support privacy-aware Conversion & Measurement<\/strong>.<\/li>\n
                        • Experimentation platforms:<\/strong> Tools to test different promotions, placements, and messaging while using Select_promotion as a measurable outcome.<\/li>\n
                        • BI and reporting dashboards:<\/strong> Data visualization and modeling layers that combine Select_promotion with revenue, margin, and CRM outcomes.<\/li>\n<\/ul>\n\n\n\n

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

                          Select_promotion becomes powerful when paired with the right metrics in Analytics<\/strong> and Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                            \n
                          • Select_promotion count:<\/strong> Raw volume of promotion selections (useful for trend monitoring).<\/li>\n
                          • Promotion selection rate (promo CTR):<\/strong> Select_promotion divided by promotion views (when impressions are tracked).<\/li>\n
                          • Downstream conversion rate:<\/strong> Purchases\/leads\/upgrades that occur after Select_promotion within a defined attribution window.<\/li>\n
                          • Revenue per Select_promotion:<\/strong> Total revenue attributed to sessions\/users who selected a promotion, divided by selections (interpret carefully).<\/li>\n
                          • Average order value after selection:<\/strong> Helps identify whether promotions attract higher- or lower-value baskets.<\/li>\n
                          • Incremental lift (test vs control):<\/strong> Best metric when experimentation is available; strengthens causal confidence.<\/li>\n
                          • Engagement depth:<\/strong> Pages\/screens per session, time, or product interactions after Select_promotion to evaluate traffic quality.<\/li>\n<\/ul>\n\n\n\n

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

                            Select_promotion is evolving as Conversion & Measurement<\/strong> faces new constraints and opportunities:<\/p>\n\n\n\n

                              \n
                            • AI-assisted merchandising and personalization:<\/strong> AI will increasingly decide which promotion to show; Select_promotion will be a key feedback signal to train and evaluate these systems in Analytics<\/strong>.<\/li>\n
                            • More automation in measurement QA:<\/strong> Automated anomaly detection will flag sudden changes in Select_promotion rates caused by broken tags or UI changes.<\/li>\n
                            • Privacy-driven measurement design:<\/strong> As consent requirements tighten, teams will rely more on aggregated reporting, modeled conversions, and server-side approaches to keep Select_promotion reliable.<\/li>\n
                            • Experimentation as a default:<\/strong> Organizations are shifting from \u201creporting clicks\u201d to proving incremental impact, using Select_promotion as a leading indicator in Conversion & Measurement<\/strong>.<\/li>\n
                            • Richer context capture:<\/strong> Expect broader use of placement IDs, creative versions, and user-state context (new vs returning, plan tier, loyalty status) to make Select_promotion analysis more actionable.<\/li>\n<\/ul>\n\n\n\n

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

                              Understanding adjacent concepts prevents misinterpretation in Analytics<\/strong>:<\/p>\n\n\n\n

                              Select_promotion vs promotion view (impression)<\/h3>\n\n\n\n
                                \n
                              • Promotion view<\/strong> means the promotion was displayed.<\/li>\n
                              • Select_promotion<\/strong> means the user interacted with it.\nBoth are valuable; together they enable selection rate and better Conversion & Measurement<\/strong> diagnostics.<\/li>\n<\/ul>\n\n\n\n

                                Select_promotion vs click tracking (generic)<\/h3>\n\n\n\n
                                  \n
                                • Generic click tracking<\/strong> often records \u201ca click happened\u201d without context.<\/li>\n
                                • Select_promotion<\/strong> should capture structured promotion metadata (which promo, where it was placed, which creative), making reporting reliable and scalable.<\/li>\n<\/ul>\n\n\n\n

                                  Select_promotion vs conversion event<\/h3>\n\n\n\n
                                    \n
                                  • A conversion event<\/strong> is the business outcome (purchase, lead, signup).<\/li>\n
                                  • Select_promotion<\/strong> is typically a precursor signal that helps explain why<\/em> conversions happened and which promotions contributed in your Analytics<\/strong> narrative.<\/li>\n<\/ul>\n\n\n\n

                                    Who Should Learn Select_promotion<\/h2>\n\n\n\n

                                    Select_promotion is worth learning because it sits at the intersection of UX decisions, promotional strategy, and measurable outcomes:<\/p>\n\n\n\n

                                      \n
                                    • Marketers:<\/strong> To evaluate creative, messaging, and promotional calendars using Analytics<\/strong> rather than intuition.<\/li>\n
                                    • Analysts:<\/strong> To build clean event schemas, interpret promo impact responsibly, and strengthen Conversion & Measurement<\/strong> reporting.<\/li>\n
                                    • Agencies:<\/strong> To provide clients with measurable promotion performance frameworks and clearer optimization roadmaps.<\/li>\n
                                    • Business owners and founders:<\/strong> To understand which offers truly drive growth and profit, not just activity.<\/li>\n
                                    • Developers and product teams:<\/strong> To implement consistent instrumentation, prevent duplicate events, and ensure Select_promotion data is trustworthy.<\/li>\n<\/ul>\n\n\n\n

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

                                      Select_promotion<\/strong> measures when a user actively chooses a promotion, creating a high-signal interaction event that supports better decision-making. It fits naturally into Conversion & Measurement<\/strong> as a bridge between promotion exposure and business outcomes, and it strengthens Analytics<\/strong> by enabling structured, comparable reporting across creative, placement, and audience segments. When standardized, QA\u2019d, and tied to downstream value, Select_promotion becomes a foundation for smarter optimization and more credible performance insights.<\/p>\n\n\n\n

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

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

                                      Select_promotion measures a user\u2019s intentional interaction with a promotional element\u2014most commonly a click or tap\u2014captured with enough context to identify which promotion was selected.<\/p>\n\n\n\n

                                      2) Do I need promotion impressions if I track Select_promotion?<\/h3>\n\n\n\n

                                      Impressions aren\u2019t strictly required, but they significantly improve Conversion & Measurement<\/strong>. With impressions, you can calculate selection rate and distinguish \u201cbad creative\u201d from \u201cbad placement or low visibility.\u201d<\/p>\n\n\n\n

                                      3) How is Select_promotion used in Analytics reporting?<\/h3>\n\n\n\n

                                      In Analytics<\/strong>, Select_promotion is analyzed by promotion name\/ID, creative, placement, device, audience segment, and then connected to downstream conversions and revenue to assess impact.<\/p>\n\n\n\n

                                      4) Can Select_promotion be used for A\/B testing promotions?<\/h3>\n\n\n\n

                                      Yes. Select_promotion is a strong leading indicator for experiments, especially when paired with conversion and value metrics to avoid optimizing for clicks that don\u2019t translate into outcomes.<\/p>\n\n\n\n

                                      5) What are common implementation mistakes with Select_promotion?<\/h3>\n\n\n\n

                                      The most common issues are inconsistent naming, missing placement context, duplicate firing, and treating Select_promotion as proof of causation rather than a behavioral signal within Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                      6) How do I tie Select_promotion to revenue or leads responsibly?<\/h3>\n\n\n\n

                                      Use clear attribution rules (session-based or time-windowed), compare against control groups when possible, and report both selection metrics and downstream conversion\/value metrics in Analytics<\/strong> to avoid over-crediting promotions.<\/p>\n\n\n\n

                                      7) Is Select_promotion only for ecommerce?<\/h3>\n\n\n\n

                                      No. Ecommerce is a common use case, but Select_promotion is equally useful for SaaS upgrades, content subscriptions, app feature adoption prompts, and any promotional module where selection signals intent.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                      Select_promotion is a practical measurement concept used in **Conversion & Measurement** to capture when a user actively chooses a promotion\u2014such as clicking a homepage banner, tapping an in-app offer tile, or selecting a discount module in a checkout flow. In modern **Analytics**, this is typically implemented as an event that represents *promotion selection* (not just exposure).<\/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-11057","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\/11057","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=11057"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/11057\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=11057"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=11057"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=11057"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}