{"id":7066,"date":"2026-03-23T23:03:24","date_gmt":"2026-03-23T23:03:24","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/store-visit-attribution\/"},"modified":"2026-03-23T23:03:24","modified_gmt":"2026-03-23T23:03:24","slug":"store-visit-attribution","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/store-visit-attribution\/","title":{"rendered":"Store Visit Attribution: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Attribution"},"content":{"rendered":"\n

Digital marketing doesn\u2019t stop at the checkout page. For retailers, restaurants, automotive, healthcare, and any business with physical locations, a large share of revenue happens offline\u2014after someone sees an ad, clicks a listing, or taps for directions. Store Visit Attribution<\/strong> is the set of methods used in Conversion & Measurement<\/strong> to connect digital marketing touchpoints to real-world store visits so teams can evaluate performance, optimize spend, and improve customer journeys.<\/p>\n\n\n\n

In the broader discipline of Attribution<\/strong>, Store Visit Attribution helps answer a crucial question: Which channels, campaigns, and messages are actually driving people to locations?<\/em> As privacy rules tighten and customer journeys span devices and channels, Store Visit Attribution has become a key part of modern Conversion & Measurement<\/strong> strategy\u2014especially for brands balancing eCommerce and brick-and-mortar growth.<\/p>\n\n\n\n

What Is Store Visit Attribution?<\/h2>\n\n\n\n

Store Visit Attribution<\/strong> is the practice of estimating or measuring how marketing interactions (ads, listings, email, social, search, etc.) contribute to visits to a physical store or location. A \u201cstore visit\u201d might mean entering a retail shop, arriving at a dealership, walking into a clinic, or checking in at a venue\u2014depending on the business.<\/p>\n\n\n\n

At its core, Store Visit Attribution links:\n– Digital exposure and engagement<\/strong> (impressions, clicks, map actions, calls)\n– to offline outcomes<\/strong> (in-person visits)<\/p>\n\n\n\n

The business meaning is straightforward: it turns location traffic into a measurable conversion event, enabling marketers to evaluate channels that influence in-store behavior. In Conversion & Measurement<\/strong>, Store Visit Attribution sits alongside online conversions (purchases, leads, sign-ups) as a way to quantify performance for omnichannel campaigns. Within Attribution<\/strong>, it helps distribute credit across touchpoints that influence the decision to visit\u2014often in combination with other conversion events like calls, form fills, and online orders.<\/p>\n\n\n\n

Why Store Visit Attribution Matters in Conversion & Measurement<\/h2>\n\n\n\n

Store Visit Attribution matters because many high-value conversions happen in person, and ignoring them can distort decision-making. A channel may look \u201cunprofitable\u201d online while consistently driving foot traffic that converts at the register.<\/p>\n\n\n\n

Strategically, Store Visit Attribution enables:\n– Smarter budget allocation<\/strong>: invest in campaigns that increase location traffic, not just clicks.\n– Better local optimization<\/strong>: understand which areas, store clusters, and audiences respond.\n– Incremental measurement<\/strong>: separate correlation (people who would have visited anyway) from lift when paired with experiments.<\/p>\n\n\n\n

From a competitive standpoint, brands that can measure offline impact often out-iterate competitors on local messaging, creative, and offers. In Conversion & Measurement<\/strong>, that can translate into more efficient customer acquisition and more accurate forecasting. In Attribution<\/strong>, it closes a major gap between digital activity and real-world outcomes.<\/p>\n\n\n\n

How Store Visit Attribution Works<\/h2>\n\n\n\n

Store Visit Attribution can be implemented in multiple ways, but in practice it follows a common workflow.<\/p>\n\n\n\n

1) Inputs: marketing and location signals<\/h3>\n\n\n\n

Typical inputs include:\n– Ad impressions and clicks (search, social, display)\n– Map interactions (directions, \u201cnear me\u201d searches, place page actions)\n– Location context (store geofences, address data, opening hours)\n– Optional first-party signals (loyalty app events, POS-linked identifiers, consented location data)<\/p>\n\n\n\n

2) Processing: matching and modeling<\/h3>\n\n\n\n

Because you can\u2019t always deterministically connect a click to a physical entry, Store Visit Attribution often uses a combination of:\n– Deterministic matching<\/strong> where possible (for example, consented app location signals tied to a known user)\n– Probabilistic methods<\/strong> and modeling to estimate whether a visit occurred after an exposure<\/p>\n\n\n\n

High-quality Conversion & Measurement<\/strong> implementations apply filters to reduce noise, such as minimum dwell time, distance thresholds, and exclusion of employees or neighboring venues.<\/p>\n\n\n\n

3) Application: counting visits and assigning credit<\/h3>\n\n\n\n

Once visits are detected or estimated, systems attribute them back to:\n– Campaigns, keywords, creatives, and audiences\n– Channels (paid search, paid social, local listings, email)\n– Geography (DMA, city, store radius)<\/p>\n\n\n\n

This is where Store Visit Attribution intersects with Attribution<\/strong> models: the visit can be credited via last-click, data-driven methods, or custom weighting\u2014depending on your measurement approach.<\/p>\n\n\n\n

4) Outputs: reporting and optimization<\/h3>\n\n\n\n

Outputs typically include:\n– Store visits as a conversion metric\n– Cost per store visit, visit rate, and lift\n– Insights by location, time, audience, and creative<\/p>\n\n\n\n

These outputs feed back into bidding, targeting, creative strategy, and local operations\u2014making Store Visit Attribution a living part of Conversion & Measurement<\/strong>, not a one-time report.<\/p>\n\n\n\n

Key Components of Store Visit Attribution<\/h2>\n\n\n\n

Successful Store Visit Attribution relies on more than a single report. Key components include:<\/p>\n\n\n\n

Data inputs and identity signals<\/h3>\n\n\n\n
    \n
  • Ad platform interaction data (impressions, clicks, view-through windows)<\/li>\n
  • Location signals (GPS, Wi\u2011Fi, Bluetooth where consented and supported)<\/li>\n
  • First-party data (CRM\/loyalty IDs, app usage, email engagement)<\/li>\n
  • Store metadata (accurate addresses, categories, hours, store IDs)<\/li>\n<\/ul>\n\n\n\n

    Measurement rules and governance<\/h3>\n\n\n\n
      \n
    • Clear definition of a \u201cvisit\u201d (dwell time, frequency caps, exclusions)<\/li>\n
    • Documented attribution windows (e.g., 1 day vs 7 days post-click)<\/li>\n
    • Privacy and consent management aligned to regulations and platform policies<\/li>\n<\/ul>\n\n\n\n

      Operational ownership<\/h3>\n\n\n\n

      Store Visit Attribution often spans teams:\n– Marketing (campaign strategy, budget decisions)\n– Analytics\/BI (validation, dashboards, experimentation)\n– Engineering\/data (tagging, pipelines, identity governance)\n– Store operations (local context, promotions, staffing impact)<\/p>\n\n\n\n

      Reporting and decision loops<\/h3>\n\n\n\n

      In Conversion & Measurement<\/strong>, value comes from action:\n– automated reporting dashboards\n– weekly optimization workflows\n– creative and landing experience iteration (including store locator and map actions)<\/p>\n\n\n\n

      Types of Store Visit Attribution<\/h2>\n\n\n\n

      Store Visit Attribution doesn\u2019t have a single universal \u201cmodel,\u201d but there are practical distinctions in how it\u2019s approached:<\/p>\n\n\n\n

      Direct (deterministic) vs modeled (probabilistic)<\/h3>\n\n\n\n
        \n
      • Deterministic<\/strong> approaches rely on consented first-party signals (e.g., app check-ins or known user sessions). They can be precise but may have limited coverage.<\/li>\n
      • Modeled<\/strong> approaches estimate visits using aggregated signals and statistical methods. Coverage is broader but depends on assumptions and calibration.<\/li>\n<\/ul>\n\n\n\n

        Click-based vs view-through visit attribution<\/h3>\n\n\n\n
          \n
        • Click-based<\/strong> ties visits to ad clicks and tends to be more conservative.<\/li>\n
        • View-through<\/strong> attributes visits after ad impressions without a click; it can be useful for upper-funnel channels but requires careful guardrails to avoid over-crediting.<\/li>\n<\/ul>\n\n\n\n

          Single-touch vs multi-touch Attribution<\/h3>\n\n\n\n
            \n
          • Single-touch<\/strong> (often last touch) assigns the visit to one interaction.<\/li>\n
          • Multi-touch<\/strong> distributes credit across multiple touchpoints, aligning Store Visit Attribution with broader Attribution<\/strong> strategy.<\/li>\n<\/ul>\n\n\n\n

            Location-level vs store-level measurement<\/h3>\n\n\n\n
              \n
            • Location-level<\/strong> aggregates performance by region or market.<\/li>\n
            • Store-level<\/strong> attempts to attribute visits to specific stores, which increases complexity and data requirements but can unlock local optimization.<\/li>\n<\/ul>\n\n\n\n

              Real-World Examples of Store Visit Attribution<\/h2>\n\n\n\n

              Example 1: Local search ads for a multi-location retailer<\/h3>\n\n\n\n

              A retailer runs paid search campaigns for \u201crunning shoes near me\u201d and \u201csports store open now.\u201d Store Visit Attribution counts visits after ad interactions and reveals that certain keywords drive high in-store traffic even when online purchases are low. In Conversion & Measurement<\/strong>, the team shifts budget toward those high-intent terms and updates ad copy to highlight same-day availability. In Attribution<\/strong>, store visits become a core conversion event alongside online revenue.<\/p>\n\n\n\n

              Example 2: Paid social for a restaurant chain with weekday promotions<\/h3>\n\n\n\n

              A restaurant promotes weekday lunch offers via paid social with location targeting. Store Visit Attribution shows strong lift within a 3\u20135 mile radius during 11am\u20132pm, but weaker results on weekends. The team refines dayparting and creative, improving cost per store visit. This ties social spend to real foot traffic\u2014critical for Conversion & Measurement<\/strong> accuracy.<\/p>\n\n\n\n

              Example 3: Omnichannel campaign with a store locator optimization<\/h3>\n\n\n\n

              A home services brand runs display and video ads and notices low click-through rates. Store Visit Attribution indicates meaningful store visit impact after exposures, but only when users subsequently engage with the store locator or map directions. The team improves the locator UX, adds local inventory\/service messaging, and tightens Attribution<\/strong> windows to prevent inflated view-through credit.<\/p>\n\n\n\n

              Benefits of Using Store Visit Attribution<\/h2>\n\n\n\n

              Store Visit Attribution can improve both measurement quality and business outcomes:<\/p>\n\n\n\n

                \n
              • More complete performance reporting<\/strong>: Offline conversions are included in Conversion & Measurement<\/strong>, reducing bias toward online-only channels.<\/li>\n
              • Better ROI decisions<\/strong>: Budget can be shifted toward campaigns that drive in-store revenue, not just web metrics.<\/li>\n
              • Improved efficiency<\/strong>: Optimize bids, audiences, and creatives based on visit outcomes and cost per visit.<\/li>\n
              • Stronger local customer experience<\/strong>: Insights help align messaging with store hours, availability, and local needs.<\/li>\n
              • Omnichannel clarity<\/strong>: Store Visit Attribution helps quantify how online discovery influences offline behavior, strengthening overall Attribution<\/strong> strategy.<\/li>\n<\/ul>\n\n\n\n

                Challenges of Store Visit Attribution<\/h2>\n\n\n\n

                Despite its value, Store Visit Attribution comes with real constraints:<\/p>\n\n\n\n

                Data accuracy and noise<\/h3>\n\n\n\n
                  \n
                • GPS drift, dense urban environments, and shared venues can misclassify visits.<\/li>\n
                • Short dwell times may represent passersby, not customers.<\/li>\n<\/ul>\n\n\n\n

                  Privacy and consent requirements<\/h3>\n\n\n\n
                    \n
                  • Location data is sensitive, and regulations plus platform policies limit granularity.<\/li>\n
                  • First-party strategies require clear consent flows and governance.<\/li>\n<\/ul>\n\n\n\n

                    Coverage and representativeness<\/h3>\n\n\n\n
                      \n
                    • Not all users share location signals, causing sampling bias.<\/li>\n
                    • Modeled estimates may not reflect smaller markets or low-traffic stores well.<\/li>\n<\/ul>\n\n\n\n

                      Cross-channel comparability<\/h3>\n\n\n\n

                      Different platforms may define \u201cvisit\u201d differently. In Conversion & Measurement<\/strong>, comparing store visit metrics across channels requires careful normalization and documentation.<\/p>\n\n\n\n

                      Over-attribution risk<\/h3>\n\n\n\n

                      View-through credit and long windows can inflate impact. Without incrementality testing, Attribution<\/strong> can mislead budget decisions.<\/p>\n\n\n\n

                      Best Practices for Store Visit Attribution<\/h2>\n\n\n\n

                      To make Store Visit Attribution actionable and trustworthy:<\/p>\n\n\n\n

                        \n
                      1. \n

                        Define \u201cstore visit\u201d precisely<\/strong>\n Document dwell-time thresholds, exclusion rules, and how repeat visits are treated.<\/p>\n<\/li>\n

                      2. \n

                        Start with a measurement hierarchy<\/strong>\n Use the most reliable signals available (first-party, consented) and treat modeled numbers as estimates with confidence bounds when possible.<\/p>\n<\/li>\n

                      3. \n

                        Align attribution windows to buying cycles<\/strong>\n Short windows may fit quick decisions (food, convenience retail). Longer windows may fit considered purchases (auto, furniture). Consistency improves Conversion & Measurement<\/strong> comparability.<\/p>\n<\/li>\n

                      4. \n

                        Use experiments to validate lift<\/strong>\n Geo-holdouts, matched markets, or incrementality tests help determine whether visits increased due to marketing\u2014strengthening Attribution<\/strong> beyond correlation.<\/p>\n<\/li>\n

                      5. \n

                        Segment by geography and store clusters<\/strong>\n Performance drivers differ by density, competition, and local demand. Store Visit Attribution is most useful when analyzed at meaningful local levels.<\/p>\n<\/li>\n

                      6. \n

                        Connect visits to business outcomes when possible<\/strong>\n Pair store visits with POS signals, loyalty redemptions, or average order value assumptions to estimate revenue impact.<\/p>\n<\/li>\n

                      7. \n

                        Build a repeatable reporting cadence<\/strong>\n Weekly and monthly reviews that tie Store Visit Attribution to optimization actions keep Conversion & Measurement<\/strong> from becoming \u201creporting-only.\u201d<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                        Tools Used for Store Visit Attribution<\/h2>\n\n\n\n

                        Store Visit Attribution is typically operationalized through a combination of tool categories:<\/p>\n\n\n\n

                          \n
                        • Ad platforms<\/strong>: provide store visit conversion reporting based on their identity and location signals; also support optimization toward visit events.<\/li>\n
                        • Analytics tools<\/strong>: unify web\/app behavior with local actions (store locator usage, directions clicks) to support Conversion & Measurement<\/strong> analysis.<\/li>\n
                        • Tag management and event tracking<\/strong>: ensure consistent collection of on-site actions that correlate with store intent (e.g., \u201cget directions,\u201d \u201ccall,\u201d \u201cfind a store\u201d).<\/li>\n
                        • CRM and loyalty systems<\/strong>: enable first-party identifiers and consented customer relationships that can improve measurement quality.<\/li>\n
                        • Data warehouses and BI dashboards<\/strong>: centralize reporting, enable segmentation by store\/region, and maintain a single source of truth for Attribution<\/strong> decisions.<\/li>\n
                        • Privacy and consent management tooling<\/strong>: supports compliant collection and use of location-related signals where applicable.<\/li>\n<\/ul>\n\n\n\n

                          The best stack is the one that produces explainable, repeatable Store Visit Attribution insights aligned with business decisions\u2014not just more metrics.<\/p>\n\n\n\n

                          Metrics Related to Store Visit Attribution<\/h2>\n\n\n\n

                          Common metrics that matter in Store Visit Attribution and Conversion & Measurement<\/strong> include:<\/p>\n\n\n\n

                            \n
                          • Store visits<\/strong> (count): the primary conversion metric, measured or modeled.<\/li>\n
                          • Visit rate<\/strong>: visits per click, visits per impression, or visits per exposed user.<\/li>\n
                          • Cost per store visit (CPSV)<\/strong>: spend divided by attributed visits; useful for channel comparison.<\/li>\n
                          • Incremental store visits<\/strong>: visits above baseline, estimated via experiments.<\/li>\n
                          • Directions clicks \/ map actions<\/strong>: leading indicators of store intent.<\/li>\n
                          • Call clicks<\/strong>: another offline-intent signal often analyzed alongside visits.<\/li>\n
                          • Store visit value<\/strong>: estimated revenue per visit (from POS averages or loyalty data) to translate visits into business value.<\/li>\n
                          • Cross-channel contribution<\/strong>: how store visits distribute across channels under your Attribution<\/strong> model.<\/li>\n<\/ul>\n\n\n\n

                            Future Trends of Store Visit Attribution<\/h2>\n\n\n\n

                            Store Visit Attribution is evolving quickly within Conversion & Measurement<\/strong> due to technology and privacy shifts:<\/p>\n\n\n\n

                              \n
                            • More modeling and aggregation<\/strong>: as user-level tracking becomes harder, aggregated and privacy-preserving methods will play a larger role.<\/li>\n
                            • Greater reliance on first-party data<\/strong>: loyalty programs, apps, and authenticated experiences will become central to durable measurement.<\/li>\n
                            • AI-assisted optimization<\/strong>: machine learning can improve prediction of visit propensity, detect anomalies, and recommend budget shifts\u2014while still requiring human governance.<\/li>\n
                            • Incrementality-first measurement<\/strong>: marketers will increasingly demand proof of lift rather than implied Attribution<\/strong> from exposure.<\/li>\n
                            • Omnichannel measurement alignment<\/strong>: organizations will unify online revenue, offline visits, and in-store sales into a single Conversion & Measurement<\/strong> framework for planning and forecasting.<\/li>\n<\/ul>\n\n\n\n

                              Store Visit Attribution vs Related Terms<\/h2>\n\n\n\n

                              Store Visit Attribution vs Offline conversion tracking<\/h3>\n\n\n\n

                              Offline conversion tracking is broader: it includes calls, appointments, in-store purchases, and other offline events. Store Visit Attribution<\/strong> is specifically focused on the visit<\/em> as the conversion outcome and the logic that connects marketing touchpoints to that visit.<\/p>\n\n\n\n

                              Store Visit Attribution vs Footfall measurement<\/h3>\n\n\n\n

                              Footfall measurement typically focuses on counting traffic at a location (often for operations or market research). Store Visit Attribution goes further by tying visits back to marketing exposures and applying Attribution<\/strong> to evaluate channel impact.<\/p>\n\n\n\n

                              Store Visit Attribution vs Multi-touch Attribution (MTA)<\/h3>\n\n\n\n

                              Multi-touch Attribution aims to distribute conversion credit across multiple interactions, usually across digital journeys. Store Visit Attribution can be an input to MTA (treating \u201cvisit\u201d as a conversion), but it also includes the unique challenge of detecting offline presence and handling location data constraints in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                              Who Should Learn Store Visit Attribution<\/h2>\n\n\n\n
                                \n
                              • Marketers<\/strong> benefit by optimizing spend toward real business outcomes, not just online proxies.<\/li>\n
                              • Analysts and data teams<\/strong> need Store Visit Attribution to build accurate omnichannel reporting, validate lift, and strengthen Attribution<\/strong> governance.<\/li>\n
                              • Agencies<\/strong> use it to prove value for local campaigns and to defend budgets with better Conversion & Measurement<\/strong> evidence.<\/li>\n
                              • Business owners and founders<\/strong> gain clarity on which marketing investments drive customers into stores, especially when eCommerce tracking underrepresents performance.<\/li>\n
                              • Developers and technical teams<\/strong> support implementation through event tracking, data pipelines, consent management, and reporting reliability.<\/li>\n<\/ul>\n\n\n\n

                                Summary of Store Visit Attribution<\/h2>\n\n\n\n

                                Store Visit Attribution<\/strong> is a measurement approach that connects digital marketing touchpoints to physical store visits, making offline behavior measurable and optimizable. It plays a critical role in Conversion & Measurement<\/strong> by expanding what counts as a conversion beyond the website. Within Attribution<\/strong>, it helps assign credit to channels and campaigns that influence real-world visits, supporting smarter budget decisions, better local strategy, and more accurate omnichannel performance evaluation.<\/p>\n\n\n\n

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

                                What is Store Visit Attribution used for?<\/h3>\n\n\n\n

                                Store Visit Attribution is used to measure how ads and other marketing touchpoints contribute to visits to physical locations, so teams can optimize campaigns based on offline outcomes within Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                Is Store Visit Attribution accurate?<\/h3>\n\n\n\n

                                It can be directionally strong but depends on data availability, consented location signals, and modeling assumptions. The most reliable programs validate Store Visit Attribution with experiments and clear visit definitions.<\/p>\n\n\n\n

                                How does Attribution work when a customer sees multiple ads before visiting?<\/h3>\n\n\n\n

                                That depends on your Attribution<\/strong> model. Some organizations use last-touch rules; others use multi-touch approaches that distribute credit across exposures and clicks. Store Visit Attribution provides the visit event; the model determines how credit is assigned.<\/p>\n\n\n\n

                                What\u2019s the difference between store visits and directions clicks?<\/h3>\n\n\n\n

                                Directions clicks indicate intent and are a leading indicator; store visits aim to represent actual arrival at a location. In Conversion & Measurement<\/strong>, it\u2019s common to track both\u2014directions for early funnel insight and Store Visit Attribution for outcome measurement.<\/p>\n\n\n\n

                                Do you need an app to measure store visits?<\/h3>\n\n\n\n

                                No. Some approaches use aggregated signals from platforms or modeled methods. However, first-party app data (with consent) can improve precision and help tie Store Visit Attribution to customer value.<\/p>\n\n\n\n

                                How should I choose an attribution window for store visits?<\/h3>\n\n\n\n

                                Pick a window that matches your buying cycle and channel behavior. Quick-decision categories often use shorter windows; considered purchases may use longer ones. Consistency is key for comparing results in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                Can Store Visit Attribution be tied to revenue?<\/h3>\n\n\n\n

                                Yes, indirectly or directly. Indirectly, you can estimate revenue using average order value or visit value. Direct linkage requires POS or loyalty integrations and strong governance, but it makes Attribution<\/strong> decisions more financially grounded.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                Digital marketing doesn\u2019t stop at the checkout page. For retailers, restaurants, automotive, healthcare, and any business with physical locations, a large share of revenue happens offline\u2014after someone sees an ad, clicks a listing, or taps for directions. **Store Visit Attribution** is the set of methods used in **Conversion & Measurement** to connect digital marketing touchpoints to real-world store visits so teams can evaluate performance, optimize spend, and improve customer journeys.<\/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":[1888],"tags":[],"class_list":["post-7066","post","type-post","status-publish","format-standard","hentry","category-attribution"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7066","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=7066"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7066\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7066"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7066"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7066"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}