{"id":11172,"date":"2026-04-01T11:57:47","date_gmt":"2026-04-01T11:57:47","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/store-visits\/"},"modified":"2026-04-01T11:57:47","modified_gmt":"2026-04-01T11:57:47","slug":"store-visits","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/store-visits\/","title":{"rendered":"Store Visits: What It Is, Key Features, Benefits, Use Cases, and How It Fits in SEM \/ Paid Search"},"content":{"rendered":"\n

Store Visits is a measurement concept in Paid Marketing<\/strong> that estimates how many people physically visit a business location after interacting with an ad. In SEM \/ Paid Search<\/strong>, it\u2019s especially valuable because it connects high-intent search behavior (like \u201cnear me\u201d or brand queries) to real-world outcomes that don\u2019t happen on a website.<\/p>\n\n\n\n

As more revenue is influenced by local intent, omnichannel shopping, and same-day decisions, Store Visits helps marketers justify budgets, optimize campaigns, and understand what online ads are doing offline. It\u2019s not a perfect \u201cground truth\u201d count, but when implemented correctly, it becomes one of the most practical bridges between digital spend and in-store performance.<\/p>\n\n\n\n

\n\n\n\n

What Is Store Visits?<\/h2>\n\n\n\n

Store Visits<\/strong> is an offline conversion metric that attributes an in-person visit to an ad interaction (such as an impression, click, or call) using aggregated location signals and modeling. In plain terms: it answers, \u201cDid our ads help bring people into our physical locations?\u201d<\/p>\n\n\n\n

The core concept<\/h3>\n\n\n\n

Store Visits focuses on foot traffic influenced by ads<\/strong>, not just online actions. While web conversions track events like purchases or form fills, Store Visits tracks a real-world behavior that typically happens outside your analytics tags.<\/p>\n\n\n\n

The business meaning<\/h3>\n\n\n\n

For retailers, restaurants, banks, automotive, healthcare clinics, and any multi-location brand, Store Visits is often closer to the revenue moment than a website click. It can support decisions like:\n– Which keywords and ads drive incremental footfall\n– Which locations benefit most from ad spend\n– Whether local campaigns are profitable even when online conversions look low<\/p>\n\n\n\n

Where it fits in Paid Marketing and SEM \/ Paid Search<\/h3>\n\n\n\n

In Paid Marketing<\/strong>, Store Visits is part of \u201coffline measurement,\u201d alongside call conversions and imported sales. In SEM \/ Paid Search<\/strong>, it is commonly used to evaluate local-intent queries, brand defense, and campaigns where the website is informational but the transaction happens in-person.<\/p>\n\n\n\n

\n\n\n\n

Why Store Visits Matters in Paid Marketing<\/h2>\n\n\n\n

Store Visits matters because many businesses can\u2019t judge success solely by online conversions. People may search, compare, and then show up in person\u2014especially when urgency, proximity, or trust is involved.<\/p>\n\n\n\n

Key reasons it\u2019s strategically important in Paid Marketing<\/strong> and SEM \/ Paid Search<\/strong>:<\/p>\n\n\n\n

    \n
  • More accurate ROI narratives:<\/strong> If sales happen in-store, online-only reporting can undervalue search ads.<\/li>\n
  • Better budget allocation:<\/strong> You can shift investment toward campaigns and locations that generate profitable foot traffic.<\/li>\n
  • Stronger local competitiveness:<\/strong> Competitors bid on local intent; Store Visits helps you see whether you\u2019re winning the in-person outcome, not just the click.<\/li>\n
  • Improved executive alignment:<\/strong> Offline-oriented stakeholders (store ops, franchisees, regional managers) understand visits more intuitively than click-through rate.<\/li>\n<\/ul>\n\n\n\n

    Used well, Store Visits becomes a decision metric\u2014not a vanity metric.<\/p>\n\n\n\n

    \n\n\n\n

    How Store Visits Works<\/h2>\n\n\n\n

    Store Visits is a modeled measurement workflow that combines ad interaction data with location and business information. Exact methods vary by platform and eligibility, but the practical flow is consistent.<\/p>\n\n\n\n

      \n
    1. \n

      Input \/ trigger: ad exposure and engagement<\/strong>\n A user sees or clicks a search ad, taps for directions, calls, or otherwise engages. In SEM \/ Paid Search<\/strong>, this often starts with a high-intent query and a location-aware ad experience.<\/p>\n<\/li>\n

    2. \n

      Analysis \/ processing: matching and modeling<\/strong>\n The ad platform uses aggregated, privacy-safe location signals (from users who have enabled location sharing and meet eligibility thresholds) and matches patterns of movement against known business locations. Data is typically modeled<\/strong>, not directly observed for every user, to protect privacy and fill gaps.<\/p>\n<\/li>\n

    3. \n

      Execution \/ application: conversion reporting<\/strong>\n Store Visits are reported as an offline conversion type inside the platform\u2019s conversion reporting. They can be segmented by campaign, ad group, keyword theme, device, location, and time period (depending on platform capabilities).<\/p>\n<\/li>\n

    4. \n

      Output \/ outcome: optimization and planning<\/strong>\n Marketers use Store Visits to adjust bids, targeting, and creative; evaluate local strategy; and estimate offline return. In Paid Marketing<\/strong>, the end goal is better spend efficiency and stronger revenue outcomes\u2014not just more visits.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

      \n\n\n\n

      Key Components of Store Visits<\/h2>\n\n\n\n

      Implementing Store Visits well requires more than \u201cturning it on.\u201d The strongest programs treat it as a measurement system with inputs, controls, and accountability.<\/p>\n\n\n\n

      Data and setup fundamentals<\/h3>\n\n\n\n
        \n
      • Accurate location data:<\/strong> Store addresses, hours, and location identifiers must be correct and consistently managed.<\/li>\n
      • Location associations:<\/strong> Ads must be connected to the correct business locations so visits can be attributed accurately.<\/li>\n
      • Conversion configuration:<\/strong> Store Visits should be clearly defined in your conversion actions and reporting views.<\/li>\n<\/ul>\n\n\n\n

        Operational processes<\/h3>\n\n\n\n
          \n
        • Governance and ownership:<\/strong> Clarify who owns location data (marketing ops, local SEO, store ops) and who owns measurement (analytics, performance marketing).<\/li>\n
        • Validation approach:<\/strong> Decide how you will sanity-check Store Visits using other signals like POS trends, regional performance, or lift tests.<\/li>\n
        • Reporting cadence:<\/strong> Offline conversions often have reporting delays, so expectations and timelines should be set with stakeholders.<\/li>\n<\/ul>\n\n\n\n

          Metrics and segmentation<\/h3>\n\n\n\n
            \n
          • Store visit rate, cost per visit, and visit value<\/strong> are only useful when segmented properly (by location, query intent, and distance).<\/li>\n
          • In SEM \/ Paid Search<\/strong>, segmentation by brand vs non-brand<\/strong> and local-intent vs research-intent<\/strong> is often critical.<\/li>\n<\/ul>\n\n\n\n
            \n\n\n\n

            Types of Store Visits<\/h2>\n\n\n\n

            Store Visits doesn\u2019t have \u201cofficial\u201d universal types, but in practice it shows up in different measurement approaches and contexts. The distinctions below help teams interpret results correctly.<\/p>\n\n\n\n

            1) Modeled Store Visits (platform-estimated)<\/h3>\n\n\n\n

            This is the most common approach: visits are estimated<\/strong> using aggregated location signals and statistical modeling. It\u2019s scalable and privacy-aware, but it is not a census count.<\/p>\n\n\n\n

            2) Verified offline outcomes (visits tied to first-party data)<\/h3>\n\n\n\n

            Some organizations complement Store Visits with first-party<\/strong> signals such as loyalty check-ins, appointment arrivals, Wi\u2011Fi opt-ins, or POS-linked customer identities (where legally permitted). This can improve confidence but requires strong data governance.<\/p>\n\n\n\n

            3) Incrementality-focused measurement (lift-based)<\/h3>\n\n\n\n

            Instead of treating Store Visits as absolute truth, teams run experiments to estimate incremental<\/strong> visits caused by ads (e.g., geo tests). This is often the most decision-useful method for budget planning in Paid Marketing<\/strong>.<\/p>\n\n\n\n

            4) Context-based distinctions<\/h3>\n\n\n\n
              \n
            • Single-location vs multi-location<\/strong> reporting needs<\/li>\n
            • Urban vs rural<\/strong> measurement stability (density impacts signal quality)<\/li>\n
            • High-frequency vs low-frequency<\/strong> visit behavior (coffee shop vs furniture)<\/li>\n<\/ul>\n\n\n\n
              \n\n\n\n

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

              Example 1: Retail chain optimizing non-brand keywords<\/h3>\n\n\n\n

              A retailer runs SEM \/ Paid Search<\/strong> campaigns for \u201crunning shoes near me\u201d and \u201csports store open now.\u201d Online purchases are low, but Store Visits show strong in-store intent. The team:\n– Raises bids on high-intent local queries within a tight radius\n– Adjusts ad copy to emphasize availability and store hours\n– Evaluates performance by region using cost per Store Visits\nResult: Paid Marketing<\/strong> performance improves even though online ROAS alone would have suggested cutting spend.<\/p>\n\n\n\n

              Example 2: Restaurant measuring lunchtime demand by daypart<\/h3>\n\n\n\n

              A quick-service restaurant uses Store Visits to understand whether ads drive lunch traffic. The team:\n– Splits campaigns by daypart and weekday\n– Tracks Store Visits and cost per visit by time window\n– Uses insights to concentrate budget on high-converting lunch periods\nResult: Better efficiency and less wasted spend during low-visit periods.<\/p>\n\n\n\n

              Example 3: Multi-location service business prioritizing the best markets<\/h3>\n\n\n\n

              A healthcare clinic network sees uneven performance across cities. Store Visits reveals which markets respond to \u201csame-day appointment\u201d searches. The team:\n– Reallocates budget toward high-response markets\n– Aligns landing pages and call handling with top locations\n– Uses Store Visits trends to plan future expansion priorities\nResult: SEM \/ Paid Search<\/strong> becomes a market intelligence channel, not just a lead channel.<\/p>\n\n\n\n

              \n\n\n\n

              Benefits of Using Store Visits<\/h2>\n\n\n\n

              When used thoughtfully, Store Visits can improve both measurement quality and performance outcomes.<\/p>\n\n\n\n

                \n
              • Better optimization signals:<\/strong> In-location businesses can optimize Paid Marketing<\/strong> toward offline behavior, not just clicks.<\/li>\n
              • More complete ROI:<\/strong> Store Visits helps quantify value where ecommerce tracking is incomplete or irrelevant.<\/li>\n
              • Improved local efficiency:<\/strong> Supports tighter radius targeting, smarter scheduling, and location-level budget control.<\/li>\n
              • Alignment with customer reality:<\/strong> Customers don\u2019t think in channels; Store Visits reflects the omnichannel path from search to store.<\/li>\n<\/ul>\n\n\n\n
                \n\n\n\n

                Challenges of Store Visits<\/h2>\n\n\n\n

                Store Visits is powerful, but it has real limitations. Treating it as an estimate with known constraints is essential.<\/p>\n\n\n\n

                  \n
                • Eligibility and volume thresholds:<\/strong> Smaller advertisers or low-traffic locations may not receive Store Visits reporting.<\/li>\n
                • Privacy and consent constraints:<\/strong> Measurement relies on aggregated signals and may change as privacy rules evolve.<\/li>\n
                • Attribution complexity:<\/strong> A visit may be influenced by multiple touches (organic, social, maps, email), not just SEM \/ Paid Search<\/strong>.<\/li>\n
                • Reporting delays and volatility:<\/strong> Offline conversions can lag and fluctuate with modeling updates.<\/li>\n
                • Location data hygiene issues:<\/strong> Incorrect addresses, duplicates, or mis-linked locations can distort results.<\/li>\n
                • Non-incremental visits:<\/strong> Some people would have visited anyway; Store Visits doesn\u2019t automatically equal incremental lift.<\/li>\n<\/ul>\n\n\n\n
                  \n\n\n\n

                  Best Practices for Store Visits<\/h2>\n\n\n\n

                  Use these practices to make Store Visits more reliable and more actionable in Paid Marketing<\/strong>.<\/p>\n\n\n\n

                    \n
                  1. \n

                    Start with location accuracy and consistency<\/strong>\n Keep store details clean, standardized, and frequently audited\u2014especially for multi-location brands.<\/p>\n<\/li>\n

                  2. \n

                    Segment by intent, not just by campaign names<\/strong>\n In SEM \/ Paid Search<\/strong>, separate:\n – Brand vs non-brand\n – \u201cNear me\/open now\u201d vs research queries\n – Competitor terms (if used) vs category terms<\/p>\n<\/li>\n

                  3. \n

                    Use Store Visits as part of a measurement portfolio<\/strong>\n Combine Store Visits with calls, direction requests, appointment bookings, and POS trends. No single metric tells the full story.<\/p>\n<\/li>\n

                  4. \n

                    Model a visit value (carefully)<\/strong>\n Estimate average revenue per visit using store-level analytics or historical conversion rates. Revisit assumptions regularly by region and season.<\/p>\n<\/li>\n

                  5. \n

                    Validate with experiments when possible<\/strong>\n Use geo holdouts or budget split tests to estimate incrementality. This strengthens confidence in Paid Marketing<\/strong> ROI discussions.<\/p>\n<\/li>\n

                  6. \n

                    Align reporting windows with offline reality<\/strong>\n Discuss lag times, seasonality, and local events with stakeholders so Store Visits isn\u2019t misread week to week.<\/p>\n<\/li>\n

                  7. \n

                    Operationalize location-level insights<\/strong>\n If one location has high Store Visits but poor in-store conversion, the fix may be operational (staffing, inventory), not advertising.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                    \n\n\n\n

                    Tools Used for Store Visits<\/h2>\n\n\n\n

                    Store Visits measurement typically spans multiple systems. The goal is to connect ad exposure to physical outcomes while maintaining privacy and governance.<\/p>\n\n\n\n

                      \n
                    • Ad platforms (search advertising platforms):<\/strong> Where Store Visits conversions are typically reported and used for bidding and optimization in SEM \/ Paid Search<\/strong>.<\/li>\n
                    • Analytics tools:<\/strong> Used to compare online behavior with offline patterns (e.g., location pages, \u201cdirections\u201d clicks, click-to-call events).<\/li>\n
                    • Tag management and event tracking systems:<\/strong> Helpful for tracking local-intent micro-conversions that often precede Store Visits.<\/li>\n
                    • CRM and customer data platforms:<\/strong> Useful when blending first-party customer outcomes with campaign reporting (where consent and policy allow).<\/li>\n
                    • Point-of-sale and store operations systems:<\/strong> Provide contextual validation\u2014sales trends, transaction counts, average order value.<\/li>\n
                    • Reporting dashboards \/ BI tools:<\/strong> Combine Store Visits with spend, calls, and revenue to create decision-ready views for Paid Marketing<\/strong> leaders.<\/li>\n<\/ul>\n\n\n\n
                      \n\n\n\n

                      Metrics Related to Store Visits<\/h2>\n\n\n\n

                      Store Visits is rarely used alone. These related metrics help interpret performance and guide optimization.<\/p>\n\n\n\n

                      Core Store Visits performance metrics<\/h3>\n\n\n\n
                        \n
                      • Store Visits (count):<\/strong> The estimated number of in-person visits attributed to ads.<\/li>\n
                      • Store visit rate:<\/strong> Store Visits divided by ad interactions (often clicks), useful for comparing segments.<\/li>\n
                      • Cost per Store Visit:<\/strong> Spend divided by Store Visits; a key efficiency metric in Paid Marketing<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                        Value and ROI metrics<\/h3>\n\n\n\n
                          \n
                        • Estimated revenue per visit:<\/strong> An assumption-based value derived from historical in-store conversion and average transaction size.<\/li>\n
                        • Offline ROAS (modeled):<\/strong> Revenue estimate divided by ad spend, built on visit value assumptions.<\/li>\n
                        • Incremental visits (from testing):<\/strong> The most decision-oriented metric when lift studies are available.<\/li>\n<\/ul>\n\n\n\n

                          Diagnostic and context metrics<\/h3>\n\n\n\n
                            \n
                          • Distance \/ radius performance:<\/strong> How visit likelihood changes with proximity.<\/li>\n
                          • Time-to-visit lag:<\/strong> Typical delay between ad interaction and store visit.<\/li>\n
                          • Location-level variance:<\/strong> Identifies outliers caused by operations, competition, or tracking issues.<\/li>\n<\/ul>\n\n\n\n
                            \n\n\n\n

                            Future Trends of Store Visits<\/h2>\n\n\n\n

                            Store Visits is evolving as measurement moves toward privacy-first methods and more automation in Paid Marketing<\/strong>.<\/p>\n\n\n\n

                              \n
                            • More modeling, fewer deterministic signals:<\/strong> As privacy constraints tighten, Store Visits will increasingly rely on aggregated modeling and statistical methods.<\/li>\n
                            • First-party data emphasis:<\/strong> Brands will invest more in consented loyalty, appointments, and identity-safe matching to complement Store Visits.<\/li>\n
                            • Experimentation becomes standard:<\/strong> Incrementality testing will matter more than raw attribution as channels overlap.<\/li>\n
                            • AI-driven optimization:<\/strong> Automated bidding and creative systems will use offline signals (including Store Visits) to optimize toward business outcomes, especially in SEM \/ Paid Search<\/strong>.<\/li>\n
                            • Omnichannel planning:<\/strong> Store Visits will be interpreted alongside in-store inventory, local fulfillment options, and regional demand forecasting.<\/li>\n<\/ul>\n\n\n\n
                              \n\n\n\n

                              Store Visits vs Related Terms<\/h2>\n\n\n\n

                              Store Visits vs Foot Traffic<\/h3>\n\n\n\n

                              Foot traffic<\/strong> is a broad concept describing how many people enter a location, regardless of source. Store Visits<\/strong> is specifically the ad-attributed<\/strong> (often modeled) portion used in Paid Marketing<\/strong> reporting.<\/p>\n\n\n\n

                              Store Visits vs Offline Conversions<\/h3>\n\n\n\n

                              Offline conversions<\/strong> is the umbrella category for any conversion happening outside the website (store purchases, signed contracts, in-person appointments). Store Visits is one type of offline conversion focused on the act of visiting.<\/p>\n\n\n\n

                              Store Visits vs Direction Requests \/ Local Actions<\/h3>\n\n\n\n

                              Direction requests and similar \u201clocal actions\u201d are online signals of intent<\/strong> (clicks for directions, calls, location page views). They can correlate with Store Visits, but they are not the same as a measured\/estimated physical visit.<\/p>\n\n\n\n

                              \n\n\n\n

                              Who Should Learn Store Visits<\/h2>\n\n\n\n
                                \n
                              • Marketers:<\/strong> To optimize Paid Marketing<\/strong> toward real business outcomes when ecommerce isn\u2019t the whole story.<\/li>\n
                              • Analysts:<\/strong> To interpret modeled offline metrics correctly, design validation plans, and build location-level dashboards.<\/li>\n
                              • Agencies:<\/strong> To prove impact for multi-location clients and create smarter SEM \/ Paid Search<\/strong> strategies.<\/li>\n
                              • Business owners and operators:<\/strong> To connect ad spend to store performance and make budget decisions with confidence.<\/li>\n
                              • Developers and marketing ops:<\/strong> To support data quality, location governance, and integrations across analytics, CRM, and reporting.<\/li>\n<\/ul>\n\n\n\n
                                \n\n\n\n

                                Summary of Store Visits<\/h2>\n\n\n\n

                                Store Visits<\/strong> is an offline conversion measurement that estimates how many people physically visit a business after interacting with ads. It matters because it helps Paid Marketing<\/strong> teams evaluate performance beyond the website, especially for local-intent campaigns. In SEM \/ Paid Search<\/strong>, Store Visits connects high-intent queries to real-world outcomes, enabling smarter optimization, better budget allocation, and stronger omnichannel ROI analysis when used with proper segmentation, validation, and governance.<\/p>\n\n\n\n

                                \n\n\n\n

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

                                1) What does Store Visits measure in Paid Marketing?<\/h3>\n\n\n\n

                                Store Visits measures the estimated number of in-person visits to a business location that are attributed to ad interactions. It helps quantify offline impact when conversions happen in-store.<\/p>\n\n\n\n

                                2) Are Store Visits exact or estimated?<\/h3>\n\n\n\n

                                Store Visits is typically estimated<\/strong> using aggregated location signals and modeling. It should be treated as a directional, decision-support metric\u2014not a perfect headcount.<\/p>\n\n\n\n

                                3) How can I use Store Visits to optimize SEM \/ Paid Search?<\/h3>\n\n\n\n

                                Use Store Visits to identify which query themes, locations, devices, and time windows drive in-person outcomes. Then adjust bids, budgets, radius targeting, and ad messaging to prioritize segments with strong cost per Store Visit and strong incremental lift potential.<\/p>\n\n\n\n

                                4) Why am I not seeing Store Visits in my reports?<\/h3>\n\n\n\n

                                Common reasons include insufficient traffic volume, missing or incorrect location associations, or platform eligibility thresholds. Also, some industries, regions, or account setups may have limited offline measurement support.<\/p>\n\n\n\n

                                5) What\u2019s a good cost per Store Visit?<\/h3>\n\n\n\n

                                There\u2019s no universal benchmark. A \u201cgood\u201d cost per Store Visit depends on average visit value, in-store conversion rate, margins, and whether visits are incremental. Many teams set targets by region or store format rather than using one global number.<\/p>\n\n\n\n

                                6) Can Store Visits replace POS or sales tracking?<\/h3>\n\n\n\n

                                No. Store Visits complements POS and sales tracking by showing marketing-attributed<\/strong> foot traffic. The strongest Paid Marketing<\/strong> measurement blends Store Visits with first-party revenue signals and, when possible, incrementality testing.<\/p>\n\n\n\n

                                7) How do I validate Store Visits without violating privacy?<\/h3>\n\n\n\n

                                Validate at an aggregated level: compare trends by region and time period against POS transaction counts, staffing\/inventory changes, local events, and controlled geo experiments. Avoid attempts to identify individual visitors; focus on pattern consistency and lift.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                Store Visits is a measurement concept in **Paid Marketing** that estimates how many people physically visit a business location after interacting with an ad. In **SEM \/ Paid Search**, it\u2019s especially valuable because it connects high-intent search behavior (like \u201cnear me\u201d or brand queries) to real-world outcomes that don\u2019t happen on a website.<\/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":[1913],"tags":[],"class_list":["post-11172","post","type-post","status-publish","format-standard","hentry","category-sem-paid-search"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/11172","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=11172"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/11172\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=11172"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=11172"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=11172"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}