{"id":11391,"date":"2026-04-01T20:27:56","date_gmt":"2026-04-01T20:27:56","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/shopping-ads-report\/"},"modified":"2026-04-01T20:27:56","modified_gmt":"2026-04-01T20:27:56","slug":"shopping-ads-report","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/shopping-ads-report\/","title":{"rendered":"Shopping Ads Report: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Shopping Ads"},"content":{"rendered":"\n

A Shopping Ads Report<\/strong> is the reporting view (or exported dataset) you use to understand how your Shopping Ads<\/strong> campaigns are performing inside a broader Paid Marketing<\/strong> program. It turns ad spend, product catalog data, and user behavior into a set of metrics and breakdowns you can act on\u2014what\u2019s selling, what\u2019s wasting budget, and what needs better data or strategy.<\/p>\n\n\n\n

In modern Paid Marketing<\/strong>, shopping campaigns often contain thousands of products and fast-changing auction dynamics. A well-structured Shopping Ads Report<\/strong> is what keeps those moving parts measurable and manageable. It helps teams connect product decisions (price, availability, feed quality) with media decisions (bidding, targeting, negatives, budget allocation) so optimization is based on evidence rather than guesswork.<\/p>\n\n\n\n

What Is Shopping Ads Report?<\/h2>\n\n\n\n

A Shopping Ads Report<\/strong> is a performance report that summarizes results from Shopping Ads<\/strong> campaigns and breaks them down by dimensions such as product, brand, category, device, geography, audience, and time. It can live inside an ad platform UI, a BI dashboard, or a spreadsheet export, but the purpose is the same: make shopping campaign performance observable and explainable.<\/p>\n\n\n\n

The core concept is simple: Shopping Ads<\/strong> do not optimize like text ads because the \u201ckeywords\u201d are often implicit (derived from product data and user intent signals). A Shopping Ads Report<\/strong> provides the visibility you need to understand which products and attributes are driving revenue and which ones are consuming spend without returns.<\/p>\n\n\n\n

From a business perspective, the Shopping Ads Report<\/strong> is where finance and marketing meet. It\u2019s how you validate whether Paid Marketing<\/strong> is producing profitable demand for the products you actually want to sell, at margins that make sense, with a customer acquisition cost you can sustain.<\/p>\n\n\n\n

Why Shopping Ads Report Matters in Paid Marketing<\/h2>\n\n\n\n

A Shopping Ads Report<\/strong> matters because it ties ad performance to the product catalog\u2014the true \u201cinventory\u201d of your growth strategy. In Paid Marketing<\/strong>, you\u2019re not just buying clicks; you\u2019re buying distribution for products under real constraints like margin, stock, seasonality, shipping thresholds, and returns.<\/p>\n\n\n\n

Key reasons it drives outcomes:<\/p>\n\n\n\n

    \n
  • Strategic budget allocation:<\/strong> It shows which categories, brands, or price tiers deserve more investment and which should be capped or paused.<\/li>\n
  • Profit-aware optimization:<\/strong> It helps you move beyond ROAS as a single headline metric and incorporate margin, COGS, and lifetime value where possible.<\/li>\n
  • Competitive resilience:<\/strong> Shopping auctions change quickly. A consistent Shopping Ads Report<\/strong> reveals early signals of CPC inflation, impression loss, or ranking drops.<\/li>\n
  • Cross-team clarity:<\/strong> Merchandising, growth, and analytics can align on one set of facts, reducing \u201copinion-driven\u201d debates.<\/li>\n<\/ul>\n\n\n\n

    In short, a Shopping Ads Report<\/strong> is a decision system for scaling Shopping Ads<\/strong> responsibly within a larger Paid Marketing<\/strong> mix.<\/p>\n\n\n\n

    How Shopping Ads Report Works<\/h2>\n\n\n\n

    In practice, a Shopping Ads Report<\/strong> works as a workflow that converts raw campaign activity into decisions:<\/p>\n\n\n\n

      \n
    1. \n

      Inputs (data sources and triggers)<\/strong>\n Data enters from the ad platform (spend, clicks, impressions), conversion tracking (orders, revenue), and your product feed or catalog (titles, categories, price, availability). Changes like new inventory, price updates, or bid strategy adjustments create new patterns the report must capture.<\/p>\n<\/li>\n

    2. \n

      Processing (normalization and attribution context)<\/strong>\n Reporting tools align time zones, currencies, and attribution windows. They also reconcile product identifiers (SKU, item group, variant IDs) so performance can be compared consistently across Shopping Ads<\/strong> structures.<\/p>\n<\/li>\n

    3. \n

      Application (analysis and optimization decisions)<\/strong>\n Teams segment performance (for example, brand vs. non-brand queries, high-margin vs. low-margin products, mobile vs. desktop) and decide what to change\u2014bids, budgets, feed improvements, product exclusions, or campaign structure.<\/p>\n<\/li>\n

    4. \n

      Outputs (actions and measurable results)<\/strong>\n The output is a set of changes plus an expectation: improved efficiency, higher revenue, increased impression share on priority products, or reduced wasted spend. The next Shopping Ads Report<\/strong> confirms whether the change worked.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

      This \u201creport \u2192 decision \u2192 change \u2192 validation\u201d loop is the operational backbone of Paid Marketing<\/strong> for commerce.<\/p>\n\n\n\n

      Key Components of Shopping Ads Report<\/h2>\n\n\n\n

      A strong Shopping Ads Report<\/strong> usually includes:<\/p>\n\n\n\n

      Core data inputs<\/h3>\n\n\n\n
        \n
      • Campaign and ad group structure:<\/strong> How products are grouped and targeted.<\/li>\n
      • Product feed attributes:<\/strong> Title, brand, category, product type, custom labels, price, sale price, availability.<\/li>\n
      • Conversion tracking data:<\/strong> Purchases, revenue, and (when available) profit signals.<\/li>\n
      • Auction signals:<\/strong> Impression share or comparable visibility metrics, device, location, audience segments.<\/li>\n<\/ul>\n\n\n\n

        Essential breakdown dimensions<\/h3>\n\n\n\n
          \n
        • Product ID \/ SKU or item group<\/li>\n
        • Brand, category, product type<\/li>\n
        • Price bands (and sale vs. non-sale)<\/li>\n
        • Device and geography<\/li>\n
        • Time (day\/week, seasonality)<\/li>\n<\/ul>\n\n\n\n

          Governance and responsibilities<\/h3>\n\n\n\n
            \n
          • Marketing operators<\/strong> interpret the Shopping Ads Report<\/strong> to adjust bids, budgets, and structure.<\/li>\n
          • Merchandising<\/strong> uses it to prioritize assortments and promotions.<\/li>\n
          • Data\/analytics<\/strong> maintains metric definitions and dashboards to keep Paid Marketing<\/strong> reporting consistent.<\/li>\n
          • Feed or engineering teams<\/strong> fix attribute quality issues that suppress performance in Shopping Ads<\/strong>.<\/li>\n<\/ul>\n\n\n\n

            Types of Shopping Ads Report<\/h2>\n\n\n\n

            There aren\u2019t universal \u201cofficial\u201d types everywhere, but in real operations, Shopping Ads Report<\/strong> formats tend to fall into a few practical categories:<\/p>\n\n\n\n

              \n
            1. \n

              Product performance reports<\/strong>\n Break down spend and revenue by SKU, item group, brand, category, or custom labels. This is the most common view for Shopping Ads<\/strong> optimization.<\/p>\n<\/li>\n

            2. \n

              Search query or intent reports (shopping-focused)<\/strong>\n Show how user intent (search terms or query themes) relates to product performance, helping you refine negatives, prioritize titles, and separate brand vs. non-brand strategy in Paid Marketing<\/strong>.<\/p>\n<\/li>\n

            3. \n

              Auction and visibility reports<\/strong>\n Focus on impression share, rank\/position proxies, and competitive pressure indicators to explain why volume changed.<\/p>\n<\/li>\n

            4. \n

              Diagnostics and feed-quality reports<\/strong>\n Highlight disapprovals, missing attributes, policy issues, or price\/availability mismatches that can quietly throttle Shopping Ads<\/strong> delivery.<\/p>\n<\/li>\n

            5. \n

              Executive summaries vs. operator dashboards<\/strong>\n An executive Shopping Ads Report<\/strong> may emphasize revenue, ROAS, and contribution to total sales, while an operator view needs granular levers (SKU-level inefficiency, device splits, and bid strategy segments).<\/p>\n<\/li>\n<\/ol>\n\n\n\n

              Real-World Examples of Shopping Ads Report<\/h2>\n\n\n\n

              Example 1: Retailer reallocating budget to high-margin categories<\/h3>\n\n\n\n

              A home goods retailer reviews a Shopping Ads Report<\/strong> by category and discovers that low-margin accessories generate high ROAS but low profit, while mid-ROAS furniture items produce better contribution margin. They restructure campaigns by margin tiers and set tighter efficiency targets for accessories. The result is a Paid Marketing<\/strong> mix that protects profit while maintaining top-line growth.<\/p>\n\n\n\n

              Example 2: DTC brand fixing feed issues to regain volume<\/h3>\n\n\n\n

              A DTC apparel brand sees a sudden impression drop in Shopping Ads<\/strong>. The Shopping Ads Report<\/strong> combined with diagnostics reveals a spike in disapprovals caused by inconsistent availability values after a catalog update. Fixing feed rules restores eligibility, and the report validates recovery via impressions, clicks, and returning conversion volume.<\/p>\n\n\n\n

              Example 3: Seasonal promotion analysis and pacing<\/h3>\n\n\n\n

              During a promotional week, an electronics seller uses a daily Shopping Ads Report<\/strong> to track price-drop items versus regular-price items. They identify that discounted SKUs are winning auctions on mobile but struggling on desktop due to higher CPCs. They adjust device bid modifiers (or device-level targeting equivalents) and shift budget to the best-performing time windows, improving overall Paid Marketing<\/strong> efficiency.<\/p>\n\n\n\n

              Benefits of Using Shopping Ads Report<\/h2>\n\n\n\n

              Using a Shopping Ads Report<\/strong> consistently can deliver:<\/p>\n\n\n\n

                \n
              • Performance improvements:<\/strong> Identify high-converting products and scale them with smarter bids and budgets in Shopping Ads<\/strong>.<\/li>\n
              • Cost savings:<\/strong> Cut spend on SKUs with poor conversion rates, low inventory, or weak profit contribution.<\/li>\n
              • Operational efficiency:<\/strong> Replace scattered screenshots and ad hoc exports with a repeatable reporting cadence that supports faster decisions in Paid Marketing<\/strong>.<\/li>\n
              • Better customer experience:<\/strong> Insights into product availability, pricing accuracy, and top-selling variants encourage cleaner catalogs and fewer \u201cout of stock\u201d ad clicks.<\/li>\n
              • More reliable forecasting:<\/strong> Trend lines from a Shopping Ads Report<\/strong> support seasonality planning and promo pacing.<\/li>\n<\/ul>\n\n\n\n

                Challenges of Shopping Ads Report<\/h2>\n\n\n\n

                A Shopping Ads Report<\/strong> can mislead if the data or structure is weak. Common challenges include:<\/p>\n\n\n\n

                  \n
                • Attribution limitations:<\/strong> Conversion windows, cross-device behavior, and channel interactions can skew ROAS, especially when Paid Marketing<\/strong> is evaluated in isolation.<\/li>\n
                • SKU identity and mapping issues:<\/strong> Variant vs. parent grouping changes can break historical comparisons.<\/li>\n
                • Feed complexity:<\/strong> Small attribute errors (brand formatting, category mismatches, price inconsistencies) can reduce eligibility or relevance in Shopping Ads<\/strong>.<\/li>\n
                • Lag and volatility:<\/strong> Auction dynamics change quickly; a weekly report can hide daily swings, while a daily report can overreact to noise.<\/li>\n
                • Profit blindness:<\/strong> Revenue-based reporting can reward products that look efficient but are unprofitable after shipping, returns, and COGS.<\/li>\n<\/ul>\n\n\n\n

                  Best Practices for Shopping Ads Report<\/h2>\n\n\n\n

                  To make your Shopping Ads Report<\/strong> consistently actionable:<\/p>\n\n\n\n

                    \n
                  1. \n

                    Define \u201cgood\u201d beyond ROAS<\/strong>\n Add margin tiers or contribution metrics where possible, even if approximate. Align Paid Marketing<\/strong> targets with real business outcomes.<\/p>\n<\/li>\n

                  2. \n

                    Standardize segmentation that matches decisions<\/strong>\n Segment by dimensions you can act on: brand, category, price band, margin label, and inventory status. If you can\u2019t change it, don\u2019t over-segment it.<\/p>\n<\/li>\n

                  3. \n

                    Use consistent time windows and annotations<\/strong>\n Compare like-for-like (same days of week, same promo periods). Annotate major changes: feed updates, bid strategy shifts, budget changes, and site outages.<\/p>\n<\/li>\n

                  4. \n

                    Track both efficiency and volume<\/strong>\n Pair ROAS or CPA with impressions, clicks, and conversion volume. A Shopping Ads Report<\/strong> should explain \u201cwhy revenue changed,\u201d not only \u201chow efficient it was.\u201d<\/p>\n<\/li>\n

                  5. \n

                    Create a repeatable optimization cadence<\/strong>\n – Daily: pacing, disapprovals, sudden CPC\/traffic shifts\n – Weekly: product\/category reallocation, query negatives, bid strategy checks\n – Monthly: structure changes, feed attribute upgrades, profit model updates<\/p>\n<\/li>\n

                  6. \n

                    Validate changes with holdouts when feasible<\/strong>\n For larger accounts, test changes on a subset of products or categories to reduce risk in Shopping Ads<\/strong>.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                    Tools Used for Shopping Ads Report<\/h2>\n\n\n\n

                    You don\u2019t need a single \u201cperfect\u201d platform; you need a reliable system. Common tool groups include:<\/p>\n\n\n\n

                      \n
                    • Ad platforms and native reporting<\/strong> for Shopping Ads<\/strong> performance, visibility metrics, and segmentation controls.<\/li>\n
                    • Analytics tools<\/strong> to validate on-site behavior, conversion paths, and tracking health across Paid Marketing<\/strong> channels.<\/li>\n
                    • Tag management and event tracking systems<\/strong> to keep purchase, revenue, and product events consistent.<\/li>\n
                    • Product feed management tools<\/strong> to enrich attributes, manage rules, and reduce disapprovals that impact Shopping Ads<\/strong> eligibility.<\/li>\n
                    • Reporting dashboards and BI tools<\/strong> to consolidate the Shopping Ads Report<\/strong> with inventory, margin, and CRM data.<\/li>\n
                    • Automation tools<\/strong> (scripts, rules, workflows) to flag anomalies\u2014like spend spikes on out-of-stock SKUs\u2014or to pause poor performers based on thresholds.<\/li>\n
                    • CRM and order systems<\/strong> to incorporate customer value, repeat purchase rates, and refunds into Paid Marketing<\/strong> evaluation.<\/li>\n<\/ul>\n\n\n\n

                      Metrics Related to Shopping Ads Report<\/h2>\n\n\n\n

                      A practical Shopping Ads Report<\/strong> typically tracks:<\/p>\n\n\n\n

                      Performance and efficiency<\/h3>\n\n\n\n
                        \n
                      • Impressions, clicks, click-through rate (CTR)<\/li>\n
                      • Cost, average CPC<\/li>\n
                      • Conversions (orders), conversion rate (CVR)<\/li>\n
                      • Revenue, ROAS (or cost\/revenue ratio)<\/li>\n
                      • Cost per acquisition (CPA)<\/li>\n<\/ul>\n\n\n\n

                        Commercial quality<\/h3>\n\n\n\n
                          \n
                        • Average order value (AOV)<\/li>\n
                        • Refund\/return rate (if available)<\/li>\n
                        • Contribution margin or profit (when integrated)<\/li>\n
                        • New vs. returning customer share (where measurable)<\/li>\n<\/ul>\n\n\n\n

                          Visibility and competitiveness<\/h3>\n\n\n\n
                            \n
                          • Impression share (or similar visibility metrics)<\/li>\n
                          • Lost impressions due to budget or rank (where provided)<\/li>\n
                          • Price competitiveness indicators (when available through feeds or internal data)<\/li>\n<\/ul>\n\n\n\n

                            Catalog and operations signals<\/h3>\n\n\n\n
                              \n
                            • Percentage of products eligible\/approved<\/li>\n
                            • Out-of-stock click share (clicks going to low-availability items)<\/li>\n
                            • Revenue concentration (dependence on a small set of SKUs)<\/li>\n<\/ul>\n\n\n\n

                              Future Trends of Shopping Ads Report<\/h2>\n\n\n\n

                              The Shopping Ads Report<\/strong> is evolving alongside automation and privacy changes in Paid Marketing<\/strong>:<\/p>\n\n\n\n

                                \n
                              • More automation, more need for diagnosis:<\/strong> As bidding and targeting become more automated, reporting must explain \u201cwhat the system decided\u201d and whether it aligns with business priorities.<\/li>\n
                              • Incrementality and blended measurement:<\/strong> Teams will rely more on experiments, modeled conversions, and blended ROAS to interpret Shopping Ads<\/strong> impact across channels.<\/li>\n
                              • Product-level personalization:<\/strong> Reporting will increasingly segment by audience intent clusters, lifecycle stage, and predicted value rather than only by SKU and category.<\/li>\n
                              • Tighter data governance:<\/strong> With privacy constraints, durable first-party data and clean event definitions become essential for a trustworthy Shopping Ads Report<\/strong>.<\/li>\n
                              • Profit and supply chain integration:<\/strong> Expect more reporting that accounts for inventory risk, fulfillment costs, and returns\u2014making Paid Marketing<\/strong> optimization more \u201cretail-aware.\u201d<\/li>\n<\/ul>\n\n\n\n

                                Shopping Ads Report vs Related Terms<\/h2>\n\n\n\n

                                Shopping Ads Report vs Search Term Report<\/h3>\n\n\n\n

                                A Search Term Report focuses on the exact user queries that triggered ads. A Shopping Ads Report<\/strong> is broader: it emphasizes product and catalog performance as well as campaign outcomes. Search terms help refine intent and negatives; shopping reporting tells you which products win or lose once they enter the auction.<\/p>\n\n\n\n

                                Shopping Ads Report vs Product Feed Diagnostics<\/h3>\n\n\n\n

                                Feed diagnostics tell you whether products are eligible and correctly formatted for Shopping Ads<\/strong>. A Shopping Ads Report<\/strong> tells you what happened after eligibility\u2014traffic quality, cost, and revenue. You need both: diagnostics for health, reporting for performance.<\/p>\n\n\n\n

                                Shopping Ads Report vs ROAS Dashboard<\/h3>\n\n\n\n

                                A ROAS dashboard is usually an executive summary metric view. A Shopping Ads Report<\/strong> should include the levers behind ROAS\u2014SKU breakdowns, price bands, device splits, and visibility metrics\u2014so operators can improve outcomes in Paid Marketing<\/strong>.<\/p>\n\n\n\n

                                Who Should Learn Shopping Ads Report<\/h2>\n\n\n\n
                                  \n
                                • Marketers<\/strong> need a Shopping Ads Report<\/strong> to optimize bidding, budgets, and structure while protecting profit.<\/li>\n
                                • Analysts<\/strong> use it to build measurement frameworks, validate tracking, and connect Paid Marketing<\/strong> to business KPIs.<\/li>\n
                                • Agencies<\/strong> rely on consistent reporting to prove value, prioritize work, and communicate clearly with clients running Shopping Ads<\/strong>.<\/li>\n
                                • Business owners and founders<\/strong> benefit by understanding which products actually scale profitably and where ad spend is being wasted.<\/li>\n
                                • Developers and technical teams<\/strong> support clean data pipelines, reliable product identifiers, and accurate conversion tracking\u2014foundational to any trustworthy Shopping Ads Report<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                                  Summary of Shopping Ads Report<\/h2>\n\n\n\n

                                  A Shopping Ads Report<\/strong> is the practical reporting layer that makes Shopping Ads<\/strong> performance measurable and improvable within Paid Marketing<\/strong>. It combines ad delivery metrics, conversion outcomes, and product feed attributes to show what\u2019s driving revenue, what\u2019s driving waste, and what actions will move results. When used with consistent segmentation, clean data, and profit-aware targets, it becomes an evergreen system for scaling shopping campaigns sustainably.<\/p>\n\n\n\n

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

                                  1) What should a Shopping Ads Report include at minimum?<\/h3>\n\n\n\n

                                  At minimum: impressions, clicks, cost, conversions, revenue, ROAS\/CPA, and a product-level breakdown (SKU or item group). Without product granularity, you can\u2019t reliably optimize Shopping Ads<\/strong>.<\/p>\n\n\n\n

                                  2) How often should I review Shopping Ads Report data?<\/h3>\n\n\n\n

                                  For active accounts, review pacing and anomalies daily, then do deeper optimization weekly. Use monthly reviews for structural changes and larger Paid Marketing<\/strong> strategy shifts.<\/p>\n\n\n\n

                                  3) Why do my Shopping Ads look profitable in reports but not in the business?<\/h3>\n\n\n\n

                                  Common reasons include high return rates, low margins, shipping costs, discounting, or attribution over-crediting. Enhance the Shopping Ads Report<\/strong> with margin tiers, refunds, and customer value where possible.<\/p>\n\n\n\n

                                  4) Which breakdown is most useful for Shopping Ads optimization: product, category, or brand?<\/h3>\n\n\n\n

                                  Start with product or item group to find waste and winners, then roll up to category\/brand to guide budget allocation. A strong Paid Marketing<\/strong> approach uses both: granular fixes plus strategic reallocations.<\/p>\n\n\n\n

                                  5) How do I use Shopping Ads Report insights to reduce wasted spend?<\/h3>\n\n\n\n

                                  Look for SKUs with high cost and low conversions, out-of-stock items still receiving clicks, and categories with declining CVR. Then adjust exclusions, bids, budgets, and feed quality to improve relevance in Shopping Ads<\/strong>.<\/p>\n\n\n\n

                                  6) What\u2019s the difference between tracking issues and real performance decline?<\/h3>\n\n\n\n

                                  Tracking issues often show sudden conversion drops with stable traffic and spend, or mismatches between analytics and ad platform orders. Real declines usually coincide with CPC changes, impression loss, or feed\/eligibility problems visible in the Shopping Ads Report<\/strong> and diagnostics.<\/p>\n\n\n\n

                                  7) Can small businesses benefit from a Shopping Ads Report, or is it only for large catalogs?<\/h3>\n\n\n\n

                                  Small businesses benefit greatly. Even with a limited catalog, a Shopping Ads Report<\/strong> helps identify which products deserve budget, which need better pricing or titles, and how Paid Marketing<\/strong> investment translates into sales.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                  A **Shopping Ads Report** is the reporting view (or exported dataset) you use to understand how your **Shopping Ads** campaigns are performing inside a broader **Paid Marketing** program. It turns ad spend, product catalog data, and user behavior into a set of metrics and breakdowns you can act on\u2014what\u2019s selling, what\u2019s wasting budget, and what needs better data or strategy.<\/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":[1914],"tags":[],"class_list":["post-11391","post","type-post","status-publish","format-standard","hentry","category-shopping-ads"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/11391","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=11391"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/11391\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=11391"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=11391"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=11391"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}