{"id":6720,"date":"2026-03-23T09:51:25","date_gmt":"2026-03-23T09:51:25","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/retail-media-clean-room\/"},"modified":"2026-03-23T09:51:25","modified_gmt":"2026-03-23T09:51:25","slug":"retail-media-clean-room","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/retail-media-clean-room\/","title":{"rendered":"Retail Media Clean Room: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Commerce & Retail Media"},"content":{"rendered":"\n

A Retail Media Clean Room<\/strong> is a privacy-preserving way for retailers and brands to collaborate on data for targeting, measurement, and insights\u2014without directly sharing raw, identifiable customer information. In Commerce & Retail Media<\/strong>, it has become a foundational concept because the industry depends on first-party shopper data, yet faces growing expectations around privacy, security, and responsible data use.<\/p>\n\n\n\n

As retail media budgets grow, marketers want proof: which ads drove incremental sales, which audiences responded, and how to optimize spend across onsite, offsite, and in-store touchpoints. A Retail Media Clean Room<\/strong> helps answer those questions in Commerce & Retail Media<\/strong> by enabling controlled analysis of sensitive datasets while reducing leakage risk and strengthening governance.<\/p>\n\n\n\n

\n\n\n\n

What Is Retail Media Clean Room?<\/h2>\n\n\n\n

A Retail Media Clean Room<\/strong> is a secure computing environment where a retailer\u2019s first-party data (such as shopper transactions and loyalty activity) can be matched and analyzed with a brand\u2019s data (such as CRM lists or campaign exposures) under strict controls. Instead of exporting raw customer-level data, the parties run approved queries and receive aggregated, privacy-safe outputs<\/strong>.<\/p>\n\n\n\n

At its core, the concept is simple: bring data to a protected space, run restricted analysis, export only safe results.<\/em> The business meaning is powerful\u2014brands can measure and optimize retail media while retailers maintain stewardship of shopper data.<\/p>\n\n\n\n

Within Commerce & Retail Media<\/strong>, a Retail Media Clean Room<\/strong> sits at the intersection of:\n– retail media networks and ad platforms,\n– customer data (online and offline),\n– attribution and incrementality measurement,\n– privacy and compliance workflows.<\/p>\n\n\n\n

It plays an operational role in Commerce & Retail Media<\/strong> by enabling measurement and audience collaboration when direct identifiers, third-party cookies, or broad data sharing are inappropriate or prohibited.<\/p>\n\n\n\n

\n\n\n\n

Why Retail Media Clean Room Matters in Commerce & Retail Media<\/h2>\n\n\n\n

A Retail Media Clean Room<\/strong> matters because it helps retail media deliver on its promise: measurable, commerce-linked advertising performance with stronger privacy protections.<\/p>\n\n\n\n

Key strategic advantages in Commerce & Retail Media<\/strong> include:<\/p>\n\n\n\n

    \n
  • Proof of business impact:<\/strong> Connect ad exposure to sales outcomes, often using retailer point-of-sale or transaction data.<\/li>\n
  • Better budget allocation:<\/strong> Identify which tactics drive incremental conversions versus simply capturing existing demand.<\/li>\n
  • Stronger partner relationships:<\/strong> Brands gain transparency and confidence; retailers demonstrate mature governance and measurement.<\/li>\n
  • Reduced data risk:<\/strong> Collaboration happens without exchanging raw customer-level datasets, lowering the probability of unintended disclosure.<\/li>\n
  • Competitive differentiation:<\/strong> In crowded Commerce & Retail Media<\/strong> landscapes, credible measurement and privacy posture can be a deciding factor for spend.<\/li>\n<\/ul>\n\n\n\n
    \n\n\n\n

    How Retail Media Clean Room Works<\/h2>\n\n\n\n

    A Retail Media Clean Room<\/strong> is less about a single \u201ctool\u201d and more about a controlled workflow. In practice, it commonly follows four stages:<\/p>\n\n\n\n

      \n
    1. \n

      Inputs (data onboarding and permissions)<\/strong>\n – Retailer provides eligible datasets (transactions, product catalog mappings, loyalty segments, ad exposure logs).\n – Brand provides eligible datasets (first-party customer lists, conversions outside the retailer, media logs).\n – Data is prepared with privacy controls such as hashing\/pseudonymization, field minimization, and policy checks.<\/p>\n<\/li>\n

    2. \n

      Processing (matching and query execution)<\/strong>\n – Records are matched using approved methods (for example, pseudonymous identifiers).\n – Analysts run pre-approved queries or templates (e.g., overlap, reach, conversion lift, cohort analysis).\n – Guardrails enforce privacy rules: minimum aggregation thresholds, restricted joins, query auditing, and output controls.<\/p>\n<\/li>\n

    3. \n

      Application (insights and audience workflows)<\/strong>\n – Outputs inform optimization: bidding, creative, product selection, and retail media placements.\n – Some setups support privacy-safe audience activation (e.g., building a segment for retailer-managed targeting) without exporting identities.<\/p>\n<\/li>\n

    4. \n

      Outputs (approved results only)<\/strong>\n – Exports are typically aggregated tables, model coefficients, or summary reports.\n – Raw row-level customer data is not released; results are monitored for leakage risk.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

      This is why the Retail Media Clean Room<\/strong> is so relevant to Commerce & Retail Media<\/strong>: it makes advanced measurement possible while respecting data boundaries.<\/p>\n\n\n\n

      \n\n\n\n

      Key Components of Retail Media Clean Room<\/h2>\n\n\n\n

      A well-designed Retail Media Clean Room<\/strong> typically includes the following components:<\/p>\n\n\n\n

      Data inputs<\/h3>\n\n\n\n
        \n
      • Retailer first-party data:<\/strong> transactions, onsite behavior, loyalty attributes, product taxonomy, store-level signals.<\/li>\n
      • Ad exposure data:<\/strong> impressions, clicks, viewability or engagement signals (where applicable), frequency.<\/li>\n
      • Brand first-party data:<\/strong> CRM\/loyalty lists, email engagement, site\/app events, offline conversions (if permitted).<\/li>\n
      • Metadata and mappings:<\/strong> SKU-to-brand mapping, campaign taxonomy, time windows, store\/region normalization.<\/li>\n<\/ul>\n\n\n\n

        Privacy and security controls<\/h3>\n\n\n\n
          \n
        • Access control (least privilege), role-based permissions, and approvals<\/li>\n
        • Query restrictions, aggregation thresholds, suppression rules<\/li>\n
        • Audit logs for datasets, queries, and exports<\/li>\n
        • Data retention policies and secure deletion<\/li>\n<\/ul>\n\n\n\n

          Governance and responsibilities<\/h3>\n\n\n\n
            \n
          • Clear definitions of \u201callowed questions\u201d and \u201callowed outputs\u201d<\/li>\n
          • Legal\/compliance review of use cases and contracts<\/li>\n
          • Data stewardship by retailer; analysis enablement by measurement teams<\/li>\n
          • Documentation for methodology, assumptions, and limitations<\/li>\n<\/ul>\n\n\n\n

            Measurement and analytics layer<\/h3>\n\n\n\n
              \n
            • Standardized templates for reporting and experimentation<\/li>\n
            • Incrementality frameworks (test\/control, geo tests, or matched cohorts)<\/li>\n
            • Controls for bias, seasonality, and confounders<\/li>\n<\/ul>\n\n\n\n
              \n\n\n\n

              Types of Retail Media Clean Room<\/h2>\n\n\n\n

              \u201cTypes\u201d are not always formally standardized, but in Commerce & Retail Media<\/strong> there are practical distinctions that affect how a Retail Media Clean Room<\/strong> is used.<\/p>\n\n\n\n

              1) Retailer-hosted vs neutral-hosted environments<\/h3>\n\n\n\n
                \n
              • Retailer-hosted:<\/strong> the retailer controls the environment, datasets, and permissible exports\u2014often simpler for shopper privacy but may be less flexible for brands.<\/li>\n
              • Neutral-hosted:<\/strong> a separate environment designed for multi-party collaboration, potentially supporting comparisons across partners while enforcing strong governance.<\/li>\n<\/ul>\n\n\n\n

                2) Measurement-only vs measurement + activation<\/h3>\n\n\n\n
                  \n
                • Measurement-only:<\/strong> focused on reporting, attribution, and lift.<\/li>\n
                • Measurement + activation:<\/strong> additionally enables privacy-safe audience creation for retailer-managed targeting (without exporting identities to the brand).<\/li>\n<\/ul>\n\n\n\n

                  3) Single-retailer vs multi-retailer analysis<\/h3>\n\n\n\n
                    \n
                  • Single-retailer:<\/strong> deep insights and optimization within one retail ecosystem.<\/li>\n
                  • Multi-retailer:<\/strong> more complex; requires careful standardization, consistent definitions, and strict privacy controls to avoid re-identification or unintended cross-context use.<\/li>\n<\/ul>\n\n\n\n
                    \n\n\n\n

                    Real-World Examples of Retail Media Clean Room<\/h2>\n\n\n\n

                    Example 1: New product launch incrementality<\/h3>\n\n\n\n

                    A brand runs sponsored placements for a new SKU. Using a Retail Media Clean Room<\/strong>, the brand and retailer analyze exposed vs non-exposed shoppers with a matched methodology to estimate incremental sales lift, not just attributed conversions. The output informs whether the launch budget should shift toward conquesting categories or reinforcing loyal buyers\u2014classic Commerce & Retail Media<\/strong> decision-making.<\/p>\n\n\n\n

                    Example 2: Omnichannel measurement across digital and in-store<\/h3>\n\n\n\n

                    A retailer provides aggregated store-region sales outcomes while the brand contributes campaign exposure by region and timeframe. Inside the Retail Media Clean Room<\/strong>, analysts estimate how offsite ads influenced in-store purchases, accounting for baseline trends. This ties media spend to real-world commerce outcomes, a core requirement in Commerce & Retail Media<\/strong>.<\/p>\n\n\n\n

                    Example 3: Audience overlap and suppression strategy<\/h3>\n\n\n\n

                    A brand wants to avoid spending on shoppers who already purchased in the last 14 days. Through a Retail Media Clean Room<\/strong>, the retailer can build a suppression segment based on transaction recency and apply it in retailer-managed targeting. The brand receives performance deltas (reach, frequency, sales) without receiving the suppressed identities.<\/p>\n\n\n\n

                    \n\n\n\n

                    Benefits of Using Retail Media Clean Room<\/h2>\n\n\n\n

                    A Retail Media Clean Room<\/strong> can deliver measurable business improvements when implemented with strong methodology:<\/p>\n\n\n\n

                      \n
                    • More accurate performance analysis:<\/strong> Stronger linkage between media exposure and commerce outcomes than platform-only reporting.<\/li>\n
                    • Incrementality-focused optimization:<\/strong> Helps reduce wasted spend by identifying what truly drives incremental sales.<\/li>\n
                    • Faster learning cycles:<\/strong> Standard query templates and governed datasets reduce one-off manual analyses.<\/li>\n
                    • Better customer experience:<\/strong> Suppression, frequency management, and relevance improvements can reduce ad fatigue.<\/li>\n
                    • Lower operational risk:<\/strong> Better controls for privacy, access, and auditability compared with ad hoc file sharing.<\/li>\n<\/ul>\n\n\n\n

                      In Commerce & Retail Media<\/strong>, these benefits translate into smarter investment decisions and more defensible reporting.<\/p>\n\n\n\n

                      \n\n\n\n

                      Challenges of Retail Media Clean Room<\/h2>\n\n\n\n

                      A Retail Media Clean Room<\/strong> is not a magic box. Common challenges include:<\/p>\n\n\n\n

                        \n
                      • Data standardization:<\/strong> Different naming conventions, SKU mappings, and campaign taxonomies can distort results.<\/li>\n
                      • Identity and match limitations:<\/strong> Pseudonymous matching may yield imperfect overlap, which affects measurement confidence.<\/li>\n
                      • Methodology disagreements:<\/strong> Brands and retailers may differ on attribution windows, definitions of incrementality, or baseline adjustments.<\/li>\n
                      • Latency and freshness:<\/strong> Transaction and exposure data can lag, limiting real-time optimization.<\/li>\n
                      • Privacy constraints on outputs:<\/strong> Aggregation thresholds and restricted queries can make granular analysis impossible, requiring smarter experimental design.<\/li>\n
                      • Resourcing:<\/strong> Clean room analysis often needs analytics, data engineering, and governance capacity\u2014especially at scale in Commerce & Retail Media<\/strong>.<\/li>\n<\/ul>\n\n\n\n
                        \n\n\n\n

                        Best Practices for Retail Media Clean Room<\/h2>\n\n\n\n

                        To make a Retail Media Clean Room<\/strong> successful long-term, focus on repeatable measurement and disciplined governance:<\/p>\n\n\n\n

                          \n
                        1. \n

                          Start with prioritized use cases<\/strong>\n – Begin with 2\u20133 high-value questions: incrementality, audience overlap, and category growth.\n – Avoid boiling the ocean with dozens of exploratory requests.<\/p>\n<\/li>\n

                        2. \n

                          Define shared measurement standards<\/strong>\n – Align on attribution windows, conversion definitions, and reporting grain (daily\/weekly, SKU\/category).\n – Document assumptions and \u201cknown limitations\u201d so stakeholders interpret outputs correctly.<\/p>\n<\/li>\n

                        3. \n

                          Invest in experiment design<\/strong>\n – Use test\/control where possible; otherwise apply matched cohorts and robustness checks.\n – Plan for seasonality, promotions, and stock availability\u2014especially important in Commerce & Retail Media<\/strong>.<\/p>\n<\/li>\n

                        4. \n

                          Build privacy into the workflow<\/strong>\n – Enforce least-privilege access, query approvals, and export review.\n – Use aggregation thresholds and suppression rules consistently.<\/p>\n<\/li>\n

                        5. \n

                          Operationalize with templates and dashboards<\/strong>\n – Standardize recurring queries and output formats.\n – Create a measurement calendar tied to campaign cycles and business reviews.<\/p>\n<\/li>\n

                        6. \n

                          Monitor data quality continuously<\/strong>\n – Validate feeds, track missingness, and maintain mapping tables (SKU, store, region, campaign).\n – Add automated checks to catch breaks before they reach reporting.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                          \n\n\n\n

                          Tools Used for Retail Media Clean Room<\/h2>\n\n\n\n

                          A Retail Media Clean Room<\/strong> typically relies on a stack of tool categories rather than a single product:<\/p>\n\n\n\n

                            \n
                          • Cloud data warehouses and secure compute environments:<\/strong> to host protected datasets, enforce permissions, and run queries.<\/li>\n
                          • Analytics and data science tools:<\/strong> for cohorting, causal inference, lift modeling, and forecasting.<\/li>\n
                          • Retail media ad platforms:<\/strong> to provide exposure logs and enable retailer-managed audience activation.<\/li>\n
                          • CRM and customer data systems:<\/strong> to supply brand first-party segments and support lifecycle analysis.<\/li>\n
                          • Tagging and event collection tools:<\/strong> for consistent campaign taxonomy and conversion events (where applicable).<\/li>\n
                          • Reporting dashboards and BI tools:<\/strong> to distribute approved aggregated outputs to stakeholders.<\/li>\n
                          • Governance and security systems:<\/strong> identity access management, audit logging, and data loss prevention controls.<\/li>\n<\/ul>\n\n\n\n

                            In Commerce & Retail Media<\/strong>, the \u201cbest\u201d stack is the one that produces reliable, privacy-safe outputs repeatedly\u2014not the one with the most features.<\/p>\n\n\n\n

                            \n\n\n\n

                            Metrics Related to Retail Media Clean Room<\/h2>\n\n\n\n

                            A Retail Media Clean Room<\/strong> supports measurement across performance, incrementality, and operational health. Common metrics include:<\/p>\n\n\n\n

                            Performance and commerce outcomes<\/h3>\n\n\n\n
                              \n
                            • Incremental sales and incremental revenue<\/li>\n
                            • Conversion rate and purchase frequency lift<\/li>\n
                            • New-to-brand or new-to-category rate (where defined and permitted)<\/li>\n
                            • Average order value and basket size changes<\/li>\n<\/ul>\n\n\n\n

                              Efficiency and ROI<\/h3>\n\n\n\n
                                \n
                              • Return on ad spend (ROAS), with clarity on attributed vs incremental<\/li>\n
                              • Cost per incremental purchase<\/li>\n
                              • Cost per new customer (where methodology supports it)<\/li>\n<\/ul>\n\n\n\n

                                Audience and reach quality<\/h3>\n\n\n\n
                                  \n
                                • Reach, frequency, and effective frequency<\/li>\n
                                • Audience overlap rates (brand CRM vs retailer shoppers)<\/li>\n
                                • Suppression impact (waste reduction, frequency control)<\/li>\n<\/ul>\n\n\n\n

                                  Data and process health<\/h3>\n\n\n\n
                                    \n
                                  • Match rate (with careful interpretation)<\/li>\n
                                  • Data latency (days to availability)<\/li>\n
                                  • Query turnaround time and reuse rate of templates<\/li>\n
                                  • Percentage of outputs passing privacy checks without rework<\/li>\n<\/ul>\n\n\n\n
                                    \n\n\n\n

                                    Future Trends of Retail Media Clean Room<\/h2>\n\n\n\n

                                    The Retail Media Clean Room<\/strong> is evolving quickly alongside Commerce & Retail Media<\/strong> maturity:<\/p>\n\n\n\n

                                      \n
                                    • AI-assisted measurement workflows:<\/strong> more automation in anomaly detection, query generation (with guardrails), and model selection\u2014paired with stricter governance.<\/li>\n
                                    • Improved incrementality standards:<\/strong> wider use of experimentation frameworks and clearer separation between attribution and causal lift.<\/li>\n
                                    • Greater interoperability:<\/strong> pressure to normalize taxonomies and measurement definitions across retailers, while still respecting privacy boundaries.<\/li>\n
                                    • Privacy-by-design expansion:<\/strong> stronger controls on re-identification risk, more rigorous auditing, and tighter retention policies.<\/li>\n
                                    • More omnichannel linkage:<\/strong> better alignment of onsite, offsite, and in-store outcomes, which is central to the promise of Commerce & Retail Media<\/strong>.<\/li>\n<\/ul>\n\n\n\n
                                      \n\n\n\n

                                      Retail Media Clean Room vs Related Terms<\/h2>\n\n\n\n

                                      Retail Media Clean Room vs Data Clean Room<\/h3>\n\n\n\n

                                      A data clean room is a broad concept for privacy-safe data collaboration in many industries. A Retail Media Clean Room<\/strong> is specifically tuned for retailer data, retail media exposure logs, and commerce outcomes like transactions and baskets\u2014making it purpose-built for Commerce & Retail Media<\/strong> use cases.<\/p>\n\n\n\n

                                      Retail Media Clean Room vs Customer Data Platform (CDP)<\/h3>\n\n\n\n

                                      A CDP centralizes and activates a company\u2019s own customer data for segmentation and personalization. A Retail Media Clean Room<\/strong> focuses on controlled collaboration<\/strong> between parties (retailer and brand) with restricted outputs. CDPs are about owning and operationalizing first-party data; clean rooms are about privacy-safe joint analysis.<\/p>\n\n\n\n

                                      Retail Media Clean Room vs Marketing Mix Modeling (MMM)<\/h3>\n\n\n\n

                                      MMM estimates the contribution of channels using aggregated time-series data. A Retail Media Clean Room<\/strong> can support shopper-level matching and controlled cohort analysis (with privacy safeguards), often producing more granular insights for retail media optimization. They can complement each other: MMM for macro allocation, clean room analysis for retailer-specific learning.<\/p>\n\n\n\n

                                      \n\n\n\n

                                      Who Should Learn Retail Media Clean Room<\/h2>\n\n\n\n

                                      Understanding Retail Media Clean Room<\/strong> concepts helps different roles collaborate effectively in Commerce & Retail Media<\/strong>:<\/p>\n\n\n\n

                                        \n
                                      • Marketers:<\/strong> to set realistic measurement expectations, choose incrementality methods, and interpret outputs correctly.<\/li>\n
                                      • Analysts:<\/strong> to design experiments, validate data quality, and translate aggregated outputs into decisions.<\/li>\n
                                      • Agencies:<\/strong> to standardize reporting across clients and retailers, and to defend recommendations with sound methodology.<\/li>\n
                                      • Business owners and founders:<\/strong> to evaluate retail media investments and demand credible, privacy-safe performance proof.<\/li>\n
                                      • Developers and data engineers:<\/strong> to implement secure data pipelines, permissions, auditability, and scalable query workflows.<\/li>\n<\/ul>\n\n\n\n
                                        \n\n\n\n

                                        Summary of Retail Media Clean Room<\/h2>\n\n\n\n

                                        A Retail Media Clean Room<\/strong> is a privacy-safe environment that lets retailers and brands analyze sensitive datasets together while limiting exposure of raw customer-level data. It matters because modern Commerce & Retail Media<\/strong> depends on first-party shopper data, yet requires stronger governance and defensible measurement.<\/p>\n\n\n\n

                                        Used well, a Retail Media Clean Room<\/strong> strengthens incrementality analysis, improves optimization, and supports responsible collaboration\u2014helping Commerce & Retail Media<\/strong> teams make better decisions with lower data risk.<\/p>\n\n\n\n

                                        \n\n\n\n

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

                                        1) What problem does a Retail Media Clean Room solve?<\/h3>\n\n\n\n

                                        A Retail Media Clean Room<\/strong> enables brands and retailers to measure and learn from shopper and ad exposure data without directly sharing raw customer-level information. It reduces privacy risk while still producing actionable aggregated insights.<\/p>\n\n\n\n

                                        2) Is a Retail Media Clean Room mainly for targeting or for measurement?<\/h3>\n\n\n\n

                                        It can support both, but many organizations start with measurement (lift, overlap, performance validation). Activation is often retailer-managed and governed more tightly than reporting outputs.<\/p>\n\n\n\n

                                        3) How does a Retail Media Clean Room fit into Commerce & Retail Media planning?<\/h3>\n\n\n\n

                                        In Commerce & Retail Media<\/strong>, it helps teams connect media activity to commerce outcomes, validate incrementality, and inform budget allocation, audience strategy, and campaign design with stronger evidence.<\/p>\n\n\n\n

                                        4) Do brands get access to the retailer\u2019s customer-level data in a clean room?<\/h3>\n\n\n\n

                                        Typically no. A Retail Media Clean Room<\/strong> is designed to prevent raw data export. Brands usually receive aggregated results and approved segments managed by the retailer, depending on governance.<\/p>\n\n\n\n

                                        5) What are the biggest limitations to expect?<\/h3>\n\n\n\n

                                        Common limitations include imperfect match rates, delays in data availability, restricted query flexibility due to privacy rules, and the need for careful experiment design to avoid misleading conclusions.<\/p>\n\n\n\n

                                        6) How do you know if clean room results are trustworthy?<\/h3>\n\n\n\n

                                        Look for transparent methodology, documented definitions, repeatable query templates, robustness checks (e.g., placebo tests), and clear separation between attributed results and incremental lift\u2014especially for Commerce & Retail Media<\/strong> decision-making.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                        A **Retail Media Clean Room** is a privacy-preserving way for retailers and brands to collaborate on data for targeting, measurement, and insights\u2014without directly sharing raw, identifiable customer information. In **Commerce & Retail Media**, it has become a foundational concept because the industry depends on first-party shopper data, yet faces growing expectations around privacy, security, and responsible data use.<\/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":[1886],"tags":[],"class_list":["post-6720","post","type-post","status-publish","format-standard","hentry","category-commerce-retail-media"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/6720","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=6720"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/6720\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=6720"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=6720"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=6720"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}