{"id":7039,"date":"2026-03-23T22:05:23","date_gmt":"2026-03-23T22:05:23","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/de-duplication\/"},"modified":"2026-03-23T22:05:23","modified_gmt":"2026-03-23T22:05:23","slug":"de-duplication","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/de-duplication\/","title":{"rendered":"De-duplication: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Attribution"},"content":{"rendered":"\n

De-duplication is the disciplined practice of ensuring the same customer action (like a purchase, lead, or signup) is counted once\u2014even if multiple systems, tags, or channels report it. In Conversion & Measurement<\/strong>, it is one of the most important \u201cdata hygiene\u201d concepts because it directly determines whether your numbers reflect reality or inflated activity. In Attribution<\/strong>, De-duplication prevents multiple touchpoints from claiming the same conversion as if each created a separate result.<\/p>\n\n\n\n

Modern marketing stacks make De-duplication unavoidable: one conversion can be recorded by a website pixel, a server-side event, a CRM update, and an ad platform\u2019s click-through reporting\u2014often within seconds. Without De-duplication, teams overestimate performance, misallocate budgets, and make decisions based on misleading trends. With it, your Conversion & Measurement<\/strong> strategy becomes stable, comparable over time, and credible across stakeholders.<\/p>\n\n\n\n

What Is De-duplication?<\/h2>\n\n\n\n

De-duplication<\/strong> is the process of identifying and removing duplicate records or duplicate conversion events so that each real-world action is represented once in reporting and analysis. In a marketing context, duplicates usually happen when:<\/p>\n\n\n\n

    \n
  • The same conversion is tracked in multiple places (browser + server, analytics + CRM).<\/li>\n
  • A user triggers the same event more than once (double form submission, page refresh).<\/li>\n
  • Multiple channels can legitimately touch the same user, but reporting mistakenly counts multiple conversions instead of one.<\/li>\n<\/ul>\n\n\n\n

    The core concept is simple: one person\u2019s one action should equal one counted outcome<\/em>, even if multiple systems observed it.<\/p>\n\n\n\n

    From a business perspective, De-duplication protects decision-making. It ensures KPIs like conversion rate, cost per acquisition, and ROAS reflect actual outcomes\u2014not measurement artifacts. Within Conversion & Measurement<\/strong>, De-duplication sits between data collection and reporting, acting as a quality gate. Within Attribution<\/strong>, it ensures credit is assigned to touchpoints for a single conversion event rather than multiplying conversions and falsely \u201cproving\u201d success across every channel.<\/p>\n\n\n\n

    Why De-duplication Matters in Conversion & Measurement<\/h2>\n\n\n\n

    In Conversion & Measurement<\/strong>, every downstream decision depends on the integrity of conversion counts and revenue totals. If you can\u2019t trust those, you can\u2019t trust experiments, forecasts, or channel comparisons.<\/p>\n\n\n\n

    De-duplication creates business value by:<\/p>\n\n\n\n

      \n
    • Preventing wasted spend:<\/strong> Duplicate conversions make campaigns look more efficient than they are, leading to overspending in underperforming channels.<\/li>\n
    • Improving optimization:<\/strong> Bidding, creative testing, and landing page improvements rely on accurate feedback loops.<\/li>\n
    • Reducing internal conflict:<\/strong> When analytics, CRM, and ad platforms disagree, De-duplication is often the missing alignment layer.<\/li>\n
    • Enabling fair evaluation:<\/strong> Agencies and internal teams can be evaluated on consistent rules, not on whichever system inflates results.<\/li>\n<\/ul>\n\n\n\n

      As a competitive advantage, strong De-duplication helps you move faster. Teams spend less time reconciling numbers and more time improving the funnel. In Attribution<\/strong>, it prevents a common failure mode: multiple platforms each claiming the same conversion, causing leadership to believe \u201ceverything works,\u201d when budget should actually shift.<\/p>\n\n\n\n

      How De-duplication Works<\/h2>\n\n\n\n

      De-duplication<\/strong> can be implemented at different points, but the practical workflow usually looks like this:<\/p>\n\n\n\n

        \n
      1. \n

        Input \/ trigger (data capture)<\/strong>\n Conversion events and identifiers enter your ecosystem from sources like web analytics, tag managers, server-side tracking, ad platform postbacks, payment processors, and CRM systems. Each source may record the event with different timestamps, IDs, and fields.<\/p>\n<\/li>\n

      2. \n

        Analysis \/ processing (matching and rules)<\/strong>\n The system attempts to decide whether two records represent the same real-world conversion. This can use:\n – Unique event IDs (best case)\n – Transaction\/order IDs\n – Click IDs (where available)\n – Email\/phone hashes (where permitted)\n – Timestamp windows + session\/user identifiers\n Then rules determine what \u201cduplicate\u201d means (e.g., same order ID within 24 hours).<\/p>\n<\/li>\n

      3. \n

        Execution \/ application (selection or merging)<\/strong>\n Once duplicates are detected, you apply a policy:\n – Keep one \u201csource of truth\u201d record and suppress the rest\n – Merge fields (e.g., keep CRM revenue, keep analytics channel)\n – Attribute credit to multiple touches but count only one conversion<\/p>\n<\/li>\n

      4. \n

        Output \/ outcome (clean reporting and modeling)<\/strong>\n Cleaned conversions feed dashboards, cohort analysis, Attribution<\/strong> models, and budget decisions. The output is fewer discrepancies, more stable KPIs, and more reliable Conversion & Measurement<\/strong> across tools.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        Key Components of De-duplication<\/h2>\n\n\n\n

        Effective De-duplication<\/strong> is rarely a single setting. It\u2019s a combination of data design, process, and governance:<\/p>\n\n\n\n

          \n
        • Tracking design:<\/strong> A consistent event schema (event name, parameters, conversion definitions) reduces ambiguity.<\/li>\n
        • Identifiers:<\/strong> Event IDs, order IDs, user IDs, and click IDs improve match accuracy.<\/li>\n
        • Data pipeline or collection layer:<\/strong> Tag management, server-side collection, CDPs, or ETL processes often host De-duplication logic.<\/li>\n
        • Rules and hierarchies:<\/strong> Clear decisions about which system wins when duplicates occur (e.g., payment processor as revenue truth).<\/li>\n
        • Consent and privacy controls:<\/strong> What identifiers can be used depends on legal requirements and user consent.<\/li>\n
        • QA and monitoring:<\/strong> Ongoing checks for duplicate rates, sudden spikes, and cross-platform discrepancies.<\/li>\n
        • Ownership:<\/strong> A defined owner (analytics lead, marketing ops, data engineering) to maintain De-duplication rules as campaigns and systems evolve.<\/li>\n<\/ul>\n\n\n\n

          In Conversion & Measurement<\/strong> and Attribution<\/strong>, the strongest implementations treat De-duplication as a maintained product, not a one-time fix.<\/p>\n\n\n\n

          Types of De-duplication<\/h2>\n\n\n\n

          There aren\u2019t universally standardized \u201cofficial\u201d types, but in practice De-duplication<\/strong> commonly appears in these contexts:<\/p>\n\n\n\n

            \n
          1. \n

            Event-level De-duplication<\/strong>\n Removes duplicate conversion events (e.g., two \u201cpurchase\u201d events for the same order). This is the most direct Conversion & Measurement<\/strong> use case.<\/p>\n<\/li>\n

          2. \n

            Identity-level De-duplication<\/strong>\n Resolves duplicate user\/customer profiles (e.g., the same person as two contacts in a CRM). This affects audience counts, lifecycle reporting, and Attribution<\/strong> linking across sessions\/devices.<\/p>\n<\/li>\n

          3. \n

            Lead\/contact De-duplication<\/strong>\n Common in B2B: the same lead submits multiple forms, or multiple team members submit on behalf of one account.<\/p>\n<\/li>\n

          4. \n

            Cross-system De-duplication (platform reconciliation)<\/strong>\n Aligns conversions recorded by analytics, ad platforms, backend systems, and CRM. This is crucial when each system has different measurement rules.<\/p>\n<\/li>\n

          5. \n

            Reporting-layer De-duplication<\/strong>\n Occurs in BI tools and dashboards where multiple data sources are blended. Without careful joins and keys, duplicates can be introduced even if source data is clean.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

            Real-World Examples of De-duplication<\/h2>\n\n\n\n

            Example 1: Purchase tracked by browser pixel and server-side event<\/h3>\n\n\n\n

            An ecommerce site sends a \u201cPurchase\u201d event from the browser (client-side) and also from the server after payment confirmation. Without De-duplication<\/strong>, the same order may be counted twice in Conversion & Measurement<\/strong>, inflating revenue and conversion rate. A robust fix is to use the order ID as a unique key and suppress any duplicate purchases with the same order ID. In Attribution<\/strong>, you still assign credit to touchpoints, but only for one conversion per order.<\/p>\n\n\n\n

            Example 2: Paid social and paid search both claim the same lead<\/h3>\n\n\n\n

            A user clicks a paid social ad, later clicks a paid search ad, then submits a form. Each ad platform may report a conversion, and your analytics tool also records the form submit. De-duplication in your central reporting ensures the business sees one lead, not three. Then Attribution<\/strong> can decide how to split credit (first touch vs last touch vs multi-touch) without multiplying the outcome.<\/p>\n\n\n\n

            Example 3: CRM duplicates inflate pipeline and mislead channel performance<\/h3>\n\n\n\n

            A B2B company imports leads from chat, demo forms, and webinar registrations. One person uses a work email once and a personal email another time, producing two CRM contacts and two \u201cnew leads\u201d in reporting. Identity-focused De-duplication<\/strong> (with careful rules) reduces inflated lead counts and improves Conversion & Measurement<\/strong> of lead-to-opportunity rate. Attribution<\/strong> improves because downstream revenue is tied to the correct person\/account.<\/p>\n\n\n\n

            Benefits of Using De-duplication<\/h2>\n\n\n\n

            When De-duplication<\/strong> is implemented well, the benefits show up across performance, finance, and operations:<\/p>\n\n\n\n

              \n
            • More accurate KPIs:<\/strong> Conversion rate, CAC\/CPA, ROAS, and LTV calculations become more reliable.<\/li>\n
            • Better budget allocation:<\/strong> Channels stop \u201cdouble-counting\u201d success, making comparative performance fairer.<\/li>\n
            • Cleaner experimentation:<\/strong> A\/B tests and incrementality analyses depend on trustworthy conversion totals.<\/li>\n
            • Operational efficiency:<\/strong> Less time spent reconciling dashboards, fewer disputes in weekly reporting.<\/li>\n
            • Improved customer experience:<\/strong> Reduced duplicate outreach (e.g., duplicate lead records causing repeated sales calls).<\/li>\n
            • Stronger <\/strong>Attribution****: Multi-touch credit assignment becomes meaningful because the underlying conversions are not inflated.<\/li>\n<\/ul>\n\n\n\n

              In Conversion & Measurement<\/strong>, De-duplication is often the difference between \u201cnumbers we report\u201d and \u201cnumbers we can act on.\u201d<\/p>\n\n\n\n

              Challenges of De-duplication<\/h2>\n\n\n\n

              De-duplication<\/strong> sounds straightforward until you run into real-world constraints:<\/p>\n\n\n\n

                \n
              • Inconsistent identifiers:<\/strong> Not every conversion has an order ID; not every lead has a stable user ID.<\/li>\n
              • Timing and latency:<\/strong> Systems record events at different times; strict matching windows can miss duplicates or over-merge separate conversions.<\/li>\n
              • Cross-device behavior:<\/strong> One person can appear as multiple users; aggressive De-duplication can undercount.<\/li>\n
              • Platform limitations:<\/strong> Walled-garden reporting may not provide enough detail to fully reconcile conversion events.<\/li>\n
              • Privacy and consent constraints:<\/strong> Some identifiers cannot be used without consent, reducing match accuracy.<\/li>\n
              • Business definition disagreements:<\/strong> Teams may disagree on what counts as \u201cthe same conversion\u201d (e.g., repeat purchases vs duplicates).<\/li>\n
              • Risk of over-deduping:<\/strong> Removing legitimate repeat conversions can be as damaging as counting duplicates.<\/li>\n<\/ul>\n\n\n\n

                Strong Conversion & Measurement<\/strong> practice acknowledges these trade-offs and documents them so Attribution<\/strong> outputs are interpreted correctly.<\/p>\n\n\n\n

                Best Practices for De-duplication<\/h2>\n\n\n\n

                To implement De-duplication<\/strong> that scales, focus on policy and design\u2014not just tooling:<\/p>\n\n\n\n

                  \n
                • Define conversions precisely:<\/strong> Document what counts as a conversion, including repeat purchase rules and time windows.<\/li>\n
                • Use stable unique keys where possible:<\/strong> Order ID, invoice ID, lead ID, or a generated event ID drastically improves accuracy.<\/li>\n
                • Set a source-of-truth hierarchy:<\/strong> For example, backend\/payment for revenue, CRM for qualified pipeline stages, analytics for on-site behavior.<\/li>\n
                • Separate \u201ccounting\u201d from \u201ccredit\u201d:<\/strong> Count conversions once, then apply Attribution<\/strong> to distribute credit across touchpoints.<\/li>\n
                • Build reconciliation reports:<\/strong> Regularly compare totals across systems and track the duplicate rate over time.<\/li>\n
                • Monitor for instrumentation drift:<\/strong> Tag changes, new checkout flows, or app updates often reintroduce duplicates.<\/li>\n
                • Version control your rules:<\/strong> When De-duplication logic changes, annotate reports so trend breaks are explainable.<\/li>\n
                • Start conservative:<\/strong> If uncertain, avoid aggressive merges that can undercount; iterate as you learn more.<\/li>\n<\/ul>\n\n\n\n

                  These practices keep Conversion & Measurement<\/strong> consistent even as channels, tags, and privacy requirements change.<\/p>\n\n\n\n

                  Tools Used for De-duplication<\/h2>\n\n\n\n

                  De-duplication<\/strong> is typically enabled by a combination of systems rather than a single tool:<\/p>\n\n\n\n

                    \n
                  • Analytics tools:<\/strong> Used to define conversions, validate event counts, and investigate discrepancies. They often support event IDs or transaction IDs for De-duplication.<\/li>\n
                  • Tag management and server-side collection:<\/strong> Helpful for generating consistent event IDs and reducing double-firing. Server-side approaches can also reduce client-side duplication.<\/li>\n
                  • CRM systems:<\/strong> Common place for lead\/contact De-duplication rules, field matching, and lifecycle stage governance.<\/li>\n
                  • Data warehouses and ETL\/ELT pipelines:<\/strong> Often where cross-system De-duplication is enforced at scale using SQL logic and repeatable transformations.<\/li>\n
                  • Reporting dashboards \/ BI tools:<\/strong> Can introduce duplicates via joins; good modeling practices prevent double counting in blended reports.<\/li>\n
                  • Advertising platforms:<\/strong> Provide conversion reporting that must be reconciled; while they may dedupe within their own environment, cross-platform De-duplication still belongs in your Conversion & Measurement<\/strong> layer for consistent Attribution<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                    The key is not the brand of tool, but whether your stack supports consistent IDs, repeatable rules, and transparent auditing.<\/p>\n\n\n\n

                    Metrics Related to De-duplication<\/h2>\n\n\n\n

                    You can\u2019t improve De-duplication<\/strong> without measuring its impact. Useful metrics include:<\/p>\n\n\n\n

                      \n
                    • Duplicate rate:<\/strong> Percentage of conversions flagged as duplicates (by type and source).<\/li>\n
                    • Match rate \/ join success rate:<\/strong> How often records link across systems (analytics \u2194 CRM \u2194 orders).<\/li>\n
                    • Discrepancy ratio:<\/strong> Difference between systems (e.g., analytics purchases vs backend orders).<\/li>\n
                    • Suppressed conversions count:<\/strong> Number of events removed by De-duplication rules (tracked over time).<\/li>\n
                    • Reconciliation time:<\/strong> Hours spent each reporting cycle aligning numbers\u2014should drop as processes mature.<\/li>\n
                    • CPA\/ROAS before vs after dedupe:<\/strong> Helps quantify how much duplication previously inflated performance.<\/li>\n
                    • Attribution stability:<\/strong> How often channel contribution swings due to measurement noise rather than real behavior.<\/li>\n<\/ul>\n\n\n\n

                      In Conversion & Measurement<\/strong>, these indicators act like quality diagnostics for the entire reporting pipeline.<\/p>\n\n\n\n

                      Future Trends of De-duplication<\/h2>\n\n\n\n

                      De-duplication<\/strong> is evolving as measurement becomes more privacy-aware and less dependent on third-party identifiers:<\/p>\n\n\n\n

                        \n
                      • More first-party data reliance:<\/strong> Stronger dependence on server-side events, order IDs, and CRM keys to support De-duplication.<\/li>\n
                      • Modeled and aggregated measurement:<\/strong> When user-level signals are limited, systems rely more on modeling\u2014raising the need to separate modeled estimates from deduped \u201cground truth.\u201d<\/li>\n
                      • AI-assisted entity resolution:<\/strong> Machine learning can help detect duplicates and merge identities, but requires strict governance to avoid false merges.<\/li>\n
                      • Clean-room style collaboration:<\/strong> Privacy-safe matching approaches will influence how De-duplication and Attribution<\/strong> work across publishers and advertisers.<\/li>\n
                      • Greater auditability expectations:<\/strong> Finance and leadership teams increasingly expect explainable Conversion & Measurement<\/strong> logic, including documented De-duplication rules.<\/li>\n<\/ul>\n\n\n\n

                        The direction is clear: as tracking signals get noisier, De-duplication becomes even more central to trustworthy Attribution<\/strong>.<\/p>\n\n\n\n

                        De-duplication vs Related Terms<\/h2>\n\n\n\n

                        De-duplication vs Data cleansing<\/strong>\nData cleansing is broader: it includes correcting formats, filling missing fields, and standardizing values. De-duplication is a specific subset focused on removing or merging duplicates that distort counts in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                        De-duplication vs Identity resolution<\/strong>\nIdentity resolution connects identifiers across devices and systems to represent one person. De-duplication may use identity resolution outputs, but it also applies to event records (like orders) even when identity is unknown. Identity resolution supports Attribution<\/strong>; De-duplication ensures the conversion being attributed is not double-counted.<\/p>\n\n\n\n

                        De-duplication vs Attribution modeling<\/strong>\nAttribution modeling decides how to assign credit for a conversion across touchpoints. De-duplication ensures there is one conversion to credit. If you skip De-duplication, Attribution<\/strong> results can look better simply because the same outcome is counted multiple times.<\/p>\n\n\n\n

                        Who Should Learn De-duplication<\/h2>\n\n\n\n
                          \n
                        • Marketers:<\/strong> To interpret performance reports correctly and avoid optimizing toward inflated results in Conversion & Measurement<\/strong>.<\/li>\n
                        • Analysts:<\/strong> To build consistent dashboards and trustworthy datasets that support Attribution<\/strong> and forecasting.<\/li>\n
                        • Agencies:<\/strong> To set realistic expectations, prevent reporting disputes, and prove impact credibly across channels.<\/li>\n
                        • Business owners and founders:<\/strong> To protect budget decisions and understand why different systems show different \u201ctruths.\u201d<\/li>\n
                        • Developers and marketing engineers:<\/strong> To implement event IDs, server-side tracking, and data pipeline logic that makes De-duplication reliable and auditable.<\/li>\n<\/ul>\n\n\n\n

                          Summary of De-duplication<\/h2>\n\n\n\n

                          De-duplication<\/strong> is the practice of ensuring each real conversion is counted once, even when multiple platforms observe it. It matters because accurate Conversion & Measurement<\/strong> depends on clean conversion counts, not inflated duplicates. It fits at the intersection of tracking, data pipelines, and reporting governance, and it directly strengthens Attribution<\/strong> by ensuring credit is assigned to one true outcome rather than to multiple copies of the same event.<\/p>\n\n\n\n

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

                          1) What is De-duplication in marketing analytics?<\/h3>\n\n\n\n

                          De-duplication is the process of detecting and removing duplicate conversion or customer records so reporting reflects one real action once. It\u2019s a core control in Conversion & Measurement<\/strong> because it prevents inflated conversions and revenue.<\/p>\n\n\n\n

                          2) Why do ad platforms and analytics tools show different conversion numbers?<\/h3>\n\n\n\n

                          They use different attribution windows, counting rules, and identifiers. Without cross-system De-duplication and reconciliation, the same conversion can be counted multiple times across platforms.<\/p>\n\n\n\n

                          3) How does De-duplication affect Attribution?<\/h3>\n\n\n\n

                          Attribution<\/strong> assigns credit across touchpoints for a conversion. De-duplication ensures there\u2019s only one conversion to receive credit, preventing misleading channel performance where multiple platforms \u201cclaim\u201d separate conversions for the same outcome.<\/p>\n\n\n\n

                          4) What identifiers are best for De-duplication?<\/h3>\n\n\n\n

                          Order\/transaction IDs and generated event IDs are typically strongest for purchase events. For leads, a CRM lead ID plus consistent form submission IDs helps. When identifiers are weak, teams may rely on timestamp windows and partial identity matching with careful safeguards.<\/p>\n\n\n\n

                          5) Can De-duplication cause undercounting?<\/h3>\n\n\n\n

                          Yes. Overly aggressive rules can merge separate legitimate conversions (like repeat purchases) into one. Good Conversion & Measurement<\/strong> includes clear time windows and business definitions to avoid over-deduping.<\/p>\n\n\n\n

                          6) Where should De-duplication happen: in analytics, CRM, or the data warehouse?<\/h3>\n\n\n\n

                          Ideally, De-duplication is enforced where you create your \u201csingle source of reporting truth,\u201d often a warehouse or centralized reporting layer, while also applying local dedupe controls (like CRM lead rules) upstream. The right answer depends on your stack and governance maturity.<\/p>\n\n\n\n

                          7) How do I know if I have a De-duplication problem?<\/h3>\n\n\n\n

                          Common signals include sudden conversion spikes after tagging changes, large gaps between backend orders and reported purchases, frequent \u201cnumbers don\u2019t match\u201d debates, and inconsistent Attribution<\/strong> outcomes that swing without real campaign changes. Tracking duplicate rate and discrepancy ratios will confirm it.<\/p>\n","protected":false},"excerpt":{"rendered":"

                          De-duplication is the disciplined practice of ensuring the same customer action (like a purchase, lead, or signup) is counted once\u2014even if multiple systems, tags, or channels report it. In **Conversion & Measurement**, it is one of the most important \u201cdata hygiene\u201d concepts because it directly determines whether your numbers reflect reality or inflated activity. In **Attribution**, De-duplication prevents multiple touchpoints from claiming the same conversion as if each created a separate result.<\/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-7039","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\/7039","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=7039"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7039\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7039"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7039"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7039"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}