{"id":7097,"date":"2026-03-24T00:13:52","date_gmt":"2026-03-24T00:13:52","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/attribution-roas\/"},"modified":"2026-03-24T00:13:52","modified_gmt":"2026-03-24T00:13:52","slug":"attribution-roas","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/attribution-roas\/","title":{"rendered":"Attribution ROAS: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Attribution"},"content":{"rendered":"\n

Attribution ROAS is a way to calculate return on ad spend using an Attribution<\/strong> approach\u2014meaning revenue credit is distributed across marketing touchpoints based on a chosen model, rather than being assigned to a single \u201cwinner\u201d click. In Conversion & Measurement<\/strong>, it answers a deceptively simple question: Which channels, campaigns, and keywords are truly driving revenue when customer journeys span multiple sessions and devices?<\/em><\/p>\n\n\n\n

Modern Conversion & Measurement<\/strong> is rarely straightforward. Prospects might discover you via SEO, click a retargeting ad later, and finally convert after an email reminder. Attribution ROAS<\/strong> matters because it reshapes how teams judge performance, allocate budgets, and scale growth\u2014especially when last-click metrics hide the real contributors.<\/p>\n\n\n\n

What Is Attribution ROAS?<\/h2>\n\n\n\n

Attribution ROAS<\/strong> is return on ad spend (ROAS) calculated using attributed revenue that is assigned to marketing touchpoints according to an Attribution<\/strong> model.<\/p>\n\n\n\n

At a beginner level, the idea is:<\/p>\n\n\n\n

    \n
  • ROAS<\/strong> tells you how much revenue you earned per dollar spent on ads.<\/li>\n
  • Attribution<\/strong> decides which<\/em> touchpoints get credit for that revenue.<\/li>\n
  • Attribution ROAS<\/strong> combines them by using attributed revenue<\/em> (not just \u201cfinal click\u201d revenue) as the numerator.<\/li>\n<\/ul>\n\n\n\n

    The core concept is that revenue should be credited in a way that reflects how people actually buy. From a business perspective, Attribution ROAS<\/strong> helps avoid underinvesting in \u201cassist\u201d channels (like prospecting or upper-funnel campaigns) and overinvesting in channels that merely capture demand at the end.<\/p>\n\n\n\n

    Within Conversion & Measurement<\/strong>, Attribution ROAS is a practical performance metric used to evaluate advertising efficiency under a defined credit-allocation logic. Within Attribution<\/strong>, it is one of the most common ways to translate attribution outcomes into budget decisions.<\/p>\n\n\n\n

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

    Attribution ROAS<\/strong> matters because most businesses don\u2019t have single-touch conversions. People research, compare, abandon carts, return via different channels, and respond to offers at different times. If your Conversion & Measurement<\/strong> strategy relies only on last-click ROAS, you risk optimizing for the final step rather than what created demand.<\/p>\n\n\n\n

    Key reasons it\u2019s strategically important:<\/p>\n\n\n\n

      \n
    • Budget allocation becomes more realistic.<\/strong> Attribution ROAS helps justify spend on campaigns that influence conversions but don\u2019t \u201cclose\u201d them.<\/li>\n
    • Creative and audience strategy improves.<\/strong> By seeing which touchpoints contribute earlier in the journey, teams can tailor messaging by stage.<\/li>\n
    • Cross-channel performance becomes comparable.<\/strong> Attribution<\/strong> helps normalize the evaluation of search, social, display, affiliates, and email within the same decision frame.<\/li>\n
    • Competitive advantage grows over time.<\/strong> Organizations that measure contribution more accurately can scale with fewer blind spots in Conversion & Measurement<\/strong>.<\/li>\n<\/ul>\n\n\n\n

      Ultimately, Attribution ROAS is a bridge between measurement and action: it changes what you pause, what you scale, and what you test next.<\/p>\n\n\n\n

      How Attribution ROAS Works<\/h2>\n\n\n\n

      In practice, Attribution ROAS<\/strong> is less about a single formula and more about a repeatable measurement workflow inside Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

        \n
      1. \n

        Inputs: capture cost and conversion data<\/strong>\n You collect ad spend (by channel\/campaign\/ad set\/keyword), conversion events, and revenue (transaction value, subscription value, or lead value). You also capture user journeys through touchpoints (impressions, clicks, sessions) to support Attribution<\/strong>.<\/p>\n<\/li>\n

      2. \n

        Processing: apply an Attribution model to assign credit<\/strong>\n An attribution model determines how much revenue credit each touchpoint receives. Instead of assigning 100% of revenue to the last click, you might split it across multiple interactions. The model can be rule-based (like linear) or algorithmic (data-driven).<\/p>\n<\/li>\n

      3. \n

        Application: aggregate attributed revenue back to marketing entities<\/strong>\n Attributed revenue is rolled up to the levels you manage: channel, campaign, ad group, keyword, creative, audience segment, landing page, or even content cluster. This makes the metric operational in Conversion & Measurement<\/strong>.<\/p>\n<\/li>\n

      4. \n

        Output: calculate Attribution ROAS and make decisions<\/strong>\n Attribution ROAS is typically calculated as:\nAttributed Revenue \u00f7 Ad Spend<\/strong>\n The output supports decisions like reallocating budget, adjusting bids, changing audience targeting, revising creatives, or investing more in SEO\/content that assists paid performance.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        The crucial nuance: Attribution ROAS is only as credible as your attribution rules and your data quality<\/strong>. It\u2019s a decision metric, not a universal truth.<\/p>\n\n\n\n

        Key Components of Attribution ROAS<\/h2>\n\n\n\n

        Strong Attribution ROAS<\/strong> depends on several building blocks working together across Conversion & Measurement<\/strong> and Attribution<\/strong>:<\/p>\n\n\n\n

          \n
        • Conversion definitions and event taxonomy<\/strong>: Clear rules for what counts as a conversion (purchase, qualified lead, trial start), and how events are named and tracked.<\/li>\n
        • Revenue and value mapping<\/strong>: Transaction revenue for ecommerce, or standardized lead values and downstream conversion values for B2B\/lead gen.<\/li>\n
        • Cost data integration<\/strong>: Accurate spend data by channel and campaign, including fees or commissions where applicable.<\/li>\n
        • Identity and journey stitching<\/strong>: Methods to connect sessions and touchpoints (first-party identifiers, consented user IDs, or modeled paths).<\/li>\n
        • Attribution model governance<\/strong>: Agreement on which model is used for which decisions, and documentation so stakeholders interpret Attribution ROAS consistently.<\/li>\n
        • Reporting and decision cadence<\/strong>: Dashboards, weekly reviews, and experimentation workflows that turn Attribution ROAS into action\u2014not just analysis.<\/li>\n
        • Team responsibilities<\/strong>: Clear ownership between marketing, analytics, and engineering for tagging, data pipelines, and QA.<\/li>\n<\/ul>\n\n\n\n

          Types of Attribution ROAS<\/h2>\n\n\n\n

          There aren\u2019t \u201cofficial\u201d types of Attribution ROAS so much as contexts and Attribution models<\/strong> that produce different ROAS outcomes. The most relevant distinctions are:<\/p>\n\n\n\n

          By attribution model (most common)<\/h3>\n\n\n\n
            \n
          • Last-click Attribution ROAS<\/strong>: All credit to the final touchpoint before conversion. Simple, but often biased toward bottom-funnel.<\/li>\n
          • First-click Attribution ROAS<\/strong>: All credit to the first touchpoint. Useful for evaluating acquisition, but can over-credit early interactions.<\/li>\n
          • Linear Attribution ROAS<\/strong>: Splits credit evenly across touchpoints. Good for broad fairness, but not always behaviorally accurate.<\/li>\n
          • Time-decay Attribution ROAS<\/strong>: More credit to touches closer to conversion. Reflects recency, can under-credit early demand creation.<\/li>\n
          • Position-based Attribution ROAS<\/strong>: Heavier credit to first and last touch, some to the middle. Popular compromise model.<\/li>\n
          • Data-driven Attribution ROAS<\/strong>: Credit is assigned based on observed patterns in your data. Powerful but sensitive to data volume, tracking gaps, and platform constraints.<\/li>\n<\/ul>\n\n\n\n

            By measurement scope<\/h3>\n\n\n\n
              \n
            • Platform Attribution ROAS<\/strong>: Calculated within a single ad platform\u2019s view. Useful for channel optimization, but limited cross-channel.<\/li>\n
            • Cross-channel Attribution ROAS<\/strong>: Uses a shared measurement layer to compare channels consistently within Conversion & Measurement<\/strong>.<\/li>\n
            • Incrementality-informed Attribution ROAS<\/strong>: Uses lift tests or causal methods to adjust for what would have happened anyway. Best for strategic budgeting, harder to operationalize.<\/li>\n<\/ul>\n\n\n\n

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

              Example 1: Ecommerce brand balancing prospecting and retargeting<\/h3>\n\n\n\n

              A direct-to-consumer store sees retargeting campaigns with extremely high last-click ROAS, while prospecting looks weak. With Attribution ROAS<\/strong> using a position-based model, prospecting receives meaningful partial credit for initiating journeys that later convert through retargeting and branded search. In Conversion & Measurement<\/strong>, this prevents the team from cutting prospecting spend and shrinking future demand.<\/p>\n\n\n\n

              Example 2: B2B SaaS with long sales cycles and lead values<\/h3>\n\n\n\n

              A SaaS company tracks trial signups and downstream pipeline revenue. They assign lead values based on historical conversion to paid and apply Attribution<\/strong> across content, paid search, and LinkedIn ads. Attribution ROAS<\/strong> highlights that \u201chow-to\u201d content campaigns rarely get last-click credit but materially influence high-value deals. The result is better alignment between Conversion & Measurement<\/strong> and the actual revenue cycle.<\/p>\n\n\n\n

              Example 3: Multi-location business comparing paid search vs paid social<\/h3>\n\n\n\n

              A regional service provider runs paid search for \u201cnear me\u201d queries and paid social for awareness. Last-click suggests paid social is inefficient. Cross-channel Attribution ROAS<\/strong> with time-decay shows paid social drives assisted conversions that later close through search. The business uses this insight to adjust creative sequencing and landing pages, improving overall Conversion & Measurement<\/strong> performance.<\/p>\n\n\n\n

              Benefits of Using Attribution ROAS<\/h2>\n\n\n\n

              When implemented thoughtfully, Attribution ROAS<\/strong> delivers practical benefits:<\/p>\n\n\n\n

                \n
              • Better performance decisions<\/strong>: You optimize using a fuller picture of contribution, not just the final interaction.<\/li>\n
              • More efficient spend<\/strong>: Budgets shift from \u201ccredit hogs\u201d to channels that actually create demand, improving blended ROAS over time.<\/li>\n
              • Stronger full-funnel strategy<\/strong>: Teams can justify upper-funnel investment because Attribution<\/strong> shows its downstream role.<\/li>\n
              • Improved customer experience<\/strong>: Understanding journey touchpoints encourages more coherent messaging, frequency control, and better landing-page alignment.<\/li>\n
              • Clearer experimentation<\/strong>: Attribution ROAS can guide where to run holdouts or lift tests to validate incremental impact within Conversion & Measurement<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                Challenges of Attribution ROAS<\/h2>\n\n\n\n

                Attribution ROAS<\/strong> is powerful, but it has real limitations that teams must manage:<\/p>\n\n\n\n

                  \n
                • Tracking loss and privacy constraints<\/strong>: Consent requirements, browser restrictions, and identifier loss reduce journey visibility, affecting Attribution<\/strong> accuracy.<\/li>\n
                • Cross-device and cross-browser gaps<\/strong>: Users who research on mobile and buy on desktop can break paths, skewing Attribution ROAS.<\/li>\n
                • Model bias and overconfidence<\/strong>: Different models can produce very different ROAS results; treating one as \u201ctruth\u201d is risky.<\/li>\n
                • Inconsistent conversion definitions<\/strong>: If teams disagree on what counts as a conversion or how revenue is assigned, Conversion & Measurement<\/strong> outputs become political.<\/li>\n
                • Offline conversions and delayed revenue<\/strong>: Retail, call centers, and B2B pipelines require careful integration to avoid undercounting value.<\/li>\n
                • Attribution windows and lag<\/strong>: Short windows can under-credit slow-burn channels; long windows can over-credit stale touches.<\/li>\n<\/ul>\n\n\n\n

                  Best Practices for Attribution ROAS<\/h2>\n\n\n\n

                  To make Attribution ROAS<\/strong> useful and trustworthy in Conversion & Measurement<\/strong>, prioritize these practices:<\/p>\n\n\n\n

                    \n
                  1. \n

                    Define decisions first, then pick the model<\/strong>\n Use different views for different decisions (e.g., last-click for on-platform bid tuning, cross-channel for budget allocation). Document how Attribution<\/strong> is used.<\/p>\n<\/li>\n

                  2. \n

                    Standardize conversion and value logic<\/strong>\n Ensure revenue, refunds, discounts, and lead values are consistently applied. Attribution ROAS is only meaningful if \u201crevenue\u201d is defined correctly.<\/p>\n<\/li>\n

                  3. \n

                    Validate with experiments<\/strong>\n Use geo tests, holdouts, or incrementality experiments to sanity-check whether high Attribution ROAS channels truly drive lift.<\/p>\n<\/li>\n

                  4. \n

                    Control granularity<\/strong>\n Don\u2019t over-optimize at tiny levels (like individual keywords) if your attribution data is sparse. Aggregate where needed for stability.<\/p>\n<\/li>\n

                  5. \n

                    Monitor data quality continuously<\/strong>\n Track event firing rates, deduplication issues, UTM\/tagging consistency, and spend ingestion. Broken data silently corrupts Attribution ROAS.<\/p>\n<\/li>\n

                  6. \n

                    Report multiple lenses<\/strong>\n Pair Attribution ROAS with blended ROAS, CAC, and contribution margin so executives get a robust Conversion & Measurement<\/strong> narrative.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                    Tools Used for Attribution ROAS<\/h2>\n\n\n\n

                    You don\u2019t need a single \u201cAttribution ROAS tool.\u201d Instead, most teams use a stack that supports Attribution<\/strong> and operational reporting:<\/p>\n\n\n\n

                      \n
                    • Analytics tools<\/strong>: Event tracking, path analysis, conversion reporting, cohort views, and attribution model comparisons.<\/li>\n
                    • Tag management systems<\/strong>: Centralized control over pixels, event schemas, and deployment governance.<\/li>\n
                    • Ad platforms<\/strong>: Cost, impressions, clicks, and platform-reported conversions (useful but not always consistent cross-channel).<\/li>\n
                    • CRM systems<\/strong>: Lead status, pipeline stages, closed-won revenue, and offline conversion feedback loops for Conversion & Measurement<\/strong>.<\/li>\n
                    • Data warehouses and ETL\/ELT pipelines<\/strong>: Joining cost + conversion + CRM data, deduplicating events, and creating attributed revenue tables.<\/li>\n
                    • Reporting dashboards\/BI<\/strong>: Stakeholder-friendly views of Attribution ROAS by channel, campaign, and funnel stage.<\/li>\n
                    • SEO tools (supporting role)<\/strong>: Keyword and landing-page insights that help interpret assisted conversions and brand\/non-brand dynamics.<\/li>\n<\/ul>\n\n\n\n

                      Metrics Related to Attribution ROAS<\/h2>\n\n\n\n

                      Attribution ROAS<\/strong> is best interpreted alongside complementary metrics in Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                        \n
                      • Blended ROAS<\/strong>: Total revenue \u00f7 total ad spend, without attribution splitting. Good for \u201cbig picture\u201d health.<\/li>\n
                      • CAC (Customer Acquisition Cost)<\/strong>: Spend \u00f7 new customers, often paired with Attribution ROAS for unit economics.<\/li>\n
                      • MER (Marketing Efficiency Ratio)<\/strong>: Total revenue \u00f7 total marketing spend (including non-ad costs), useful for executive budgeting.<\/li>\n
                      • Conversion rate and assisted conversions<\/strong>: Reveal whether Attribution ROAS changes because of more conversions or simply credit redistribution.<\/li>\n
                      • AOV \/ LTV<\/strong>: Average order value and lifetime value help interpret whether attributed gains are high-quality.<\/li>\n
                      • Contribution margin<\/strong>: Revenue can be misleading without profitability; margin-based views make Attribution<\/strong> more business-realistic.<\/li>\n
                      • Time to convert and touchpoint depth<\/strong>: Helps diagnose whether certain channels drive longer journeys that require different evaluation windows.<\/li>\n<\/ul>\n\n\n\n

                        Future Trends of Attribution ROAS<\/h2>\n\n\n\n

                        Attribution ROAS<\/strong> is evolving as Conversion & Measurement<\/strong> adapts to privacy, automation, and AI:<\/p>\n\n\n\n

                          \n
                        • More modeled measurement<\/strong>: With fewer deterministic identifiers, modeled paths and aggregated reporting will play a larger role in Attribution<\/strong>.<\/li>\n
                        • Incrementality becomes more mainstream<\/strong>: Teams will increasingly pair Attribution ROAS with lift testing to avoid optimizing to non-incremental conversions.<\/li>\n
                        • AI-assisted insights (with guardrails)<\/strong>: AI can surface anomalies, predict diminishing returns, and recommend budget shifts\u2014but inputs still need governance.<\/li>\n
                        • First-party data strategies<\/strong>: Stronger consented data collection and CRM integration will improve cross-channel Attribution ROAS reliability.<\/li>\n
                        • Media mix and attribution convergence<\/strong>: Marketing mix modeling and attribution will be used together\u2014MMM for strategic allocation, attribution for tactical optimization within Conversion & Measurement<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                          Attribution ROAS vs Related Terms<\/h2>\n\n\n\n

                          Attribution ROAS vs ROAS<\/h3>\n\n\n\n

                          Plain ROAS often implicitly relies on a platform\u2019s default crediting (frequently last-click or platform-specific rules). Attribution ROAS<\/strong> explicitly uses an Attribution<\/strong> model to distribute revenue credit across touchpoints, making it more suitable for cross-channel decision-making.<\/p>\n\n\n\n

                          Attribution ROAS vs Multi-Touch Attribution (MTA)<\/h3>\n\n\n\n

                          Multi-touch attribution is the broader methodology of assigning credit across touchpoints. Attribution ROAS<\/strong> is a metric outcome<\/em> that uses those credits to compute return on ad spend. In other words: MTA is the method; Attribution ROAS is a decision-friendly number produced by that method.<\/p>\n\n\n\n

                          Attribution ROAS vs Marketing Mix Modeling (MMM)<\/h3>\n\n\n\n

                          MMM estimates channel impact using aggregated data (often weekly spend and revenue) and controls for external factors. It\u2019s great for high-level budgeting but less granular for campaign optimization. Attribution ROAS<\/strong> is typically more tactical and user-journey-oriented within Conversion & Measurement<\/strong>, though both can complement each other.<\/p>\n\n\n\n

                          Who Should Learn Attribution ROAS<\/h2>\n\n\n\n
                            \n
                          • Marketers<\/strong> benefit by making smarter optimization choices across funnel stages and channels, not just chasing last-click wins.<\/li>\n
                          • Analysts<\/strong> use Attribution ROAS to translate complex Attribution<\/strong> outputs into business decisions and measurement narratives.<\/li>\n
                          • Agencies<\/strong> need it to explain performance credibly, defend upper-funnel investment, and align reporting with client goals in Conversion & Measurement<\/strong>.<\/li>\n
                          • Business owners and founders<\/strong> gain clarity on what actually drives revenue, improving budgeting and forecasting.<\/li>\n
                          • Developers and data engineers<\/strong> support the pipelines, identity logic, and data quality that make Attribution ROAS reliable and scalable.<\/li>\n<\/ul>\n\n\n\n

                            Summary of Attribution ROAS<\/h2>\n\n\n\n

                            Attribution ROAS<\/strong> is ROAS calculated using attributed revenue assigned across customer touchpoints based on an Attribution<\/strong> model. It matters because modern journeys are multi-channel and multi-session, making last-click measurement incomplete. In Conversion & Measurement<\/strong>, Attribution ROAS helps teams allocate budget, evaluate performance fairly, and scale what truly contributes to revenue\u2014while staying mindful of model limitations, data quality, and privacy constraints.<\/p>\n\n\n\n

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

                            1) What is Attribution ROAS and how is it calculated?<\/h3>\n\n\n\n

                            Attribution ROAS is attributed revenue \u00f7 ad spend<\/strong>, where attributed revenue is distributed across touchpoints using an Attribution<\/strong> model (linear, time-decay, position-based, data-driven, etc.).<\/p>\n\n\n\n

                            2) Is Attribution ROAS better than last-click ROAS?<\/h3>\n\n\n\n

                            It\u2019s often more decision-useful for cross-channel budgeting in Conversion & Measurement<\/strong>, but it depends on your goal. Last-click can still be helpful for certain tactical optimizations; Attribution ROAS is better for understanding contribution across the journey.<\/p>\n\n\n\n

                            3) Which Attribution model should I use for Attribution ROAS?<\/h3>\n\n\n\n

                            Start with the decision you\u2019re trying to make. Many teams use a simple rule-based model (like position-based) for clarity, then validate with experiments. If you have strong data coverage, data-driven Attribution<\/strong> can be useful\u2014just monitor stability.<\/p>\n\n\n\n

                            4) How does Attribution affect ROAS reporting across channels?<\/h3>\n\n\n\n

                            Attribution<\/strong> changes which channel gets credit for revenue. Channels that introduce or nurture demand often gain credit under multi-touch models, while channels that close conversions may lose some last-click credit. That shift is the point of Attribution ROAS.<\/p>\n\n\n\n

                            5) Can I use Attribution ROAS for SEO or email, not just ads?<\/h3>\n\n\n\n

                            You can apply the same concept to any channel with measurable costs and attributable value, but \u201cspend\u201d may be less direct for SEO and email. In Conversion & Measurement<\/strong>, teams often use Attribution ROAS primarily for paid media and use complementary efficiency metrics for owned channels.<\/p>\n\n\n\n

                            6) What are the biggest reasons Attribution ROAS can be misleading?<\/h3>\n\n\n\n

                            Common issues include missing cross-device paths, inconsistent conversion value mapping, platform-specific reporting differences, and overreliance on a single model without incrementality checks. Treat Attribution ROAS as a strong signal\u2014not absolute truth.<\/p>\n","protected":false},"excerpt":{"rendered":"

                            Attribution ROAS is a way to calculate return on ad spend using an **Attribution** approach\u2014meaning revenue credit is distributed across marketing touchpoints based on a chosen model, rather than being assigned to a single \u201cwinner\u201d click. In **Conversion & Measurement**, it answers a deceptively simple question: *Which channels, campaigns, and keywords are truly driving revenue when customer journeys span multiple sessions and devices?*<\/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-7097","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\/7097","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=7097"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7097\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7097"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7097"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7097"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}