{"id":11274,"date":"2026-04-01T16:09:08","date_gmt":"2026-04-01T16:09:08","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/amazon-marketing-cloud-2\/"},"modified":"2026-04-01T16:09:08","modified_gmt":"2026-04-01T16:09:08","slug":"amazon-marketing-cloud-2","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/amazon-marketing-cloud-2\/","title":{"rendered":"Amazon Marketing Cloud: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Shopping Ads"},"content":{"rendered":"\n

Amazon Marketing Cloud is increasingly important for teams running Paid Marketing<\/strong> on Amazon because it helps answer questions that standard dashboards can\u2019t\u2014especially when you need to understand how multiple ad exposures influence shopping behavior. In the world of Shopping Ads<\/strong>, where Sponsored Ads and other Amazon media compete for the same customer attention, marketers need better ways to measure incrementality, sequence, and cross-campaign impact.<\/p>\n\n\n\n

At a high level, Amazon Marketing Cloud<\/strong> (often shortened to AMC<\/strong>) is a privacy-safe analytics environment that lets advertisers analyze Amazon Ads event data in more flexible ways. It\u2019s not just \u201cmore reporting\u201d; it\u2019s a way to create consistent measurement logic and then apply those insights to optimize Paid Marketing<\/strong> decisions, including audience strategy, creative sequencing, and budget allocation for Shopping Ads<\/strong>.<\/p>\n\n\n\n

What Is Amazon Marketing Cloud?<\/h2>\n\n\n\n

Amazon Marketing Cloud<\/strong> is a secure analytics solution designed to help advertisers run deeper analysis on Amazon Ads signals\u2014typically at an aggregated, privacy-protected level\u2014so they can understand how marketing touchpoints relate to outcomes like product detail page views, add-to-cart behavior, and purchases.<\/p>\n\n\n\n

The core concept behind Amazon Marketing Cloud<\/strong> is \u201cadvanced measurement in a controlled environment.\u201d Rather than relying only on prebuilt reports, AMC enables custom analysis that can answer specific business questions such as:<\/p>\n\n\n\n

    \n
  • Which combination of ad types contributes most to conversion?<\/li>\n
  • How long is the typical consideration window for a product category?<\/li>\n
  • What happens when customers see Sponsored Brands before Sponsored Products?<\/li>\n<\/ul>\n\n\n\n

    From a business standpoint, Amazon Marketing Cloud<\/strong> helps teams make better trade-offs in Paid Marketing<\/strong>: where to spend, who to target, and how to coordinate campaigns. Within Shopping Ads<\/strong>, it supports optimization beyond last-click performance by revealing the relationships between exposure, consideration, and conversion across multiple campaign types.<\/p>\n\n\n\n

    Why Amazon Marketing Cloud Matters in Paid Marketing<\/h2>\n\n\n\n

    Modern Paid Marketing<\/strong> requires more than \u201coptimize to ROAS.\u201d Brands need to understand customer journeys, overlap between audiences, and how ad formats work together. Amazon Marketing Cloud<\/strong> matters because it enables:<\/p>\n\n\n\n

      \n
    • Cross-campaign understanding:<\/strong> Instead of evaluating each campaign in isolation, you can analyze combinations of exposures and their outcomes.<\/li>\n
    • Better budget allocation:<\/strong> When you understand which touchpoints assist conversions, you can fund upper-funnel tactics without guessing.<\/li>\n
    • Cleaner experimentation:<\/strong> AMC supports more rigorous measurement approaches that help distinguish correlation from likely impact.<\/li>\n
    • Competitive advantage:<\/strong> Many advertisers still optimize Shopping Ads<\/strong> with limited visibility into paths to purchase. Teams using AMC can build a measurement discipline that compounds over time.<\/li>\n<\/ul>\n\n\n\n

      For brands competing in crowded categories, Amazon Marketing Cloud<\/strong> becomes a strategic measurement layer that helps justify spend, improve efficiency, and align stakeholders on what \u201cworking\u201d really means in Paid Marketing<\/strong>.<\/p>\n\n\n\n

      How Amazon Marketing Cloud Works<\/h2>\n\n\n\n

      In practice, Amazon Marketing Cloud<\/strong> works less like a single \u201cbutton\u201d and more like a measurement workflow that turns ad event data into actionable decisions. A simple way to understand it is:<\/p>\n\n\n\n

        \n
      1. \n

        Input (data and questions)<\/strong>\n Advertisers bring a measurement question (for example, \u201cDoes video exposure improve conversion rate for branded search?\u201d). AMC uses privacy-safe Amazon Ads signals related to impressions, clicks, and conversions across eligible ad products.<\/p>\n<\/li>\n

      2. \n

        Analysis (custom queries and logic)<\/strong>\n Teams define the logic needed to answer the question\u2014such as lookback windows, exposure sequences, frequency thresholds, or audience overlap rules. This is where AMC differs from standard reporting: it supports deeper, customizable analysis.<\/p>\n<\/li>\n

      3. \n

        Application (activation and optimization)<\/strong>\n Insights can inform how you structure Paid Marketing<\/strong>: shifting budget, adjusting frequency, changing creative sequencing, or refining audiences. In many cases, you can also build audiences based on defined behaviors and use them in Amazon advertising workflows (where eligible).<\/p>\n<\/li>\n

      4. \n

        Output (measurement and iteration)<\/strong>\n The output is typically aggregated findings, tested hypotheses, and operational changes\u2014applied back into Shopping Ads<\/strong> planning. Over time, teams iterate to improve performance, reduce waste, and standardize learnings.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        The key is that Amazon Marketing Cloud<\/strong> is designed to support privacy-aware analysis. Results are intended to be aggregated and governed, which shapes how you plan queries and interpret outputs.<\/p>\n\n\n\n

        Key Components of Amazon Marketing Cloud<\/h2>\n\n\n\n

        While implementations vary by organization, Amazon Marketing Cloud<\/strong> programs usually include these components:<\/p>\n\n\n\n

        Data inputs and signals<\/h3>\n\n\n\n

        AMC relies on Amazon Ads event-level signals in a controlled environment. Instead of exporting raw user-level data into external systems, analysis happens within a governed framework designed to protect privacy.<\/p>\n\n\n\n

        Querying and analysis layer<\/h3>\n\n\n\n

        Teams use a query-driven approach to create definitions (for example: \u201cexposed users = anyone with at least one impression in the last 14 days\u201d). This supports repeatable measurement across Paid Marketing<\/strong> initiatives and consistent evaluation of Shopping Ads<\/strong> tactics.<\/p>\n\n\n\n

        Audience definitions (where applicable)<\/h3>\n\n\n\n

        A major operational use of Amazon Marketing Cloud<\/strong> is translating insights into audiences\u2014such as users exposed to certain campaigns but not converted\u2014so you can tailor messaging, bidding, or suppression strategies.<\/p>\n\n\n\n

        Measurement governance<\/h3>\n\n\n\n

        To keep AMC useful, teams define:\n– Naming conventions for campaigns and line items\n– Consistent attribution windows and exposure rules\n– Documentation for query logic and assumptions\n– Access controls and review processes for analysis<\/p>\n\n\n\n

        People and responsibilities<\/h3>\n\n\n\n

        Successful AMC usage typically requires collaboration:\n– Marketers define hypotheses and decisions\n– Analysts build and validate logic\n– Media operators apply changes in Paid Marketing<\/strong>\n– Stakeholders align on KPIs for Shopping Ads<\/strong> and beyond<\/p>\n\n\n\n

        Types of Amazon Marketing Cloud<\/h2>\n\n\n\n

        Amazon Marketing Cloud<\/strong> doesn\u2019t have \u201ctypes\u201d in the same way an ad format does, but there are practical distinctions in how it\u2019s used:<\/p>\n\n\n\n

        1) Measurement-focused AMC usage<\/h3>\n\n\n\n

        This is the most common approach: using AMC to understand paths to purchase, frequency effects, and cross-campaign impact. It\u2019s especially valuable when Shopping Ads<\/strong> performance looks strong but growth is constrained, and you need to find the next lever.<\/p>\n\n\n\n

        2) Audience and activation-focused usage<\/h3>\n\n\n\n

        Here, the goal is to translate observed behaviors into audiences\u2014like \u201chigh-intent viewers\u201d or \u201cbrand search clickers who didn\u2019t purchase\u201d\u2014and then use Paid Marketing<\/strong> to move those segments down-funnel.<\/p>\n\n\n\n

        3) Experimentation and incrementality-focused usage<\/h3>\n\n\n\n

        More mature teams use Amazon Marketing Cloud<\/strong> to support structured tests (for example, comparing exposed vs. control-like groups using consistent rules). This helps reduce over-crediting Shopping Ads<\/strong> that may simply be capturing existing demand.<\/p>\n\n\n\n

        Real-World Examples of Amazon Marketing Cloud<\/h2>\n\n\n\n

        Example 1: Frequency cap guidance for Sponsored and display campaigns<\/h3>\n\n\n\n

        A brand running Shopping Ads<\/strong> and display notices ROAS drops as spend increases. Using Amazon Marketing Cloud<\/strong>, the team analyzes performance by frequency (1\u20132 exposures vs. 7+ exposures) and finds diminishing returns after a threshold. They adjust Paid Marketing<\/strong> budgets and refine reach tactics to prioritize incremental users rather than repeated exposure.<\/p>\n\n\n\n

        Example 2: Cross-format sequencing to increase conversion rate<\/h3>\n\n\n\n

        A category with longer consideration cycles uses AMC to study sequences: customers who first see awareness creative and later click a product-focused ad convert at a higher rate than those who only see bottom-funnel ads. The team restructures Shopping Ads<\/strong> campaigns to align messaging and timing, improving conversion rate without increasing bids.<\/p>\n\n\n\n

        Example 3: Suppression to reduce wasted spend on existing customers<\/h3>\n\n\n\n

        A subscription-like product identifies repeat purchasers. With Amazon Marketing Cloud<\/strong>, the team builds a suppression approach (where eligible) to reduce Paid Marketing<\/strong> spend on users likely to purchase anyway, then reallocates budget to acquisition-focused Shopping Ads<\/strong> and prospecting audiences.<\/p>\n\n\n\n

        Benefits of Using Amazon Marketing Cloud<\/h2>\n\n\n\n

        Amazon Marketing Cloud<\/strong> can create meaningful improvements across performance and operations:<\/p>\n\n\n\n

          \n
        • More accurate decision-making:<\/strong> Better insight into assist behavior and multi-touch paths than simple last-click views.<\/li>\n
        • Efficiency gains:<\/strong> By identifying waste (excess frequency, audience overlap), teams can reduce inefficient spend in Paid Marketing<\/strong>.<\/li>\n
        • Stronger audience strategy:<\/strong> Behavioral audiences and suppression strategies can improve the quality of traffic driven by Shopping Ads<\/strong>.<\/li>\n
        • Better alignment with stakeholders:<\/strong> A shared measurement layer helps reduce debates about which campaigns \u201cdeserve credit.\u201d<\/li>\n
        • Faster learning loops:<\/strong> Repeatable query frameworks allow teams to test, learn, and apply insights more consistently.<\/li>\n<\/ul>\n\n\n\n

          Challenges of Amazon Marketing Cloud<\/h2>\n\n\n\n

          Despite its upside, Amazon Marketing Cloud<\/strong> has real hurdles:<\/p>\n\n\n\n

            \n
          • Skill and resourcing:<\/strong> Query-based analysis requires analytical capability and time. Many teams need analyst support to operationalize AMC.<\/li>\n
          • Data interpretation risk:<\/strong> Multi-touch insights can be misread if teams confuse correlation with causation. Incrementality requires careful design.<\/li>\n
          • Operational complexity:<\/strong> Insights only matter if they change how Paid Marketing<\/strong> is run\u2014campaign structures, naming, and processes must support measurement.<\/li>\n
          • Access and eligibility constraints:<\/strong> Availability and capabilities can depend on account setup and Amazon Ads eligibility.<\/li>\n
          • Privacy and aggregation limitations:<\/strong> AMC is designed for privacy-safe outputs, which may limit certain granular analyses marketers are used to elsewhere.<\/li>\n<\/ul>\n\n\n\n

            Best Practices for Amazon Marketing Cloud<\/h2>\n\n\n\n

            To get consistent value from Amazon Marketing Cloud<\/strong>, focus on operational habits\u2014not one-off analyses:<\/p>\n\n\n\n

              \n
            1. \n

              Start with decisions, not dashboards<\/strong>\n Define the decision you want to make (budget shift, audience change, creative sequencing), then build AMC analysis to support it.<\/p>\n<\/li>\n

            2. \n

              Standardize taxonomy and naming<\/strong>\n Clean campaign naming makes Shopping Ads<\/strong> analysis dramatically easier, especially when you need to group by theme, product line, or funnel stage.<\/p>\n<\/li>\n

            3. \n

              Use consistent windows and definitions<\/strong>\n Decide on lookback windows, exposure thresholds, and conversion definitions so results can be compared across time.<\/p>\n<\/li>\n

            4. \n

              Prioritize a small set of \u201cevergreen\u201d queries<\/strong>\n Build a measurement starter set (frequency, overlap, pathing) and run it regularly as part of Paid Marketing<\/strong> operations.<\/p>\n<\/li>\n

            5. \n

              Document assumptions and caveats<\/strong>\n Record what was included\/excluded and why. This prevents teams from over-generalizing insights from one period to the next.<\/p>\n<\/li>\n

            6. \n

              Close the loop with testing<\/strong>\n Use AMC insights to propose changes, then validate them with structured experiments or at least controlled rollouts in Shopping Ads<\/strong>.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

              Tools Used for Amazon Marketing Cloud<\/h2>\n\n\n\n

              Even though Amazon Marketing Cloud<\/strong> is a distinct environment, it typically lives inside a broader measurement stack for Paid Marketing<\/strong> and Shopping Ads<\/strong>:<\/p>\n\n\n\n

                \n
              • Ad platforms and campaign management tools:<\/strong> Used to implement changes (bidding, budgets, targeting, creatives) based on AMC insights.<\/li>\n
              • Analytics and BI tools:<\/strong> For internal reporting, visualization, and stakeholder dashboards using aggregated AMC outputs.<\/li>\n
              • Tagging and taxonomy management processes:<\/strong> Not a \u201ctool\u201d per se, but critical systems for consistent naming and classification.<\/li>\n
              • CRM and customer data systems (where applicable):<\/strong> Help align lifecycle strategy and internal segmentation with advertising goals, while respecting privacy constraints.<\/li>\n
              • Experimentation frameworks:<\/strong> Methods and templates for incrementality tests, holdouts, and structured learning agendas.<\/li>\n<\/ul>\n\n\n\n

                The goal is to make AMC insights actionable\u2014integrated into how teams plan, launch, and evaluate Paid Marketing<\/strong>, rather than treated as separate research.<\/p>\n\n\n\n

                Metrics Related to Amazon Marketing Cloud<\/h2>\n\n\n\n

                Amazon Marketing Cloud<\/strong> supports deeper analysis of metrics you likely already track, plus new angles on them:<\/p>\n\n\n\n

                  \n
                • Path-to-purchase metrics:<\/strong> Common sequences of exposures and the time lag between first exposure and conversion.<\/li>\n
                • Frequency vs. performance:<\/strong> Conversion rate, CPA, or ROAS by number of impressions\/exposures.<\/li>\n
                • Reach and overlap:<\/strong> Unique reach across campaigns and audience overlap that can cause Shopping Ads<\/strong> to compete against each other.<\/li>\n
                • Assisted conversion indicators:<\/strong> How often an ad type appears earlier in journeys that later convert.<\/li>\n
                • Incrementality-oriented metrics (where tested):<\/strong> Lift in conversion rate, lift in new-to-brand outcomes, or reduced wasted impressions after suppression.<\/li>\n<\/ul>\n\n\n\n

                  A practical approach is to pick 3\u20135 AMC-driven metrics that directly influence Paid Marketing<\/strong> actions (for example, frequency thresholds and overlap rates).<\/p>\n\n\n\n

                  Future Trends of Amazon Marketing Cloud<\/h2>\n\n\n\n

                  Several trends are shaping how Amazon Marketing Cloud<\/strong> evolves within Paid Marketing<\/strong>:<\/p>\n\n\n\n

                    \n
                  • AI-assisted analysis:<\/strong> Expect more automation in translating business questions into analysis frameworks, making AMC accessible to more teams.<\/li>\n
                  • More advanced audience strategies:<\/strong> Personalization and segmentation will likely become more central as advertisers try to differentiate Shopping Ads<\/strong> beyond bid wars.<\/li>\n
                  • Privacy-driven measurement design:<\/strong> As the industry continues shifting toward privacy-safe analytics, clean-room-style measurement approaches like AMC will become more common.<\/li>\n
                  • Tighter measurement-to-activation loops:<\/strong> The operational advantage will come from shortening the time between insight and action\u2014updating audiences, creatives, and budget rules faster.<\/li>\n
                  • Standardization across organizations:<\/strong> Larger teams will formalize AMC playbooks, query libraries, and governance to scale learnings across brands and regions.<\/li>\n<\/ul>\n\n\n\n

                    Amazon Marketing Cloud vs Related Terms<\/h2>\n\n\n\n

                    Amazon Marketing Cloud vs Amazon DSP reporting<\/h3>\n\n\n\n

                    Amazon DSP reporting provides platform dashboards and standard insights. Amazon Marketing Cloud<\/strong> goes further by enabling custom analysis across touchpoints with flexible definitions, which is often crucial for understanding how different Paid Marketing<\/strong> efforts influence Shopping Ads<\/strong> outcomes.<\/p>\n\n\n\n

                    Amazon Marketing Cloud vs Amazon Attribution<\/h3>\n\n\n\n

                    Attribution tools generally focus on assigning credit to marketing interactions, often across channels or traffic sources, depending on the setup. Amazon Marketing Cloud<\/strong> is primarily an analysis environment for Amazon Ads signals where you can ask deeper questions about sequences, overlap, and frequency\u2014useful for refining how you run Amazon-centric Paid Marketing<\/strong>.<\/p>\n\n\n\n

                    Amazon Marketing Cloud vs a customer data platform (CDP)<\/h3>\n\n\n\n

                    A CDP is designed to unify first-party customer data for activation across channels. Amazon Marketing Cloud<\/strong> is not a general-purpose CDP; it\u2019s purpose-built for privacy-safe analysis of Amazon Ads interactions and for improving outcomes in Amazon advertising, including Shopping Ads<\/strong>.<\/p>\n\n\n\n

                    Who Should Learn Amazon Marketing Cloud<\/h2>\n\n\n\n
                      \n
                    • Marketers:<\/strong> To make smarter optimization decisions and build measurement literacy beyond basic ROAS.<\/li>\n
                    • Analysts:<\/strong> To develop repeatable frameworks for pathing, frequency, and audience overlap that improve Paid Marketing<\/strong> performance.<\/li>\n
                    • Agencies:<\/strong> To differentiate services with deeper insights and more defensible recommendations for Shopping Ads<\/strong> strategy.<\/li>\n
                    • Business owners and founders:<\/strong> To understand what drives profitable growth on Amazon and avoid scaling spend blindly.<\/li>\n
                    • Developers and technical practitioners:<\/strong> To support data workflows, governance, and repeatable analysis patterns that make AMC operational.<\/li>\n<\/ul>\n\n\n\n

                      Summary of Amazon Marketing Cloud<\/h2>\n\n\n\n

                      Amazon Marketing Cloud (AMC)<\/strong> is a privacy-safe analytics environment that helps advertisers analyze Amazon Ads interactions in more customizable ways than standard reporting. It matters because it improves how teams measure and optimize Paid Marketing<\/strong>, especially when multiple campaigns and formats influence the same outcomes. For Shopping Ads<\/strong>, Amazon Marketing Cloud supports better decisions about sequencing, frequency, audience strategy, and budget allocation\u2014turning measurement into an advantage rather than a limitation.<\/p>\n\n\n\n

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

                      1) What is Amazon Marketing Cloud used for?<\/h3>\n\n\n\n

                      Amazon Marketing Cloud<\/strong> is used for deeper, privacy-safe analysis of Amazon advertising interactions\u2014such as frequency impact, path-to-purchase patterns, and audience overlap\u2014so teams can improve Paid Marketing<\/strong> decisions and outcomes.<\/p>\n\n\n\n

                      2) Is Amazon Marketing Cloud the same as an attribution tool?<\/h3>\n\n\n\n

                      Not exactly. Attribution tools focus on credit assignment models. Amazon Marketing Cloud<\/strong> is an analysis environment where you can define custom logic and answer questions about how different ad exposures relate to outcomes, which can inform attribution thinking but isn\u2019t limited to it.<\/p>\n\n\n\n

                      3) How does Amazon Marketing Cloud help Shopping Ads performance?<\/h3>\n\n\n\n

                      For Shopping Ads<\/strong>, Amazon Marketing Cloud can reveal whether certain ad sequences convert better, whether you\u2019re over-serving ads to the same users, and where overlap between campaigns is causing inefficiency\u2014insights that can lead to better targeting and budget allocation.<\/p>\n\n\n\n

                      4) Do you need an analyst to use AMC?<\/h3>\n\n\n\n

                      You often need analytical support to get the most from Amazon Marketing Cloud<\/strong>, especially for designing consistent queries and interpreting results responsibly. Some teams start with a small set of standardized analyses and expand as capability grows.<\/p>\n\n\n\n

                      5) What kinds of questions should I ask first in Amazon Marketing Cloud?<\/h3>\n\n\n\n

                      Start with questions tied to clear actions, such as: \u201cWhat frequency level shows diminishing returns?\u201d \u201cWhich campaigns overlap heavily?\u201d or \u201cDo upper-funnel exposures improve branded conversion rates?\u201d These directly guide Paid Marketing<\/strong> optimizations.<\/p>\n\n\n\n

                      6) Can Amazon Marketing Cloud prove incrementality?<\/h3>\n\n\n\n

                      It can support incrementality-oriented analysis, but \u201cproving\u201d incrementality requires careful experimental design and governance. Use AMC to structure comparisons and learning agendas, then validate changes through controlled tests where possible.<\/p>\n\n\n\n

                      7) What\u2019s the biggest mistake teams make with Amazon Marketing Cloud?<\/h3>\n\n\n\n

                      The most common mistake is treating AMC as a one-time report rather than a repeatable measurement system. The value comes from consistent definitions, documented logic, and ongoing iteration that improves how Paid Marketing<\/strong> and Shopping Ads<\/strong> are planned and executed.<\/p>\n","protected":false},"excerpt":{"rendered":"

                      Amazon Marketing Cloud is increasingly important for teams running **Paid Marketing** on Amazon because it helps answer questions that standard dashboards can\u2019t\u2014especially when you need to understand how multiple ad exposures influence shopping behavior. In the world of **Shopping Ads**, where Sponsored Ads and other Amazon media compete for the same customer attention, marketers need better ways to measure incrementality, sequence, and cross-campaign impact.<\/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-11274","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\/11274","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=11274"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/11274\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=11274"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=11274"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=11274"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}