{"id":10847,"date":"2026-03-30T01:10:35","date_gmt":"2026-03-30T01:10:35","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/third-party-data\/"},"modified":"2026-03-30T01:10:35","modified_gmt":"2026-03-30T01:10:35","slug":"third-party-data","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/third-party-data\/","title":{"rendered":"Third-party Data: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Programmatic Advertising"},"content":{"rendered":"\n

Third-party Data is information about people, households, devices, or contexts that a company acquires from an external provider rather than collecting directly. In Paid Marketing<\/strong>, it has traditionally been used to expand reach, target new audiences, and inform bidding decisions\u2014especially inside Programmatic Advertising<\/strong>, where automation depends on signals to decide who sees an ad and at what price.<\/p>\n\n\n\n

Third-party Data matters because it can help marketers move beyond the limits of their own customer lists, uncover new demand, and scale campaigns faster. At the same time, it comes with serious tradeoffs: privacy constraints, data quality variation, and growing restrictions on cross-site tracking. Understanding what Third-party Data is (and what it is not) is now a core skill for anyone running modern Paid Marketing<\/strong> programs and buying media through Programmatic Advertising<\/strong> platforms.<\/p>\n\n\n\n

What Is Third-party Data?<\/h2>\n\n\n\n

Third-party Data is data collected by an entity that does not have a direct relationship with the end user\u2014and then packaged and sold or licensed to other businesses. The key characteristic is the lack of a direct interaction between the advertiser and the consumer at the point of data collection.<\/p>\n\n\n\n

At a conceptual level, Third-party Data is used as an \u201coutside-in\u201d signal. It can describe:\n– Who<\/strong> someone might be (demographics or inferred interests)\n– What<\/strong> they might want (purchase intent or behavioral segments)\n– Where\/when<\/strong> to reach them (context, location patterns, device signals)\n– How<\/strong> they behave across properties (aggregated browsing behavior, when legally and technically feasible)<\/p>\n\n\n\n

From a business standpoint, Third-party Data is a way to buy audience intelligence rather than earning it through direct customer interactions. In Paid Marketing<\/strong>, it is often applied to prospecting (new customer acquisition), lookalike expansion, and brand reach campaigns. Inside Programmatic Advertising<\/strong>, it can influence targeting, bidding, frequency controls, and measurement models\u2014though the exact mechanics depend on the buying environment and privacy rules.<\/p>\n\n\n\n

Why Third-party Data Matters in Paid Marketing<\/h2>\n\n\n\n

In practice, Third-party Data has been attractive because it promises scale and speed. When first-party customer data is small or incomplete, external data can help teams reach relevant audiences without waiting months to build a robust dataset.<\/p>\n\n\n\n

Strategically, Third-party Data can support Paid Marketing<\/strong> by:\n– Expanding addressable audiences<\/strong> beyond site visitors and CRM contacts\n– Improving targeting precision<\/strong> for acquisition campaigns (when the data is accurate)\n– Accelerating testing<\/strong> by providing pre-built segments for rapid experimentation\n– Enabling new market entry<\/strong> when a brand has little historical demand data\n– Supporting omnichannel planning<\/strong> by offering broader market or category insights<\/p>\n\n\n\n

In Programmatic Advertising<\/strong>, this matters because the entire ecosystem is designed to make automated decisions quickly. Better signals can mean better bids, more efficient reach, and fewer wasted impressions\u2014assuming the data is compliant, current, and actually predictive.<\/p>\n\n\n\n

How Third-party Data Works<\/h2>\n\n\n\n

Third-party Data is more of an operating model than a single procedure, but it typically flows through a repeatable workflow in Paid Marketing<\/strong> and Programmatic Advertising<\/strong>:<\/p>\n\n\n\n

    \n
  1. \n

    Input (data sourcing and licensing)<\/strong>\n A provider collects data from publishers, apps, panels, transactions, surveys, or other permitted sources, then aggregates it into segments. An advertiser licenses access to those segments, often via a data marketplace, a data management layer, or direct contracts.<\/p>\n<\/li>\n

  2. \n

    Processing (normalization and identity mapping)<\/strong>\n The data is cleaned, categorized, and mapped to identifiers used in ad systems (for example, cohort IDs, device signals, or publisher-specific IDs). In privacy-restricted environments, matching may be probabilistic or done within controlled \u201cclean room\u201d style workflows rather than via direct user-level identifiers.<\/p>\n<\/li>\n

  3. \n

    Execution (activation in campaigns)<\/strong>\n The advertiser activates Third-party Data as targeting or optimization inputs: selecting audience segments, applying bid adjustments, excluding existing customers, or shaping frequency and reach. This is most visible in Programmatic Advertising<\/strong>, where audience segments can influence real-time bidding and inventory selection.<\/p>\n<\/li>\n

  4. \n

    Output (measurement and learning)<\/strong>\n Performance is evaluated using campaign outcomes (CPA, ROAS, incremental lift) and operational metrics (match rate, reach, frequency). The final step is learning: refining segments, combining with first-party signals, and deciding whether the Third-party Data is worth its cost and risk.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

    Key Components of Third-party Data<\/h2>\n\n\n\n

    Using Third-party Data well requires more than buying segments. The effectiveness depends on a system of inputs, governance, and measurement.<\/p>\n\n\n\n

    Data inputs and segment definitions<\/h3>\n\n\n\n

    Third-party Data can be built from behavioral events, content consumption, declared preferences, or aggregated purchase signals. What matters is how a segment is defined: \u201cauto intenders\u201d can mean very different things across providers.<\/p>\n\n\n\n

    Identity and activation pathways<\/h3>\n\n\n\n

    Activation depends on how data is matched to ad environments. That might happen via:\n– Publisher-specific identifiers in walled environments\n– Contextual or cohort-based approaches\n– Modeled audiences where user-level tracking is limited<\/p>\n\n\n\n

    Compliance and governance<\/h3>\n\n\n\n

    Because Third-party Data touches privacy, teams need clear responsibilities for:\n– Data procurement and vendor due diligence\n– Consent and usage limitations\n– Retention policies and access controls\n– Documentation for audits and legal reviews<\/p>\n\n\n\n

    Measurement and experimentation<\/h3>\n\n\n\n

    The final component is a testing discipline: holdouts, lift studies, and consistent attribution approaches. Without rigorous evaluation, Third-party Data can look valuable while simply re-labeling people you would have reached anyway.<\/p>\n\n\n\n

    Types of Third-party Data<\/h2>\n\n\n\n

    While there is no single universal taxonomy, the most useful distinctions for Paid Marketing<\/strong> and Programmatic Advertising<\/strong> are about what the data represents<\/em> and how it is created<\/em>:<\/p>\n\n\n\n

    Demographic and firmographic segments<\/h3>\n\n\n\n

    Age ranges, household composition, income bands (often modeled), job roles, industry categories, and company size. These are commonly used for broad targeting and creative tailoring.<\/p>\n\n\n\n

    Interest and behavioral segments<\/h3>\n\n\n\n

    Inferred interests based on content consumption or browsing patterns. They can help top-of-funnel prospecting but vary widely in accuracy and freshness.<\/p>\n\n\n\n

    Intent and in-market segments<\/h3>\n\n\n\n

    Signals suggesting near-term purchase consideration (e.g., researching mortgages or comparing phones). These segments can perform well for acquisition, but they are also the most sensitive to privacy restrictions and signal loss.<\/p>\n\n\n\n

    Location and mobility-derived segments<\/h3>\n\n\n\n

    Aggregated location patterns or inferred \u201cfrequent visitors\u201d to certain places. Use cases include local campaigns and footfall-oriented categories, but these require careful compliance and are increasingly constrained.<\/p>\n\n\n\n

    Modeled and synthetic audiences<\/h3>\n\n\n\n

    Audiences created through statistical modeling rather than direct observation. As direct identifiers decline, modeled Third-party Data becomes more common, making validation and lift testing essential.<\/p>\n\n\n\n

    Real-World Examples of Third-party Data<\/h2>\n\n\n\n

    1) Scaling acquisition for a new product launch<\/h3>\n\n\n\n

    A consumer brand launching in a new category has limited first-party data. They use Third-party Data intent segments to find likely buyers, then run Programmatic Advertising<\/strong> prospecting campaigns. Performance is measured against a control group using the same creative and inventory but without the third-party segment to estimate incremental impact.<\/p>\n\n\n\n

    2) B2B targeting with firmographic overlays<\/h3>\n\n\n\n

    A SaaS company uses Third-party Data firmographics to focus Paid Marketing<\/strong> spend on mid-market and enterprise accounts in specific industries. In Programmatic Advertising<\/strong>, the firmographic segment is combined with contextual targeting on relevant publications to reduce wasted impressions and improve lead quality.<\/p>\n\n\n\n

    3) Retail category expansion with audience testing<\/h3>\n\n\n\n

    A retailer wants to grow a seasonal category. They test multiple Third-party Data audiences (interest vs. intent vs. demographic) with identical budgets and creative. The winner is chosen based on incremental conversions and new-customer rate, not just click-through rate, because some segments inflate engagement without driving sales.<\/p>\n\n\n\n

    Benefits of Using Third-party Data<\/h2>\n\n\n\n

    When it\u2019s accurate, compliant, and properly tested, Third-party Data can deliver meaningful upside in Paid Marketing<\/strong>:<\/p>\n\n\n\n

      \n
    • Faster scaling of prospecting<\/strong> by reaching relevant audiences beyond your owned channels <\/li>\n
    • Improved media efficiency<\/strong> by reducing spend on low-propensity impressions <\/li>\n
    • Better funnel coverage<\/strong> by supporting awareness, consideration, and conversion strategies with different segments <\/li>\n
    • Creative relevance<\/strong> by matching messaging to audience needs (e.g., intent vs. lifestyle) <\/li>\n
    • Operational speed<\/strong> in Programmatic Advertising<\/strong>, where pre-built segments can accelerate experimentation<\/li>\n<\/ul>\n\n\n\n

      It can also help with planning: market-level insights and category demand signals can inform where to allocate budget, what to test, and which audiences to prioritize.<\/p>\n\n\n\n

      Challenges of Third-party Data<\/h2>\n\n\n\n

      Third-party Data is not a guaranteed performance lever. Common problems include:<\/p>\n\n\n\n

      Data quality and freshness<\/h3>\n\n\n\n

      Segments can be outdated, overly broad, or built from weak proxies. Two vendors can label similar segments but deliver very different results.<\/p>\n\n\n\n

      Privacy and regulatory constraints<\/h3>\n\n\n\n

      Usage may be limited by consent requirements, regional laws, platform policies, and browser\/app tracking restrictions. What is technically possible in Programmatic Advertising<\/strong> is increasingly shaped by privacy-by-design defaults.<\/p>\n\n\n\n

      Signal loss and identity fragmentation<\/h3>\n\n\n\n

      With reduced cross-site identifiers, match rates can drop and audience portability can decline. The same Third-party Data segment may behave differently across environments.<\/p>\n\n\n\n

      Measurement ambiguity<\/h3>\n\n\n\n

      Attribution can over-credit Third-party Data if the audience overlaps heavily with users already likely to convert. Without incrementality testing, teams may mistake correlation for causation.<\/p>\n\n\n\n

      Brand and reputational risk<\/h3>\n\n\n\n

      If data collection practices are unclear or misaligned with user expectations, the downside is not just performance\u2014it can be trust and compliance exposure.<\/p>\n\n\n\n

      Best Practices for Third-party Data<\/h2>\n\n\n\n

      To use Third-party Data responsibly and effectively in Paid Marketing<\/strong>:<\/p>\n\n\n\n

        \n
      1. \n

        Start with a clear hypothesis<\/strong>\n Define what the data should improve: prospecting CPA, new-customer rate, qualified leads, or reach in a specific segment.<\/p>\n<\/li>\n

      2. \n

        Demand transparent segment documentation<\/strong>\n Ask how segments are built, how often they refresh, what geographic limitations apply, and what the expected accuracy is.<\/p>\n<\/li>\n

      3. \n

        Test incrementality, not just efficiency<\/strong>\n Use holdouts, geo tests, or lift studies where possible. In Programmatic Advertising<\/strong>, compare against contextual-only or broad targeting baselines.<\/p>\n<\/li>\n

      4. \n

        Combine with first-party strategy<\/strong>\n Treat Third-party Data as a complement, not a foundation. Use it to discover audiences, then build stronger first-party signals through content, CRM capture, and lifecycle marketing.<\/p>\n<\/li>\n

      5. \n

        Control overlap and frequency<\/strong>\n Layer segments thoughtfully and monitor reach\/frequency to avoid paying more to hit the same people repeatedly.<\/p>\n<\/li>\n

      6. \n

        Plan for portability limits<\/strong>\n Expect that audiences won\u2019t work the same way across every channel. Document what works where and maintain channel-specific playbooks.<\/p>\n<\/li>\n

      7. \n

        Implement governance early<\/strong>\n Maintain a data inventory, usage policies, and vendor reviews. Ensure legal, security, and marketing teams agree on acceptable use.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        Tools Used for Third-party Data<\/h2>\n\n\n\n

        Third-party Data is operationalized through a stack rather than a single tool. Common tool categories in Paid Marketing<\/strong> and Programmatic Advertising<\/strong> include:<\/p>\n\n\n\n

          \n
        • Demand-side platforms (DSPs)<\/strong> for activating segments, bidding, frequency management, and reporting in programmatic buys <\/li>\n
        • Data management and audience platforms<\/strong> for segment storage, distribution, overlap analysis, and taxonomy management <\/li>\n
        • Customer data platforms (CDPs) and CRM systems<\/strong> to connect first-party data with acquisition and suppression strategies <\/li>\n
        • Analytics tools<\/strong> for cohort analysis, funnel measurement, and post-click performance evaluation <\/li>\n
        • Experimentation and lift measurement frameworks<\/strong> to evaluate incrementality and audience value <\/li>\n
        • Reporting dashboards and BI systems<\/strong> to unify costs, performance, reach, and audience diagnostics across channels<\/li>\n<\/ul>\n\n\n\n

          The practical point: Third-party Data is only as useful as your ability to activate it reliably and measure it honestly.<\/p>\n\n\n\n

          Metrics Related to Third-party Data<\/h2>\n\n\n\n

          Evaluating Third-party Data requires both performance metrics and data quality diagnostics.<\/p>\n\n\n\n

          Performance and ROI metrics<\/h3>\n\n\n\n
            \n
          • CPA \/ CPL \/ CAC<\/strong> (cost per acquisition\/lead\/customer) <\/li>\n
          • ROAS<\/strong> (return on ad spend) and contribution margin where available <\/li>\n
          • Conversion rate<\/strong> by audience segment <\/li>\n
          • New-customer rate<\/strong> (or \u201cnet new\u201d) to detect audience recycling <\/li>\n
          • Incremental lift<\/strong> in conversions or revenue (preferred when feasible)<\/li>\n<\/ul>\n\n\n\n

            Media efficiency and delivery metrics<\/h3>\n\n\n\n
              \n
            • CPM<\/strong> and effective CPM changes when applying segments <\/li>\n
            • Reach and frequency<\/strong> to detect saturation <\/li>\n
            • Win rate<\/strong> and auction dynamics shifts in Programmatic Advertising<\/strong> when targeting is constrained<\/li>\n<\/ul>\n\n\n\n

              Data quality and activation metrics<\/h3>\n\n\n\n
                \n
              • Match rate \/ addressability rate<\/strong> (where applicable) <\/li>\n
              • Segment overlap<\/strong> with existing customers or other segments <\/li>\n
              • Recency\/freshness indicators<\/strong> (refresh cadence, decay assumptions) <\/li>\n
              • Stability over time<\/strong> (does performance collapse after initial weeks?)<\/li>\n<\/ul>\n\n\n\n

                Future Trends of Third-party Data<\/h2>\n\n\n\n

                Third-party Data is evolving rapidly under privacy pressure and changing platform policies. Key trends shaping Paid Marketing<\/strong> include:<\/p>\n\n\n\n

                  \n
                • \n

                  Shift from user-level tracking to modeling and aggregation<\/strong>\n More Third-party Data will be probabilistic or cohort-based, increasing the importance of validation and lift testing.<\/p>\n<\/li>\n

                • \n

                  Growth of contextual and commerce-based signals<\/strong>\n As cross-site identity weakens, context, product-level signals, and retailer media ecosystems become more central in Programmatic Advertising<\/strong> strategies.<\/p>\n<\/li>\n

                • \n

                  Clean room and privacy-safe collaboration patterns<\/strong>\n More measurement and audience insights will be produced through controlled environments that reduce raw data sharing.<\/p>\n<\/li>\n

                • \n

                  AI-assisted segmentation and optimization<\/strong>\n AI can help discover patterns and predict propensities, but it also increases the risk of opaque segments. Marketers will need stronger governance and explainability standards.<\/p>\n<\/li>\n

                • \n

                  Greater emphasis on first-party resilience<\/strong>\n Third-party Data will increasingly serve as a supplement for scale and testing, while durable advantage comes from first-party data, content, and customer relationships.<\/p>\n<\/li>\n<\/ul>\n\n\n\n

                  Third-party Data vs Related Terms<\/h2>\n\n\n\n

                  Third-party Data vs First-party Data<\/h3>\n\n\n\n

                  First-party data<\/strong> is collected directly from your customers and audiences (website behavior, CRM records, email engagement). It is typically more trustworthy and permission-aligned. Third-party Data<\/strong> is purchased or licensed from external sources and is often less transparent. In Paid Marketing<\/strong>, first-party data is usually best for retention and high-intent targeting; Third-party Data is often used for prospecting and scale.<\/p>\n\n\n\n

                  Third-party Data vs Second-party Data<\/h3>\n\n\n\n

                  Second-party data<\/strong> is another company\u2019s first-party data shared through a direct partnership (for example, a publisher sharing audience segments with an advertiser). It can be higher quality than Third-party Data due to clearer provenance, but it tends to be less scalable and more relationship-driven. In Programmatic Advertising<\/strong>, second-party arrangements often appear as preferred deals or curated marketplace packages.<\/p>\n\n\n\n

                  Third-party Data vs Contextual Targeting<\/h3>\n\n\n\n

                  Contextual targeting<\/strong> places ads based on the content being viewed rather than the user\u2019s historical behavior. It doesn\u2019t require the same type of cross-site tracking, making it more privacy-resilient. Third-party Data targets audiences; contextual targets environments. Many effective Paid Marketing<\/strong> strategies blend both.<\/p>\n\n\n\n

                  Who Should Learn Third-party Data<\/h2>\n\n\n\n
                    \n
                  • Marketers<\/strong> need to understand when Third-party Data helps (prospecting scale, testing) and when it harms (wasted spend, compliance risk). <\/li>\n
                  • Analysts<\/strong> should know how to validate segment value, detect overlap, and design incrementality tests for Paid Marketing<\/strong>. <\/li>\n
                  • Agencies<\/strong> must evaluate vendors objectively and build repeatable frameworks for audience testing across Programmatic Advertising<\/strong> channels. <\/li>\n
                  • Business owners and founders<\/strong> benefit from understanding cost, risk, and realistic performance expectations\u2014especially when budgeting for growth. <\/li>\n
                  • Developers and data engineers<\/strong> are increasingly involved in data governance, identity workflows, and privacy-safe measurement pipelines.<\/li>\n<\/ul>\n\n\n\n

                    Summary of Third-party Data<\/h2>\n\n\n\n

                    Third-party Data is externally sourced audience or market information used to inform targeting, optimization, and measurement. In Paid Marketing<\/strong>, it has been a powerful way to scale acquisition and explore new audiences. In Programmatic Advertising<\/strong>, it can influence automated buying decisions, from bidding to reach management.<\/p>\n\n\n\n

                    Its value is real but not automatic. Data quality, privacy constraints, identity fragmentation, and measurement limitations mean Third-party Data must be tested rigorously and governed carefully. Used thoughtfully\u2014often alongside first-party and contextual strategies\u2014it can still play a meaningful role in an evergreen, performance-oriented Paid Marketing<\/strong> program.<\/p>\n\n\n\n

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

                    1) What is Third-party Data and when should I use it?<\/h3>\n\n\n\n

                    Third-party Data is audience or market data you license from an external provider. Use it when you need prospecting scale, want to test new audience hypotheses, or lack sufficient first-party signals\u2014while ensuring you can measure incrementality and meet privacy requirements.<\/p>\n\n\n\n

                    2) Is Third-party Data still effective for Paid Marketing today?<\/h3>\n\n\n\n

                    It can be, but effectiveness varies more than it used to. Platform restrictions and reduced identifiers mean some segments are less precise or less portable. The best approach is controlled testing against contextual and broad-targeting baselines.<\/p>\n\n\n\n

                    3) How does Third-party Data work in Programmatic Advertising?<\/h3>\n\n\n\n

                    In Programmatic Advertising<\/strong>, Third-party Data is typically activated as audience segments that influence which impressions you bid on and how much you bid. The segment is mapped to identifiers available in the buying environment, then performance is measured at the segment level.<\/p>\n\n\n\n

                    4) How do I know if a Third-party Data segment is high quality?<\/h3>\n\n\n\n

                    Look for transparent definitions, refresh cadence, and evidence of predictive performance. Then validate with your own experiments: compare against a control group, check overlap with existing customers, and monitor performance stability over time.<\/p>\n\n\n\n

                    5) What are the biggest risks of using Third-party Data?<\/h3>\n\n\n\n

                    The biggest risks are privacy\/compliance exposure, poor data accuracy, wasted spend due to overlap, and misleading attribution. There is also reputational risk if data sourcing practices don\u2019t align with user expectations or regulations.<\/p>\n\n\n\n

                    6) Should I prioritize Third-party Data or contextual targeting?<\/h3>\n\n\n\n

                    Prioritize what you can measure and sustain. Contextual targeting is often more privacy-resilient and consistent across environments. Third-party Data can add value for certain prospecting goals, but it should earn its place through incrementality testing.<\/p>\n\n\n\n

                    7) Can I combine Third-party Data with first-party data?<\/h3>\n\n\n\n

                    Yes\u2014and that\u2019s often the best approach. Use first-party data for suppression, customer lifecycle segmentation, and high-intent signals, then use Third-party Data to expand reach and discover new audiences in Paid Marketing<\/strong> while keeping measurement disciplined.<\/p>\n","protected":false},"excerpt":{"rendered":"

                    Third-party Data is information about people, households, devices, or contexts that a company acquires from an external provider rather than collecting directly. In **Paid Marketing**, it has traditionally been used to expand reach, target new audiences, and inform bidding decisions\u2014especially inside **Programmatic Advertising**, where automation depends on signals to decide who sees an ad and at what price.<\/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":[1911],"tags":[],"class_list":["post-10847","post","type-post","status-publish","format-standard","hentry","category-programmatic-advertising"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/10847","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=10847"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/10847\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=10847"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=10847"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=10847"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}