{"id":10724,"date":"2026-03-29T20:36:13","date_gmt":"2026-03-29T20:36:13","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/device-graph\/"},"modified":"2026-03-29T20:36:13","modified_gmt":"2026-03-29T20:36:13","slug":"device-graph","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/device-graph\/","title":{"rendered":"Device Graph: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Programmatic Advertising"},"content":{"rendered":"\n

Modern customers don\u2019t behave on a single screen. They research on a laptop, browse on a phone, stream on a TV, and convert inside an app\u2014often all within the same day. A Device Graph<\/strong> is the mechanism that helps marketers understand those fragmented signals as one connected journey, which is especially important in Paid Marketing<\/strong> where budgets, targeting, and measurement depend on knowing who you\u2019re reaching and how often.<\/p>\n\n\n\n

In Programmatic Advertising<\/strong>, decisions are made in milliseconds: which user to bid on, what creative to show, and how much to pay. Without a reliable way to connect devices and identifiers, campaigns can waste spend through duplicate targeting, misread frequency, and broken attribution. A well-governed Device Graph<\/strong> helps unify identity signals so Paid Marketing<\/strong> teams can plan, activate, and measure cross-device campaigns with more confidence.<\/p>\n\n\n\n

What Is Device Graph?<\/h2>\n\n\n\n

A Device Graph<\/strong> is a structured set of relationships that links multiple devices, browsers, apps, and identifiers to a single person or household with an assigned level of confidence. In plain terms, it\u2019s a map that says: \u201cThese identifiers likely belong to the same user,\u201d so marketing systems can treat them as one audience.<\/p>\n\n\n\n

The core concept is identity resolution across devices. A Device Graph<\/strong> typically connects items like mobile ad IDs, cookie IDs (where available), hashed emails, login IDs, IP addresses, and other privacy-safe signals into clusters. Each cluster represents a user or household, depending on the graph\u2019s design.<\/p>\n\n\n\n

From a business perspective, Device Graph<\/strong> capability reduces waste and improves decision-making across the funnel:<\/p>\n\n\n\n

    \n
  • Planning:<\/strong> estimating true reach and audience size<\/li>\n
  • Activation:<\/strong> targeting consistently across channels and devices<\/li>\n
  • Measurement:<\/strong> understanding conversions that occur on a different device than the ad exposure<\/li>\n<\/ul>\n\n\n\n

    In Paid Marketing<\/strong>, the Device Graph<\/strong> is most commonly used to improve audience targeting, frequency management, sequential messaging, and attribution modeling. Inside Programmatic Advertising<\/strong>, it supports real-time bidding workflows by informing which identity signals can be used for targeting and measurement in a privacy-aware way.<\/p>\n\n\n\n

    Why Device Graph Matters in Paid Marketing<\/h2>\n\n\n\n

    A Device Graph<\/strong> matters because cross-device behavior is the default, not the exception. If your marketing systems treat every device as a different person, you create structural inefficiencies that compound as spend increases.<\/p>\n\n\n\n

    Key strategic benefits in Paid Marketing<\/strong> include:<\/p>\n\n\n\n

      \n
    • More accurate reach and frequency:<\/strong> Avoids hitting the same person repeatedly across devices while under-serving others.<\/li>\n
    • Stronger audience performance:<\/strong> Helps retargeting and lookalike strategies reflect real users, not duplicated device IDs.<\/li>\n
    • Better attribution and incrementality analysis:<\/strong> Connects exposures and conversions that happen on different devices, which is common in Programmatic Advertising<\/strong>.<\/li>\n
    • Improved customer experience:<\/strong> Enables consistent messaging and reduces \u201cad fatigue\u201d caused by mismanaged frequency.<\/li>\n
    • Competitive advantage:<\/strong> Teams with stronger identity foundations can optimize faster, personalize more safely, and report more credibly to stakeholders.<\/li>\n<\/ul>\n\n\n\n

      For growing brands, the value is often simplest: spend less to get the same results, or get more results from the same budget\u2014because you\u2019re targeting and measuring with fewer blind spots.<\/p>\n\n\n\n

      How Device Graph Works<\/h2>\n\n\n\n

      A Device Graph<\/strong> is both data-driven and operational. While implementations vary, the real-world workflow often looks like this:<\/p>\n\n\n\n

        \n
      1. \n

        Inputs (identity signals are collected)<\/strong>\n Data arrives from ad platforms, websites, apps, CRMs, clean rooms, and analytics. Signals may include logins, consented first-party identifiers, device IDs, cookie-based identifiers (where they still exist), IP-derived hints, timestamps, and event patterns.<\/p>\n<\/li>\n

      2. \n

        Processing (identity resolution and confidence scoring)<\/strong>\n The system determines which identifiers belong together. Some links are direct (for example, a user logs in with the same email on two devices). Others are inferred based on probabilistic signals (for example, repeated co-usage patterns). The graph typically assigns confidence levels or uses thresholds to decide whether to create or update a cluster.<\/p>\n<\/li>\n

      3. \n

        Activation (audiences are built and pushed to channels)<\/strong>\n Once identities are connected, the Device Graph<\/strong> enables cross-device audience building (prospecting, retargeting, suppression, and frequency rules). In Programmatic Advertising<\/strong>, these audiences can guide bidding, creative sequencing, and pacing.<\/p>\n<\/li>\n

      4. \n

        Outputs (measurement and optimization improve)<\/strong>\n Marketers can measure outcomes across devices, reduce duplication in reporting, and adjust strategies based on more realistic user-level insights. In Paid Marketing<\/strong>, this often shows up as improved conversion efficiency and clearer attribution narratives.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        The important nuance: a Device Graph<\/strong> is never \u201cperfect.\u201d It\u2019s a model of reality with defined data sources, rules, and uncertainty\u2014so governance and ongoing validation matter.<\/p>\n\n\n\n

        Key Components of Device Graph<\/h2>\n\n\n\n

        A high-quality Device Graph<\/strong> is built from more than data. It\u2019s a combination of systems, processes, and accountability.<\/p>\n\n\n\n

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

        Common inputs include:<\/p>\n\n\n\n

          \n
        • First-party identifiers: consented emails, phone numbers, customer IDs, login events<\/li>\n
        • Device identifiers: mobile ad IDs (where available), app instance IDs<\/li>\n
        • Web identifiers: cookies or alternative web signals depending on environment<\/li>\n
        • Network and context signals: IP-derived hints, user agent, timestamps, event patterns<\/li>\n
        • Conversion events: purchases, sign-ups, leads, subscription starts<\/li>\n<\/ul>\n\n\n\n

          Identity resolution methods<\/h3>\n\n\n\n
            \n
          • Matching logic (deterministic vs probabilistic)<\/li>\n
          • Confidence scoring and thresholds<\/li>\n
          • Rules for merging and splitting identity clusters over time<\/li>\n<\/ul>\n\n\n\n

            Activation and integration layer<\/h3>\n\n\n\n
              \n
            • Audience creation and syncing to Programmatic Advertising<\/strong> platforms<\/li>\n
            • Suppression lists and exclusions for Paid Marketing<\/strong><\/li>\n
            • Frequency and recency controls across channels<\/li>\n<\/ul>\n\n\n\n

              Governance and responsibilities<\/h3>\n\n\n\n
                \n
              • Consent and privacy compliance processes<\/li>\n
              • Data quality ownership (marketing ops, analytics, data engineering)<\/li>\n
              • Documentation of matching rules and measurement assumptions<\/li>\n<\/ul>\n\n\n\n

                Types of Device Graph<\/h2>\n\n\n\n

                \u201cTypes\u201d are less about formal categories and more about how the graph is built and used. The most practical distinctions are:<\/p>\n\n\n\n

                Deterministic vs probabilistic graphs<\/h3>\n\n\n\n
                  \n
                • Deterministic Device Graph:<\/strong> Built from direct, high-confidence links (like logins or verified identifiers). Typically more accurate but may have less coverage.<\/li>\n
                • Probabilistic Device Graph:<\/strong> Built from statistical inference and patterns. Typically broader coverage but requires careful validation and conservative use in sensitive cases.<\/li>\n<\/ul>\n\n\n\n

                  People-based vs household-based graphs<\/h3>\n\n\n\n
                    \n
                  • Person-level graphs:<\/strong> Aim to represent an individual user. Useful for personalized messaging and user-level frequency.<\/li>\n
                  • Household-level graphs:<\/strong> Group devices at a household. Often used for connected TV planning, shared devices, and broader reach management in Paid Marketing<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                    First-party vs partner-augmented graphs<\/h3>\n\n\n\n
                      \n
                    • First-party graphs:<\/strong> Built from a brand\u2019s own consented data. Strong for owned audiences and measurement consistency.<\/li>\n
                    • Partner-augmented graphs:<\/strong> Extended with external identity partners or interoperable frameworks. Useful for scale in Programmatic Advertising<\/strong>, but requires stronger governance and transparency.<\/li>\n<\/ul>\n\n\n\n

                      Real-World Examples of Device Graph<\/h2>\n\n\n\n

                      Example 1: Cross-device retargeting with frequency control<\/h3>\n\n\n\n

                      A retail brand runs Programmatic Advertising<\/strong> for cart abandoners. Without a Device Graph<\/strong>, the same user might see retargeting ads on mobile and desktop as if they were two people\u2014doubling impressions and cost. With a Device Graph<\/strong>, the brand applies a unified frequency cap, reducing waste and improving conversion rate from fewer, better-timed touches in Paid Marketing<\/strong>.<\/p>\n\n\n\n

                      Example 2: App install to web purchase attribution<\/h3>\n\n\n\n

                      A subscription service drives app installs through Paid Marketing<\/strong>. Many users install via mobile but later complete purchase on a laptop. Using a Device Graph<\/strong>, the brand can connect the install exposure to the web conversion more reliably, leading to better budget allocation and more realistic ROI reporting in Programmatic Advertising<\/strong> campaigns.<\/p>\n\n\n\n

                      Example 3: Sequential messaging across devices<\/h3>\n\n\n\n

                      An education company wants a sequence: awareness video \u2192 consideration ad \u2192 lead-gen offer. The user watches a video on connected TV, researches on mobile, then fills a form on desktop. A Device Graph<\/strong> enables coordinated sequencing so the user isn\u2019t stuck seeing the same top-funnel ad repeatedly, improving experience and lead efficiency in Paid Marketing<\/strong>.<\/p>\n\n\n\n

                      Benefits of Using Device Graph<\/h2>\n\n\n\n

                      When implemented responsibly, a Device Graph<\/strong> can improve both performance and operational clarity.<\/p>\n\n\n\n

                        \n
                      • Higher conversion efficiency:<\/strong> Better retargeting, cleaner exclusions, and smarter sequencing.<\/li>\n
                      • Lower wasted spend:<\/strong> Reduced duplicate impressions across devices and channels.<\/li>\n
                      • Improved frequency management:<\/strong> A more accurate view of how often a person is exposed across Programmatic Advertising<\/strong> inventory.<\/li>\n
                      • More reliable measurement:<\/strong> Stronger cross-device attribution, better de-duplicated reach, and clearer funnel reporting.<\/li>\n
                      • Better audience experience:<\/strong> Less repetition and more relevant creative progression.<\/li>\n
                      • Stronger learning loops:<\/strong> Cleaner data makes experimentation (A\/B tests, holdouts, incrementality) more trustworthy in Paid Marketing<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                        Challenges of Device Graph<\/h2>\n\n\n\n

                        A Device Graph<\/strong> is powerful, but it introduces real technical and strategic complexity.<\/p>\n\n\n\n

                          \n
                        • Privacy and consent constraints:<\/strong> Identity data must be collected and used with appropriate consent and lawful basis. Policies vary by region and platform.<\/li>\n
                        • Signal loss and fragmentation:<\/strong> Changes in platform policies and identifier availability reduce certain data inputs, affecting coverage and measurement.<\/li>\n
                        • Accuracy vs scale trade-offs:<\/strong> Probabilistic approaches can inflate reach or mis-link devices if thresholds aren\u2019t conservative.<\/li>\n
                        • Data quality issues:<\/strong> Inconsistent event tracking, mismatched timestamps, and poor CRM hygiene weaken graph reliability.<\/li>\n
                        • Integration burden:<\/strong> Connecting analytics, CDPs, ad platforms, and data warehouses is a non-trivial engineering and operations effort.<\/li>\n
                        • Measurement expectations:<\/strong> Stakeholders may treat graph-based attribution as \u201ctruth\u201d when it is a modeled estimate.<\/li>\n<\/ul>\n\n\n\n

                          Best Practices for Device Graph<\/h2>\n\n\n\n

                          Strong outcomes come from disciplined implementation, not just \u201chaving a graph.\u201d<\/p>\n\n\n\n

                          Build from first-party foundations<\/h3>\n\n\n\n

                          Prioritize consented first-party identifiers (logins, customer IDs, hashed contact data) and ensure tracking consistency across web and app. A Device Graph<\/strong> built on reliable first-party signals is more stable for long-term Paid Marketing<\/strong> strategy.<\/p>\n\n\n\n

                          Use confidence thresholds and documented rules<\/h3>\n\n\n\n

                          Define how links are formed, when identities are merged, and when they are split. Keep a clear record of assumptions so Programmatic Advertising<\/strong> reporting doesn\u2019t become a black box.<\/p>\n\n\n\n

                          Validate with experiments and sanity checks<\/h3>\n\n\n\n

                          Use holdouts, geo tests, and incrementality experiments to ensure the Device Graph<\/strong> improves outcomes rather than just changing attribution credit. Cross-check de-duplicated reach against plausible user counts.<\/p>\n\n\n\n

                          Apply governance and privacy-by-design<\/h3>\n\n\n\n

                          Limit access to sensitive data, use hashing\/pseudonymization where appropriate, and align data retention policies with business needs and regulations. Make privacy review part of any Paid Marketing<\/strong> expansion plan.<\/p>\n\n\n\n

                          Operationalize in campaign workflows<\/h3>\n\n\n\n

                          The Device Graph<\/strong> should influence daily decisions: frequency caps, suppression, sequencing, and budget allocation. If it only appears in a quarterly report, value will be limited.<\/p>\n\n\n\n

                          Tools Used for Device Graph<\/h2>\n\n\n\n

                          A Device Graph<\/strong> typically sits across a stack rather than inside one tool. Common tool categories include:<\/p>\n\n\n\n

                            \n
                          • Customer data platforms (CDPs) and identity resolution systems:<\/strong> Unify first-party data, build identity clusters, and manage audience creation for Paid Marketing<\/strong>.<\/li>\n
                          • Ad platforms and DSPs:<\/strong> Activate audiences and apply frequency\/recency controls in Programmatic Advertising<\/strong>.<\/li>\n
                          • Analytics and attribution tools:<\/strong> Measure cross-device paths, de-duplicate conversions, and support experimentation.<\/li>\n
                          • CRM systems:<\/strong> Store customer records and lifecycle stages that feed deterministic identity links.<\/li>\n
                          • Data warehouses and ETL pipelines:<\/strong> Centralize event data, standardize identifiers, and support governance and auditing.<\/li>\n
                          • Reporting dashboards and BI tools:<\/strong> Communicate de-duplicated reach, frequency, and conversion performance to stakeholders.<\/li>\n<\/ul>\n\n\n\n

                            The key is interoperability: identity signals must flow reliably from collection to resolution to activation and measurement.<\/p>\n\n\n\n

                            Metrics Related to Device Graph<\/h2>\n\n\n\n

                            To evaluate a Device Graph<\/strong>, measure both marketing outcomes and graph quality.<\/p>\n\n\n\n

                            Marketing performance metrics<\/h3>\n\n\n\n
                              \n
                            • Conversion rate (by audience and device)<\/li>\n
                            • Cost per acquisition (CPA) \/ cost per lead (CPL)<\/li>\n
                            • Return on ad spend (ROAS) or revenue per mille (RPM)<\/li>\n
                            • Incremental lift (from controlled tests)<\/li>\n<\/ul>\n\n\n\n

                              Reach and efficiency metrics<\/h3>\n\n\n\n
                                \n
                              • De-duplicated reach (estimated people\/households reached)<\/li>\n
                              • Cross-device frequency distribution (not just average frequency)<\/li>\n
                              • Waste indicators (duplicate conversions, overlapping audience delivery)<\/li>\n<\/ul>\n\n\n\n

                                Graph quality and operational metrics<\/h3>\n\n\n\n
                                  \n
                                • Match rate (share of events\/users connected to a cluster)<\/li>\n
                                • Deterministic vs probabilistic share (coverage mix)<\/li>\n
                                • Confidence score distribution (are you relying on low-confidence links?)<\/li>\n
                                • Identity stability over time (excessive merging\/splitting can signal issues)<\/li>\n
                                • Time to resolution (how quickly new signals update the graph)<\/li>\n<\/ul>\n\n\n\n

                                  In Programmatic Advertising<\/strong>, improvements often show up as better frequency control and steadier performance when scaling budgets.<\/p>\n\n\n\n

                                  Future Trends of Device Graph<\/h2>\n\n\n\n

                                  The Device Graph<\/strong> space is evolving quickly as privacy expectations and platform capabilities change.<\/p>\n\n\n\n

                                    \n
                                  • More first-party centric identity:<\/strong> Brands will rely more on authenticated experiences, consented identifiers, and server-side data collection to sustain Paid Marketing<\/strong> performance.<\/li>\n
                                  • Privacy-preserving computation:<\/strong> Clean rooms, aggregation, and on-device processing approaches will shape how graphs are built and activated without exposing raw identifiers.<\/li>\n
                                  • AI-driven identity and optimization:<\/strong> Machine learning will improve confidence scoring, anomaly detection, and audience optimization\u2014while also increasing the need for explainability and governance.<\/li>\n
                                  • Channel convergence:<\/strong> As streaming, retail media, and mobile ecosystems mature, Programmatic Advertising<\/strong> will increasingly demand cross-environment identity strategies (web, app, CTV) that a Device Graph<\/strong> can support.<\/li>\n
                                  • Measurement model shifts:<\/strong> More emphasis on incrementality, media mix modeling, and blended measurement will complement graph-based attribution, especially when deterministic signals are limited.<\/li>\n<\/ul>\n\n\n\n

                                    Device Graph vs Related Terms<\/h2>\n\n\n\n

                                    Device Graph vs Identity Graph<\/h3>\n\n\n\n

                                    An Identity Graph<\/strong> is a broader concept that can include people, devices, households, and even offline identifiers. A Device Graph<\/strong> is typically focused on mapping devices and digital identifiers to users or households for activation and measurement in Paid Marketing<\/strong> and Programmatic Advertising<\/strong>.<\/p>\n\n\n\n

                                    Device Graph vs Cross-Device Tracking<\/h3>\n\n\n\n

                                    Cross-device tracking is the practice of observing behavior across devices. A Device Graph<\/strong> is the structured model that enables cross-device tracking to be actionable\u2014by linking identifiers into usable audience clusters and measurement units.<\/p>\n\n\n\n

                                    Device Graph vs Attribution<\/h3>\n\n\n\n

                                    Attribution is the method used to assign credit to marketing touchpoints. A Device Graph<\/strong> improves attribution inputs by connecting exposures and conversions across devices, but it does not replace attribution logic (rules-based, data-driven, or incrementality-based).<\/p>\n\n\n\n

                                    Who Should Learn Device Graph<\/h2>\n\n\n\n
                                      \n
                                    • Performance marketers:<\/strong> To reduce wasted spend, improve retargeting, and scale responsibly in Paid Marketing<\/strong>.<\/li>\n
                                    • Programmatic traders and media buyers:<\/strong> To understand identity signals, frequency control, and audience activation within Programmatic Advertising<\/strong>.<\/li>\n
                                    • Marketing analysts:<\/strong> To interpret de-duplicated reach, cross-device attribution, and experiment results accurately.<\/li>\n
                                    • Agencies and consultants:<\/strong> To design measurement frameworks and explain identity-driven trade-offs to clients.<\/li>\n
                                    • Business owners and founders:<\/strong> To evaluate vendors, understand reporting claims, and invest in the right data foundations.<\/li>\n
                                    • Developers and data engineers:<\/strong> To implement event pipelines, identity stitching logic, and governance controls that make the Device Graph<\/strong> usable.<\/li>\n<\/ul>\n\n\n\n

                                      Summary of Device Graph<\/h2>\n\n\n\n

                                      A Device Graph<\/strong> connects devices and identifiers into user- or household-level clusters so marketers can target and measure across screens. It matters because customers are inherently cross-device, and Paid Marketing<\/strong> efficiency depends on accurate reach, frequency, and attribution. In Programmatic Advertising<\/strong>, a Device Graph<\/strong> supports audience activation, sequencing, suppression, and more credible measurement. The best implementations balance accuracy, scale, privacy, and operational integration\u2014treating the graph as a governed system, not a one-time project.<\/p>\n\n\n\n

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

                                      1) What is a Device Graph used for?<\/h3>\n\n\n\n

                                      A Device Graph<\/strong> is used to connect multiple devices and identifiers to the same person or household so campaigns can manage reach, frequency, targeting, and measurement across devices in Paid Marketing<\/strong>.<\/p>\n\n\n\n

                                      2) How does Device Graph improve Programmatic Advertising performance?<\/h3>\n\n\n\n

                                      In Programmatic Advertising<\/strong>, a Device Graph<\/strong> can reduce duplicate targeting, enable unified frequency caps, improve retargeting precision, and connect conversions that happen on different devices\u2014often improving efficiency metrics like CPA or ROAS.<\/p>\n\n\n\n

                                      3) Is a Device Graph always accurate?<\/h3>\n\n\n\n

                                      No. A Device Graph<\/strong> is a model built from available signals and rules. Deterministic links are usually higher confidence, while probabilistic links trade some accuracy for scale. Validation and conservative thresholds are essential.<\/p>\n\n\n\n

                                      4) Do you need first-party data to build a Device Graph?<\/h3>\n\n\n\n

                                      You can build limited cross-device connections without first-party data, but robust and durable identity linking increasingly depends on consented first-party signals (like logins or customer IDs), especially for long-term Paid Marketing<\/strong> measurement.<\/p>\n\n\n\n

                                      5) How is a Device Graph different from a CDP?<\/h3>\n\n\n\n

                                      A CDP is a broader system for collecting, organizing, and activating customer data. A Device Graph<\/strong> is specifically the identity mapping layer (often inside or connected to a CDP) that links devices and identifiers for activation and analytics.<\/p>\n\n\n\n

                                      6) What should you measure to know if your Device Graph is working?<\/h3>\n\n\n\n

                                      Track outcomes (CPA, ROAS, conversion rate), delivery efficiency (de-duplicated reach, frequency distribution), and graph quality indicators (match rate, confidence distribution). Pair these with experiments to confirm incremental impact in Paid Marketing<\/strong>.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                      Modern customers don\u2019t behave on a single screen. They research on a laptop, browse on a phone, stream on a TV, and convert inside an app\u2014often all within the same day. A **Device Graph** is the mechanism that helps marketers understand those fragmented signals as one connected journey, which is especially important in **Paid Marketing** where budgets, targeting, and measurement depend on knowing who you\u2019re reaching and how often.<\/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-10724","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\/10724","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=10724"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/10724\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=10724"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=10724"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=10724"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}