{"id":7061,"date":"2026-03-23T22:52:29","date_gmt":"2026-03-23T22:52:29","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/privacy-safe-measurement\/"},"modified":"2026-03-23T22:52:29","modified_gmt":"2026-03-23T22:52:29","slug":"privacy-safe-measurement","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/privacy-safe-measurement\/","title":{"rendered":"Privacy-safe Measurement: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Attribution"},"content":{"rendered":"\n

Privacy expectations, regulations, and platform changes have reshaped how marketers track performance. Privacy-safe Measurement<\/strong> is the discipline of measuring marketing impact while reducing the use of identifiable personal data and respecting user choices. In Conversion & Measurement<\/strong>, it helps teams answer \u201cwhat worked?\u201d without relying on fragile identifiers or invasive tracking. In Attribution<\/strong>, it supports more trustworthy decision-making by leaning on aggregated, consented, and modeled signals instead of stitching together individual-level journeys at any cost.<\/p>\n\n\n\n

Privacy-safe Measurement<\/strong> matters because measurement is only valuable if it\u2019s durable, compliant, and credible. When teams overdepend on third-party cookies, device identifiers, or uncontrolled data sharing, reporting becomes unstable and risk increases. A privacy-safe approach strengthens your Conversion & Measurement<\/strong> foundation and keeps Attribution<\/strong> useful even as data availability changes.<\/p>\n\n\n\n

2) What Is Privacy-safe Measurement?<\/h2>\n\n\n\n

Privacy-safe Measurement<\/strong> is a set of methods, controls, and analytics practices used to evaluate marketing performance while minimizing privacy risk. It prioritizes:\n– collecting only what you need (data minimization),\n– honoring consent and user preferences,\n– using aggregated or de-identified data where possible,\n– and applying statistical techniques when granular tracking isn\u2019t appropriate or available.<\/p>\n\n\n\n

At its core, Privacy-safe Measurement<\/strong> is not \u201cno measurement.\u201d It\u2019s measurement designed to be resilient and respectful. Business-wise, it helps organizations maintain visibility into growth drivers\u2014acquisition, retention, and revenue\u2014without building strategy on personally identifying data that can\u2019t be responsibly governed.<\/p>\n\n\n\n

Within Conversion & Measurement<\/strong>, it sits alongside tagging, analytics, experimentation, and reporting. In Attribution<\/strong>, it provides safer inputs (like aggregated conversions, modeled conversions, or incrementality results) so budget allocation reflects reality rather than just what can be easily tracked.<\/p>\n\n\n\n

3) Why Privacy-safe Measurement Matters in Conversion & Measurement<\/h2>\n\n\n\n

Privacy-safe Measurement<\/strong> is strategically important because measurement quality is now a competitive advantage. Teams that adapt can still optimize campaigns, forecast performance, and justify spend, while teams that don\u2019t often operate with blind spots.<\/p>\n\n\n\n

Key business outcomes include:\n– More reliable decision-making:<\/strong> When identifiers disappear or consent rates vary, naive tracking breaks. Privacy-safe methods keep Conversion & Measurement<\/strong> stable.\n– Lower legal and reputational risk:<\/strong> Safer data handling reduces exposure and builds trust with customers and partners.\n– Better marketing efficiency:<\/strong> Stronger signals and cleaner governance reduce wasted spend driven by misleading Attribution<\/strong>.\n– Future-proof analytics:<\/strong> By investing in first-party, aggregated, and modeled approaches, your Conversion & Measurement<\/strong> system becomes less dependent on any single browser, platform, or device-level ID.<\/p>\n\n\n\n

4) How Privacy-safe Measurement Works<\/h2>\n\n\n\n

In practice, Privacy-safe Measurement<\/strong> combines technical implementation, policy controls, and analytics methods. A helpful workflow looks like this:<\/p>\n\n\n\n

1) Input (signals and permissions)<\/strong>\n– Consent status and preference signals (what can be collected and for what purpose)\n– First-party events (on-site\/app behavior, purchases, leads)\n– Contextual campaign data (channel, creative, landing page, geo, time)\n– Platform-provided aggregated reporting (where user-level data is restricted)<\/p>\n\n\n\n

2) Processing (privacy-aware collection and transformation)<\/strong>\n– Server-side collection where appropriate, with controls to reduce unnecessary data\n– Data minimization and retention limits\n– Pseudonymization or aggregation to reduce identifiability\n– Quality checks (deduplication, bot filtering, event validation)<\/p>\n\n\n\n

3) Execution (measurement and analysis)<\/strong>\n– Conversion reporting using aggregated or consented events\n– Modeled performance where direct observation is limited\n– Experimentation and incrementality tests to validate channel impact\n– Blended Attribution<\/strong> approaches that combine observable and modeled signals<\/p>\n\n\n\n

4) Output (decision-ready insights)<\/strong>\n– Channel performance dashboards with confidence notes (observed vs modeled)\n– Budget recommendations aligned to incrementality, not only last-click\n– KPI trends that remain comparable over time in Conversion & Measurement<\/strong>\n– Governance reporting (consent rates, data coverage, compliance checks)<\/p>\n\n\n\n

This is what makes Privacy-safe Measurement<\/strong> practical: it acknowledges real-world constraints and still produces actionable guidance for Attribution<\/strong> and optimization.<\/p>\n\n\n\n

5) Key Components of Privacy-safe Measurement<\/h2>\n\n\n\n

Effective Privacy-safe Measurement<\/strong> requires more than a tracking script. The major components typically include:<\/p>\n\n\n\n

Data inputs<\/h3>\n\n\n\n
    \n
  • First-party event data (e.g., add-to-cart, signup, purchase)<\/li>\n
  • Consent and preference metadata<\/li>\n
  • Campaign parameters (source\/medium, creative IDs where applicable)<\/li>\n
  • Offline outcomes (CRM stages, closed-won revenue, refunds)<\/li>\n<\/ul>\n\n\n\n

    Systems and infrastructure<\/h3>\n\n\n\n
      \n
    • Tag management and event schemas<\/li>\n
    • Server-side data collection endpoints (when used) with strict controls<\/li>\n
    • Data warehouse or lake for governed storage<\/li>\n
    • Identity and access management to restrict sensitive data exposure<\/li>\n<\/ul>\n\n\n\n

      Processes and governance<\/h3>\n\n\n\n
        \n
      • Data minimization policies and retention schedules<\/li>\n
      • Consent management workflows aligned to measurement needs<\/li>\n
      • Documentation: event definitions, KPI logic, and Attribution<\/strong> rules<\/li>\n
      • Cross-functional ownership (marketing, analytics, legal\/privacy, engineering)<\/li>\n<\/ul>\n\n\n\n

        Metrics and reporting standards<\/h3>\n\n\n\n
          \n
        • Clear definitions for conversions, qualified leads, revenue, and churn<\/li>\n
        • Separate reporting for observed vs modeled results<\/li>\n
        • Regular audits for data quality and drift within Conversion & Measurement<\/strong><\/li>\n<\/ul>\n\n\n\n

          6) Types of Privacy-safe Measurement (Practical Distinctions)<\/h2>\n\n\n\n

          While Privacy-safe Measurement<\/strong> is a concept, teams commonly implement it through a few distinct approaches:<\/p>\n\n\n\n

          Aggregated conversion measurement<\/h3>\n\n\n\n

          Conversions are reported in aggregate (by campaign, time window, or cohort) rather than as user-level logs. This supports Attribution<\/strong> without exposing individual journeys.<\/p>\n\n\n\n

          First-party and consented measurement<\/h3>\n\n\n\n

          Measurement relies on data collected directly by the business (web\/app\/CRM), using consent and purpose limits. This strengthens Conversion & Measurement<\/strong> fundamentals.<\/p>\n\n\n\n

          Modeled measurement<\/h3>\n\n\n\n

          Statistical models estimate conversions or channel impact when observation is incomplete. Modeled results should be clearly labeled to avoid overconfidence in Attribution<\/strong>.<\/p>\n\n\n\n

          Experiment-led measurement (incrementality)<\/h3>\n\n\n\n

          Controlled tests (holdouts, geo tests, lift tests) estimate causal impact. This is often the most privacy-compatible way to validate channel value within Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

          Privacy-enhancing techniques (selective use)<\/h3>\n\n\n\n

          In some environments, teams may apply methods like aggregation thresholds, noise injection, or secure computation to reduce re-identification risk. These techniques support privacy goals but require careful design and interpretation.<\/p>\n\n\n\n

          7) Real-World Examples of Privacy-safe Measurement<\/h2>\n\n\n\n

          Example 1: E-commerce acquisition with blended reporting<\/h3>\n\n\n\n

          A retailer runs paid search and paid social campaigns. Direct user-level tracking is inconsistent due to consent and device limits. The team implements Privacy-safe Measurement<\/strong> by:\n– standardizing purchase events and server-confirmed order IDs (for deduplication),\n– using aggregated platform conversion reports,\n– and validating channel impact with periodic holdout tests.<\/p>\n\n\n\n

          Outcome: Conversion & Measurement<\/strong> becomes more stable week to week, and Attribution<\/strong> shifts from \u201cwho got the last click\u201d to \u201cwhich channel drives incremental orders.\u201d<\/p>\n\n\n\n

          Example 2: B2B lead gen with CRM-based outcomes<\/h3>\n\n\n\n

          A SaaS company cares about pipeline and revenue, not just form fills. They adopt Privacy-safe Measurement<\/strong> by:\n– capturing consented lead events on-site,\n– passing qualified lead and opportunity stages back into analytics in aggregated form,\n– and using cohort reporting by industry and campaign theme instead of user-level profiling.<\/p>\n\n\n\n

          Outcome: Attribution<\/strong> improves because channels are evaluated on downstream revenue quality, while Conversion & Measurement<\/strong> remains aligned with privacy and data governance.<\/p>\n\n\n\n

          Example 3: Mobile app campaigns with modeled conversions<\/h3>\n\n\n\n

          An app marketer faces limited device-level tracking. They implement Privacy-safe Measurement<\/strong> using:\n– aggregated install and purchase reporting,\n– controlled experiments on budget changes,\n– and modeled conversion estimates to fill gaps.<\/p>\n\n\n\n

          Outcome: The team can still optimize creative and spend using privacy-aware signals, and Attribution<\/strong> becomes a triangulation of observed data + experiments + models.<\/p>\n\n\n\n

          8) Benefits of Using Privacy-safe Measurement<\/h2>\n\n\n\n

          Adopting Privacy-safe Measurement<\/strong> can deliver tangible advantages:<\/p>\n\n\n\n

            \n
          • More dependable performance reads:<\/strong> Fewer breaks from browser changes or consent variability, strengthening Conversion & Measurement<\/strong> continuity.<\/li>\n
          • Better budget allocation:<\/strong> Incrementality and blended Attribution<\/strong> reduce the risk of overfunding channels that merely capture demand.<\/li>\n
          • Operational efficiency:<\/strong> Cleaner event schemas, fewer duplicate tags, and better governance reduce analytics fire drills.<\/li>\n
          • Improved customer experience:<\/strong> Less invasive tracking can reduce friction and build trust, supporting long-term brand health.<\/li>\n
          • Stronger partner readiness:<\/strong> Privacy-safe practices make it easier to work with enterprise customers and regulated industries.<\/li>\n<\/ul>\n\n\n\n

            9) Challenges of Privacy-safe Measurement<\/h2>\n\n\n\n

            Privacy-safe Measurement<\/strong> is powerful, but it comes with trade-offs:<\/p>\n\n\n\n

              \n
            • Reduced granularity:<\/strong> Less user-level data can limit path analysis and micro-segmentation.<\/li>\n
            • Model risk:<\/strong> Modeled conversions can be misunderstood or treated as exact. This can distort Attribution<\/strong> if uncertainty isn\u2019t communicated.<\/li>\n
            • Implementation complexity:<\/strong> Server-side collection, consent logic, and data governance require coordination across teams.<\/li>\n
            • Data reconciliation:<\/strong> Aligning platform reports, analytics totals, and backend revenue can be difficult in Conversion & Measurement<\/strong>.<\/li>\n
            • Testing constraints:<\/strong> Incrementality experiments require time, statistical rigor, and sometimes temporary performance sacrifices.<\/li>\n<\/ul>\n\n\n\n

              10) Best Practices for Privacy-safe Measurement<\/h2>\n\n\n\n

              To implement Privacy-safe Measurement<\/strong> effectively:<\/p>\n\n\n\n

              1) Define measurement goals before tools<\/strong>\nTie Conversion & Measurement<\/strong> to business outcomes (revenue, pipeline, retention) and decide what level of detail is truly necessary.<\/p>\n\n\n\n

              2) Standardize events and conversion definitions<\/strong>\nCreate a shared conversion taxonomy (primary vs secondary conversions) and document KPI logic. Clean definitions improve Attribution<\/strong> more than extra tracking.<\/p>\n\n\n\n

              3) Minimize data and control access<\/strong>\nCollect only what supports decisions, set retention limits, and restrict sensitive data access. Governance is part of measurement quality.<\/p>\n\n\n\n

              4) Separate observed and modeled reporting<\/strong>\nLabel modeled values clearly, include confidence ranges where feasible, and avoid mixing them invisibly with observed conversions.<\/p>\n\n\n\n

              5) Use experiments to validate channel value<\/strong>\nRun holdouts or geo tests on major spend channels. Incrementality is often the most reliable backbone for privacy-safe Attribution<\/strong>.<\/p>\n\n\n\n

              6) Audit regularly<\/strong>\nMonitor consent rates, tag firing, deduplication, and backend alignment. In Conversion & Measurement<\/strong>, small tracking errors compound quickly.<\/p>\n\n\n\n

              11) Tools Used for Privacy-safe Measurement<\/h2>\n\n\n\n

              Privacy-safe Measurement<\/strong> is enabled by tool categories and workflows rather than a single product:<\/p>\n\n\n\n

                \n
              • Analytics tools:<\/strong> Event-based analytics and configurable reporting that supports aggregation and cohort analysis within Conversion & Measurement<\/strong>.<\/li>\n
              • Tag management systems:<\/strong> Centralize governance of what fires when, reduce tag sprawl, and enforce consistent event schemas.<\/li>\n
              • Consent management platforms:<\/strong> Capture and store user preferences, pass consent states to analytics and advertising systems, and support compliant measurement.<\/li>\n
              • Data warehouses and ETL\/ELT pipelines:<\/strong> Governed storage, controlled transformations, and reproducible reporting for Attribution<\/strong> and finance alignment.<\/li>\n
              • CRM systems:<\/strong> Connect marketing activity to qualified leads, pipeline, and revenue\u2014often the most business-relevant outcome.<\/li>\n
              • Experimentation platforms:<\/strong> Support A\/B tests, holdouts, and lift studies that validate incremental impact.<\/li>\n
              • Reporting dashboards\/BI:<\/strong> Communicate observed vs modeled performance, trends, and uncertainty in a way stakeholders can trust.<\/li>\n<\/ul>\n\n\n\n

                12) Metrics Related to Privacy-safe Measurement<\/h2>\n\n\n\n

                Because Privacy-safe Measurement<\/strong> changes how data is collected and interpreted, teams should track metrics that reflect both performance and measurement quality:<\/p>\n\n\n\n

                Performance metrics<\/h3>\n\n\n\n
                  \n
                • Conversions (primary and secondary)<\/li>\n
                • Revenue, margin, or pipeline value<\/li>\n
                • Cost per acquisition (CPA) or cost per qualified lead<\/li>\n
                • Return on ad spend (ROAS) \/ marketing ROI (with clear definitions)<\/li>\n<\/ul>\n\n\n\n

                  Efficiency and funnel metrics<\/h3>\n\n\n\n
                    \n
                  • Conversion rate by channel and landing experience<\/li>\n
                  • Time-to-convert (often aggregated by cohort)<\/li>\n
                  • Lead-to-opportunity and opportunity-to-close rates (B2B)<\/li>\n<\/ul>\n\n\n\n

                    Measurement quality metrics (often overlooked)<\/h3>\n\n\n\n
                      \n
                    • Consent rate and coverage by region\/device<\/li>\n
                    • Modeled vs observed conversion share<\/li>\n
                    • Deduplication rate (how often duplicates occur and are resolved)<\/li>\n
                    • Data latency (time from event to reporting)<\/li>\n
                    • Match\/reconciliation rate between analytics and backend revenue<\/li>\n<\/ul>\n\n\n\n

                      These metrics keep Conversion & Measurement<\/strong> honest and help prevent fragile Attribution<\/strong> narratives.<\/p>\n\n\n\n

                      13) Future Trends of Privacy-safe Measurement<\/h2>\n\n\n\n

                      Several trends are shaping the next phase of Privacy-safe Measurement<\/strong> within Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                        \n
                      • More modeling, but with better transparency:<\/strong> Expect wider use of statistical approaches, paired with clearer labeling, uncertainty ranges, and auditability.<\/li>\n
                      • Automation in data governance:<\/strong> Policy-driven data retention, access controls, and consent enforcement will become more standardized.<\/li>\n
                      • AI-assisted analysis (with constraints):<\/strong> AI can speed up anomaly detection, forecasting, and scenario planning, but it will also amplify errors if input data is biased or inconsistent.<\/li>\n
                      • Shift toward incrementality for budget decisions:<\/strong> As deterministic paths fade, incrementality testing will increasingly anchor Attribution<\/strong> strategies.<\/li>\n
                      • Stronger alignment between marketing and finance:<\/strong> Businesses will demand measurement systems that reconcile with revenue reality, pushing Conversion & Measurement<\/strong> toward more backend truth and less front-end guesswork.<\/li>\n<\/ul>\n\n\n\n

                        14) Privacy-safe Measurement vs Related Terms<\/h2>\n\n\n\n

                        Privacy-safe Measurement vs cookieless measurement<\/h3>\n\n\n\n

                        Cookieless measurement often means \u201cmeasure without third-party cookies,\u201d but it may still rely on other identifiers or questionable workarounds. Privacy-safe Measurement<\/strong> is broader: it focuses on minimizing privacy risk through consent, aggregation, governance, and method choice\u2014not just swapping one identifier for another in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                        Privacy-safe Measurement vs Marketing Mix Modeling (MMM)<\/h3>\n\n\n\n

                        MMM is a top-down approach that estimates channel contribution using aggregated time-series data (spend, sales, seasonality). MMM can be part of Privacy-safe Measurement<\/strong>, but the term also includes bottom-up systems like first-party event measurement and experimentation. In Attribution<\/strong>, MMM tends to guide strategic budget allocation, while other privacy-safe methods support campaign-level optimization.<\/p>\n\n\n\n

                        Privacy-safe Measurement vs incrementality testing<\/h3>\n\n\n\n

                        Incrementality testing is a method (experiments to measure causal lift). Privacy-safe Measurement<\/strong> is the overall discipline that may include incrementality as a core pillar. When Attribution<\/strong> is uncertain, incrementality often becomes the \u201cground truth\u201d reference point.<\/p>\n\n\n\n

                        15) Who Should Learn Privacy-safe Measurement<\/h2>\n\n\n\n
                          \n
                        • Marketers:<\/strong> To plan campaigns with realistic expectations and avoid optimizing to misleading Attribution<\/strong> signals.<\/li>\n
                        • Analysts:<\/strong> To design robust Conversion & Measurement<\/strong> frameworks, communicate uncertainty, and reconcile multiple data sources.<\/li>\n
                        • Agencies:<\/strong> To deliver resilient reporting and strategy across clients with different consent rates, tech stacks, and regulations.<\/li>\n
                        • Business owners and founders:<\/strong> To understand what performance numbers mean, what they don\u2019t mean, and how to invest confidently.<\/li>\n
                        • Developers and data engineers:<\/strong> To implement consent-aware tracking, server-side collection, governance controls, and reliable data pipelines that enable Privacy-safe Measurement<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                          16) Summary of Privacy-safe Measurement<\/h2>\n\n\n\n

                          Privacy-safe Measurement<\/strong> is the practice of measuring marketing performance in a way that respects user privacy, reduces reliance on personal identifiers, and remains durable amid platform and regulatory change. It strengthens Conversion & Measurement<\/strong> by improving data quality, governance, and resilience. It supports better Attribution<\/strong> by combining aggregated reporting, consented first-party data, modeling, and incrementality testing to produce insights that stakeholders can trust.<\/p>\n\n\n\n

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

                          1) What is Privacy-safe Measurement in simple terms?<\/h3>\n\n\n\n

                          Privacy-safe Measurement<\/strong> is a way to track marketing results using consented, minimized, and often aggregated data so you can improve performance without over-collecting personal information.<\/p>\n\n\n\n

                          2) Does Privacy-safe Measurement mean I can\u2019t do Attribution anymore?<\/h3>\n\n\n\n

                          No. It changes Attribution<\/strong> from a purely user-level story to a blended approach that may include aggregated conversions, modeling, and incrementality tests to estimate true impact.<\/p>\n\n\n\n

                          3) What should I prioritize first in Conversion & Measurement to become more privacy-safe?<\/h3>\n\n\n\n

                          Start with clear conversion definitions, a clean event schema, consent-aware tagging, and regular reconciliation to backend outcomes. Those fundamentals make everything else in Conversion & Measurement<\/strong> more reliable.<\/p>\n\n\n\n

                          4) How do modeled conversions affect decision-making?<\/h3>\n\n\n\n

                          Modeled conversions can be useful for trend direction and budget planning, but they are estimates. Good Privacy-safe Measurement<\/strong> labels modeled vs observed numbers so Attribution<\/strong> doesn\u2019t become overconfident.<\/p>\n\n\n\n

                          5) Is server-side tracking automatically privacy-safe?<\/h3>\n\n\n\n

                          Not automatically. Server-side collection can improve control and data quality, but it must still follow consent, data minimization, retention limits, and access controls to qualify as Privacy-safe Measurement<\/strong>.<\/p>\n\n\n\n

                          6) What\u2019s the best way to prove a channel is incremental with privacy constraints?<\/h3>\n\n\n\n

                          Run incrementality tests such as holdouts or geo experiments. These approaches often provide the clearest causal evidence for Attribution<\/strong> while fitting well within privacy-aware Conversion & Measurement<\/strong> practices.<\/p>\n\n\n\n

                          7) Which teams need to be involved to implement Privacy-safe Measurement well?<\/h3>\n\n\n\n

                          Marketing, analytics, engineering, and privacy\/legal should collaborate. Privacy-safe Measurement<\/strong> is as much governance and system design as it is reporting.<\/p>\n","protected":false},"excerpt":{"rendered":"

                          Privacy expectations, regulations, and platform changes have reshaped how marketers track performance. **Privacy-safe Measurement** is the discipline of measuring marketing impact while reducing the use of identifiable personal data and respecting user choices. In **Conversion & Measurement**, it helps teams answer \u201cwhat worked?\u201d without relying on fragile identifiers or invasive tracking. In **Attribution**, it supports more trustworthy decision-making by leaning on aggregated, consented, and modeled signals instead of stitching together individual-level journeys at any cost.<\/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-7061","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\/7061","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=7061"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7061\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7061"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7061"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7061"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}