{"id":7365,"date":"2026-03-24T10:07:02","date_gmt":"2026-03-24T10:07:02","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/tracking-benchmark\/"},"modified":"2026-03-24T10:07:02","modified_gmt":"2026-03-24T10:07:02","slug":"tracking-benchmark","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/tracking-benchmark\/","title":{"rendered":"Tracking Benchmark: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Tracking"},"content":{"rendered":"\n

A Tracking Benchmark<\/strong> is the reference point you use to judge whether your measurement setup and results are \u201cgood,\u201d \u201cnormal,\u201d or \u201coff-track.\u201d In Conversion & Measurement<\/strong>, it answers questions like: Are we capturing the right events? Is attribution stable? Are conversion rates changing because performance improved\u2014or because Tracking broke?<\/em><\/p>\n\n\n\n

Modern marketing depends on many interconnected systems\u2014websites, apps, ad platforms, CRM, and analytics\u2014so Tracking<\/strong> quality and consistency can change without anyone noticing. A solid Tracking Benchmark<\/strong> turns measurement from guesswork into a repeatable practice by establishing what \u201chealthy\u201d data looks like and how far results can drift before you investigate.<\/p>\n\n\n\n

What Is Tracking Benchmark?<\/h2>\n\n\n\n

A Tracking Benchmark<\/strong> is a defined baseline (or set of baselines) for your measurement signals\u2014events, conversions, revenue, traffic, and data quality indicators\u2014used to compare current performance against expected patterns. It is not only a performance target; it also functions as a diagnostic tool for Tracking<\/strong> reliability.<\/p>\n\n\n\n

At its core, the concept is simple: you select key measurement outputs and supporting quality checks, define their normal ranges, and then monitor deviations. In business terms, a Tracking Benchmark<\/strong> helps you separate real marketing change<\/em> (campaign impact, pricing changes, seasonality) from measurement change<\/em> (tag failures, consent shifts, attribution logic changes).<\/p>\n\n\n\n

Within Conversion & Measurement<\/strong>, it sits between implementation and decision-making: you instrument your funnel, verify what\u2019s being collected, then benchmark it so teams can trust trends over time. Inside Tracking<\/strong>, it becomes the \u201ccontrol\u201d that keeps analytics and reporting stable as your site, consent banners, and ad platform integrations evolve.<\/p>\n\n\n\n

Why Tracking Benchmark Matters in Conversion & Measurement<\/h2>\n\n\n\n

A Tracking Benchmark<\/strong> is strategically important because decisions are only as good as the measurement behind them. Without benchmarks, teams often \u201coptimize\u201d based on noisy or broken data, which can lead to wasted budget and incorrect conclusions about what works.<\/p>\n\n\n\n

From a business value perspective, benchmarks reduce the risk of misallocating spend after a tracking regression. In Conversion & Measurement<\/strong>, even small instrumentation issues\u2014like double-firing purchase events or losing UTMs\u2014can inflate or deflate ROAS, CAC, and pipeline reports.<\/p>\n\n\n\n

Marketing outcomes improve because teams can move faster with confidence. If you know your expected conversion volume, event match rates, and funnel step ratios, you can spot anomalies early and fix them before they distort tests, bidding algorithms, and forecasts.<\/p>\n\n\n\n

Competitive advantage comes from operational excellence. Organizations that maintain a reliable Tracking Benchmark<\/strong> can scale channels, run experiments, and attribute results more consistently than competitors whose reporting swings due to measurement drift.<\/p>\n\n\n\n

How Tracking Benchmark Works<\/h2>\n\n\n\n

A Tracking Benchmark<\/strong> is applied as an operating workflow\u2014part measurement design, part monitoring discipline\u2014within Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

    \n
  1. \n

    Inputs (what you monitor)<\/strong>\n You choose a set of KPIs and diagnostic signals: conversion counts, revenue, lead quality, plus Tracking<\/strong> health indicators like event coverage, deduplication rates, and attribution consistency.<\/p>\n<\/li>\n

  2. \n

    Processing (how you define \u201cnormal\u201d)<\/strong>\n You establish baselines using historical data (e.g., last 8\u201312 weeks), segmented by channel, device, geo, or landing page type. You also document expected behavior: which events should fire, on which pages, and under what consent conditions.<\/p>\n<\/li>\n

  3. \n

    Execution (how you compare and alert)<\/strong>\n You compare current performance to the benchmark ranges. This can be manual (weekly QA) or automated (dashboards with anomaly detection). Importantly, you decide escalation thresholds, like \u201cinvestigate if purchases drop >20% day-over-day.\u201d<\/p>\n<\/li>\n

  4. \n

    Outputs (what you do with the result)<\/strong>\n The outcome is not just a report; it\u2019s action: fix tagging, update documentation, adjust attribution notes, or annotate dashboards. Over time, your Tracking Benchmark<\/strong> becomes part of routine Tracking<\/strong> governance and release management.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

    Key Components of Tracking Benchmark<\/h2>\n\n\n\n

    A dependable Tracking Benchmark<\/strong> is built from several complementary elements:<\/p>\n\n\n\n

      \n
    • Measurement plan and event taxonomy<\/strong>: Clear definitions of conversions, micro-conversions, and required parameters (value, currency, content IDs). This keeps Conversion & Measurement<\/strong> aligned across teams.<\/li>\n
    • Data sources<\/strong>: Web\/app analytics events, server-side events (where used), ad platform conversion signals, CRM outcomes, and ecommerce\/order systems.<\/li>\n
    • Baseline windows and segmentation<\/strong>: Time ranges, seasonality considerations, and segment rules (brand vs non-brand, new vs returning, paid vs organic).<\/li>\n
    • Quality controls for Tracking<\/strong>: <\/li>\n
    • Event firing coverage (expected vs observed) <\/li>\n
    • Duplicate event rate <\/li>\n
    • Missing parameter rate (e.g., value, transaction_id) <\/li>\n
    • UTM presence and format compliance <\/li>\n
    • Consent-related data loss estimates where applicable<\/li>\n
    • Governance and ownership<\/strong>: Named owners for implementation, QA, reporting, and change approvals. Without this, the Tracking Benchmark<\/strong> drifts as the site changes.<\/li>\n
    • Documentation and change log<\/strong>: Notes on site releases, tag updates, consent changes, and attribution setting changes\u2014critical context for interpreting benchmark shifts in Conversion & Measurement<\/strong>.<\/li>\n<\/ul>\n\n\n\n

      Types of Tracking Benchmark<\/h2>\n\n\n\n

      \u201cTracking Benchmark\u201d isn\u2019t a single formal standard, but in practice it\u2019s used in several common ways:<\/p>\n\n\n\n

        \n
      1. \n

        Performance benchmarks (outcome-focused)<\/strong>\n Baselines for conversion rate, CPA, ROAS, lead-to-opportunity rate, or revenue per session\u2014used to evaluate marketing effectiveness in Conversion & Measurement<\/strong>.<\/p>\n<\/li>\n

      2. \n

        Tracking health benchmarks (instrumentation-focused)<\/strong>\n Baselines for event volumes, parameter completeness, deduplication, and attribution stability\u2014used to ensure Tracking<\/strong> integrity.<\/p>\n<\/li>\n

      3. \n

        Channel-specific benchmarks<\/strong>\n Separate baselines by paid search, paid social, email, affiliates, or organic\u2014because each channel has different click behavior, attribution patterns, and conversion lags.<\/p>\n<\/li>\n

      4. \n

        Funnel-step benchmarks<\/strong>\n Expected ratios between steps (product view \u2192 add to cart \u2192 checkout \u2192 purchase). These are powerful for detecting broken events or UX issues.<\/p>\n<\/li>\n

      5. \n

        Pre\/post-change benchmarks<\/strong>\n Benchmarks created around major changes: new checkout, consent banner updates, tagging migrations, or new conversion definitions. This helps isolate measurement shifts from real performance shifts.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        Real-World Examples of Tracking Benchmark<\/h2>\n\n\n\n

        Example 1: Ecommerce purchase event stability after a checkout update<\/h3>\n\n\n\n

        A retailer rolls out a new checkout UI. Their Tracking Benchmark<\/strong> includes purchase event volume, revenue totals vs backend orders, and duplicate transaction rates. In Conversion & Measurement<\/strong>, the dashboard flags a spike in purchases but revenue doesn\u2019t match backend orders\u2014indicating duplicate event fires. The team fixes the event trigger and restores trustworthy Tracking<\/strong> before paid bidding algorithms learn the wrong signal.<\/p>\n\n\n\n

        Example 2: Lead-gen form tracking across paid social and search<\/h3>\n\n\n\n

        A B2B company benchmarks form_submit events, CRM-qualified lead rate, and the share of leads missing UTM parameters. After a landing page experiment, conversions appear to drop 30% in paid social. The Tracking Benchmark<\/strong> shows sessions are steady but form_submit events fell while CRM lead creation stayed flat\u2014meaning the form event broke, not demand. Conversion & Measurement<\/strong> stays accurate, and budget decisions aren\u2019t made on faulty Tracking<\/strong>.<\/p>\n\n\n\n

        Example 3: Consent changes affecting attribution and reporting<\/h3>\n\n\n\n

        A publisher introduces a stricter consent flow. Their Tracking Benchmark<\/strong> includes \u201cmeasured conversions per 1,000 sessions\u201d and the ratio of modeled\/estimated conversions (if used internally) versus observed. Post-change, tracked conversions decline, but on-site engagement and subscriptions in the billing system remain stable. The benchmark helps stakeholders interpret the shift as measurement loss rather than marketing failure\u2014leading to updated reporting notes and new baseline ranges for Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

        Benefits of Using Tracking Benchmark<\/h2>\n\n\n\n

        A well-maintained Tracking Benchmark<\/strong> delivers practical benefits:<\/p>\n\n\n\n

          \n
        • Higher measurement confidence<\/strong>: Teams can trust trends, not just point-in-time numbers, strengthening Conversion & Measurement<\/strong> decisions.<\/li>\n
        • Faster detection of Tracking issues<\/strong>: Benchmarks highlight anomalies quickly\u2014especially after site releases, tag changes, or platform setting updates.<\/li>\n
        • Better budget efficiency<\/strong>: Reduced risk of overspending due to inflated conversion signals or underspending due to missing conversions.<\/li>\n
        • Improved experimentation<\/strong>: A stable benchmark reduces false positives\/negatives in A\/B tests by ensuring events are firing consistently.<\/li>\n
        • Smoother cross-team alignment<\/strong>: Benchmarks create shared expectations between marketing, analytics, product, and engineering on what \u201ccorrect\u201d Tracking<\/strong> looks like.<\/li>\n
        • Better customer experience<\/strong>: Funnel-step benchmarks can uncover UX breakpoints (e.g., checkout errors) before support tickets surge.<\/li>\n<\/ul>\n\n\n\n

          Challenges of Tracking Benchmark<\/h2>\n\n\n\n

          A Tracking Benchmark<\/strong> also comes with real constraints:<\/p>\n\n\n\n

            \n
          • Seasonality and promotions<\/strong>: Baselines can be misleading if you don\u2019t adjust for holidays, launches, pricing changes, or campaign bursts in Conversion & Measurement<\/strong>.<\/li>\n
          • Attribution variability<\/strong>: Channel mix shifts, view-through changes, and conversion lag can move numbers even when Tracking<\/strong> is correct.<\/li>\n
          • Data loss and privacy changes<\/strong>: Consent, browser restrictions, and ad platform changes can reduce observability. Benchmarks must be updated thoughtfully to avoid normalizing bad data.<\/li>\n
          • Implementation complexity<\/strong>: Multiple tags, server-side forwarding (where used), and CRM integration create more failure points\u2014and more signals to benchmark.<\/li>\n
          • Metric definition drift<\/strong>: If \u201cconversion\u201d is redefined (e.g., MQL vs any lead), old benchmarks become invalid unless you version them.<\/li>\n
          • Over-reliance on one system<\/strong>: If analytics data is treated as the source of truth without reconciliation to backend orders or CRM, your Tracking Benchmark<\/strong> can reinforce incorrect assumptions.<\/li>\n<\/ul>\n\n\n\n

            Best Practices for Tracking Benchmark<\/h2>\n\n\n\n

            To make a Tracking Benchmark<\/strong> durable and useful:<\/p>\n\n\n\n

              \n
            1. \n

              Benchmark both outcomes and Tracking health<\/strong>\n Pair business KPIs (revenue, leads) with instrumentation KPIs (event completeness, duplicates). This is essential for reliable Conversion & Measurement<\/strong>.<\/p>\n<\/li>\n

            2. \n

              Use ranges, not single numbers<\/strong>\n Define acceptable variance bands by segment (channel\/device). Real performance is naturally noisy.<\/p>\n<\/li>\n

            3. \n

              Version your benchmarks<\/strong>\n When conversion definitions, consent logic, or attribution settings change, create a new benchmark period and annotate the change.<\/p>\n<\/li>\n

            4. \n

              Reconcile to a source of truth<\/strong>\n Regularly compare analytics conversions to backend systems (orders, subscriptions, CRM). This keeps Tracking<\/strong> anchored to reality.<\/p>\n<\/li>\n

            5. \n

              Automate monitoring where possible<\/strong>\n Use scheduled checks for drops\/spikes in key events and parameter completeness, especially for high-impact conversions.<\/p>\n<\/li>\n

            6. \n

              Establish ownership and a release checklist<\/strong>\n Require QA against the Tracking Benchmark<\/strong> after major site deploys, checkout changes, tag updates, or template revisions.<\/p>\n<\/li>\n

            7. \n

              Document \u201cexpected behavior\u201d<\/strong>\n Write down what should fire, when, and with which parameters. Good documentation is part of good Conversion & Measurement<\/strong> hygiene.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

              Tools Used for Tracking Benchmark<\/h2>\n\n\n\n

              A Tracking Benchmark<\/strong> is enabled by systems that collect, validate, and report measurement signals. Common tool categories include:<\/p>\n\n\n\n

                \n
              • Analytics tools<\/strong>: For event collection, funnel reporting, segmentation, and anomaly spotting within Conversion & Measurement<\/strong>.<\/li>\n
              • Tag management systems<\/strong>: For deploying and maintaining client-side Tracking<\/strong> tags, triggers, and variables with change history.<\/li>\n
              • Server-side measurement and event routing (where applicable)<\/strong>: To improve control, reduce client-side fragility, and support consistent data delivery.<\/li>\n
              • Ad platforms<\/strong>: To compare platform-reported conversions with analytics and backend truth, and to monitor conversion signal health used for optimization.<\/li>\n
              • CRM and marketing automation<\/strong>: To connect leads and pipeline outcomes back to campaign sources and validate lead quality benchmarks.<\/li>\n
              • Data warehouse \/ BI and reporting dashboards<\/strong>: For standardized metrics, multi-source reconciliation, and stakeholder-ready benchmark reporting.<\/li>\n
              • QA and monitoring utilities<\/strong>: For validating event payloads, spotting missing parameters, and checking that critical pages trigger expected events.<\/li>\n<\/ul>\n\n\n\n

                Metrics Related to Tracking Benchmark<\/h2>\n\n\n\n

                The right metrics depend on your funnel, but these are commonly benchmarked in Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                Performance metrics<\/strong>\n– Conversion rate (by channel, device, landing page)\n– Cost per acquisition (CPA) and return on ad spend (ROAS)\n– Average order value (AOV) or revenue per visitor\/session\n– Lead-to-qualified-lead rate; qualified-lead-to-opportunity rate<\/p>\n\n\n\n

                Tracking quality metrics<\/strong>\n– Event volume baselines for key actions (purchase, lead, signup)\n– Duplicate event rate (especially purchases)\n– Missing parameter rate (value, currency, transaction_id, content IDs)\n– UTM coverage rate and taxonomy compliance\n– Attribution stability checks (share of conversions by channel over time)<\/p>\n\n\n\n

                Efficiency and reliability metrics<\/strong>\n– Time-to-detect Tracking issues (MTTD)\n– Time-to-fix measurement issues (MTTR)\n– Percentage of releases that pass measurement QA against the Tracking Benchmark<\/strong><\/p>\n\n\n\n

                Future Trends of Tracking Benchmark<\/h2>\n\n\n\n

                Several trends are shaping how Tracking Benchmark<\/strong> practices evolve:<\/p>\n\n\n\n

                  \n
                • More automation and anomaly detection<\/strong>: Teams will increasingly rely on automated checks to detect event regressions, sudden channel shifts, and funnel breaks in Conversion & Measurement<\/strong>.<\/li>\n
                • AI-assisted diagnosis<\/strong>: AI can help classify anomalies (seasonality vs Tracking break) by correlating changes across channels, devices, and backend systems\u2014while still requiring human validation.<\/li>\n
                • Privacy-driven measurement adaptation<\/strong>: As consent and platform restrictions reduce observability, benchmarks will include stronger reconciliation to first-party systems and clearer documentation of what is measurable versus modeled.<\/li>\n
                • Greater emphasis on measurement governance<\/strong>: More organizations will formalize Tracking<\/strong> ownership, versioning, and change management to keep benchmarks meaningful.<\/li>\n
                • Personalization and multi-touch complexity<\/strong>: As journeys span devices and sessions, benchmarks will focus more on funnel health and outcome reconciliation, not just last-click channel totals.<\/li>\n<\/ul>\n\n\n\n

                  Tracking Benchmark vs Related Terms<\/h2>\n\n\n\n

                  Tracking Benchmark vs KPI Benchmark<\/strong>\nA KPI benchmark usually focuses on outcomes (e.g., \u201cour target conversion rate is 3%\u201d). A Tracking Benchmark<\/strong> includes outcome baselines and<\/em> measurement integrity checks, which is critical in Conversion & Measurement<\/strong> when data collection can fail.<\/p>\n\n\n\n

                  Tracking Benchmark vs Baseline<\/strong>\nA baseline is the reference number or range. A Tracking Benchmark<\/strong> is the broader practice: choosing baselines, defining variance thresholds, monitoring, and responding\u2014often with governance and documentation.<\/p>\n\n\n\n

                  Tracking Benchmark vs Conversion Rate Benchmark<\/strong>\nA conversion rate benchmark looks at a single metric. A Tracking Benchmark<\/strong> typically covers a basket of metrics and quality signals (event coverage, duplicates, attribution stability) to ensure conversion rate changes aren\u2019t caused by broken Tracking<\/strong>.<\/p>\n\n\n\n

                  Who Should Learn Tracking Benchmark<\/h2>\n\n\n\n
                    \n
                  • Marketers<\/strong> benefit because budget allocation, testing, and creative optimization depend on trustworthy Conversion & Measurement<\/strong> signals.<\/li>\n
                  • Analysts<\/strong> use a Tracking Benchmark<\/strong> to validate data pipelines, explain anomalies, and protect stakeholders from misinterpretation.<\/li>\n
                  • Agencies<\/strong> need benchmarks to prove performance credibly, reduce reporting disputes, and manage multi-client Tracking<\/strong> consistency.<\/li>\n
                  • Business owners and founders<\/strong> gain confidence that growth decisions are based on real demand and revenue\u2014not measurement noise.<\/li>\n
                  • Developers and product teams<\/strong> benefit because benchmarks create clear acceptance criteria for releases: \u201cthe funnel still tracks correctly.\u201d<\/li>\n<\/ul>\n\n\n\n

                    Summary of Tracking Benchmark<\/h2>\n\n\n\n

                    A Tracking Benchmark<\/strong> is the set of reference ranges and quality checks used to evaluate both marketing outcomes and measurement integrity. It matters because Conversion & Measurement<\/strong> only works when your data is consistent and explainable over time. By benchmarking performance metrics alongside Tracking<\/strong> health indicators, teams can detect issues faster, interpret changes correctly, and make better optimization and budgeting decisions.<\/p>\n\n\n\n

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

                    1) What is a Tracking Benchmark in plain language?<\/h3>\n\n\n\n

                    A Tracking Benchmark<\/strong> is your \u201cnormal range\u201d for key conversions and tracking signals, used to spot when performance changes are real versus when measurement is broken or drifting.<\/p>\n\n\n\n

                    2) How often should I update a Tracking Benchmark?<\/h3>\n\n\n\n

                    Update it after major changes (site redesign, checkout changes, consent updates, conversion definition changes) and review it periodically (monthly or quarterly) to account for seasonality and channel mix shifts in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                    3) What\u2019s the difference between Tracking Benchmark and performance targets?<\/h3>\n\n\n\n

                    Targets are goals you want to hit. A Tracking Benchmark<\/strong> is a reference for what typically happens and what data quality looks like, helping you trust the numbers before you set or judge targets.<\/p>\n\n\n\n

                    4) Which metrics are best to include first?<\/h3>\n\n\n\n

                    Start with 1\u20132 primary conversions (purchase or lead), 2\u20133 funnel-step events, and 2\u20133 Tracking<\/strong> health metrics (duplicates, missing parameters, UTM coverage). Expand once these are stable.<\/p>\n\n\n\n

                    5) How do I know whether a drop is a Tracking issue or real demand?<\/h3>\n\n\n\n

                    Compare multiple signals: sessions, funnel-step ratios, backend orders\/CRM records, and channel splits. If business outcomes are stable but tracked events drop, it\u2019s often a Tracking<\/strong> problem; if multiple independent systems show a drop, it\u2019s more likely real.<\/p>\n\n\n\n

                    6) Do small businesses need Tracking Benchmark practices?<\/h3>\n\n\n\n

                    Yes. Even lightweight Conversion & Measurement<\/strong> benefits from a simple Tracking Benchmark<\/strong>\u2014especially for high-stakes actions like purchases, booked calls, or trial signups.<\/p>\n\n\n\n

                    7) Can Tracking Benchmark help with attribution disagreements?<\/h3>\n\n\n\n

                    It can reduce confusion by documenting expected channel shares, conversion lag, and known measurement limits. While it won\u2019t \u201csolve\u201d attribution philosophy, it makes changes in attribution reporting easier to detect, explain, and communicate.<\/p>\n","protected":false},"excerpt":{"rendered":"

                    A **Tracking Benchmark** is the reference point you use to judge whether your measurement setup and results are \u201cgood,\u201d \u201cnormal,\u201d or \u201coff-track.\u201d In **Conversion & Measurement**, it answers questions like: *Are we capturing the right events? Is attribution stable? Are conversion rates changing because performance improved\u2014or because Tracking broke?*<\/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":[1890],"tags":[],"class_list":["post-7365","post","type-post","status-publish","format-standard","hentry","category-tracking"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7365","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=7365"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7365\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7365"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7365"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7365"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}