{"id":7054,"date":"2026-03-23T22:37:19","date_gmt":"2026-03-23T22:37:19","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/modeled-lift\/"},"modified":"2026-03-23T22:37:19","modified_gmt":"2026-03-23T22:37:19","slug":"modeled-lift","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/modeled-lift\/","title":{"rendered":"Modeled Lift: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Attribution"},"content":{"rendered":"\n

Modeled Lift is an approach to estimating the incremental impact of marketing when you can\u2019t directly observe it with perfect tracking or clean experiments. In Conversion & Measurement<\/strong>, it fills the gap between what your analytics can prove<\/em> and what the business still needs to decide<\/em>\u2014budget allocation, channel strategy, and campaign optimization.<\/p>\n\n\n\n

Within Attribution<\/strong>, Modeled Lift helps answer a tougher question than \u201cWho got credit?\u201d: \u201cWhat actually changed because marketing happened?\u201d As privacy constraints grow, signal loss increases, and customer journeys stretch across devices and walled gardens, Modeled Lift becomes a core capability for dependable Conversion & Measurement<\/strong> strategy.<\/p>\n\n\n\n

What Is Modeled Lift?<\/h2>\n\n\n\n

Modeled Lift<\/strong> is an estimated measure of incremental conversions or revenue caused by a marketing activity, calculated using statistical or machine-learning models rather than relying solely on directly observed conversion paths.<\/p>\n\n\n\n

At its core, it tries to quantify the difference between:<\/p>\n\n\n\n

    \n
  • What happened with marketing<\/strong> (observed outcomes), and <\/li>\n
  • What would have happened without marketing<\/strong> (a counterfactual baseline)<\/li>\n<\/ul>\n\n\n\n

    The business meaning is straightforward: Modeled Lift is your best modeled estimate of incrementality<\/strong>\u2014how many conversions, sign-ups, purchases, or qualified leads marketing truly added<\/em>, not just touched<\/em>.<\/p>\n\n\n\n

    In Conversion & Measurement<\/strong>, Modeled Lift often shows up when tracking is incomplete (cookie restrictions, iOS limitations, consent decline, cross-device journeys) or when controlled experiments can\u2019t cover every campaign. In Attribution<\/strong>, it complements path-based crediting by focusing on causal impact rather than exposure sequence alone.<\/p>\n\n\n\n

    Why Modeled Lift Matters in Conversion & Measurement<\/h2>\n\n\n\n

    Modeled Lift matters because modern marketing decisions require more than directional reporting. When measurement degrades, teams tend to over-credit easy-to-track channels and under-invest in channels that drive demand but don\u2019t \u201cclose the loop\u201d cleanly.<\/p>\n\n\n\n

    In a strong Conversion & Measurement<\/strong> program, Modeled Lift supports:<\/p>\n\n\n\n

      \n
    • Smarter budget allocation<\/strong> by estimating incremental ROI instead of optimizing to last-touch outcomes <\/li>\n
    • Better channel planning<\/strong> when upper-funnel influence is real but hard to attribute directly <\/li>\n
    • Resilience to tracking loss<\/strong> by reducing dependence on perfect identifiers and complete event capture<\/li>\n<\/ul>\n\n\n\n

      From a competitive standpoint, teams that use Modeled Lift responsibly can react faster to market changes, avoid \u201cfalse efficiency,\u201d and defend spend with evidence that aligns with business outcomes. In Attribution<\/strong>, this helps prevent credit inflation where multiple touchpoints claim the same conversion without proving they caused it.<\/p>\n\n\n\n

      How Modeled Lift Works<\/h2>\n\n\n\n

      Modeled Lift is more practical than mystical: it\u2019s a structured way to estimate the counterfactual. While implementations vary, most follow a consistent workflow.<\/p>\n\n\n\n

      1) Inputs (signals and context)<\/h3>\n\n\n\n

      A Modeled Lift approach typically starts with a mix of:<\/p>\n\n\n\n

        \n
      • Conversion outcomes (purchases, leads, subscriptions)<\/li>\n
      • Marketing exposures or spend (impressions, clicks, cost, reach)<\/li>\n
      • Audience and market context (geo, seasonality, pricing changes, inventory)<\/li>\n
      • Time-based patterns (day-of-week, promos, macro shifts)<\/li>\n<\/ul>\n\n\n\n

        In Conversion & Measurement<\/strong>, data quality and alignment (definitions, timestamps, deduplication) matter as much as the modeling method.<\/p>\n\n\n\n

        2) Modeling (baseline vs incremental impact)<\/h3>\n\n\n\n

        The model estimates a baseline outcome expected without the marketing input, then calculates incremental impact above that baseline. Depending on the approach, it might:<\/p>\n\n\n\n

          \n
        • Compare similar groups (treated vs control, matched audiences)<\/li>\n
        • Use time-series forecasting to predict \u201cno-campaign\u201d outcomes<\/li>\n
        • Learn response curves from historical spend and results<\/li>\n<\/ul>\n\n\n\n

          This is where Modeled Lift becomes a bridge between Attribution<\/strong> reporting and causal inference.<\/p>\n\n\n\n

          3) Application (decisioning and optimization)<\/h3>\n\n\n\n

          The output is operationalized in planning and optimization, such as:<\/p>\n\n\n\n

            \n
          • Reallocating spend across channels or regions<\/li>\n
          • Setting guardrails for bidding and frequency<\/li>\n
          • Calibrating conversion reporting to avoid over-counting<\/li>\n<\/ul>\n\n\n\n

            In Conversion & Measurement<\/strong>, the goal is not just a number\u2014it\u2019s a number you can safely act on.<\/p>\n\n\n\n

            4) Outputs (lift estimates with uncertainty)<\/h3>\n\n\n\n

            A responsible Modeled Lift output includes both magnitude and confidence, for example:<\/p>\n\n\n\n

              \n
            • Incremental conversions and lift percentage<\/li>\n
            • Incremental revenue or profit<\/li>\n
            • Uncertainty ranges (confidence intervals) or model error<\/li>\n<\/ul>\n\n\n\n

              In Attribution<\/strong>, these outputs help teams understand where credit is likely overstated and where impact is underestimated.<\/p>\n\n\n\n

              Key Components of Modeled Lift<\/h2>\n\n\n\n

              Modeled Lift depends on more than an algorithm. It\u2019s a measurement system with people, process, and governance.<\/p>\n\n\n\n

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

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

                \n
              • Conversion events and revenue (online and, when possible, offline)<\/li>\n
              • Spend, impressions, clicks, reach, and frequency<\/li>\n
              • Campaign metadata (targeting, creative, placements)<\/li>\n
              • Customer data signals (new vs returning, cohorts, segments)<\/li>\n
              • External factors (promotions, holidays, competitor events)<\/li>\n<\/ul>\n\n\n\n

                Measurement and modeling processes<\/h3>\n\n\n\n

                Key processes that make Modeled Lift credible:<\/p>\n\n\n\n

                  \n
                • Clear conversion definitions and consistent attribution windows<\/li>\n
                • Deduplication rules across channels and devices<\/li>\n
                • Baseline selection (what \u201cno marketing\u201d reasonably means)<\/li>\n
                • Calibration using experiments when available<\/li>\n<\/ul>\n\n\n\n

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

                  In mature Conversion & Measurement<\/strong> teams:<\/p>\n\n\n\n

                    \n
                  • Marketing owns decisions and hypotheses<\/li>\n
                  • Analytics\/data science owns methods and validation<\/li>\n
                  • Data engineering owns pipelines and quality controls<\/li>\n
                  • Finance helps align lift outputs with profitability and forecasting<\/li>\n<\/ul>\n\n\n\n

                    Modeled Lift works best when it is reviewed like any other business-critical estimate: assumptions documented, monitored, and periodically recalibrated.<\/p>\n\n\n\n

                    Types of Modeled Lift<\/h2>\n\n\n\n

                    Modeled Lift isn\u2019t a single standardized model. The most useful distinctions are about where lift is estimated<\/em> and how causal inference is approached<\/em>.<\/p>\n\n\n\n

                    Experiment-calibrated modeled lift<\/h3>\n\n\n\n

                    Uses controlled tests (A\/B, geo tests, holdouts) to measure lift directly in some areas, then models lift for the rest. This is often the most defensible blend for Conversion & Measurement<\/strong> because it anchors modeling to observed incrementality.<\/p>\n\n\n\n

                    Observational modeled lift (non-experimental)<\/h3>\n\n\n\n

                    Estimates lift from historical and contextual patterns when experiments aren\u2019t feasible. Methods may include matching, time-series models, or causal inference techniques. In Attribution<\/strong>, it can correct for bias, but it also requires stronger assumptions.<\/p>\n\n\n\n

                    Channel-level vs campaign-level lift<\/h3>\n\n\n\n
                      \n
                    • Channel-level Modeled Lift<\/strong> is useful for strategic budget planning and media mix shifts. <\/li>\n
                    • Campaign-level Modeled Lift<\/strong> supports execution decisions like creative rotation, audience expansion, and frequency controls.<\/li>\n<\/ul>\n\n\n\n

                      The right level depends on your data stability and decision cadence.<\/p>\n\n\n\n

                      Real-World Examples of Modeled Lift<\/h2>\n\n\n\n

                      Example 1: Paid social with incomplete conversion tracking<\/h3>\n\n\n\n

                      A subscription brand sees declining tracked conversions from paid social after consent and platform changes. Using Modeled Lift, the team combines spend, reach, on-site sign-ups, and cohort retention to estimate incremental subscriptions attributable to social. In Conversion & Measurement<\/strong>, this prevents the team from cutting a channel that still drives net-new customers. In Attribution<\/strong>, it offsets under-crediting caused by missing identifiers.<\/p>\n\n\n\n

                      Example 2: Retail promotions across regions (geo-based modeling)<\/h3>\n\n\n\n

                      A retailer runs a promotion supported by streaming and display. They can\u2019t hold out users cleanly, but they can vary spend across regions. A geo-based Modeled Lift approach estimates incremental store visits and sales while controlling for seasonality and promo intensity. This supports Attribution<\/strong> decisions about which channels truly moved demand rather than simply appearing in paths.<\/p>\n\n\n\n

                      Example 3: B2B pipeline impact with long sales cycles<\/h3>\n\n\n\n

                      A B2B SaaS company needs to understand whether brand campaigns improve pipeline quality, not just form fills. A Modeled Lift approach ties campaign exposure and spend to downstream metrics like opportunity creation and win rate, controlling for sales capacity and seasonality. In Conversion & Measurement<\/strong>, this aligns marketing optimization with revenue outcomes rather than short-term leads.<\/p>\n\n\n\n

                      Benefits of Using Modeled Lift<\/h2>\n\n\n\n

                      When used carefully, Modeled Lift improves both decision quality and measurement resilience.<\/p>\n\n\n\n

                        \n
                      • More accurate investment decisions:<\/strong> Shifts focus from credited conversions to incremental outcomes. <\/li>\n
                      • Better efficiency:<\/strong> Helps reduce spend on tactics that harvest existing demand without adding new conversions. <\/li>\n
                      • Improved customer experience:<\/strong> Enables frequency and saturation management by understanding diminishing returns. <\/li>\n
                      • Cross-channel clarity:<\/strong> Supports planning where traditional Attribution<\/strong> struggles (upper funnel, cross-device, walled gardens). <\/li>\n
                      • Stronger stakeholder alignment:<\/strong> Provides finance-friendly incremental metrics for forecasts and business cases.<\/li>\n<\/ul>\n\n\n\n

                        In short, Modeled Lift makes Conversion & Measurement<\/strong> more durable when perfect tracking is unrealistic.<\/p>\n\n\n\n

                        Challenges of Modeled Lift<\/h2>\n\n\n\n

                        Modeled Lift is powerful, but it\u2019s not magic\u2014and misuse can create false confidence.<\/p>\n\n\n\n

                        Technical challenges<\/h3>\n\n\n\n
                          \n
                        • Data sparsity at granular levels (campaign\/creative\/segment)<\/li>\n
                        • Misaligned time windows (exposure vs conversion vs reporting)<\/li>\n
                        • Unstable IDs and deduplication issues<\/li>\n
                        • Pipeline complexity and delayed data availability<\/li>\n<\/ul>\n\n\n\n

                          Strategic risks<\/h3>\n\n\n\n
                            \n
                          • Confusing correlation with causation when assumptions are weak<\/li>\n
                          • Overfitting models to past conditions that no longer apply<\/li>\n
                          • Treating model outputs as precise truths instead of estimates<\/li>\n<\/ul>\n\n\n\n

                            Measurement limitations<\/h3>\n\n\n\n
                              \n
                            • Some outcomes are influenced by many unobserved factors (brand, PR, product changes)<\/li>\n
                            • Lift estimates can drift over time without recalibration<\/li>\n
                            • Attribution<\/strong> disagreements intensify when different methods produce different \u201ctruths\u201d<\/li>\n<\/ul>\n\n\n\n

                              A good Conversion & Measurement<\/strong> practice is to pair Modeled Lift with transparent uncertainty and periodic validation.<\/p>\n\n\n\n

                              Best Practices for Modeled Lift<\/h2>\n\n\n\n
                                \n
                              • Start with a decision, not a model:<\/strong> Define what you will change if lift is higher or lower (budget, targeting, frequency). <\/li>\n
                              • Calibrate whenever possible:<\/strong> Use holdouts, geo tests, or incrementality experiments to anchor assumptions. <\/li>\n
                              • Model at the right granularity:<\/strong> Prefer stable aggregation (weekly\/channel\/region) before forcing daily\/creative-level lift. <\/li>\n
                              • Control for confounders:<\/strong> Seasonality, pricing, promotions, distribution, and competitor events can easily distort lift. <\/li>\n
                              • Track uncertainty and stability:<\/strong> Monitor error, drift, and sensitivity to key assumptions. <\/li>\n
                              • Create a measurement playbook:<\/strong> Document definitions, windows, data sources, and governance so Attribution<\/strong> and finance align. <\/li>\n
                              • Use lift to guide, not to decorate:<\/strong> If Modeled Lift doesn\u2019t change decisions, it\u2019s a reporting project\u2014not a measurement strategy.<\/li>\n<\/ul>\n\n\n\n

                                Tools Used for Modeled Lift<\/h2>\n\n\n\n

                                Modeled Lift typically relies on a stack rather than a single tool. In Conversion & Measurement<\/strong> and Attribution<\/strong> workflows, common tool categories include:<\/p>\n\n\n\n

                                  \n
                                • Analytics tools:<\/strong> Event collection, cohort analysis, funnel reporting, and conversion definitions <\/li>\n
                                • Tagging and data collection systems:<\/strong> Server-side tagging, consent-aware collection, and identity resolution where appropriate <\/li>\n
                                • Ad platforms and clean-room-like workflows:<\/strong> Exposure and spend data, aggregated reporting, and privacy-safe joins <\/li>\n
                                • CRM and marketing automation:<\/strong> Lead status, pipeline stages, offline conversion capture, and lifecycle outcomes <\/li>\n
                                • Data warehouses and transformation pipelines:<\/strong> Centralized datasets, consistent metrics layers, and reproducible modeling tables <\/li>\n
                                • Experimentation frameworks:<\/strong> Holdouts, geo experiments, and incrementality test design <\/li>\n
                                • BI and reporting dashboards:<\/strong> Modeled vs observed comparisons, trend monitoring, and stakeholder communication<\/li>\n<\/ul>\n\n\n\n

                                  The key is interoperability: Modeled Lift is only as credible as the consistency of the underlying Conversion & Measurement<\/strong> data.<\/p>\n\n\n\n

                                  Metrics Related to Modeled Lift<\/h2>\n\n\n\n

                                  To make Modeled Lift actionable, pair lift outputs with business and diagnostic metrics.<\/p>\n\n\n\n

                                  Core lift metrics<\/h3>\n\n\n\n
                                    \n
                                  • Incremental conversions<\/strong> (additional conversions attributable to marketing)<\/li>\n
                                  • Lift percentage<\/strong> (incremental vs baseline)<\/li>\n
                                  • Incremental revenue \/ gross profit<\/strong><\/li>\n
                                  • Incremental ROAS (iROAS)<\/strong> or profit per dollar<\/li>\n<\/ul>\n\n\n\n

                                    Efficiency and outcome metrics<\/h3>\n\n\n\n
                                      \n
                                    • Incremental CPA\/CAC<\/strong> (cost per incremental acquisition)<\/li>\n
                                    • Marginal return \/ diminishing returns curves<\/strong><\/li>\n
                                    • Payback period<\/strong> (especially for subscription and high-LTV models)<\/li>\n<\/ul>\n\n\n\n

                                      Model health metrics<\/h3>\n\n\n\n
                                        \n
                                      • Forecast error (e.g., MAPE\/RMSE depending on approach)<\/li>\n
                                      • Stability over time (drift checks)<\/li>\n
                                      • Sensitivity to assumptions (what changes the result most)<\/li>\n<\/ul>\n\n\n\n

                                        In Attribution<\/strong>, these metrics help reconcile \u201ccredited\u201d performance with incremental performance.<\/p>\n\n\n\n

                                        Future Trends of Modeled Lift<\/h2>\n\n\n\n

                                        Modeled Lift is evolving as the industry adapts to privacy, automation, and platform fragmentation.<\/p>\n\n\n\n

                                          \n
                                        • More modeled measurement by default:<\/strong> As deterministic tracking declines, Conversion & Measurement<\/strong> will lean more on aggregated and modeled approaches. <\/li>\n
                                        • Automation of calibration loops:<\/strong> Systems will increasingly trigger experiments or holdouts to refresh lift estimates. <\/li>\n
                                        • Better integration with bidding and budget tools:<\/strong> Incremental metrics will increasingly inform optimization, not just reporting. <\/li>\n
                                        • Privacy-first modeling practices:<\/strong> More aggregation, cohort-based approaches, and governance to ensure compliance and trust. <\/li>\n
                                        • Richer incrementality for omnichannel:<\/strong> Improved linkage between online exposures and offline outcomes through better data hygiene and consented CRM signals.<\/li>\n<\/ul>\n\n\n\n

                                          In practice, Modeled Lift will become less of a \u201cdata science project\u201d and more of a standard capability inside Conversion & Measurement<\/strong> programs.<\/p>\n\n\n\n

                                          Modeled Lift vs Related Terms<\/h2>\n\n\n\n

                                          Modeled Lift vs Conversion Lift<\/h3>\n\n\n\n

                                          \u201cConversion lift\u201d often refers to lift measured directly via controlled experiments (holdout tests). Modeled Lift<\/strong> may incorporate experiments, but it can also estimate lift when direct tests aren\u2019t available. In Conversion & Measurement<\/strong>, conversion lift is usually the strongest evidence; modeled lift expands coverage.<\/p>\n\n\n\n

                                          Modeled Lift vs Marketing Mix Modeling (MMM)<\/h3>\n\n\n\n

                                          MMM estimates the contribution of channels using aggregated time-series and spend data, often for long-term planning. Modeled Lift is broader: it can be MMM-like, experiment-calibrated, or user-level observational modeling. In Attribution<\/strong>, MMM is typically top-down, while Modeled Lift can be top-down or blended.<\/p>\n\n\n\n

                                          Modeled Lift vs Multi-Touch Attribution (MTA)<\/h3>\n\n\n\n

                                          MTA assigns credit across touchpoints in a customer journey, often based on rules or probabilistic models. Attribution<\/strong> via MTA explains credit distribution<\/em>; Modeled Lift estimates incrementality<\/em>. A channel can receive a lot of MTA credit yet produce low incremental lift if it mainly captures existing intent.<\/p>\n\n\n\n

                                          Who Should Learn Modeled Lift<\/h2>\n\n\n\n
                                            \n
                                          • Marketers:<\/strong> To invest based on incremental impact, not just tracked conversions, strengthening Conversion & Measurement<\/strong> decisions. <\/li>\n
                                          • Analysts and data scientists:<\/strong> To design defensible lift methods, validate assumptions, and integrate results into Attribution<\/strong> reporting. <\/li>\n
                                          • Agencies:<\/strong> To prove value beyond vanity metrics and align optimization with incrementality. <\/li>\n
                                          • Business owners and founders:<\/strong> To understand which spend actually grows the business and to manage risk when tracking is incomplete. <\/li>\n
                                          • Developers and data engineers:<\/strong> To build reliable pipelines, governance, and reporting layers that make Modeled Lift trustworthy.<\/li>\n<\/ul>\n\n\n\n

                                            Summary of Modeled Lift<\/h2>\n\n\n\n

                                            Modeled Lift is an estimate of the incremental conversions or revenue driven by marketing, derived from statistical modeling rather than purely observed paths. It matters because modern Conversion & Measurement<\/strong> faces signal loss, fragmented journeys, and incomplete tracking, making naive reporting increasingly misleading. Used alongside experiments and solid governance, Modeled Lift strengthens Attribution<\/strong> by shifting the focus from \u201cwho got credit\u201d to \u201cwhat actually changed because we marketed.\u201d<\/p>\n\n\n\n

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

                                            1) What is Modeled Lift in simple terms?<\/h3>\n\n\n\n

                                            Modeled Lift is an estimated count (or value) of extra conversions caused by marketing, calculated by modeling what would have happened without the marketing activity and comparing it to what actually happened.<\/p>\n\n\n\n

                                            2) Is Modeled Lift the same as incrementality?<\/h3>\n\n\n\n

                                            Modeled Lift is a way to estimate<\/em> incrementality. True incrementality is best proven with experiments; Modeled Lift approximates it when experiments are limited or used only for calibration.<\/p>\n\n\n\n

                                            3) How does Modeled Lift help Attribution decisions?<\/h3>\n\n\n\n

                                            Attribution<\/strong> assigns credit, but credit doesn\u2019t guarantee causal impact. Modeled Lift helps evaluate whether credited touchpoints actually increased conversions, reducing over-crediting of \u201ceasy-to-track\u201d channels.<\/p>\n\n\n\n

                                            4) When should I use Modeled Lift instead of running an experiment?<\/h3>\n\n\n\n

                                            Use Modeled Lift when experiments are too costly, slow, or operationally difficult to run continuously. A strong Conversion & Measurement<\/strong> strategy often uses experiments periodically to calibrate and validate modeled estimates.<\/p>\n\n\n\n

                                            5) What data do I need to calculate Modeled Lift?<\/h3>\n\n\n\n

                                            At minimum: conversion outcomes, marketing activity data (spend and\/or exposures), and context variables like time, region, and promotions. Better results come from clean definitions, consistent windows, and reliable deduplication.<\/p>\n\n\n\n

                                            6) Can Modeled Lift be used for upper-funnel channels?<\/h3>\n\n\n\n

                                            Yes. Upper-funnel channels often suffer in last-click Attribution<\/strong>. Modeled Lift can estimate their incremental impact using reach\/spend patterns, geo variation, time-series modeling, and experiment calibration when available.<\/p>\n\n\n\n

                                            7) How do I know if my Modeled Lift numbers are trustworthy?<\/h3>\n\n\n\n

                                            Look for validation against controlled tests, stable performance over time, clear assumptions, and transparent uncertainty. In Conversion & Measurement<\/strong>, trust comes from repeatable methods, not from a single \u201cperfect\u201d report.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                            Modeled Lift is an approach to estimating the incremental impact of marketing when you can\u2019t directly observe it with perfect tracking or clean experiments. In **Conversion & Measurement**, it fills the gap between what your analytics can *prove* and what the business still needs to *decide*\u2014budget allocation, channel strategy, and campaign optimization.<\/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-7054","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\/7054","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=7054"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7054\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7054"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7054"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7054"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}