{"id":11469,"date":"2026-04-01T23:22:31","date_gmt":"2026-04-01T23:22:31","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/video-ads-forecast\/"},"modified":"2026-04-01T23:22:31","modified_gmt":"2026-04-01T23:22:31","slug":"video-ads-forecast","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/video-ads-forecast\/","title":{"rendered":"Video Ads Forecast: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Video Ads"},"content":{"rendered":"\n

A Video Ads Forecast<\/strong> is a structured prediction of how your Video Ads<\/strong> are likely to perform\u2014before you spend the full budget\u2014based on historical data, current market signals, targeting choices, creative assumptions, and measurement rules. In Paid Marketing<\/strong>, forecasting turns \u201cwe think this will work\u201d into a plan with expected reach, views, conversions, and cost ranges that stakeholders can evaluate and improve.<\/p>\n\n\n\n

A modern Video Ads Forecast<\/strong> matters because video inventory, auction dynamics, and audience behavior change quickly. With better forecasting, teams can set realistic goals, allocate spend across platforms and formats, pressure-test creative and landing page assumptions, and reduce the risk of scaling campaigns that look promising but won\u2019t hit efficiency targets.<\/p>\n\n\n\n

What Is Video Ads Forecast?<\/h2>\n\n\n\n

Video Ads Forecast<\/strong> is the practice of estimating future outcomes for Video Ads<\/strong> campaigns\u2014such as impressions, reach, views, view-through rate, clicks, conversions, CPA, or ROAS\u2014using data and assumptions. It can be as simple as a spreadsheet using last quarter\u2019s averages or as advanced as a statistical model incorporating seasonality, spend curves, and creative fatigue.<\/p>\n\n\n\n

The core concept is that expected results are not random: they\u2019re influenced by controllable inputs (budget, bids, targeting, placements, creative) and external factors (competition, seasonality, platform changes). A Video Ads Forecast<\/strong> makes those relationships explicit so teams can make better decisions.<\/p>\n\n\n\n

In business terms, Video Ads Forecast<\/strong> supports planning and accountability. It helps leaders understand what outcomes a budget is likely to buy, what \u201cgood\u201d performance should look like, and where the biggest risks and opportunities are.<\/p>\n\n\n\n

Within Paid Marketing<\/strong>, forecasting sits between goal-setting and execution. It informs budgets, KPI targets, and pacing decisions. Within Video Ads<\/strong>, it provides guardrails for creative strategy, audience sequencing, and format mix (short-form, skippable, non-skippable, vertical, in-feed, CTV-style inventory).<\/p>\n\n\n\n

Why Video Ads Forecast Matters in Paid Marketing<\/h2>\n\n\n\n

In Paid Marketing<\/strong>, forecasting is how you align strategy with reality. A Video Ads Forecast<\/strong> is especially valuable because video performance is multi-stage: people may watch, engage, and convert later\u2014sometimes on a different device\u2014making \u201cwhat will happen\u201d less obvious than in simple click-to-purchase campaigns.<\/p>\n\n\n\n

Key reasons it matters:<\/p>\n\n\n\n

    \n
  • Budget confidence:<\/strong> A credible Video Ads Forecast<\/strong> helps finance and leadership approve spend because there\u2019s a measurable expectation, not just optimism.<\/li>\n
  • Goal realism:<\/strong> Forecasting forces clarity on what KPIs are achievable given the audience size, auction costs, and conversion rates.<\/li>\n
  • Scenario planning:<\/strong> Teams can compare \u201cconservative vs. aggressive\u201d outcomes and decide how much risk to take.<\/li>\n
  • Faster learning:<\/strong> A forecast becomes a baseline. When actuals deviate, you can diagnose whether the issue is creative, targeting, landing page performance, or measurement.<\/li>\n
  • Competitive advantage:<\/strong> Brands that forecast well can scale earlier, avoid overspending in overheated auctions, and reallocate budgets faster than competitors.<\/li>\n<\/ul>\n\n\n\n

    How Video Ads Forecast Works<\/h2>\n\n\n\n

    A Video Ads Forecast<\/strong> is part math and part operational discipline. In practice, most teams follow a workflow like this:<\/p>\n\n\n\n

      \n
    1. \n

      Inputs (what you control and what you know)<\/strong>\n – Budget, flight dates, geo, placements, format mix\n – Audience size estimates and frequency caps\n – Historical CPM\/CPV, CTR, view rate, conversion rate\n – Conversion value assumptions (AOV, LTV, lead-to-sale rate)<\/p>\n<\/li>\n

    2. \n

      Analysis (turn inputs into expectations)<\/strong>\n – Normalize past data (remove one-off spikes, separate prospecting vs retargeting)\n – Apply seasonality and competition adjustments\n – Model spend-to-result curves (diminishing returns at higher spend)\n – Define attribution and conversion windows to match reporting reality<\/p>\n<\/li>\n

    3. \n

      Application (build the plan)<\/strong>\n – Set KPI targets per campaign and per audience stage\n – Allocate budget across formats and funnel stages\n – Create pacing rules (daily\/weekly spend and performance thresholds)\n – Establish a test plan (creative variants, audiences, landing pages)<\/p>\n<\/li>\n

    4. \n

      Outputs (what you deliver)<\/strong>\n – Forecast ranges for reach, views, conversions, CPA\/ROAS\n – Best\/base\/worst-case scenarios\n – Assumptions list and measurement definitions\n – A monitoring plan that compares actuals vs forecast weekly<\/p>\n<\/li>\n<\/ol>\n\n\n\n

      A strong Video Ads Forecast<\/strong> is not \u201cone and done.\u201d It is updated as new data arrives, especially after the first 7\u201314 days of learning.<\/p>\n\n\n\n

      Key Components of Video Ads Forecast<\/h2>\n\n\n\n

      A reliable Video Ads Forecast<\/strong> usually includes the following building blocks:<\/p>\n\n\n\n

      Data inputs<\/h3>\n\n\n\n
        \n
      • Platform delivery data:<\/strong> impressions, CPM, views, CPV, completion rate, frequency<\/li>\n
      • On-site analytics:<\/strong> sessions, engaged sessions, add-to-cart, lead submits, purchases<\/li>\n
      • Conversion tracking data:<\/strong> modeled and observed conversions, offline conversions if available<\/li>\n
      • Creative metadata:<\/strong> hook type, length, format, CTA, messaging theme<\/li>\n
      • Business data:<\/strong> AOV, margin, repeat rate, lead quality, sales cycle length<\/li>\n<\/ul>\n\n\n\n

        Forecast logic and assumptions<\/h3>\n\n\n\n
          \n
        • Clear definitions for \u201cview,\u201d \u201cconversion,\u201d and attribution window<\/li>\n
        • Separation of Video Ads<\/strong> objectives (awareness vs performance) so metrics align<\/li>\n
        • A plan for diminishing returns as spend increases<\/li>\n
        • Assumptions about creative fatigue and refresh cadence<\/li>\n<\/ul>\n\n\n\n

          Governance and responsibilities<\/h3>\n\n\n\n
            \n
          • Who owns the model (analyst, performance marketer, or ops)<\/li>\n
          • Who approves assumptions (channel lead, finance, client)<\/li>\n
          • How often forecasts are updated (weekly during launches, monthly for steady-state)<\/li>\n<\/ul>\n\n\n\n

            Outputs and communication<\/h3>\n\n\n\n
              \n
            • A forecast table with ranges, not single-point guesses<\/li>\n
            • Notes explaining what would cause over- or under-performance<\/li>\n
            • A simple readout that non-technical stakeholders can understand<\/li>\n<\/ul>\n\n\n\n

              Types of Video Ads Forecast<\/h2>\n\n\n\n

              \u201cTypes\u201d of Video Ads Forecast<\/strong> are usually best understood as approaches and time horizons rather than formal categories:<\/p>\n\n\n\n

              By method: top-down vs bottom-up<\/h3>\n\n\n\n
                \n
              • Top-down:<\/strong> Start from budget and typical efficiency (CPM\/CPV, CVR) to estimate outcomes. Faster, good for early planning.<\/li>\n
              • Bottom-up:<\/strong> Start from audience size, reachable frequency, and funnel conversion rates, then estimate spend needed. Better for constraint-based planning.<\/li>\n<\/ul>\n\n\n\n

                By horizon: short-term vs long-term<\/h3>\n\n\n\n
                  \n
                • Short-term forecasting (days\/weeks):<\/strong> Used for pacing, creative testing decisions, and auction volatility.<\/li>\n
                • Long-term forecasting (quarters):<\/strong> Used for annual planning, staffing, and broader Paid Marketing<\/strong> allocation.<\/li>\n<\/ul>\n\n\n\n

                  By uncertainty handling: point estimate vs range<\/h3>\n\n\n\n
                    \n
                  • Point estimate:<\/strong> One number (e.g., CPA = $45). Simple but risky.<\/li>\n
                  • Range-based forecast:<\/strong> Best\/base\/worst-case or percentile bands. More honest and more useful.<\/li>\n<\/ul>\n\n\n\n

                    By scope: platform-level vs cross-channel<\/h3>\n\n\n\n
                      \n
                    • Platform-level:<\/strong> Specific to one ad environment and its Video Ads<\/strong> formats.<\/li>\n
                    • Cross-channel:<\/strong> Aggregates video performance across multiple platforms, sometimes integrating search and shopping impact.<\/li>\n<\/ul>\n\n\n\n

                      Real-World Examples of Video Ads Forecast<\/h2>\n\n\n\n

                      1) E-commerce prospecting with short-form video<\/h3>\n\n\n\n

                      A retailer plans a 6-week push. The Video Ads Forecast<\/strong> uses last season\u2019s CPM and view rate, then applies a conservative conversion rate due to a new landing page. It outputs expected reach and conversions at three budget tiers, plus a trigger: \u201cIf CPV rises 20% in week 1, shift spend to higher-intent audiences.\u201d This helps Paid Marketing<\/strong> leaders approve the budget while managing risk in Video Ads<\/strong> prospecting.<\/p>\n\n\n\n

                      2) SaaS lead generation with sequential messaging<\/h3>\n\n\n\n

                      A SaaS company runs Video Ads<\/strong> for awareness, then retargets viewers with a demo offer. The Video Ads Forecast<\/strong> models two stages: view-to-visit and visit-to-lead, plus a lead-to-opportunity rate from CRM history. The plan identifies that improving lead quality (not just lead volume) is the lever, so the team forecasts outcomes for different gating strategies and form lengths.<\/p>\n\n\n\n

                      3) Agency planning for a seasonal brand campaign<\/h3>\n\n\n\n

                      An agency builds a quarterly Video Ads Forecast<\/strong> for a seasonal product launch. It includes scenario ranges based on competitor pressure and inventory costs. The agency also forecasts creative fatigue, budgeting for fresh edits every two weeks. This makes the Paid Marketing<\/strong> plan operational: the client understands not only expected results, but also the production and optimization cadence required.<\/p>\n\n\n\n

                      Benefits of Using Video Ads Forecast<\/h2>\n\n\n\n

                      A well-built Video Ads Forecast<\/strong> delivers practical benefits:<\/p>\n\n\n\n

                        \n
                      • Better performance planning:<\/strong> You can set KPIs that match audience size and funnel reality, improving the odds your Video Ads<\/strong> hit targets.<\/li>\n
                      • Cost control:<\/strong> Forecasting highlights when efficiency will degrade as spend scales, preventing wasteful budget increases.<\/li>\n
                      • Smarter experiments:<\/strong> You can forecast the expected lift from creative tests and prioritize the highest-impact variables.<\/li>\n
                      • Operational efficiency:<\/strong> Teams spend less time debating opinions and more time executing against measurable assumptions.<\/li>\n
                      • Improved audience experience:<\/strong> Frequency and reach forecasts help avoid overexposure, which protects brand perception and reduces creative burnout.<\/li>\n<\/ul>\n\n\n\n

                        Challenges of Video Ads Forecast<\/h2>\n\n\n\n

                        Forecasting is powerful, but it has real limitations\u2014especially in Paid Marketing<\/strong> environments where platforms and behavior change quickly.<\/p>\n\n\n\n

                          \n
                        • Attribution uncertainty:<\/strong> Views influence conversions that may not be credited cleanly, and modeled conversions can shift reporting.<\/li>\n
                        • Auction volatility:<\/strong> CPM\/CPV can change due to seasonality, competitor spend, and inventory supply.<\/li>\n
                        • Creative variability:<\/strong> One creative can outperform another by multiples; forecasting averages can hide this spread.<\/li>\n
                        • Signal loss and privacy constraints:<\/strong> Reduced tracking granularity can make it harder to connect Video Ads<\/strong> exposure to downstream revenue.<\/li>\n
                        • Data quality issues:<\/strong> Inconsistent UTMs, broken tags, or CRM mismatches can distort historical baselines.<\/li>\n
                        • Overconfidence:<\/strong> The biggest risk is treating a Video Ads Forecast<\/strong> as a promise instead of an informed estimate with assumptions.<\/li>\n<\/ul>\n\n\n\n

                          Best Practices for Video Ads Forecast<\/h2>\n\n\n\n

                          To make a Video Ads Forecast<\/strong> trustworthy and actionable:<\/p>\n\n\n\n

                            \n
                          1. \n

                            Forecast ranges, not certainties<\/strong>\n Use best\/base\/worst-case bands and state what conditions drive each scenario.<\/p>\n<\/li>\n

                          2. \n

                            Separate campaign intents<\/strong>\n Don\u2019t mix awareness-focused Video Ads<\/strong> benchmarks with direct-response retargeting metrics.<\/p>\n<\/li>\n

                          3. \n

                            Model diminishing returns<\/strong>\n As spend rises, costs often increase and conversion rates can drop. Build that into the forecast.<\/p>\n<\/li>\n

                          4. \n

                            Document assumptions explicitly<\/strong>\n Include attribution windows, conversion definitions, expected creative refresh rate, and expected landing page CVR.<\/p>\n<\/li>\n

                          5. \n

                            Calibrate early and often<\/strong>\n Update the forecast after initial learning. In many Paid Marketing<\/strong> teams, weekly recalibration is the difference between accuracy and drift.<\/p>\n<\/li>\n

                          6. \n

                            Use leading indicators<\/strong>\n Watch early signals like hook rate (first seconds retention), view rate, and CTR to predict later conversion performance.<\/p>\n<\/li>\n

                          7. \n

                            Treat forecasting as a system<\/strong>\n The model is only part of it; the operational loop (monitor \u2192 diagnose \u2192 adjust) is what makes Video Ads Forecast<\/strong> valuable.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                            Tools Used for Video Ads Forecast<\/h2>\n\n\n\n

                            A Video Ads Forecast<\/strong> can be built with lightweight tools or advanced analytics stacks. Common tool categories include:<\/p>\n\n\n\n

                              \n
                            • Ad platform reporting and planners:<\/strong> For delivery estimates, historical CPM\/CPV, reach, and frequency insights related to Video Ads<\/strong> inventory.<\/li>\n
                            • Web and app analytics tools:<\/strong> To measure on-site behavior after video exposure and validate funnel assumptions.<\/li>\n
                            • Tag management and conversion measurement:<\/strong> To maintain clean event definitions, reduce tracking errors, and support consistent Paid Marketing<\/strong> reporting.<\/li>\n
                            • CRM and revenue systems:<\/strong> To connect leads to pipeline and revenue, enabling forecasts beyond \u201ccost per lead.\u201d<\/li>\n
                            • Experimentation and lift measurement:<\/strong> To estimate incrementality when attribution is incomplete.<\/li>\n
                            • BI dashboards and reporting layers:<\/strong> To compare forecast vs actuals, track pacing, and communicate changes to stakeholders.<\/li>\n
                            • Automation and data pipelines:<\/strong> To refresh datasets and keep the Video Ads Forecast<\/strong> updated without manual rework.<\/li>\n<\/ul>\n\n\n\n

                              Metrics Related to Video Ads Forecast<\/h2>\n\n\n\n

                              The best metrics depend on your objective, but most Video Ads Forecast<\/strong> models rely on a mix of delivery, engagement, and business outcomes:<\/p>\n\n\n\n

                              Delivery and cost metrics<\/h3>\n\n\n\n
                                \n
                              • Impressions, reach, frequency<\/li>\n
                              • CPM (cost per thousand impressions)<\/li>\n
                              • CPV\/CPCV (cost per view \/ completed view)<\/li>\n<\/ul>\n\n\n\n

                                Engagement and quality metrics for Video Ads<\/h3>\n\n\n\n
                                  \n
                                • View rate and video completion rate<\/li>\n
                                • Watch time or average view duration<\/li>\n
                                • CTR (click-through rate) for traffic-driving Video Ads<\/strong><\/li>\n
                                • Viewability (where applicable)<\/li>\n<\/ul>\n\n\n\n

                                  Conversion and ROI metrics<\/h3>\n\n\n\n
                                    \n
                                  • Conversion rate (session-to-purchase, click-to-lead, or view-assisted where defined)<\/li>\n
                                  • CPA\/CPL (cost per acquisition\/lead)<\/li>\n
                                  • ROAS (return on ad spend) and contribution margin<\/li>\n
                                  • LTV (lifetime value) and payback period (when available)<\/li>\n<\/ul>\n\n\n\n

                                    Incrementality and brand impact (when measured)<\/h3>\n\n\n\n
                                      \n
                                    • Conversion lift or holdout lift<\/li>\n
                                    • Brand search lift or direct traffic lift (carefully interpreted)<\/li>\n
                                    • Attention or engagement quality proxies (used cautiously and consistently)<\/li>\n<\/ul>\n\n\n\n

                                      Future Trends of Video Ads Forecast<\/h2>\n\n\n\n

                                      Video Ads Forecast<\/strong> is evolving fast as Paid Marketing<\/strong> measurement becomes more model-driven.<\/p>\n\n\n\n

                                        \n
                                      • More automation, more human oversight:<\/strong> Automated forecasting will improve, but teams will still need to validate assumptions and guard against \u201cblack box\u201d errors.<\/li>\n
                                      • Incrementality becomes central:<\/strong> As deterministic tracking weakens, forecasts will lean more on experiments, geo tests, and modeled lift.<\/li>\n
                                      • Creative intelligence enters forecasting:<\/strong> Expect stronger links between creative features (length, pacing, on-screen text) and predicted performance.<\/li>\n
                                      • Cross-format planning grows:<\/strong> Forecasting will increasingly unify short-form social video with longer-form and living-room style video inventory to reflect real viewing behavior.<\/li>\n
                                      • Privacy-aware measurement design:<\/strong> Forecasts will rely more on aggregated signals and less on user-level paths, changing how Video Ads<\/strong> success is predicted and validated.<\/li>\n<\/ul>\n\n\n\n

                                        Video Ads Forecast vs Related Terms<\/h2>\n\n\n\n

                                        Video Ads Forecast vs media plan<\/h3>\n\n\n\n

                                        A media plan describes what you will run (audiences, formats, budgets, timing). A Video Ads Forecast<\/strong> estimates what the plan will produce (reach, views, conversions, CPA\/ROAS) and how uncertain those outcomes are.<\/p>\n\n\n\n

                                        Video Ads Forecast vs budget forecast<\/h3>\n\n\n\n

                                        A budget forecast focuses on spend over time (pacing, cash flow, allocations). A Video Ads Forecast<\/strong> connects spend to marketing outcomes, translating dollars into expected Video Ads<\/strong> performance.<\/p>\n\n\n\n

                                        Video Ads Forecast vs sales forecast<\/h3>\n\n\n\n

                                        A sales forecast estimates revenue the business expects to close. A Video Ads Forecast<\/strong> estimates marketing outputs and outcomes attributable to video campaigns; it can feed a sales forecast, but it doesn\u2019t replace pipeline modeling or sales cycle dynamics.<\/p>\n\n\n\n

                                        Who Should Learn Video Ads Forecast<\/h2>\n\n\n\n
                                          \n
                                        • Marketers:<\/strong> To set achievable KPIs, plan tests, and make smarter budget tradeoffs in Paid Marketing<\/strong>.<\/li>\n
                                        • Analysts:<\/strong> To build models, quantify uncertainty, and connect Video Ads<\/strong> signals to business outcomes.<\/li>\n
                                        • Agencies:<\/strong> To justify recommendations, align clients on expectations, and reduce surprises during scaling.<\/li>\n
                                        • Business owners and founders:<\/strong> To understand what growth targets are realistic and when to invest in creative, landing pages, or measurement.<\/li>\n
                                        • Developers and data teams:<\/strong> To implement clean tracking, data pipelines, and governance that make forecasting reliable.<\/li>\n<\/ul>\n\n\n\n

                                          Summary of Video Ads Forecast<\/h2>\n\n\n\n

                                          A Video Ads Forecast<\/strong> is an estimate of future Video Ads<\/strong> results\u2014grounded in data, assumptions, and measurement rules\u2014used to plan and optimize Paid Marketing<\/strong>. It matters because video outcomes depend on auction dynamics, creative quality, audience size, and attribution limitations. When done well, forecasting improves budget allocation, reduces waste, accelerates learning, and provides a practical baseline for evaluating performance.<\/p>\n\n\n\n

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

                                          1) What is a Video Ads Forecast and what should it include?<\/h3>\n\n\n\n

                                          A Video Ads Forecast<\/strong> should include expected ranges for reach, views, conversions, and efficiency (CPM\/CPV, CPA\/ROAS), plus a clear list of assumptions (attribution window, conversion definitions, audience scope, and creative refresh cadence).<\/p>\n\n\n\n

                                          2) How accurate can a Video Ads Forecast be?<\/h3>\n\n\n\n

                                          Accuracy varies by data quality, stability of auctions, and how often you recalibrate. Range-based forecasts that are updated weekly are typically more useful than single-number predictions set once per quarter.<\/p>\n\n\n\n

                                          3) Which metrics matter most when forecasting Video Ads performance?<\/h3>\n\n\n\n

                                          Most teams start with CPM or CPV, view rate, CTR (if driving traffic), conversion rate, and CPA\/ROAS. For upper-funnel Video Ads<\/strong>, completion rate, reach, and frequency often matter more than clicks.<\/p>\n\n\n\n

                                          4) How do you forecast conversions when attribution is incomplete?<\/h3>\n\n\n\n

                                          Use multiple perspectives: platform-reported conversions, on-site analytics trends, and incrementality methods (holdouts or lift tests). A good Video Ads Forecast<\/strong> will explain these measurement limits rather than hiding them.<\/p>\n\n\n\n

                                          5) Should I build separate forecasts for prospecting and retargeting?<\/h3>\n\n\n\n

                                          Yes. Prospecting and retargeting have different audience sizes, frequency dynamics, and conversion rates. Splitting them makes the Paid Marketing<\/strong> plan more realistic and easier to optimize.<\/p>\n\n\n\n

                                          6) How often should a Video Ads Forecast be updated?<\/h3>\n\n\n\n

                                          For new launches, update after the first learning period and then weekly. For stable accounts, monthly updates are common\u2014plus immediate updates when creative, targeting, or budgets change significantly.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                          A **Video Ads Forecast** is a structured prediction of how your **Video Ads** are likely to perform\u2014before you spend the full budget\u2014based on historical data, current market signals, targeting choices, creative assumptions, and measurement rules. In **Paid Marketing**, forecasting turns \u201cwe think this will work\u201d into a plan with expected reach, views, conversions, and cost ranges that stakeholders can evaluate and improve.<\/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":[1915],"tags":[],"class_list":["post-11469","post","type-post","status-publish","format-standard","hentry","category-video-ads"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/11469","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=11469"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/11469\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=11469"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=11469"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=11469"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}