{"id":11199,"date":"2026-04-01T13:12:50","date_gmt":"2026-04-01T13:12:50","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/paid-search-forecast\/"},"modified":"2026-04-01T13:12:50","modified_gmt":"2026-04-01T13:12:50","slug":"paid-search-forecast","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/paid-search-forecast\/","title":{"rendered":"Paid Search Forecast: What It Is, Key Features, Benefits, Use Cases, and How It Fits in SEM \/ Paid Search"},"content":{"rendered":"\n

A Paid Search Forecast<\/strong> is a structured estimate of what your search ads are likely to deliver\u2014spend, clicks, conversions, and revenue\u2014over a defined time period and set of assumptions. In Paid Marketing<\/strong>, forecasting turns \u201cwe think this will work\u201d into an evidence-based plan for budgets, targets, and trade-offs. Inside SEM \/ Paid Search<\/strong>, it helps teams decide which keywords to bid on, how aggressively to scale, and what performance is realistic given auction dynamics.<\/p>\n\n\n\n

Modern Paid Marketing<\/strong> is too fast and too competitive to rely on intuition alone. A solid Paid Search Forecast<\/strong> supports planning cycles, reduces budget waste, sets credible expectations with stakeholders, and gives you a way to pressure-test growth ideas before you spend.<\/p>\n\n\n\n

What Is Paid Search Forecast?<\/h2>\n\n\n\n

At its simplest, a Paid Search Forecast<\/strong> is a prediction of paid search outcomes based on historical performance, current account structure, auction conditions, and planned changes. It typically answers questions like:<\/p>\n\n\n\n

    \n
  • If we spend $X next month in SEM \/ Paid Search<\/strong>, how many clicks and conversions should we expect?<\/li>\n
  • What cost per acquisition (CPA) or return on ad spend (ROAS) is realistic at different budget levels?<\/li>\n
  • What happens if average CPC rises, conversion rate drops, or impression share changes?<\/li>\n<\/ul>\n\n\n\n

    The core concept is that paid search performance is not linear. As you spend more, you often expand into higher-cost auctions, lower-intent queries, or less efficient placements. A business-ready Paid Search Forecast<\/strong> accounts for diminishing returns and constraints like impression share, budget caps, and keyword coverage.<\/p>\n\n\n\n

    In Paid Marketing<\/strong>, this forecasting discipline sits between strategy and execution: it translates goals (growth, profitability, pipeline) into measurable SEM \/ Paid Search<\/strong> plans (budgets, targets, and expected output).<\/p>\n\n\n\n

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

    A strong Paid Search Forecast<\/strong> creates clarity where paid search can feel uncertain. It matters because it directly influences decisions that are expensive to reverse.<\/p>\n\n\n\n

    Key reasons it\u2019s valuable in Paid Marketing<\/strong>:<\/p>\n\n\n\n

      \n
    • Budget allocation and prioritization:<\/strong> Forecasting helps decide how much to invest in SEM \/ Paid Search<\/strong> versus other channels, and how to split spend across brand, non-brand, and competitor campaigns.<\/li>\n
    • Target-setting that teams can actually hit:<\/strong> It aligns leadership expectations with auction reality, seasonality, and account maturity.<\/li>\n
    • Scenario planning:<\/strong> You can model best-case, expected-case, and worst-case outcomes to manage risk.<\/li>\n
    • Operational readiness:<\/strong> Forecasts inform staffing, landing page capacity, sales follow-up, inventory, and customer support.<\/li>\n
    • Competitive advantage:<\/strong> If you can anticipate when auctions tighten (seasonality, competitor launches), you can adjust faster and protect margins.<\/li>\n<\/ul>\n\n\n\n

      In short, Paid Search Forecast<\/strong> improves both strategic planning and day-to-day execution in Paid Marketing<\/strong>, especially when SEM \/ Paid Search<\/strong> is a major growth lever.<\/p>\n\n\n\n

      How Paid Search Forecast Works<\/h2>\n\n\n\n

      A practical Paid Search Forecast<\/strong> is less about a perfect prediction and more about a transparent model that ties assumptions to outcomes. A common workflow looks like this:<\/p>\n\n\n\n

        \n
      1. \n

        Inputs (what you know or decide)<\/strong>\n – Historic performance by campaign\/keyword (impressions, CTR, CPC, CVR, CPA\/ROAS)\n – Budget constraints, target CPA\/ROAS, and conversion definitions\n – Seasonality factors and planned promotions\n – Planned changes (new landing pages, new match types, new geos)<\/p>\n<\/li>\n

      2. \n

        Processing (how you translate inputs into expected outcomes)<\/strong>\n – Estimate traffic potential (impressions and clicks) based on share and demand\n – Project cost using expected CPC and expected click volume\n – Project conversions using expected conversion rate and click volume\n – If revenue is tracked, project revenue using AOV\/LTV assumptions<\/p>\n<\/li>\n

      3. \n

        Application (how teams use it in SEM \/ Paid Search)<\/strong>\n – Set campaign budgets and bid strategy guardrails\n – Choose which keyword groups to expand or pause\n – Align goals with stakeholders (finance, leadership, sales)<\/p>\n<\/li>\n

      4. \n

        Outputs (what the forecast delivers)<\/strong>\n – Expected spend, clicks, conversions, CPA\/ROAS\n – Ranges (confidence bands) and sensitivity analysis (what moves the model most)\n – A set of assumptions that can be monitored and updated<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        In real accounts, the best Paid Search Forecast<\/strong> models are iterative: you update them as auction conditions and onsite performance change.<\/p>\n\n\n\n

        Key Components of Paid Search Forecast<\/h2>\n\n\n\n

        A reliable Paid Search Forecast<\/strong> is built from components that connect business goals to measurable mechanics in SEM \/ Paid Search<\/strong>.<\/p>\n\n\n\n

        Data inputs<\/h3>\n\n\n\n
          \n
        • Account history:<\/strong> performance trends by campaign, keyword theme, device, geo, audience<\/li>\n
        • Auction\/competition signals:<\/strong> impression share, lost IS (budget\/rank), CPC trends<\/li>\n
        • Website and funnel data:<\/strong> landing page conversion rate, lead-to-sale rate, revenue per conversion<\/li>\n
        • Seasonality:<\/strong> month-over-month demand shifts, promo periods, industry peaks<\/li>\n<\/ul>\n\n\n\n

          Core metrics and assumptions<\/h3>\n\n\n\n
            \n
          • Expected CPC<\/strong>, CTR<\/strong>, conversion rate (CVR)<\/strong>, and conversion value<\/strong><\/li>\n
          • Budget pacing constraints and impression share ceilings<\/li>\n
          • Diminishing returns when scaling beyond \u201ccore\u201d keywords<\/li>\n<\/ul>\n\n\n\n

            Process and governance<\/h3>\n\n\n\n
              \n
            • Forecast ownership (analyst\/manager), review cadence (weekly\/monthly\/quarterly)<\/li>\n
            • Documented assumptions and change logs<\/li>\n
            • Stakeholder alignment on what counts as a conversion and how revenue is attributed<\/li>\n<\/ul>\n\n\n\n

              Connection to business planning<\/h3>\n\n\n\n

              The forecast should map to how the business measures success in Paid Marketing<\/strong>\u2014pipeline, CAC, gross margin, payback period\u2014not just clicks.<\/p>\n\n\n\n

              Types of Paid Search Forecast<\/h2>\n\n\n\n

              There isn\u2019t a single official taxonomy, but in practice, Paid Search Forecast<\/strong> approaches fall into a few useful distinctions.<\/p>\n\n\n\n

              1) Top-down vs. bottom-up<\/h3>\n\n\n\n
                \n
              • Top-down:<\/strong> Start with a budget or target (e.g., \u201c$100k\/month\u201d or \u201c500 leads\u201d), then model what performance metrics must be true to achieve it.<\/li>\n
              • Bottom-up:<\/strong> Start at campaign\/keyword level, forecast each segment, then roll up totals. Bottom-up is usually more accurate for SEM \/ Paid Search<\/strong> accounts with diverse intent.<\/li>\n<\/ul>\n\n\n\n

                2) Deterministic vs. scenario-based<\/h3>\n\n\n\n
                  \n
                • Deterministic:<\/strong> One set of assumptions produces one output (\u201cthe forecast\u201d).<\/li>\n
                • Scenario-based:<\/strong> Multiple scenarios (conservative\/expected\/aggressive) show ranges and risk.<\/li>\n<\/ul>\n\n\n\n

                  3) Short-term pacing vs. long-range planning<\/h3>\n\n\n\n
                    \n
                  • Short-term:<\/strong> Weekly\/monthly pacing to ensure spend and lead flow stay on track.<\/li>\n
                  • Long-range:<\/strong> Quarterly\/annual planning for Paid Marketing<\/strong> budgets, hiring, and growth targets.<\/li>\n<\/ul>\n\n\n\n

                    4) Keyword-theme vs. portfolio-level<\/h3>\n\n\n\n
                      \n
                    • Theme-level:<\/strong> Models by intent buckets (brand, non-brand, competitor, local, product category).<\/li>\n
                    • Portfolio-level:<\/strong> Treats the whole account as a system, focusing on total spend and blended efficiency.<\/li>\n<\/ul>\n\n\n\n

                      Real-World Examples of Paid Search Forecast<\/h2>\n\n\n\n

                      Example 1: SaaS lead generation planning<\/h3>\n\n\n\n

                      A SaaS company uses a Paid Search Forecast<\/strong> to estimate how many demo requests they can produce next quarter. They model brand and non-brand separately in SEM \/ Paid Search<\/strong>, because brand CPC and CVR behave very differently. The forecast includes a lead-to-opportunity conversion rate from CRM data, so Paid Marketing<\/strong> leadership can translate ad spend into pipeline.<\/p>\n\n\n\n

                      Example 2: Ecommerce seasonal budget ramp<\/h3>\n\n\n\n

                      An ecommerce brand plans a holiday ramp. The Paid Search Forecast<\/strong> includes seasonal uplift in search demand and expected CPC inflation from competitors. The team runs scenarios: maintain ROAS vs. maximize revenue. This helps Paid Marketing<\/strong> decide whether to accept a temporary ROAS dip in exchange for higher total sales, while keeping guardrails to avoid unprofitable scaling in SEM \/ Paid Search<\/strong>.<\/p>\n\n\n\n

                      Example 3: Multi-location service business expansion<\/h3>\n\n\n\n

                      A local services company is expanding into new cities. With limited historical data per new geo, the Paid Search Forecast<\/strong> uses analog markets (similar CPC and conversion behavior) to estimate initial CPA and lead volume. The model flags that call handling capacity is a constraint, so the Paid Marketing<\/strong> plan includes staffing changes alongside the SEM \/ Paid Search<\/strong> expansion.<\/p>\n\n\n\n

                      Benefits of Using Paid Search Forecast<\/h2>\n\n\n\n

                      Using Paid Search Forecast<\/strong> consistently can improve both performance and decision quality.<\/p>\n\n\n\n

                        \n
                      • Better efficiency at scale:<\/strong> Forecasting highlights where scaling will likely increase CPA due to diminishing returns.<\/li>\n
                      • Faster alignment with stakeholders:<\/strong> Finance and leadership get clear expectations grounded in SEM \/ Paid Search<\/strong> mechanics.<\/li>\n
                      • Smarter testing:<\/strong> You can quantify what a conversion-rate lift is worth, helping prioritize landing page or offer tests in Paid Marketing<\/strong>.<\/li>\n
                      • More stable lead\/revenue flow:<\/strong> Pacing forecasts reduce end-of-month scramble and underdelivery.<\/li>\n
                      • Reduced waste:<\/strong> Forecast-driven budgets avoid overfunding low-ceiling campaigns and underfunding high-intent segments.<\/li>\n<\/ul>\n\n\n\n

                        Challenges of Paid Search Forecast<\/h2>\n\n\n\n

                        A Paid Search Forecast<\/strong> is only as good as its data and assumptions. Common challenges include:<\/p>\n\n\n\n

                          \n
                        • Auction volatility:<\/strong> CPC and impression share can change quickly due to competitor behavior, seasonality, or new entrants.<\/li>\n
                        • Attribution uncertainty:<\/strong> If conversions are undercounted (cookie loss, cross-device, offline sales), forecasts can be misleading.<\/li>\n
                        • Conversion quality variance:<\/strong> Not all leads are equal; forecasting only on volume can hurt downstream outcomes in Paid Marketing<\/strong>.<\/li>\n
                        • Model overconfidence:<\/strong> A single-point forecast can create false certainty; ranges are often more honest.<\/li>\n
                        • Operational constraints:<\/strong> Sales capacity, inventory, or site performance can cap realized results even when SEM \/ Paid Search<\/strong> demand exists.<\/li>\n<\/ul>\n\n\n\n

                          Best Practices for Paid Search Forecast<\/h2>\n\n\n\n

                          Build forecasts around controllable levers<\/h3>\n\n\n\n

                          Tie the Paid Search Forecast<\/strong> to levers you can influence: budgets, targeting scope, bid strategy constraints, landing page improvements, and negative keywords.<\/p>\n\n\n\n

                          Separate segments that behave differently<\/h3>\n\n\n\n

                          In SEM \/ Paid Search<\/strong>, brand vs. non-brand (and often remarketing vs. prospecting) should be forecast separately. Blending them can hide risk.<\/p>\n\n\n\n

                          Use ranges and sensitivity analysis<\/h3>\n\n\n\n

                          Include at least three scenarios and identify sensitivity:\n– If CPC rises 10%, what happens to volume and CPA?\n– If CVR improves by 15%, how much incremental revenue does that unlock?<\/p>\n\n\n\n

                          Calibrate with holdout periods<\/h3>\n\n\n\n

                          Compare forecasted vs. actual weekly\/monthly. When the model is wrong, update assumptions\u2014not just the output.<\/p>\n\n\n\n

                          Align on measurement definitions<\/h3>\n\n\n\n

                          Before presenting a Paid Search Forecast<\/strong>, confirm:\n– What is a conversion?\n– Is it counted once per user, per session, or per event?\n– Is revenue tracked consistently and deduped?<\/p>\n\n\n\n

                          Treat forecasting as a living process<\/h3>\n\n\n\n

                          In Paid Marketing<\/strong>, forecasts should be reviewed on a cadence that matches volatility: weekly for high-spend accounts, monthly for stable ones, and quarterly for strategic planning.<\/p>\n\n\n\n

                          Tools Used for Paid Search Forecast<\/h2>\n\n\n\n

                          A Paid Search Forecast<\/strong> usually relies on a stack rather than a single toolset. Common tool categories in Paid Marketing<\/strong> and SEM \/ Paid Search<\/strong> include:<\/p>\n\n\n\n

                            \n
                          • Ad platforms:<\/strong> Provide historical performance, keyword data, impression share, and pacing controls used to ground assumptions.<\/li>\n
                          • Analytics tools:<\/strong> Connect ad traffic to onsite behavior and conversion performance (including funnel drop-off).<\/li>\n
                          • Reporting dashboards \/ BI:<\/strong> Centralize data, create forecast vs. actual tracking, and share scenario outputs with stakeholders.<\/li>\n
                          • Spreadsheets or modeling environments:<\/strong> Where most forecasting logic lives, especially for scenario planning and sensitivity analysis.<\/li>\n
                          • CRM systems:<\/strong> Convert ad-driven leads into pipeline and revenue, improving forecast accuracy beyond top-of-funnel conversions.<\/li>\n
                          • SEO tools (supporting role):<\/strong> Help estimate query demand trends and seasonality that can influence SEM \/ Paid Search<\/strong> forecasts, especially for non-brand expansion.<\/li>\n<\/ul>\n\n\n\n

                            The key is consistency: the forecast should draw from the same sources used to evaluate performance, so learning loops stay tight.<\/p>\n\n\n\n

                            Metrics Related to Paid Search Forecast<\/h2>\n\n\n\n

                            A meaningful Paid Search Forecast<\/strong> should include metrics that reflect both delivery and business impact.<\/p>\n\n\n\n

                            Delivery and cost metrics<\/h3>\n\n\n\n
                              \n
                            • Impressions, impression share, lost IS (budget\/rank)<\/li>\n
                            • Clicks, click-through rate (CTR)<\/li>\n
                            • Average CPC, total spend<\/li>\n<\/ul>\n\n\n\n

                              Conversion and efficiency metrics<\/h3>\n\n\n\n
                                \n
                              • Conversions, conversion rate (CVR)<\/li>\n
                              • Cost per conversion \/ CPA<\/li>\n
                              • Conversion value, value per click<\/li>\n<\/ul>\n\n\n\n

                                Profit and growth metrics (when available)<\/h3>\n\n\n\n
                                  \n
                                • ROAS, contribution margin, CAC, payback period<\/li>\n
                                • Lead-to-opportunity rate, opportunity-to-close rate (for B2B)<\/li>\n
                                • Incrementality assumptions (when measuring lift)<\/li>\n<\/ul>\n\n\n\n

                                  Forecasts in Paid Marketing<\/strong> are strongest when they connect SEM \/ Paid Search<\/strong> delivery metrics to downstream outcomes, not just platform-reported conversions.<\/p>\n\n\n\n

                                  Future Trends of Paid Search Forecast<\/h2>\n\n\n\n

                                  Paid Search Forecast<\/strong> is evolving as platforms automate more decisions and privacy changes reduce deterministic tracking.<\/p>\n\n\n\n

                                    \n
                                  • More automation-aware forecasting:<\/strong> As bidding and targeting become more algorithmic, forecasts will focus more on constraints (budget, value signals, audience inputs) and less on manual bid assumptions.<\/li>\n
                                  • First-party data emphasis:<\/strong> Better CRM integration and server-side measurement improve conversion quality modeling in Paid Marketing<\/strong>.<\/li>\n
                                  • Privacy-driven uncertainty:<\/strong> Aggregated reporting and modeled conversions can widen error bands, increasing the importance of scenario ranges.<\/li>\n
                                  • Creative and landing page impact modeling:<\/strong> Forecasting will increasingly include expected CVR changes from page speed, UX, and offer tests rather than treating CVR as fixed.<\/li>\n
                                  • Portfolio optimization:<\/strong> More teams will forecast across channel mixes, using SEM \/ Paid Search<\/strong> forecasts as one component of a broader Paid Marketing<\/strong> plan.<\/li>\n<\/ul>\n\n\n\n

                                    Paid Search Forecast vs Related Terms<\/h2>\n\n\n\n

                                    Paid Search Forecast vs. Budget pacing<\/h3>\n\n\n\n
                                      \n
                                    • Budget pacing<\/strong> is operational: are you spending on schedule this week\/month?<\/li>\n
                                    • Paid Search Forecast<\/strong> is predictive: what results should that spend produce, and what changes if assumptions shift?<\/li>\n<\/ul>\n\n\n\n

                                      Paid Search Forecast vs. Media plan<\/h3>\n\n\n\n
                                        \n
                                      • A media plan<\/strong> is a channel allocation document across Paid Marketing<\/strong> (search, social, display, etc.).<\/li>\n
                                      • A Paid Search Forecast<\/strong> is the modeling layer specific to SEM \/ Paid Search<\/strong> that estimates outcomes based on auction and conversion assumptions.<\/li>\n<\/ul>\n\n\n\n

                                        Paid Search Forecast vs. Demand forecast<\/h3>\n\n\n\n
                                          \n
                                        • A demand forecast<\/strong> predicts market demand or sales overall.<\/li>\n
                                        • A Paid Search Forecast<\/strong> predicts what portion of that demand you can capture through paid search given budgets, competition, and conversion performance.<\/li>\n<\/ul>\n\n\n\n

                                          Who Should Learn Paid Search Forecast<\/h2>\n\n\n\n
                                            \n
                                          • Marketers and growth leads:<\/strong> To translate goals into budgets and set targets that reflect SEM \/ Paid Search<\/strong> realities.<\/li>\n
                                          • Analysts:<\/strong> To build models, quantify uncertainty, and connect ad performance to business outcomes in Paid Marketing<\/strong>.<\/li>\n
                                          • Agencies:<\/strong> To justify recommendations, set client expectations, and improve retention through transparent planning.<\/li>\n
                                          • Business owners and founders:<\/strong> To understand what paid search can realistically deliver before committing spend.<\/li>\n
                                          • Developers and data teams:<\/strong> To support measurement integrity, CRM integrations, and automated reporting that make forecasts trustworthy.<\/li>\n<\/ul>\n\n\n\n

                                            Summary of Paid Search Forecast<\/h2>\n\n\n\n

                                            A Paid Search Forecast<\/strong> is an estimate of future paid search performance\u2014spend, clicks, conversions, and often revenue\u2014based on historical data and explicit assumptions. It matters because it improves planning, aligns expectations, and reduces waste in Paid Marketing<\/strong>. Within SEM \/ Paid Search<\/strong>, it guides budget allocation, scaling decisions, and scenario planning by showing what\u2019s likely to happen at different spend levels and under different auction conditions.<\/p>\n\n\n\n

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

                                            1) What is a Paid Search Forecast used for?<\/h3>\n\n\n\n

                                            A Paid Search Forecast<\/strong> is used to predict outcomes (spend, clicks, conversions, revenue) so teams can plan budgets, set realistic targets, and evaluate scenarios before investing more in SEM \/ Paid Search<\/strong>.<\/p>\n\n\n\n

                                            2) How accurate can a Paid Search Forecast be?<\/h3>\n\n\n\n

                                            Accuracy depends on data quality, account stability, and how well assumptions match reality. The most reliable approach is to provide ranges (scenarios) and recalibrate frequently using forecast vs. actual results.<\/p>\n\n\n\n

                                            3) What inputs matter most in SEM \/ Paid Search forecasting?<\/h3>\n\n\n\n

                                            In SEM \/ Paid Search<\/strong>, the highest-impact inputs are usually CPC, conversion rate, impression share constraints, and conversion value (or lead quality). Small changes in CPC or CVR can dramatically change CPA and ROAS.<\/p>\n\n\n\n

                                            4) Should I forecast at keyword level or campaign level?<\/h3>\n\n\n\n

                                            If the account is large, start with intent-based segments (brand, non-brand, competitor, core categories) and forecast at campaign or theme level. Keyword-level forecasts can be useful for high-spend terms, but they\u2019re harder to maintain.<\/p>\n\n\n\n

                                            5) How do I include seasonality in a Paid Search Forecast?<\/h3>\n\n\n\n

                                            Use historical year-over-year patterns where possible, then adjust for known changes (promotions, new competitors, product launches). In Paid Marketing<\/strong>, document seasonality assumptions explicitly so stakeholders understand the \u201cwhy.\u201d<\/p>\n\n\n\n

                                            6) What\u2019s the difference between forecasting conversions and forecasting revenue?<\/h3>\n\n\n\n

                                            Conversion forecasts predict volume (leads or purchases). Revenue forecasts add average order value, lead-to-sale rates, or lifetime value assumptions. Revenue forecasting is more useful for Paid Marketing<\/strong> decisions, but it requires stronger measurement and CRM alignment.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                            A **Paid Search Forecast** is a structured estimate of what your search ads are likely to deliver\u2014spend, clicks, conversions, and revenue\u2014over a defined time period and set of assumptions. In **Paid Marketing**, forecasting turns \u201cwe think this will work\u201d into an evidence-based plan for budgets, targets, and trade-offs. Inside **SEM \/ Paid Search**, it helps teams decide which keywords to bid on, how aggressively to scale, and what performance is realistic given auction dynamics.<\/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":[1913],"tags":[],"class_list":["post-11199","post","type-post","status-publish","format-standard","hentry","category-sem-paid-search"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/11199","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=11199"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/11199\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=11199"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=11199"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=11199"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}