{"id":7183,"date":"2026-03-24T03:18:57","date_gmt":"2026-03-24T03:18:57","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/quantitative-research\/"},"modified":"2026-03-24T03:18:57","modified_gmt":"2026-03-24T03:18:57","slug":"quantitative-research","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/quantitative-research\/","title":{"rendered":"Quantitative Research: What It Is, Key Features, Benefits, Use Cases, and How It Fits in CRO"},"content":{"rendered":"\n

Quantitative Research is the discipline of using numbers\u2014measured behaviors, outcomes, and statistical patterns\u2014to answer marketing questions with evidence rather than opinion. In Conversion & Measurement<\/strong>, it shows you what is happening<\/em> across channels and journeys: which pages convert, which segments churn, where drop-offs occur, and how changes affect revenue.<\/p>\n\n\n\n

In CRO<\/strong>, Quantitative Research is the foundation for prioritization and validation. It helps teams decide what to test, estimate impact, size experiments correctly, and interpret results without being misled by randomness or biased samples. As tracking, privacy, and multi-touch journeys become more complex, modern Conversion & Measurement<\/strong> strategy relies on strong Quantitative Research to keep decision-making honest and scalable.<\/p>\n\n\n\n

What Is Quantitative Research?<\/h2>\n\n\n\n

Quantitative Research is a structured approach to answering questions using measurable data\u2014counts, rates, distributions, and statistical inference. In marketing and product, it typically draws from analytics events, ad metrics, CRM data, experiments, surveys with closed-ended questions, and transactional systems.<\/p>\n\n\n\n

The core concept is simple: if you can measure it reliably, you can analyze it systematically<\/strong>. Instead of \u201cusers seem confused,\u201d Quantitative Research asks: What percentage of users drop at step 3? How does that vary by device, traffic source, or new vs. returning?<\/em> It converts observations into metrics you can track over time.<\/p>\n\n\n\n

From a business perspective, Quantitative Research connects customer behavior to outcomes such as leads, purchases, retention, and lifetime value. In Conversion & Measurement<\/strong>, it supports monitoring performance, diagnosing funnel issues, forecasting, and proving ROI. Within CRO<\/strong>, it enables data-backed hypotheses, better test design, and clearer decisions about whether to ship, iterate, or roll back changes.<\/p>\n\n\n\n

Why Quantitative Research Matters in Conversion & Measurement<\/h2>\n\n\n\n

Quantitative Research matters because growth decisions are expensive. Creative, engineering, and media spend can rise quickly, and without solid Conversion & Measurement<\/strong>, teams can optimize the wrong thing.<\/p>\n\n\n\n

Key reasons it\u2019s strategically important:<\/p>\n\n\n\n

    \n
  • Prioritization with impact<\/strong>: Quantitative Research identifies the biggest leaks and highest-opportunity segments so CRO<\/strong> efforts focus where they\u2019ll matter most.<\/li>\n
  • Credible performance narratives<\/strong>: Stakeholders want proof. Quantitative Research turns performance changes into measurable deltas and confidence intervals rather than anecdotes.<\/li>\n
  • Competitive advantage<\/strong>: Faster learning cycles and better measurement create compounding gains\u2014especially when competitors rely on intuition or vanity metrics.<\/li>\n
  • Cross-channel clarity<\/strong>: In Conversion & Measurement<\/strong>, it helps reconcile how paid, organic, email, and product changes contribute to outcomes, even when attribution is imperfect.<\/li>\n
  • Risk reduction<\/strong>: Quantitative Research reveals variability, seasonality, and regression-to-the-mean so you avoid \u201cfalse wins\u201d and costly misinterpretations.<\/li>\n<\/ul>\n\n\n\n

    How Quantitative Research Works<\/h2>\n\n\n\n

    In practice, Quantitative Research is less a single method and more a repeatable workflow that turns raw signals into decisions. A useful model is:<\/p>\n\n\n\n

      \n
    1. \n

      Input (questions + data sources)<\/strong>\n You start with a decision you need to make (e.g., \u201cWhich landing page should we redesign first?\u201d) and identify the data needed: web analytics events, funnel steps, ad click data, CRM outcomes, experiment logs, or survey responses.<\/p>\n<\/li>\n

    2. \n

      Processing (cleaning + defining metrics)<\/strong>\n Data must be made comparable and trustworthy. That includes consistent event naming, filtering bots, handling missing values, choosing time windows, and defining metrics such as conversion rate, revenue per visitor, or activation rate. This is where Conversion & Measurement<\/strong> quality is either built or broken.<\/p>\n<\/li>\n

    3. \n

      Analysis (descriptive + diagnostic + inferential)<\/strong>\n You summarize what\u2019s happening (descriptive), find drivers and segments (diagnostic), and\u2014when appropriate\u2014use statistical methods to estimate differences or causal effects (inferential). In CRO<\/strong>, this often includes experiment analysis, cohort comparisons, and sensitivity checks.<\/p>\n<\/li>\n

    4. \n

      Application (decisions + experimentation)<\/strong>\n Insights become actions: prioritize a test backlog, change targeting, revise onboarding, or adjust pricing presentation. Quantitative Research also informs sample sizes, guardrails, and stopping rules.<\/p>\n<\/li>\n

    5. \n

      Output (learning + monitoring)<\/strong>\n The output is not just a report\u2014it\u2019s improved decisions, documented learnings, and monitoring dashboards that ensure changes hold over time. Strong Conversion & Measurement<\/strong> keeps results auditable and repeatable.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

      Key Components of Quantitative Research<\/h2>\n\n\n\n

      Effective Quantitative Research in Conversion & Measurement<\/strong> depends on several building blocks working together:<\/p>\n\n\n\n

      Data inputs<\/h3>\n\n\n\n
        \n
      • Behavioral data<\/strong>: pageviews, clicks, scrolls, form submissions, product events <\/li>\n
      • Outcome data<\/strong>: orders, qualified leads, renewals, churn, refunds <\/li>\n
      • Marketing data<\/strong>: impressions, clicks, spend, campaign parameters <\/li>\n
      • Customer data<\/strong>: CRM lifecycle stages, segments, account attributes <\/li>\n
      • Research survey data<\/strong>: structured, closed-ended responses (e.g., ratings, multiple choice)<\/li>\n<\/ul>\n\n\n\n

        Systems and processes<\/h3>\n\n\n\n
          \n
        • Tracking plan and event taxonomy<\/strong>: what you track, how it\u2019s named, and where it\u2019s collected <\/li>\n
        • Data quality checks<\/strong>: validation rules, anomaly detection, and change logs <\/li>\n
        • Experimentation program<\/strong>: hypothesis templates, test governance, and analysis standards <\/li>\n
        • Documentation<\/strong>: metric definitions, dashboard logic, and decision records<\/li>\n<\/ul>\n\n\n\n

          Metrics and analysis methods<\/h3>\n\n\n\n
            \n
          • Funnel and cohort analysis, segmentation, correlation and regression, and experiment inference. For CRO<\/strong>, the key is choosing methods that match the question and the data\u2019s limitations.<\/li>\n<\/ul>\n\n\n\n

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

            Quantitative Research improves when teams clarify ownership:\n– Marketing owns channel tagging and campaign taxonomy\n– Product\/engineering owns instrumentation integrity\n– Analytics owns definitions, pipelines, and analysis standards\n– Leadership aligns on what \u201csuccess\u201d means and which metrics are primary vs. supporting<\/p>\n\n\n\n

            Types of Quantitative Research<\/h2>\n\n\n\n

            While \u201ctypes\u201d can vary by discipline, the most useful distinctions in Conversion & Measurement<\/strong> and CRO<\/strong> are:<\/p>\n\n\n\n

            Descriptive research<\/h3>\n\n\n\n

            Summarizes what is happening: traffic trends, conversion rates, funnel drop-offs, distribution of order values. It answers \u201cwhat\u201d and \u201chow much.\u201d<\/p>\n\n\n\n

            Diagnostic research<\/h3>\n\n\n\n

            Explains why patterns might be occurring by breaking results into segments or drivers: device, channel, cohort, geography, new vs. returning. It answers \u201cwhat\u2019s driving it.\u201d<\/p>\n\n\n\n

            Causal (experimental) research<\/h3>\n\n\n\n

            Determines whether a change caused an outcome, typically via A\/B tests, multivariate tests (carefully), or controlled rollouts. This is central to CRO<\/strong>.<\/p>\n\n\n\n

            Predictive research<\/h3>\n\n\n\n

            Uses historical data to forecast outcomes: lead-to-close probability, expected revenue per session, or churn risk. In Conversion & Measurement<\/strong>, it supports planning and budgeting.<\/p>\n\n\n\n

            Attitudinal quantitative research<\/h3>\n\n\n\n

            Structured surveys that quantify beliefs and preferences (e.g., \u201crate your agreement\u201d questions). It\u2019s not a replacement for qualitative interviews, but it scales well and can be segmented.<\/p>\n\n\n\n

            Real-World Examples of Quantitative Research<\/h2>\n\n\n\n

            Example 1: Funnel leak diagnosis for a SaaS signup flow<\/h3>\n\n\n\n

            A SaaS team notices flat growth despite higher traffic. Quantitative Research in Conversion & Measurement<\/strong> reveals a sharp drop at email verification on mobile. Segmenting by device and source shows paid social mobile traffic is most affected. In CRO<\/strong>, the team prioritizes tests around verification timing, copy clarity, and alternative verification methods, using activation rate as the primary metric and support tickets as a guardrail.<\/p>\n\n\n\n

            Example 2: Landing page optimization for lead generation<\/h3>\n\n\n\n

            An agency audits B2B landing pages and finds conversion rate varies heavily by industry segment. Quantitative Research ties higher conversion to pages with shorter forms and stronger proof points. The CRO<\/strong> roadmap becomes segment-based: create tailored pages for top industries, run controlled tests, and track lead quality via CRM stage progression\u2014connecting Conversion & Measurement<\/strong> to downstream revenue, not just form fills.<\/p>\n\n\n\n

            Example 3: E-commerce checkout performance and profitability<\/h3>\n\n\n\n

            An e-commerce brand sees increased conversions after adding a discount banner, but profit declines. Quantitative Research compares cohorts and finds average order value dropped while refund rate increased. In Conversion & Measurement<\/strong>, the team updates dashboards to include margin and return rates. In CRO<\/strong>, they test eligibility rules and messaging that preserves conversion while protecting profitability.<\/p>\n\n\n\n

            Benefits of Using Quantitative Research<\/h2>\n\n\n\n

            When done well, Quantitative Research delivers benefits that compound:<\/p>\n\n\n\n

              \n
            • Higher conversion performance<\/strong>: Better funnel visibility and experiment rigor improve win rates in CRO<\/strong>.<\/li>\n
            • Lower wasted spend<\/strong>: Accurate measurement reduces spend on underperforming channels and misattributed campaigns.<\/li>\n
            • Faster decision-making<\/strong>: Clear dashboards and consistent definitions reduce debates and rework.<\/li>\n
            • Improved customer experience<\/strong>: Identifying friction points (slow pages, confusing steps, broken paths) leads to smoother journeys.<\/li>\n
            • Scalable learning<\/strong>: Quantitative Research produces reusable insights (segments, baselines, seasonality) that strengthen future Conversion & Measurement<\/strong> planning.<\/li>\n<\/ul>\n\n\n\n

              Challenges of Quantitative Research<\/h2>\n\n\n\n

              Quantitative Research is powerful, but it fails when teams ignore its constraints:<\/p>\n\n\n\n

                \n
              • Tracking gaps and inconsistent definitions<\/strong>: If \u201cconversion\u201d differs across tools, your Conversion & Measurement<\/strong> reporting becomes unreliable.<\/li>\n
              • Attribution limitations<\/strong>: Multi-device journeys, walled gardens, and privacy restrictions reduce certainty in channel contribution.<\/li>\n
              • Sampling bias<\/strong>: On-site surveys may overrepresent certain users; experiments can be skewed by targeting or traffic changes.<\/li>\n
              • False certainty<\/strong>: Statistical significance is not the same as business impact; small lifts can be noise, and big lifts can regress.<\/li>\n
              • Metric myopia<\/strong>: Over-optimizing a single KPI can harm long-term outcomes (e.g., conversion up, retention down).<\/li>\n
              • Data latency and governance<\/strong>: Delayed CRM outcomes or poor pipeline hygiene can weaken conclusions.<\/li>\n<\/ul>\n\n\n\n

                In CRO<\/strong>, these challenges commonly show up as underpowered tests, too many variants, or \u201cpeeking\u201d at results and stopping early.<\/p>\n\n\n\n

                Best Practices for Quantitative Research<\/h2>\n\n\n\n

                Practical habits improve accuracy and usefulness:<\/p>\n\n\n\n

                  \n
                1. \n

                  Start with decisions, not dashboards<\/strong>\n Define what decision the research will inform (test prioritization, budget shift, UX change) and which metric will decide it.<\/p>\n<\/li>\n

                2. \n

                  Use clear metric hierarchies<\/strong>\n Choose one primary metric, a few secondary metrics, and guardrails (e.g., refunds, churn, bounce rate). This improves Conversion & Measurement<\/strong> discipline and reduces cherry-picking in CRO<\/strong>.<\/p>\n<\/li>\n

                3. \n

                  Instrument intentionally<\/strong>\n Maintain an event taxonomy, validate tracking after releases, and document changes. Treat tracking like product infrastructure.<\/p>\n<\/li>\n

                4. \n

                  Segment thoughtfully<\/strong>\n Segment by factors that plausibly change behavior (device, intent, returning status), not just because the data allows it.<\/p>\n<\/li>\n

                5. \n

                  Respect statistical power and validity<\/strong>\n Plan sample sizes, define stopping rules, and avoid running too many simultaneous tests on the same audience unless you can control interference.<\/p>\n<\/li>\n

                6. \n

                  Connect top-of-funnel to outcomes<\/strong>\n Tie marketing actions to qualified leads, revenue, retention, and margin\u2014key to credible Conversion & Measurement<\/strong>.<\/p>\n<\/li>\n

                7. \n

                  Operationalize learning<\/strong>\n Keep a test and insights library: hypothesis, results, context, screenshots, and implementation status. This raises maturity in CRO<\/strong> over time.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                  Tools Used for Quantitative Research<\/h2>\n\n\n\n

                  Quantitative Research is enabled by tool categories rather than any single platform:<\/p>\n\n\n\n

                    \n
                  • Analytics tools<\/strong>: event and session analytics, funnels, cohorts, pathing, segmentation <\/li>\n
                  • Tag management and instrumentation<\/strong>: centralized control of tags, events, and consent logic to improve Conversion & Measurement<\/strong> consistency <\/li>\n
                  • Experimentation platforms<\/strong>: A\/B testing and feature flagging to run controlled CRO<\/strong> experiments and staged rollouts <\/li>\n
                  • Data warehouses and pipelines<\/strong>: consolidating web, product, ad, and CRM data for deeper analysis and governance <\/li>\n
                  • BI and reporting dashboards<\/strong>: standardized KPI reporting, alerting, and stakeholder visibility <\/li>\n
                  • CRM systems<\/strong>: lead status, pipeline stages, revenue outcomes\u2014critical for closing the loop <\/li>\n
                  • Ad platforms and campaign management<\/strong>: impression\/click\/spend performance and audience segmentation inputs <\/li>\n
                  • SEO tools<\/strong>: keyword and landing page performance data that informs where organic traffic converts best (useful for CRO<\/strong> prioritization)<\/li>\n<\/ul>\n\n\n\n

                    The most important \u201ctool\u201d is often a shared measurement framework: consistent definitions, QA routines, and documentation.<\/p>\n\n\n\n

                    Metrics Related to Quantitative Research<\/h2>\n\n\n\n

                    Because Quantitative Research supports Conversion & Measurement<\/strong>, it typically focuses on metrics that link behavior to outcomes:<\/p>\n\n\n\n

                    Conversion and revenue metrics<\/h3>\n\n\n\n
                      \n
                    • Conversion rate (session-to-lead, lead-to-customer, checkout completion)<\/li>\n
                    • Revenue per visitor \/ revenue per session<\/li>\n
                    • Average order value (AOV) and units per transaction<\/li>\n
                    • Lead-to-opportunity rate and opportunity-to-close rate<\/li>\n<\/ul>\n\n\n\n

                      Efficiency and ROI metrics<\/h3>\n\n\n\n
                        \n
                      • Cost per acquisition (CPA) and cost per lead (CPL)<\/li>\n
                      • Return on ad spend (ROAS) and marketing efficiency ratio (where applicable)<\/li>\n
                      • Payback period and lifetime value (LTV) to CAC ratio (when measurable responsibly)<\/li>\n<\/ul>\n\n\n\n

                        Funnel quality and experience metrics<\/h3>\n\n\n\n
                          \n
                        • Step-to-step drop-off rates<\/li>\n
                        • Time to convert \/ time to first value<\/li>\n
                        • Repeat purchase rate, retention rate, churn rate<\/li>\n
                        • Refund\/return rate and support contact rate (as guardrails in CRO<\/strong>)<\/li>\n<\/ul>\n\n\n\n

                          Measurement health metrics<\/h3>\n\n\n\n
                            \n
                          • Event coverage and missing-event rates<\/li>\n
                          • Tag firing accuracy and consent opt-in rates<\/li>\n
                          • Data freshness\/latency and anomaly frequency<\/li>\n<\/ul>\n\n\n\n

                            Future Trends of Quantitative Research<\/h2>\n\n\n\n

                            Quantitative Research is evolving quickly inside Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                              \n
                            • AI-assisted analysis<\/strong>: Automated anomaly detection, faster segmentation, and natural-language querying will speed up insight generation\u2014if definitions and governance are strong.<\/li>\n
                            • Privacy-driven measurement changes<\/strong>: Less granular identifiers and more consent constraints will push teams toward modeled conversions, aggregated reporting, and first-party data strategies.<\/li>\n
                            • Server-side and durable instrumentation<\/strong>: More resilient tracking architectures will help maintain data quality amid browser and platform changes.<\/li>\n
                            • Personalization with experimentation discipline<\/strong>: More dynamic experiences will require stronger CRO<\/strong> guardrails to avoid \u201calways-on changes\u201d that aren\u2019t measurable.<\/li>\n
                            • Incrementality focus<\/strong>: As attribution remains imperfect, more organizations will emphasize controlled tests and lift studies to validate true impact.<\/li>\n
                            • Blended research stacks<\/strong>: Teams will combine Quantitative Research with qualitative insights to understand both \u201cwhat happened\u201d and \u201cwhy it happened,\u201d improving actionability.<\/li>\n<\/ul>\n\n\n\n

                              Quantitative Research vs Related Terms<\/h2>\n\n\n\n

                              Quantitative Research vs Qualitative Research<\/h3>\n\n\n\n

                              Quantitative Research measures how much<\/em> and how often<\/em> at scale; qualitative research explores why<\/em> through interviews, usability tests, and open-ended feedback. In CRO<\/strong>, quantitative points to where friction is; qualitative helps explain the human reasons behind it. The best programs use both.<\/p>\n\n\n\n

                              Quantitative Research vs A\/B Testing<\/h3>\n\n\n\n

                              A\/B testing is one method within Quantitative Research, focused on causal inference. Quantitative Research also includes descriptive analytics, cohort analysis, and survey quantification\u2014work you often do before and after experiments in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                              Quantitative Research vs Analytics Reporting<\/h3>\n\n\n\n

                              Reporting summarizes metrics; Quantitative Research answers questions with a structured approach, including hypothesis formation, bias checks, and interpretation. Reporting is a component of Conversion & Measurement<\/strong>; research is the discipline that turns reporting into decisions.<\/p>\n\n\n\n

                              Who Should Learn Quantitative Research<\/h2>\n\n\n\n
                                \n
                              • Marketers<\/strong> benefit by understanding which campaigns truly drive outcomes and how to build credible Conversion & Measurement<\/strong> narratives.<\/li>\n
                              • Analysts<\/strong> use Quantitative Research to standardize definitions, prevent misinterpretation, and raise the quality of CRO<\/strong> decisions.<\/li>\n
                              • Agencies<\/strong> need it to justify recommendations, prove impact, and avoid optimizing surface-level KPIs that don\u2019t translate to revenue.<\/li>\n
                              • Business owners and founders<\/strong> use it to prioritize investments and avoid scaling what isn\u2019t working.<\/li>\n
                              • Developers and product teams<\/strong> benefit because instrumentation, experimentation, and data quality are engineering-adjacent; strong Quantitative Research reduces guesswork and rework.<\/li>\n<\/ul>\n\n\n\n

                                Summary of Quantitative Research<\/h2>\n\n\n\n

                                Quantitative Research is the practice of using measurable data and statistical reasoning to answer marketing and product questions with confidence. It is a cornerstone of Conversion & Measurement<\/strong>, enabling accurate funnel analysis, segmentation, forecasting, and performance monitoring. In CRO<\/strong>, Quantitative Research supports smarter prioritization, better experiment design, and more reliable interpretation\u2014so optimization decisions improve conversions without sacrificing long-term business health.<\/p>\n\n\n\n

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

                                1) What is Quantitative Research in digital marketing?<\/h3>\n\n\n\n

                                Quantitative Research is the use of measurable data\u2014such as conversion rates, cohorts, and experiment results\u2014to answer marketing questions and guide decisions within Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                2) How does Quantitative Research support CRO?<\/h3>\n\n\n\n

                                In CRO<\/strong>, Quantitative Research identifies where users drop off, helps estimate potential impact, determines sample sizes, and validates whether a change actually improved conversions with acceptable risk.<\/p>\n\n\n\n

                                3) Do I need A\/B testing to do Quantitative Research?<\/h3>\n\n\n\n

                                No. A\/B testing is one powerful method, but Quantitative Research also includes funnel analysis, segmentation, cohort tracking, and structured surveys. Many Conversion & Measurement<\/strong> insights come from these methods even before testing.<\/p>\n\n\n\n

                                4) What\u2019s the difference between correlation and causation in CRO analysis?<\/h3>\n\n\n\n

                                Correlation means two metrics move together; it doesn\u2019t prove one caused the other. Causation requires stronger evidence, typically controlled experimentation. Confusing the two is a common CRO<\/strong> mistake in Quantitative Research.<\/p>\n\n\n\n

                                5) Which data sources are most important for Conversion & Measurement?<\/h3>\n\n\n\n

                                Core sources include analytics events, ad performance data, and CRM\/revenue outcomes. Connecting these sources is essential so Quantitative Research reflects real business impact, not just on-site behavior.<\/p>\n\n\n\n

                                6) How do I avoid misleading conclusions from small sample sizes?<\/h3>\n\n\n\n

                                Plan for statistical power, avoid stopping tests early, and focus on effect sizes and confidence intervals\u2014not just \u201csignificance.\u201d This discipline improves Quantitative Research reliability in CRO<\/strong> and Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                7) Can Quantitative Research work with privacy limitations and incomplete attribution?<\/h3>\n\n\n\n

                                Yes. It may rely more on first-party data, aggregated reporting, modeled conversions, and incrementality testing. The goal remains the same: make decisions using the best available evidence and transparent assumptions in Conversion & Measurement<\/strong>.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                Quantitative Research is the discipline of using numbers\u2014measured behaviors, outcomes, and statistical patterns\u2014to answer marketing questions with evidence rather than opinion. In **Conversion & Measurement**, it shows you *what is happening* across channels and journeys: which pages convert, which segments churn, where drop-offs occur, and how changes affect revenue.<\/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":[1889],"tags":[],"class_list":["post-7183","post","type-post","status-publish","format-standard","hentry","category-cro"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7183","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=7183"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7183\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7183"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7183"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7183"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}