{"id":6963,"date":"2026-03-23T19:22:09","date_gmt":"2026-03-23T19:22:09","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/tableau\/"},"modified":"2026-03-23T19:22:09","modified_gmt":"2026-03-23T19:22:09","slug":"tableau","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/tableau\/","title":{"rendered":"Tableau: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Analytics"},"content":{"rendered":"\n

Tableau is a data visualization and business intelligence platform that helps teams explore, model, and communicate insights from data. In Conversion & Measurement<\/strong>, it\u2019s often used to unify performance data from multiple channels (paid, organic, email, product, CRM) and translate that complexity into dashboards and analyses that business stakeholders can actually act on. In Analytics<\/strong>, Tableau sits at the \u201clast mile\u201d of insight: it doesn\u2019t replace data collection or tracking, but it makes measurement understandable, comparable, and decision-ready.<\/p>\n\n\n\n

Tableau matters in modern Conversion & Measurement<\/strong> because marketing performance is increasingly multi-touch, cross-device, and privacy-constrained. Even when your tracking is solid, teams still struggle with questions like: Which campaigns drive qualified leads? Where does the funnel leak? How do conversion rates vary by audience and creative?<\/em> Tableau helps answer these questions by making data relationships visible, measurable, and shareable\u2014so optimization is based on evidence rather than opinions.<\/p>\n\n\n\n

What Is Tableau?<\/h2>\n\n\n\n

Tableau is a tool for analyzing data and building interactive visualizations\u2014charts, tables, maps, and dashboards\u2014that support data-driven decisions. For beginners, the simplest way to think about Tableau is: it connects to your data, lets you explore it visually, and helps you publish insights in a format others can use.<\/strong><\/p>\n\n\n\n

The core concept is visual analysis: instead of reading raw tables, you work with fields (dimensions and measures), filters, aggregations, and calculated metrics to discover patterns and trends. Tableau supports both self-serve analysis and governed, reusable reporting, depending on how an organization sets it up.<\/p>\n\n\n\n

From a business perspective, Tableau is valuable because it turns fragmented performance data into shared definitions and consistent reporting. In Conversion & Measurement<\/strong>, Tableau is commonly used to:<\/p>\n\n\n\n

    \n
  • Monitor funnel performance (sessions \u2192 leads \u2192 opportunities \u2192 revenue)<\/li>\n
  • Compare channel efficiency (CPA, ROAS, CAC, payback)<\/li>\n
  • Segment conversion rates by audience, geography, device, landing page, or creative<\/li>\n
  • Track experiment results and rollout impacts<\/li>\n<\/ul>\n\n\n\n

    Within Analytics<\/strong>, Tableau typically acts as the visualization and insight layer on top of data pipelines, warehouses, and tracking systems.<\/p>\n\n\n\n

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

    In practical marketing operations, measurement breaks down when data lives in silos and each team uses different definitions. Tableau matters because it enables a shared measurement narrative across stakeholders.<\/p>\n\n\n\n

    Strategically, Tableau supports Conversion & Measurement<\/strong> by:<\/p>\n\n\n\n

      \n
    • Aligning teams on definitions:<\/strong> \u201cLead,\u201d \u201cMQL,\u201d \u201cSQL,\u201d and \u201cRevenue\u201d can be standardized into a single reporting layer instead of debated weekly.<\/li>\n
    • Connecting performance to business outcomes:<\/strong> Dashboards can move beyond clicks to show downstream outcomes like pipeline, retention, or lifetime value.<\/li>\n
    • Improving speed to insight:<\/strong> Analysts can answer ad hoc questions faster without building a new report every time.<\/li>\n
    • Creating competitive advantage:<\/strong> Organizations that see performance shifts early\u2014creative fatigue, audience saturation, landing page friction\u2014optimize sooner and waste less budget.<\/li>\n<\/ul>\n\n\n\n

      In terms of marketing outcomes, Tableau supports better decisions on budget allocation, audience strategy, funnel optimization, and experiment prioritization\u2014all core to Conversion & Measurement<\/strong> and day-to-day Analytics<\/strong> work.<\/p>\n\n\n\n

      How Tableau Works<\/h2>\n\n\n\n

      Tableau is best understood as a workflow that connects data to decisions:<\/p>\n\n\n\n

        \n
      1. \n

        Input (data connections)<\/strong>\n Tableau connects to data sources such as spreadsheets, databases, data warehouses, CRM exports, or marketing performance datasets. This is where raw events, costs, conversions, and customer attributes enter the analysis.<\/p>\n<\/li>\n

      2. \n

        Processing (modeling and logic)<\/strong>\n Users define relationships, joins, and calculations to create metrics that match business logic. In Conversion & Measurement<\/strong>, this often includes calculated fields for conversion rate, blended CAC, attribution assumptions, or cohort-based retention.<\/p>\n<\/li>\n

      3. \n

        Execution (visual analysis and dashboard design)<\/strong>\n Users build worksheets and dashboards, apply filters, create segments, and design views that answer specific questions (e.g., \u201cWhich landing pages have high traffic but low form completion?\u201d).<\/p>\n<\/li>\n

      4. \n

        Output (insights and operational reporting)<\/strong>\n Tableau outputs interactive dashboards and reports that stakeholders can consume on a schedule, in meetings, or during campaign optimization cycles. The outcome is faster, more consistent Analytics<\/strong> and better-informed Conversion & Measurement<\/strong> decisions.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        In practice, Tableau\u2019s effectiveness depends less on chart style and more on data quality, metric definitions, and governance.<\/p>\n\n\n\n

        Key Components of Tableau<\/h2>\n\n\n\n

        A strong Tableau implementation for Conversion & Measurement<\/strong> typically includes the following components:<\/p>\n\n\n\n

        Data inputs and sources<\/h3>\n\n\n\n
          \n
        • Web and product usage data (sessions, events, conversions)<\/li>\n
        • Advertising performance data (impressions, clicks, spend)<\/li>\n
        • CRM and sales data (leads, opportunities, revenue)<\/li>\n
        • Email and lifecycle data (deliverability, engagement, conversions)<\/li>\n
        • Content and SEO performance data (rankings, traffic, assisted conversions)<\/li>\n<\/ul>\n\n\n\n

          Data modeling and semantic layer<\/h3>\n\n\n\n
            \n
          • Defined joins\/relationships between cost, click, lead, and revenue tables<\/li>\n
          • Standardized date handling (time zones, fiscal calendars, cohort windows)<\/li>\n
          • Calculated fields for metrics that match the business definition<\/li>\n<\/ul>\n\n\n\n

            Visual building blocks<\/h3>\n\n\n\n
              \n
            • Worksheets (single charts\/tables)<\/li>\n
            • Dashboards (collections of worksheets with shared filters)<\/li>\n
            • Stories (guided narratives for presentations, less common in operations)<\/li>\n<\/ul>\n\n\n\n

              Governance and responsibilities<\/h3>\n\n\n\n
                \n
              • Metric owners who define and approve KPIs (e.g., \u201cPaid CAC\u201d)<\/li>\n
              • Data owners who maintain pipelines and source integrity<\/li>\n
              • Dashboard owners who curate views, document definitions, and manage access<\/li>\n<\/ul>\n\n\n\n

                This combination is what makes Tableau effective within Analytics<\/strong> and reliable for Conversion & Measurement<\/strong> reporting.<\/p>\n\n\n\n

                Types of Tableau<\/h2>\n\n\n\n

                Tableau doesn\u2019t have \u201ctypes\u201d in the way a measurement model does, but there are practical distinctions in how Tableau is used:<\/p>\n\n\n\n

                Self-serve exploratory analysis<\/h3>\n\n\n\n

                Analysts and marketers explore data to answer questions, find anomalies, and test hypotheses. This mode is common when diagnosing conversion drops or investigating channel shifts.<\/p>\n\n\n\n

                Operational dashboards (always-on reporting)<\/h3>\n\n\n\n

                Recurring dashboards track KPIs such as pipeline, conversion rates, and spend efficiency. This is the backbone of ongoing Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                Executive scorecards<\/h3>\n\n\n\n

                High-level views focusing on a small set of business KPIs (revenue, CAC, ROAS, funnel velocity), often with simplified filters and strong annotation to avoid misinterpretation.<\/p>\n\n\n\n

                Governed vs. ad hoc environments<\/h3>\n\n\n\n
                  \n
                • Governed:<\/strong> Certified data sources, controlled metric definitions, standardized filters<\/li>\n
                • Ad hoc:<\/strong> Flexible exploration, faster iteration, higher risk of inconsistent definitions<\/li>\n<\/ul>\n\n\n\n

                  Most organizations need both: governed reporting for consistency and ad hoc analysis for speed.<\/p>\n\n\n\n

                  Real-World Examples of Tableau<\/h2>\n\n\n\n

                  Example 1: Multi-channel funnel dashboard for lead quality<\/h3>\n\n\n\n

                  A B2B company combines paid search spend, web conversions, and CRM outcomes to track the funnel from click to closed-won revenue. Tableau dashboards show conversion rate by campaign, but also opportunity rate and win rate\u2014preventing budget from shifting to \u201ccheap leads\u201d that never become pipeline. This is classic Conversion & Measurement<\/strong> supported by end-to-end Analytics<\/strong>.<\/p>\n\n\n\n

                  Example 2: Landing page diagnostics for conversion rate optimization<\/h3>\n\n\n\n

                  A DTC brand uses Tableau to segment conversion rate by device, page template, and traffic source. The dashboard reveals that mobile traffic from a specific social campaign has high bounce and low add-to-cart. The team tests faster page assets and clearer above-the-fold offers, then tracks the uplift over time. Tableau becomes the shared measurement layer for experimentation and Analytics<\/strong> monitoring.<\/p>\n\n\n\n

                  Example 3: Subscription cohort tracking and retention measurement<\/h3>\n\n\n\n

                  A SaaS company uses Tableau to analyze sign-up cohorts and retention by acquisition channel. Instead of optimizing only for first-touch conversion, they evaluate 30\/60\/90-day retention and LTV, shifting spend toward channels with better downstream value. This improves Conversion & Measurement<\/strong> maturity by connecting acquisition to long-term outcomes.<\/p>\n\n\n\n

                  Benefits of Using Tableau<\/h2>\n\n\n\n

                  Tableau can create measurable improvements across performance, cost, and operational efficiency:<\/p>\n\n\n\n

                    \n
                  • Faster insight cycles:<\/strong> Teams spend less time assembling reports and more time improving performance.<\/li>\n
                  • Better budget allocation:<\/strong> Clear comparisons across channels and segments reduce waste and improve ROI.<\/li>\n
                  • Improved KPI consistency:<\/strong> Standard dashboards reduce conflicting numbers in meetings.<\/li>\n
                  • Stronger collaboration:<\/strong> Marketing, sales, and product can work from the same Analytics<\/strong> view of performance.<\/li>\n
                  • Better audience and customer experience:<\/strong> Segment-level insight helps teams personalize messaging and reduce funnel friction\u2014core goals in Conversion & Measurement<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                    Challenges of Tableau<\/h2>\n\n\n\n

                    Tableau is powerful, but it doesn\u2019t magically fix measurement. Common challenges include:<\/p>\n\n\n\n

                      \n
                    • Data quality and tracking gaps:<\/strong> If events, UTMs, offline conversions, or CRM mappings are inconsistent, dashboards will mislead.<\/li>\n
                    • Metric definition conflicts:<\/strong> \u201cConversion\u201d may mean form submit, qualified lead, or purchase\u2014Tableau will visualize whatever you define, even if it\u2019s wrong.<\/li>\n
                    • Performance and scalability issues:<\/strong> Poorly designed extracts, complex joins, or unoptimized data models can lead to slow dashboards.<\/li>\n
                    • Governance and sprawl:<\/strong> Too many dashboards without ownership leads to duplicated work and inconsistent Analytics<\/strong>.<\/li>\n
                    • Privacy and attribution limitations:<\/strong> Changes in browser tracking and consent requirements can reduce visibility, affecting Conversion & Measurement<\/strong> conclusions.<\/li>\n<\/ul>\n\n\n\n

                      The main risk is false confidence: beautiful dashboards can still represent flawed logic.<\/p>\n\n\n\n

                      Best Practices for Tableau<\/h2>\n\n\n\n

                      Start with a measurement plan<\/h3>\n\n\n\n

                      Before building dashboards, define:\n– Primary business outcomes (revenue, pipeline, profit, retention)\n– Funnel stages and conversion definitions\n– Attribution assumptions and known blind spots\nThis keeps Tableau aligned with real Conversion & Measurement<\/strong> needs.<\/p>\n\n\n\n

                      Build a trusted KPI layer<\/h3>\n\n\n\n

                      Create governed metrics (e.g., \u201cMarketing Qualified Lead,\u201d \u201cPaid CAC\u201d) with:\n– Clear definitions\n– Calculation logic\n– Owner and change history\nThis reduces confusion across Analytics<\/strong> stakeholders.<\/p>\n\n\n\n

                      Design dashboards for decisions, not decoration<\/h3>\n\n\n\n

                      Good operational dashboards:\n– Answer a specific question (budget shift, funnel fix, experiment result)\n– Highlight deltas, trends, and thresholds\n– Include context like date ranges, filters, and segment definitions<\/p>\n\n\n\n

                      Validate with reconciliation checks<\/h3>\n\n\n\n

                      Regularly reconcile Tableau outputs against source systems:\n– Spend totals vs. finance\/ad platform reporting\n– Lead counts vs. CRM\n– Revenue vs. billing data\nThis is essential for trustworthy Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                      Operationalize monitoring<\/h3>\n\n\n\n

                      Use consistent review cadences:\n– Daily anomaly checks for spend\/conversions\n– Weekly funnel health reviews\n– Monthly executive scorecards\nTableau becomes more valuable when embedded into routines.<\/p>\n\n\n\n

                      Document assumptions and limitations<\/h3>\n\n\n\n

                      If certain channels can\u2019t be attributed reliably, say so. Transparency improves decision quality in Analytics<\/strong> and reduces stakeholder mistrust.<\/p>\n\n\n\n

                      Tools Used for Tableau<\/h2>\n\n\n\n

                      Tableau typically sits within a broader measurement ecosystem. In Conversion & Measurement<\/strong> and Analytics<\/strong>, teams commonly pair Tableau with:<\/p>\n\n\n\n

                        \n
                      • Data collection and analytics tools:<\/strong> systems that capture events, sessions, and conversion actions; these provide behavioral data for analysis.<\/li>\n
                      • Ad platforms and campaign managers:<\/strong> sources of spend, impressions, clicks, and campaign metadata.<\/li>\n
                      • CRM systems:<\/strong> lead lifecycle stages, opportunity outcomes, and revenue\u2014critical for tying marketing to sales impact.<\/li>\n
                      • Data warehouses and databases:<\/strong> centralized storage and transformation for scalable reporting across channels.<\/li>\n
                      • ETL\/ELT and automation tools:<\/strong> pipelines that move and transform data reliably on schedules.<\/li>\n
                      • Experimentation platforms:<\/strong> A\/B testing results and feature rollout data to measure conversion lift.<\/li>\n
                      • SEO and content measurement tools:<\/strong> keyword, page performance, and technical signals that impact organic conversion paths.<\/li>\n
                      • Governance and documentation systems:<\/strong> metric dictionaries, access controls, and change management processes.<\/li>\n<\/ul>\n\n\n\n

                        Tableau is most effective when it consumes well-modeled, well-governed data rather than raw exports.<\/p>\n\n\n\n

                        Metrics Related to Tableau<\/h2>\n\n\n\n

                        Tableau itself isn\u2019t a metric; it\u2019s the layer that helps you calculate, visualize, and monitor metrics. In Conversion & Measurement<\/strong>, common metric families include:<\/p>\n\n\n\n

                        Funnel and conversion metrics<\/h3>\n\n\n\n
                          \n
                        • Conversion rate (by step and overall)<\/li>\n
                        • Cost per lead \/ cost per acquisition<\/li>\n
                        • Lead-to-opportunity and opportunity-to-customer rates<\/li>\n
                        • Cart abandonment rate or form completion rate<\/li>\n<\/ul>\n\n\n\n

                          Efficiency and ROI metrics<\/h3>\n\n\n\n
                            \n
                          • ROAS or revenue per spend<\/li>\n
                          • CAC and blended CAC<\/li>\n
                          • Payback period<\/li>\n
                          • Contribution margin by channel (when available)<\/li>\n<\/ul>\n\n\n\n

                            Quality and downstream value metrics<\/h3>\n\n\n\n
                              \n
                            • Qualified lead rate<\/li>\n
                            • Pipeline velocity<\/li>\n
                            • Retention rate and churn<\/li>\n
                            • LTV (and LTV:CAC ratio)<\/li>\n<\/ul>\n\n\n\n

                              Operational measurement metrics<\/h3>\n\n\n\n
                                \n
                              • Data freshness (how recent is the data?)<\/li>\n
                              • Dashboard usage (who views what, and how often)<\/li>\n
                              • Error rates or reconciliation differences<\/li>\n<\/ul>\n\n\n\n

                                A mature Analytics<\/strong> program uses Tableau to monitor both performance and measurement reliability.<\/p>\n\n\n\n

                                Future Trends of Tableau<\/h2>\n\n\n\n

                                Tableau\u2019s role in Conversion & Measurement<\/strong> is evolving alongside major industry shifts:<\/p>\n\n\n\n

                                  \n
                                • AI-assisted analysis:<\/strong> Expect more automated explanations, anomaly detection, and suggested insights. The opportunity is faster discovery; the risk is overreliance without validation.<\/li>\n
                                • More governed semantic layers:<\/strong> As organizations standardize metrics, Tableau will increasingly sit on top of curated definitions to reduce inconsistency in Analytics<\/strong>.<\/li>\n
                                • Privacy-first measurement:<\/strong> With tighter consent and reduced third-party tracking, teams will emphasize modeled conversions, aggregated reporting, and first-party data integration. Tableau will be used to reconcile multiple imperfect signals rather than rely on a single \u201csource of truth.\u201d<\/li>\n
                                • Real-time or near-real-time monitoring:<\/strong> Faster pipelines will make Tableau more useful for rapid optimization, especially in high-spend campaigns.<\/li>\n
                                • Deeper personalization measurement:<\/strong> As teams personalize experiences, Tableau will help segment performance by cohort, audience, and journey stage\u2014central to next-generation Conversion & Measurement<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                                  Tableau vs Related Terms<\/h2>\n\n\n\n

                                  Tableau vs Business Intelligence (BI)<\/h3>\n\n\n\n

                                  BI is the broader discipline of turning data into decisions through reporting, dashboards, and governance. Tableau is a platform commonly used to implement BI. In Analytics<\/strong>, BI includes the strategy, processes, and people; Tableau is one of the tools.<\/p>\n\n\n\n

                                  Tableau vs Data Visualization<\/h3>\n\n\n\n

                                  Data visualization is the practice of presenting data visually (charts, graphs, maps). Tableau is a specialized tool that enables data visualization at scale, with interactive dashboards and connections to many data sources. Visualization is a capability; Tableau is a way to deliver it.<\/p>\n\n\n\n

                                  Tableau vs Data Warehouse<\/h3>\n\n\n\n

                                  A data warehouse stores and organizes data for analysis. Tableau doesn\u2019t store data as a warehouse does; it connects to data sources (including warehouses) and helps you analyze and present them. In Conversion & Measurement<\/strong>, the warehouse is often where standardized campaign, cost, and conversion data lives; Tableau is where teams consume it.<\/p>\n\n\n\n

                                  Who Should Learn Tableau<\/h2>\n\n\n\n
                                    \n
                                  • Marketers:<\/strong> To interpret performance beyond surface metrics and make better decisions on targeting, creative, and landing pages within Conversion & Measurement<\/strong>.<\/li>\n
                                  • Analysts:<\/strong> To deliver trustworthy, scalable dashboards and answer ad hoc questions with consistent Analytics<\/strong> logic.<\/li>\n
                                  • Agencies:<\/strong> To unify client reporting, prove impact, and reduce time spent on manual reporting and slide building.<\/li>\n
                                  • Business owners and founders:<\/strong> To track what drives growth, understand unit economics, and spot funnel issues early.<\/li>\n
                                  • Developers and data engineers:<\/strong> To design data models and pipelines that make Tableau dashboards fast, accurate, and governed\u2014critical for reliable Conversion & Measurement<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                                    Summary of Tableau<\/h2>\n\n\n\n

                                    Tableau is a platform for turning data into interactive dashboards and visual analyses that support better decisions. In Conversion & Measurement<\/strong>, it helps teams connect marketing activity to funnel outcomes, identify where conversions improve or degrade, and align stakeholders around consistent KPI definitions. Within Analytics<\/strong>, Tableau often serves as the insight and reporting layer that makes complex, multi-source data usable across the organization.<\/p>\n\n\n\n

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

                                    1) What is Tableau used for in marketing?<\/h3>\n\n\n\n

                                    Tableau is used to analyze marketing performance data and present it in dashboards that track KPIs like conversion rate, CAC, ROAS, and funnel progression\u2014supporting faster optimization in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                    2) Does Tableau replace an analytics tracking tool?<\/h3>\n\n\n\n

                                    No. Tableau typically consumes data collected elsewhere. Tracking tools capture events and conversions; Tableau helps model, visualize, and communicate the Analytics<\/strong> results.<\/p>\n\n\n\n

                                    3) How do I ensure Tableau dashboards show accurate conversion metrics?<\/h3>\n\n\n\n

                                    Start with agreed definitions, reconcile results against source systems (ad platforms, CRM, billing), and document assumptions. Accuracy in Conversion & Measurement<\/strong> depends on both data quality and metric logic.<\/p>\n\n\n\n

                                    4) What data sources are most important to connect for Conversion & Measurement?<\/h3>\n\n\n\n

                                    Most teams benefit from combining ad spend\/campaign data, web or product conversion data, and CRM revenue outcomes. That combination enables end-to-end Analytics<\/strong> from click to customer.<\/p>\n\n\n\n

                                    5) Is Tableau better for dashboards or deep analysis?<\/h3>\n\n\n\n

                                    It can do both. Tableau is often used for operational dashboards, but it also supports exploratory analysis to diagnose performance changes, segment audiences, and validate experiment results in Conversion & Measurement<\/strong>.<\/p>\n\n\n\n

                                    6) What skills should a beginner learn first to use Tableau effectively?<\/h3>\n\n\n\n

                                    Focus on understanding dimensions vs. measures, filters, aggregations, basic calculations (rates and ratios), and how to interpret charts. For marketers, pairing these skills with Analytics<\/strong> fundamentals is the fastest path to useful work.<\/p>\n\n\n\n

                                    7) How often should Tableau reporting be reviewed?<\/h3>\n\n\n\n

                                    It depends on spend and volatility. Many teams review key Conversion & Measurement<\/strong> dashboards daily (anomalies), weekly (optimization), and monthly (strategy and executive reporting).<\/p>\n","protected":false},"excerpt":{"rendered":"

                                    Tableau is a data visualization and business intelligence platform that helps teams explore, model, and communicate insights from data. In **Conversion & Measurement**, it\u2019s often used to unify performance data from multiple channels (paid, organic, email, product, CRM) and translate that complexity into dashboards and analyses that business stakeholders can actually act on. In **Analytics**, Tableau sits at the \u201clast mile\u201d of insight: it doesn\u2019t replace data collection or tracking, but it makes measurement understandable, comparable, and decision-ready.<\/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":[1887],"tags":[],"class_list":["post-6963","post","type-post","status-publish","format-standard","hentry","category-analytics"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/6963","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=6963"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/6963\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=6963"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=6963"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=6963"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}