{"id":10719,"date":"2026-03-29T20:25:46","date_gmt":"2026-03-29T20:25:46","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/data-onboarding\/"},"modified":"2026-03-29T20:25:46","modified_gmt":"2026-03-29T20:25:46","slug":"data-onboarding","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/data-onboarding\/","title":{"rendered":"Data Onboarding: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Programmatic Advertising"},"content":{"rendered":"\n

Data Onboarding is the process of taking offline or first-party customer data (such as CRM records) and making it usable in digital ad platforms so you can target, suppress, and measure audiences more effectively. In Paid Marketing<\/strong>, it\u2019s the bridge between what you know about customers in your business systems and what you can activate in campaigns. In Programmatic Advertising<\/strong>, it enables audience-based buying decisions at scale by translating customer identities into privacy-safe identifiers that ad tech can recognize.<\/p>\n\n\n\n

This matters because modern Paid Marketing<\/strong> increasingly depends on first-party data, measurement discipline, and responsible personalization. As privacy expectations rise and third-party identifiers become less reliable, Data Onboarding<\/strong> becomes a core capability for turning customer intelligence into measurable media outcomes\u2014without guessing who you\u2019re reaching.<\/p>\n\n\n\n

What Is Data Onboarding?<\/h2>\n\n\n\n

Data Onboarding<\/strong> is the controlled workflow of ingesting customer data from internal sources (like CRM, loyalty, email lists, purchases, or offline conversions), transforming it into a standardized format, and matching it to digital identifiers that can be used for ad targeting and measurement.<\/p>\n\n\n\n

At its core, it\u2019s about identity translation and activation<\/strong>:<\/p>\n\n\n\n

    \n
  • Translation:<\/strong> Map business records (emails, phone numbers, addresses, customer IDs) into privacy-safe identifiers.<\/li>\n
  • Activation:<\/strong> Use those identifiers to build audiences for targeting, exclusion, sequencing, or attribution.<\/li>\n<\/ul>\n\n\n\n

    From a business perspective, Data Onboarding<\/strong> helps align marketing spend with real customer value. In Paid Marketing<\/strong>, it powers higher-intent targeting, reduces wasted impressions, and enables smarter retention and upsell strategies. In Programmatic Advertising<\/strong>, it provides audience signals that inform bidding, frequency controls, and cross-channel reach\u2014especially when cookie-based targeting is limited.<\/p>\n\n\n\n

    Why Data Onboarding Matters in Paid Marketing<\/h2>\n\n\n\n

    Data Onboarding<\/strong> matters because it improves both the precision<\/em> and accountability<\/em> of media investment. Instead of relying only on broad demographics or platform interest segments, you can use your own customer data to shape who sees ads and how performance is evaluated.<\/p>\n\n\n\n

    Key strategic impacts in Paid Marketing<\/strong> include:<\/p>\n\n\n\n

      \n
    • Better audience relevance:<\/strong> Reach known customers, high-propensity leads, or lapsed buyers with tailored messaging.<\/li>\n
    • Smarter budget allocation:<\/strong> Shift spend toward segments with higher lifetime value or higher conversion likelihood.<\/li>\n
    • Cleaner measurement:<\/strong> Connect ad exposure to downstream outcomes such as purchases, renewals, or in-store sales.<\/li>\n
    • Competitive advantage:<\/strong> Brands that operationalize first-party data often learn faster and waste less spend than competitors who rely on generic targeting.<\/li>\n<\/ul>\n\n\n\n

      In Programmatic Advertising<\/strong>, these advantages compound because activation happens at scale\u2014across publishers, formats, and exchanges\u2014based on consistent audience definitions.<\/p>\n\n\n\n

      How Data Onboarding Works<\/h2>\n\n\n\n

      While implementations vary, Data Onboarding<\/strong> typically follows a practical workflow that moves from business systems to media execution.<\/p>\n\n\n\n

        \n
      1. \n

        Input \/ Trigger (data selection and purpose)<\/strong>\n – The organization selects data sources and defines the use case: prospecting, retention, churn prevention, upsell, or suppression.\n – Common inputs include CRM contacts, purchase history, loyalty membership, lead status, product usage, and offline conversion logs.<\/p>\n<\/li>\n

      2. \n

        Processing (cleaning, normalization, privacy controls)<\/strong>\n – Records are standardized (e.g., formatting phone numbers, deduplicating contacts, normalizing country codes).\n – Consent and policy rules are applied (who can be targeted, for what purpose, and for how long).\n – Data is often transformed into privacy-safe representations (commonly hashed where appropriate), reducing exposure of raw identifiers.<\/p>\n<\/li>\n

      3. \n

        Execution (matching and audience creation)<\/strong>\n – Data is matched to identifiers used in ad ecosystems (platform IDs, privacy-safe identifiers, or cohort-like groupings, depending on channel rules).\n – Audiences are created and distributed to destinations such as DSPs, social platforms, or other activation endpoints used in Paid Marketing<\/strong> and Programmatic Advertising<\/strong>.<\/p>\n<\/li>\n

      4. \n

        Output \/ Outcome (activation and measurement loop)<\/strong>\n – Campaigns run using onboarded audiences for targeting, exclusions, and frequency management.\n – Performance is tracked, and learnings feed back into segmentation and data quality improvements (a key maturity step for Data Onboarding<\/strong>).<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        Key Components of Data Onboarding<\/h2>\n\n\n\n

        Effective Data Onboarding<\/strong> requires more than uploading a list. The strongest implementations combine data discipline, governance, and activation design.<\/p>\n\n\n\n

        Data inputs<\/h3>\n\n\n\n
          \n
        • CRM contacts (email\/phone)<\/li>\n
        • Transactional data (orders, returns, subscriptions)<\/li>\n
        • Behavioral or product usage events<\/li>\n
        • Loyalty or membership status<\/li>\n
        • Offline conversions (store purchases, call center outcomes)<\/li>\n
        • Lead and pipeline stages (for B2B)<\/li>\n<\/ul>\n\n\n\n

          Systems and processes<\/h3>\n\n\n\n
            \n
          • CRM and customer databases:<\/strong> Where first-party profiles live.<\/li>\n
          • Data warehouse or data lake:<\/strong> Central storage for joining, cleaning, and versioning.<\/li>\n
          • ETL\/ELT pipelines:<\/strong> Automated data movement and transformation.<\/li>\n
          • Identity resolution logic:<\/strong> Rules for matching, deduping, and building households\/accounts where appropriate.<\/li>\n
          • Audience taxonomy:<\/strong> Standard definitions (e.g., \u201chigh-value repeat buyers,\u201d \u201ctrial users,\u201d \u201cchurn risk\u201d).<\/li>\n<\/ul>\n\n\n\n

            Governance and responsibilities<\/h3>\n\n\n\n
              \n
            • Marketing sets use cases and audience strategy for Paid Marketing<\/strong>.<\/li>\n
            • Analytics validates segment logic and measurement.<\/li>\n
            • Data\/engineering maintains pipelines, quality, and access controls.<\/li>\n
            • Legal\/privacy ensures consent, retention, and permitted activation.<\/li>\n<\/ul>\n\n\n\n

              Metrics and monitoring<\/h3>\n\n\n\n
                \n
              • Match rates, audience size stability, conversion lift, and incremental ROI (covered later).<\/li>\n<\/ul>\n\n\n\n

                Types of Data Onboarding<\/h2>\n\n\n\n

                There aren\u2019t universally \u201cformal\u201d types, but in practice Data Onboarding<\/strong> varies by what data is activated and how it\u2019s used in Programmatic Advertising<\/strong> and other paid channels.<\/p>\n\n\n\n

                1) Onboarding for targeting vs suppression<\/h3>\n\n\n\n
                  \n
                • Targeting:<\/strong> Reach known customers, high-intent prospects, or lookalike seed groups.<\/li>\n
                • Suppression:<\/strong> Exclude existing customers, recent converters, or low-value segments to reduce waste in Paid Marketing<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                  2) Onboarding for acquisition vs retention<\/h3>\n\n\n\n
                    \n
                  • Acquisition-focused:<\/strong> Build prospecting seeds and exclude current customers.<\/li>\n
                  • Retention-focused:<\/strong> Segment customers by lifecycle stage (new, active, lapsed) and tailor messaging and frequency.<\/li>\n<\/ul>\n\n\n\n

                    3) Batch onboarding vs near-real-time onboarding<\/h3>\n\n\n\n
                      \n
                    • Batch:<\/strong> Weekly or daily updates; simpler and common for many organizations.<\/li>\n
                    • Near-real-time:<\/strong> Frequent updates (hourly or event-driven) for fast-moving scenarios like cart abandonment, subscription status changes, or inventory-driven offers.<\/li>\n<\/ul>\n\n\n\n

                      4) Audience onboarding vs conversion onboarding<\/h3>\n\n\n\n
                        \n
                      • Audience onboarding:<\/strong> Send identifiers to build addressable segments.<\/li>\n
                      • Conversion onboarding:<\/strong> Send offline outcomes back for measurement, optimization, or attribution\u2014often essential for omnichannel Paid Marketing<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                        Real-World Examples of Data Onboarding<\/h2>\n\n\n\n

                        Example 1: Retail customer suppression to cut wasted spend<\/h3>\n\n\n\n

                        A retailer onboards CRM records of recent purchasers and suppresses them from acquisition campaigns. In Programmatic Advertising<\/strong>, this reduces impressions served to people who already converted, improving efficiency. The brand then reallocates budget to new-customer prospecting and measures changes in CPA and incremental revenue.<\/p>\n\n\n\n

                        Example 2: B2B account-based segmentation for pipeline acceleration<\/h3>\n\n\n\n

                        A SaaS company segments leads by lifecycle stage (MQL, SQL, opportunity) and onboards those segments for Paid Marketing<\/strong>. In Programmatic Advertising<\/strong>, decision-makers in \u201copen opportunity\u201d accounts receive mid-funnel proof points, while \u201cnew leads\u201d see education-focused ads. The analytics team compares conversion rates and sales cycle velocity by segment.<\/p>\n\n\n\n

                        Example 3: Omnichannel measurement using offline conversions<\/h3>\n\n\n\n

                        A multi-location service business onboards call center bookings and in-store appointments as offline conversions. Those conversions are used to evaluate which campaigns drive real bookings (not just clicks). Over time, Data Onboarding<\/strong> enables better bidding decisions and more accurate ROI reporting across Paid Marketing<\/strong> channels.<\/p>\n\n\n\n

                        Benefits of Using Data Onboarding<\/h2>\n\n\n\n

                        When done responsibly and consistently, Data Onboarding<\/strong> delivers improvements that compound across targeting and measurement.<\/p>\n\n\n\n

                          \n
                        • Higher relevance and conversion rates:<\/strong> Ads align with lifecycle stage, past purchases, or propensity.<\/li>\n
                        • Lower wasted spend:<\/strong> Suppression and frequency controls reduce redundant impressions in Paid Marketing<\/strong>.<\/li>\n
                        • Improved optimization signals:<\/strong> Better conversion data strengthens learning loops, especially in Programmatic Advertising<\/strong> where bidding systems react to outcomes.<\/li>\n
                        • More consistent cross-channel audiences:<\/strong> The same segment logic can be activated across multiple paid environments.<\/li>\n
                        • Better customer experience:<\/strong> Fewer irrelevant ads, more coherent sequencing, and fewer \u201cI just bought this\u201d moments.<\/li>\n<\/ul>\n\n\n\n

                          Challenges of Data Onboarding<\/h2>\n\n\n\n

                          Data Onboarding<\/strong> is powerful, but it\u2019s not plug-and-play. Common obstacles include technical, organizational, and measurement limitations.<\/p>\n\n\n\n

                            \n
                          • Data quality issues:<\/strong> Duplicates, outdated emails, inconsistent formatting, or missing consent fields reduce match success and reliability.<\/li>\n
                          • Identity match limitations:<\/strong> Not all records match to addressable identifiers; match rates vary by market, channel, and data hygiene.<\/li>\n
                          • Privacy and compliance risk:<\/strong> Using customer data for Paid Marketing<\/strong> requires clear purpose limitation, retention controls, and consent alignment.<\/li>\n
                          • Audience drift and staleness:<\/strong> Static segments degrade; lifecycle changes can make targeting inaccurate if updates aren\u2019t frequent enough.<\/li>\n
                          • Attribution complexity:<\/strong> Even with onboarded conversions, isolating incremental impact can be difficult without holdouts or experimentation.<\/li>\n
                          • Operational overhead:<\/strong> Pipelines, QA, and governance require sustained ownership\u2014not a one-time project.<\/li>\n<\/ul>\n\n\n\n

                            Best Practices for Data Onboarding<\/h2>\n\n\n\n

                            Strong Data Onboarding<\/strong> programs share a few habits that protect performance and reduce risk.<\/p>\n\n\n\n

                              \n
                            1. \n

                              Start with a clear use case<\/strong>\n – Targeting, suppression, retention, or offline measurement\u2014pick one and define success metrics before building pipelines.<\/p>\n<\/li>\n

                            2. \n

                              Standardize an audience taxonomy<\/strong>\n – Use consistent naming, inclusion rules, and refresh cadence. This reduces confusion across Paid Marketing<\/strong> teams and agencies.<\/p>\n<\/li>\n

                            3. \n

                              Prioritize data hygiene<\/strong>\n – Deduplicate, normalize formats, and version segment definitions. Small hygiene improvements can meaningfully increase match rates.<\/p>\n<\/li>\n

                            4. \n

                              Design for privacy by default<\/strong>\n – Limit fields to what\u2019s needed, apply consent checks, and set retention windows. Ensure internal access controls match the sensitivity of the data.<\/p>\n<\/li>\n

                            5. \n

                              Refresh at the speed of the business<\/strong>\n – Fast-moving segments (recent purchasers, churn risk) should update more frequently than stable segments (loyalty tiers).<\/p>\n<\/li>\n

                            6. \n

                              Close the loop with measurement<\/strong>\n – Pair Data Onboarding<\/strong> with clean reporting and, when possible, incrementality testing (holdouts, geo tests) to validate true lift.<\/p>\n<\/li>\n

                            7. \n

                              Document and monitor the pipeline<\/strong>\n – Track failures, schema changes, and audience size anomalies. Operational reliability is a competitive advantage in Programmatic Advertising<\/strong>.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                              Tools Used for Data Onboarding<\/h2>\n\n\n\n

                              Data Onboarding<\/strong> spans multiple systems. The goal is a reliable path from customer data to activation and measurement within Paid Marketing<\/strong>.<\/p>\n\n\n\n

                                \n
                              • CRM systems:<\/strong> Store customer profiles, lead stages, and sales outcomes used to define segments.<\/li>\n
                              • Customer data platforms (CDPs) and audience management layers:<\/strong> Unify events and attributes, build audiences, and manage consent logic.<\/li>\n
                              • Data warehouses and transformation tools:<\/strong> Centralize data, run SQL-based segmentation, and control versioning and lineage.<\/li>\n
                              • Identity resolution and onboarding services:<\/strong> Support privacy-safe matching and audience distribution to paid destinations used in Programmatic Advertising<\/strong>.<\/li>\n
                              • Ad platforms (DSPs and paid social environments):<\/strong> Activate onboarded segments for targeting, suppression, and optimization.<\/li>\n
                              • Analytics tools and reporting dashboards:<\/strong> Validate performance, match rates, and downstream outcomes; monitor pipeline health.<\/li>\n
                              • Tag management and server-side collection (where relevant):<\/strong> Improve event quality and reduce reliance on brittle client-side signals.<\/li>\n<\/ul>\n\n\n\n

                                Tool choice matters less than having a governed workflow with consistent definitions and measurable outcomes.<\/p>\n\n\n\n

                                Metrics Related to Data Onboarding<\/h2>\n\n\n\n

                                To manage Data Onboarding<\/strong> as a performance lever, track both data-quality metrics and campaign outcomes.<\/p>\n\n\n\n

                                Data and operational metrics<\/h3>\n\n\n\n
                                  \n
                                • Match rate:<\/strong> Percentage of records that become addressable in a given destination.<\/li>\n
                                • Audience size and stability:<\/strong> Unexpected swings often indicate pipeline or logic issues.<\/li>\n
                                • Freshness \/ latency:<\/strong> Time from a customer event (purchase, churn flag) to audience update.<\/li>\n
                                • Error rate and schema failures:<\/strong> Pipeline reliability indicators.<\/li>\n<\/ul>\n\n\n\n

                                  Paid Marketing and Programmatic Advertising performance metrics<\/h3>\n\n\n\n
                                    \n
                                  • Conversion rate (CVR) and cost per acquisition (CPA):<\/strong> Compare onboarded segments vs baseline targeting.<\/li>\n
                                  • Return on ad spend (ROAS) \/ marketing ROI:<\/strong> Especially meaningful when paired with offline conversion onboarding.<\/li>\n
                                  • Incremental lift:<\/strong> Measured via experiments rather than last-click alone.<\/li>\n
                                  • Frequency and reach quality:<\/strong> Lower wasted frequency can improve efficiency and customer experience.<\/li>\n
                                  • Customer lifetime value (LTV) or profit-based outcomes:<\/strong> For mature programs, evaluate segment-level profitability.<\/li>\n<\/ul>\n\n\n\n

                                    Future Trends of Data Onboarding<\/h2>\n\n\n\n

                                    Data Onboarding<\/strong> is evolving as identity, privacy, and automation reshape Paid Marketing<\/strong>.<\/p>\n\n\n\n

                                      \n
                                    • More automation in segmentation and activation:<\/strong> Workflow orchestration and automated QA will reduce manual list handling and improve reliability.<\/li>\n
                                    • AI-assisted audience strategy:<\/strong> Models can recommend segments, predict churn, and optimize lifecycle messaging\u2014though governance remains essential.<\/li>\n
                                    • Privacy-first identity approaches:<\/strong> Expect continued movement toward consented, durable identifiers and privacy-safe matching methods.<\/li>\n
                                    • Stronger measurement discipline:<\/strong> Incrementality testing and blended measurement approaches will become more common as deterministic tracking declines.<\/li>\n
                                    • Greater emphasis on first-party data value:<\/strong> Organizations will invest in data collection, enrichment, and governance to make Programmatic Advertising<\/strong> more resilient to ecosystem changes.<\/li>\n<\/ul>\n\n\n\n

                                      Data Onboarding vs Related Terms<\/h2>\n\n\n\n

                                      Data Onboarding vs Data Integration<\/h3>\n\n\n\n
                                        \n
                                      • Data integration<\/strong> connects systems so data can flow and be used internally.<\/li>\n
                                      • Data Onboarding<\/strong> specifically prepares and matches customer data for activation in Paid Marketing<\/strong> and Programmatic Advertising<\/strong> destinations. Integration is often a prerequisite; onboarding is the activation step.<\/li>\n<\/ul>\n\n\n\n

                                        Data Onboarding vs Identity Resolution<\/h3>\n\n\n\n
                                          \n
                                        • Identity resolution<\/strong> focuses on determining which identifiers belong to the same person or household.<\/li>\n
                                        • Data Onboarding<\/strong> uses identity logic (sometimes simple, sometimes advanced) but is broader: it includes governance, audience packaging, distribution, and measurement use cases.<\/li>\n<\/ul>\n\n\n\n

                                          Data Onboarding vs Audience Targeting<\/h3>\n\n\n\n
                                            \n
                                          • Audience targeting<\/strong> is the act of selecting who to reach in a campaign.<\/li>\n
                                          • Data Onboarding<\/strong> is the mechanism that makes your first-party\/offline audiences available for targeting and suppression across paid channels.<\/li>\n<\/ul>\n\n\n\n

                                            Who Should Learn Data Onboarding<\/h2>\n\n\n\n
                                              \n
                                            • Marketers:<\/strong> To build effective first-party strategies, reduce wasted spend, and improve targeting in Paid Marketing<\/strong>.<\/li>\n
                                            • Analysts:<\/strong> To validate segment logic, measure lift, and connect campaigns to business outcomes.<\/li>\n
                                            • Agencies:<\/strong> To operationalize client data safely, improve performance in Programmatic Advertising<\/strong>, and standardize audience frameworks.<\/li>\n
                                            • Business owners and founders:<\/strong> To understand how customer data translates into efficient growth and more defensible acquisition strategies.<\/li>\n
                                            • Developers and data engineers:<\/strong> To implement secure pipelines, enforce governance, and maintain reliable data products that support activation.<\/li>\n<\/ul>\n\n\n\n

                                              Summary of Data Onboarding<\/h2>\n\n\n\n

                                              Data Onboarding<\/strong> turns offline and first-party customer data into actionable audiences and measurable signals for digital campaigns. It matters because it improves relevance, reduces waste, and strengthens accountability in Paid Marketing<\/strong>. Within Programmatic Advertising<\/strong>, it enables scalable audience activation and more informed optimization by connecting business outcomes to media execution. Done well, it becomes a durable capability that supports performance, privacy, and long-term marketing efficiency.<\/p>\n\n\n\n

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

                                              1) What is Data Onboarding and what problem does it solve?<\/h3>\n\n\n\n

                                              Data Onboarding<\/strong> converts first-party or offline customer data into addressable audiences for targeting, suppression, and measurement. It solves the gap between internal customer knowledge and what ad platforms can use.<\/p>\n\n\n\n

                                              2) Is Data Onboarding only used for Programmatic Advertising?<\/h3>\n\n\n\n

                                              No. While it\u2019s especially common in Programmatic Advertising<\/strong>, Data Onboarding<\/strong> can also support other Paid Marketing<\/strong> channels that accept customer audiences or offline conversion signals.<\/p>\n\n\n\n

                                              3) What data is typically used for Data Onboarding?<\/h3>\n\n\n\n

                                              Common inputs include CRM contact fields (email\/phone), purchase history, loyalty status, lead stage, and offline conversions like store sales or booked appointments\u2014assuming you have the right permissions to use them.<\/p>\n\n\n\n

                                              4) How do you know if your Data Onboarding is working?<\/h3>\n\n\n\n

                                              Look at match rate, audience stability, and freshness, then tie those to outcomes such as CPA, ROAS, and incremental lift. A working program improves both operational reliability and campaign performance.<\/p>\n\n\n\n

                                              5) What are the biggest risks with Data Onboarding?<\/h3>\n\n\n\n

                                              The biggest risks are privacy\/compliance issues, poor data hygiene, and misleading measurement (for example, assuming last-click results equal incremental impact). Governance and experimentation reduce these risks.<\/p>\n\n\n\n

                                              6) How often should onboarded audiences be refreshed?<\/h3>\n\n\n\n

                                              It depends on how quickly the underlying customer behavior changes. Lifecycle segments (recent buyers, churn risk) often need frequent updates; slower-changing segments (tiered loyalty) can update less often.<\/p>\n\n\n\n

                                              7) Can small businesses benefit from Data Onboarding?<\/h3>\n\n\n\n

                                              Yes\u2014especially for suppression (excluding existing customers) and for measuring offline outcomes. Even simple, well-governed Data Onboarding<\/strong> can make Paid Marketing<\/strong> more efficient and focused.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                              Data Onboarding is the process of taking offline or first-party customer data (such as CRM records) and making it usable in digital ad platforms so you can target, suppress, and measure audiences more effectively. In **Paid Marketing**, it\u2019s the bridge between what you know about customers in your business systems and what you can activate in campaigns. In **Programmatic Advertising**, it enables audience-based buying decisions at scale by translating customer identities into privacy-safe identifiers that ad tech can recognize.<\/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":[1911],"tags":[],"class_list":["post-10719","post","type-post","status-publish","format-standard","hentry","category-programmatic-advertising"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/10719","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=10719"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/10719\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=10719"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=10719"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=10719"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}