{"id":10499,"date":"2026-03-29T12:03:49","date_gmt":"2026-03-29T12:03:49","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/lookalike-audience\/"},"modified":"2026-03-29T12:03:49","modified_gmt":"2026-03-29T12:03:49","slug":"lookalike-audience","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/lookalike-audience\/","title":{"rendered":"Lookalike Audience: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Paid Social"},"content":{"rendered":"\n

A Lookalike Audience<\/strong> is a targeting method in Paid Marketing<\/strong> that helps you reach new people who \u201clook like\u201d your best existing customers or users. Instead of guessing interests or relying purely on broad demographics, you start with a high-quality source audience (such as purchasers, subscribers, or high-LTV customers) and let an ad platform find similar profiles at scale.<\/p>\n\n\n\n

In modern Paid Social<\/strong>, where attention is fragmented and targeting options have changed due to privacy constraints, Lookalike Audience<\/strong> strategies remain one of the most reliable ways to expand reach while maintaining relevance. Used well, they bridge the gap between precision and scale\u2014two goals that often conflict in performance advertising.<\/p>\n\n\n\n

What Is Lookalike Audience?<\/h2>\n\n\n\n

A Lookalike Audience<\/strong> is an algorithmically generated audience segment created from a \u201cseed\u201d or \u201csource\u201d group of people who have already demonstrated valuable behavior. The platform analyzes patterns in the seed audience\u2014such as signals related to engagement, purchasing behavior, and inferred attributes\u2014and then identifies new users who share similar characteristics.<\/p>\n\n\n\n

At its core, the concept is simple:<\/p>\n\n\n\n

    \n
  • Start with proven value<\/strong> (your best customers or users)<\/li>\n
  • Model what they have in common<\/strong><\/li>\n
  • Find more people like them<\/strong><\/li>\n
  • Activate those users in ads<\/strong><\/li>\n<\/ul>\n\n\n\n

    From a business perspective, Lookalike Audience<\/strong> targeting is a scalable acquisition lever. In Paid Marketing<\/strong>, it sits between retargeting (high intent but limited volume) and broad prospecting (high volume but often lower efficiency). Within Paid Social<\/strong>, it\u2019s commonly used to expand customer acquisition while preserving performance benchmarks like CPA, ROAS, or lead quality.<\/p>\n\n\n\n

    Why Lookalike Audience Matters in Paid Marketing<\/h2>\n\n\n\n

    A strong Lookalike Audience<\/strong> strategy is valuable because it directly supports the goals that matter most in Paid Marketing<\/strong>:<\/p>\n\n\n\n

      \n
    • Efficient growth:<\/strong> It can reduce the cost of finding qualified prospects compared to broad interest targeting.<\/li>\n
    • Faster learning cycles:<\/strong> Starting from a high-signal seed helps platforms optimize delivery and bidding sooner.<\/li>\n
    • Scalable acquisition:<\/strong> It allows you to extend beyond your existing remarketing pool without losing relevance.<\/li>\n
    • Competitive advantage:<\/strong> Many advertisers build lookalikes from generic seed lists; teams that build better seeds (and segment them thoughtfully) often win auctions with stronger conversion rates.<\/li>\n<\/ul>\n\n\n\n

      In Paid Social<\/strong>, where competition is intense and creative fatigue is real, audience quality still matters. A Lookalike Audience<\/strong> can increase the chance your creative lands in front of people predisposed to act\u2014especially when paired with strong conversion tracking and clear value propositions.<\/p>\n\n\n\n

      How Lookalike Audience Works<\/h2>\n\n\n\n

      While implementations vary by ad platform, Lookalike Audience<\/strong> creation usually follows a practical workflow:<\/p>\n\n\n\n

        \n
      1. \n

        Input (seed audience selection)<\/strong>\n You provide a source audience, often based on first-party data or platform engagement. Examples include purchasers, high-value customers, email subscribers, app installers, or people who completed a key funnel event.<\/p>\n<\/li>\n

      2. \n

        Analysis (modeling similarity)<\/strong>\n The platform analyzes the seed for shared patterns. Depending on available signals and privacy constraints, this may include on-platform behavior, contextual signals, device and usage patterns, and inferred interests. You typically don\u2019t control the exact features used; your leverage is seed quality and segmentation.<\/p>\n<\/li>\n

      3. \n

        Execution (audience generation and activation)<\/strong>\n You choose parameters (often a similarity \u201csize\u201d or percentile). Smaller lookalikes generally mean \u201cmore similar but smaller reach,\u201d while larger ones trade similarity for scale. Then you use the Lookalike Audience<\/strong> in ad sets\/ad groups as a targeting input.<\/p>\n<\/li>\n

      4. \n

        Output (performance outcomes and learning)<\/strong>\n The outcome is measured through conversion rate, CPA, ROAS, lead quality, and incremental lift. In Paid Marketing<\/strong>, the real value is not that it\u2019s \u201csmart,\u201d but that it\u2019s measurable: you can compare it against interest targeting, broad, contextual, or other prospecting approaches.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        Key Components of Lookalike Audience<\/h2>\n\n\n\n

        A Lookalike Audience<\/strong> program works best when you treat it as a system\u2014not a one-time audience build. Key components include:<\/p>\n\n\n\n

        Data inputs (the foundation)<\/h3>\n\n\n\n
          \n
        • Customer lists:<\/strong> Email\/phone lists, ideally with consent and accurate formatting<\/li>\n
        • Conversion events:<\/strong> Purchases, qualified leads, subscriptions, app events<\/li>\n
        • Value signals:<\/strong> Revenue, LTV bands, order frequency, margin tiers (when supported)<\/li>\n
        • Engagement audiences:<\/strong> Video viewers, page engagers, form openers (useful but usually weaker than purchase signals)<\/li>\n<\/ul>\n\n\n\n

          Processes and governance<\/h3>\n\n\n\n
            \n
          • Seed definition standards:<\/strong> What qualifies someone to be included (e.g., \u201cpurchased in last 180 days\u201d)<\/li>\n
          • Refresh cadence:<\/strong> Regular updates to keep the seed aligned with current product-market fit<\/li>\n
          • Audience exclusions:<\/strong> Removing existing customers from prospecting to reduce waste<\/li>\n
          • Privacy and compliance:<\/strong> Consent, data handling, and retention rules across markets<\/li>\n<\/ul>\n\n\n\n

            Metrics and measurement<\/h3>\n\n\n\n
              \n
            • Down-funnel conversion quality:<\/strong> Not just leads\u2014qualified leads, approvals, retained users<\/li>\n
            • Incrementality mindset:<\/strong> Lookalikes can capture \u201ceasy\u201d conversions; test to confirm lift<\/li>\n<\/ul>\n\n\n\n

              Team responsibilities<\/h3>\n\n\n\n
                \n
              • Marketing:<\/strong> Audience strategy, testing plan, creative alignment<\/li>\n
              • Analytics:<\/strong> Measurement integrity, attribution checks, cohort quality<\/li>\n
              • Engineering\/ops:<\/strong> Event instrumentation, data cleanliness, feed reliability<\/li>\n<\/ul>\n\n\n\n

                In Paid Social<\/strong>, these pieces determine whether your Lookalike Audience<\/strong> behaves like a growth engine or just another audience that performs well for a week and fades.<\/p>\n\n\n\n

                Types of Lookalike Audience<\/h2>\n\n\n\n

                There isn\u2019t one universal taxonomy, but in practice, Lookalike Audience<\/strong> approaches cluster into a few meaningful variants:<\/p>\n\n\n\n

                1) Seed-based by funnel stage<\/h3>\n\n\n\n
                  \n
                • Top-of-funnel seeds:<\/strong> Page views or video views (large, low intent)<\/li>\n
                • Mid-funnel seeds:<\/strong> Add-to-cart, lead form submits (higher intent)<\/li>\n
                • Bottom-of-funnel seeds:<\/strong> Purchasers, renewals, high-LTV cohorts (highest signal)<\/li>\n<\/ul>\n\n\n\n

                  2) Similarity vs scale bands<\/h3>\n\n\n\n

                  Many platforms allow you to select audience size bands (often expressed as percentages). Common operational choices:\n– Tight lookalikes:<\/strong> Smaller size, more similarity, typically better efficiency\n– Broad lookalikes:<\/strong> Larger size, more reach, often better for scaling once creative and tracking are strong<\/p>\n\n\n\n

                  3) Value-based or quality-weighted lookalikes (when available)<\/h3>\n\n\n\n

                  Some setups allow you to weight users by value (revenue or LTV). This can shift the model toward finding prospects who resemble your best customers\u2014not just any customers.<\/p>\n\n\n\n

                  4) Geo-specific lookalikes<\/h3>\n\n\n\n

                  Lookalikes built per country\/region can outperform global seeds because purchasing behavior, pricing sensitivity, and competitive landscapes differ. This matters for international Paid Marketing<\/strong> teams.<\/p>\n\n\n\n

                  Real-World Examples of Lookalike Audience<\/h2>\n\n\n\n

                  Example 1: Ecommerce brand scaling new customer revenue<\/h3>\n\n\n\n

                  An ecommerce company builds a Lookalike Audience<\/strong> from:\n– Purchasers in the last 60 days\n– Excluding discount-only buyers\n– Optionally weighting by order value<\/p>\n\n\n\n

                  They run prospecting campaigns in Paid Social<\/strong> using tight lookalikes first, then expand to broader bands once creative winners emerge. In Paid Marketing<\/strong>, this setup often stabilizes ROAS while increasing spend capacity.<\/p>\n\n\n\n

                  Example 2: B2B SaaS improving lead quality (not just CPL)<\/h3>\n\n\n\n

                  A SaaS team creates a Lookalike Audience<\/strong> from:\n– Sales-qualified leads (SQLs) or closed-won accounts\n– Excluding free-trial users who never activated<\/p>\n\n\n\n

                  They optimize to a deeper conversion event (e.g., qualified demo request) rather than top-of-funnel lead capture. This aligns Paid Social<\/strong> delivery with business value and reduces wasted pipeline.<\/p>\n\n\n\n

                  Example 3: Mobile app driving retained users<\/h3>\n\n\n\n

                  A mobile app builds a Lookalike Audience<\/strong> from:\n– Users who hit a retention milestone (e.g., day-7 active)\n– Users who completed a monetization event<\/p>\n\n\n\n

                  They measure success by retention-adjusted CPA or ROAS. In Paid Marketing<\/strong>, this avoids the classic trap of buying cheap installs that churn immediately.<\/p>\n\n\n\n

                  Benefits of Using Lookalike Audience<\/h2>\n\n\n\n

                  When implemented with strong seeds and clear measurement, Lookalike Audience<\/strong> targeting can deliver:<\/p>\n\n\n\n

                    \n
                  • Higher relevance at scale:<\/strong> More efficient prospecting than purely broad targeting in many accounts<\/li>\n
                  • Improved conversion rates:<\/strong> Better alignment between audience predisposition and your offer<\/li>\n
                  • Lower acquisition costs:<\/strong> Often improves CPA\/CAC compared to less informed targeting<\/li>\n
                  • Faster optimization:<\/strong> Platforms get clearer signals earlier, especially in Paid Social<\/strong> auctions<\/li>\n
                  • Better user experience:<\/strong> People see ads that are more aligned with their needs, reducing wasted impressions<\/li>\n
                  • Operational efficiency:<\/strong> Once the seed and process are stable, lookalikes are easier to maintain than constant interest research<\/li>\n<\/ul>\n\n\n\n

                    For Paid Marketing<\/strong> teams trying to balance growth and efficiency, Lookalike Audience<\/strong> can be one of the most repeatable levers\u2014especially when retargeting volume is limited.<\/p>\n\n\n\n

                    Challenges of Lookalike Audience<\/h2>\n\n\n\n

                    Despite the upside, Lookalike Audience<\/strong> isn\u2019t a guaranteed win. Common issues include:<\/p>\n\n\n\n

                      \n
                    • Weak or noisy seed data:<\/strong> If your seed includes low-quality users, the model learns the wrong patterns.<\/li>\n
                    • Insufficient volume:<\/strong> Too-small seed sizes may limit performance or cause instability.<\/li>\n
                    • Changing privacy and tracking signals:<\/strong> Less deterministic tracking can reduce modeling fidelity and measurement clarity.<\/li>\n
                    • Over-reliance on platform \u201cblack box\u201d:<\/strong> You can\u2019t fully control how similarity is determined; strategy must focus on inputs, creative, and measurement.<\/li>\n
                    • Audience overlap and saturation:<\/strong> Multiple lookalikes can compete against each other and inflate costs.<\/li>\n
                    • Misaligned optimization events:<\/strong> Optimizing to shallow events (clicks, low-quality leads) often produces shallow outcomes.<\/li>\n
                    • Attribution bias:<\/strong> Lookalikes may capture users who would have converted anyway; incrementality testing matters in Paid Marketing<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                      Best Practices for Lookalike Audience<\/h2>\n\n\n\n

                      Use these practices to make Lookalike Audience<\/strong> work reliably in Paid Social<\/strong> and beyond:<\/p>\n\n\n\n

                      Build better seeds, not just bigger ones<\/h3>\n\n\n\n
                        \n
                      • Prefer purchase, qualified lead, retention, or revenue<\/strong> signals over engagement-only seeds.<\/li>\n
                      • Segment seeds by meaningful cohorts (high-LTV, repeat buyers, specific product lines).<\/li>\n
                      • Exclude refunded, fraudulent, or low-margin segments when possible.<\/li>\n<\/ul>\n\n\n\n

                        Refresh and test systematically<\/h3>\n\n\n\n
                          \n
                        • Refresh seed audiences on a schedule that matches your sales cycle (e.g., 30\/60\/90\/180 days).<\/li>\n
                        • Test lookalike sizes in a structured way: tight vs broad, one variable at a time.<\/li>\n
                        • Use controlled budgets so you can compare performance without constant learning resets.<\/li>\n<\/ul>\n\n\n\n

                          Pair with clean exclusions<\/h3>\n\n\n\n
                            \n
                          • Exclude existing customers from acquisition campaigns when appropriate.<\/li>\n
                          • Exclude recent converters to reduce overlap with retargeting.<\/li>\n
                          • Watch for overlap between multiple Lookalike Audience<\/strong> ad sets that target similar users.<\/li>\n<\/ul>\n\n\n\n

                            Align creative to the seed\u2019s intent<\/h3>\n\n\n\n
                              \n
                            • A lookalike from high-LTV buyers should see creative aligned with premium value, not bargain-only messaging.<\/li>\n
                            • Match landing pages to the promise in the ad to protect conversion rate and quality signals.<\/li>\n<\/ul>\n\n\n\n

                              Measure what the business cares about<\/h3>\n\n\n\n
                                \n
                              • Track downstream metrics: qualified leads, activated trials, retained users, margin-adjusted ROAS.<\/li>\n
                              • In Paid Marketing<\/strong>, add incrementality tests where possible (geo tests, holdouts, or controlled experiments).<\/li>\n<\/ul>\n\n\n\n

                                Tools Used for Lookalike Audience<\/h2>\n\n\n\n

                                Lookalike Audience<\/strong> work is operationalized through a mix of platform and supporting tools. Vendor-neutral categories include:<\/p>\n\n\n\n

                                  \n
                                • Ad platforms (Paid Social platforms):<\/strong> Where you build the seed audiences, generate lookalikes, and activate campaigns.<\/li>\n
                                • Analytics tools:<\/strong> To validate funnel behavior, cohort performance, and post-click engagement.<\/li>\n
                                • Tag management and event instrumentation:<\/strong> Ensures conversion events are consistent and trustworthy.<\/li>\n
                                • CRM systems:<\/strong> Source of customer lifecycle stages (MQL\/SQL\/Closed Won) and a backbone for B2B seed quality.<\/li>\n
                                • CDPs and data pipelines:<\/strong> Unify identity and events, support cleaner audience creation, and improve data governance.<\/li>\n
                                • Attribution and experimentation tooling:<\/strong> Helps interpret performance beyond last-click and assess incrementality.<\/li>\n
                                • Reporting dashboards:<\/strong> Monitor audience-level performance, overlap, and fatigue over time.<\/li>\n<\/ul>\n\n\n\n

                                  In Paid Marketing<\/strong>, the biggest tool advantage is not a fancy feature\u2014it\u2019s reliable data flow and consistent definitions.<\/p>\n\n\n\n

                                  Metrics Related to Lookalike Audience<\/h2>\n\n\n\n

                                  To evaluate Lookalike Audience<\/strong> performance in Paid Social<\/strong>, focus on metrics that reflect both efficiency and quality:<\/p>\n\n\n\n

                                    \n
                                  • CPA \/ CAC:<\/strong> Cost per acquisition (define \u201cacquisition\u201d carefully\u2014purchase vs lead)<\/li>\n
                                  • ROAS:<\/strong> Return on ad spend (prefer contribution-margin views if available)<\/li>\n
                                  • CVR:<\/strong> Conversion rate from click to desired action<\/li>\n
                                  • CPM and CPC:<\/strong> Useful diagnostics for auction competitiveness and creative resonance<\/li>\n
                                  • Qualified lead rate:<\/strong> % of leads that become sales-accepted or sales-qualified<\/li>\n
                                  • LTV \/ retention metrics:<\/strong> For subscriptions and apps, measure what happens after the install or signup<\/li>\n
                                  • Incremental lift:<\/strong> Evidence the lookalike drove additional conversions, not just captured existing demand<\/li>\n
                                  • Frequency and reach:<\/strong> Helps detect saturation, especially with smaller lookalikes<\/li>\n<\/ul>\n\n\n\n

                                    A Lookalike Audience<\/strong> that \u201cwins\u201d on CPC but loses on retention is not a real win for Paid Marketing<\/strong> outcomes.<\/p>\n\n\n\n

                                    Future Trends of Lookalike Audience<\/h2>\n\n\n\n

                                    Lookalike Audience<\/strong> targeting is evolving alongside broader changes in Paid Marketing<\/strong>:<\/p>\n\n\n\n

                                      \n
                                    • More modeling, fewer deterministic identifiers:<\/strong> Platforms will rely more on aggregated signals and probabilistic modeling.<\/li>\n
                                    • First-party data becomes central:<\/strong> Brands with strong CRM hygiene, server-side event capture, and clear lifecycle stages will build better seeds.<\/li>\n
                                    • Value optimization and quality signals:<\/strong> More emphasis on predicting long-term value rather than just short-term conversions.<\/li>\n
                                    • Automation with guardrails:<\/strong> More automated audience expansion and bidding, paired with clearer measurement frameworks to prevent \u201cblack box drift.\u201d<\/li>\n
                                    • Privacy-driven measurement changes:<\/strong> Expect more reliance on experiments, conversion modeling, and triangulation across analytics systems.<\/li>\n<\/ul>\n\n\n\n

                                      In Paid Social<\/strong>, the teams that treat Lookalike Audience<\/strong> as part of a measurement-and-data system\u2014not a one-off hack\u2014will be best positioned as targeting continues to change.<\/p>\n\n\n\n

                                      Lookalike Audience vs Related Terms<\/h2>\n\n\n\n

                                      Lookalike Audience vs Retargeting<\/h3>\n\n\n\n
                                        \n
                                      • Retargeting<\/strong> targets people who already interacted with you (visited site, added to cart, engaged with content).<\/li>\n
                                      • A Lookalike Audience<\/strong> targets new people who resemble converters or high-value users.<\/li>\n
                                      • In Paid Marketing<\/strong>, retargeting is usually higher intent but limited scale; lookalikes extend prospecting reach.<\/li>\n<\/ul>\n\n\n\n

                                        Lookalike Audience vs Interest Targeting<\/h3>\n\n\n\n
                                          \n
                                        • Interest targeting<\/strong> uses declared or inferred interests\/behaviors (e.g., \u201cfitness,\u201d \u201ctravel\u201d).<\/li>\n
                                        • Lookalike Audience<\/strong> uses your seed audience as the source of truth for similarity.<\/li>\n
                                        • In Paid Social<\/strong>, interest targeting can be useful for messaging angles, but lookalikes often align more directly with conversion propensity\u2014if the seed is strong.<\/li>\n<\/ul>\n\n\n\n

                                          Lookalike Audience vs Broad Targeting<\/h3>\n\n\n\n
                                            \n
                                          • Broad targeting<\/strong> uses minimal constraints (often just geo\/age) and relies on the platform to find converters.<\/li>\n
                                          • A Lookalike Audience<\/strong> adds a structured prior: \u201cfind people like these proven users.\u201d<\/li>\n
                                          • Broad can scale well with strong creative and conversion signals; lookalikes can provide a more controlled ramp, especially in newer accounts.<\/li>\n<\/ul>\n\n\n\n

                                            Who Should Learn Lookalike Audience<\/h2>\n\n\n\n
                                              \n
                                            • Marketers:<\/strong> To scale acquisition efficiently and build repeatable Paid Marketing<\/strong> playbooks.<\/li>\n
                                            • Analysts:<\/strong> To evaluate cohort quality, attribution bias, and true incrementality of Paid Social<\/strong> growth.<\/li>\n
                                            • Agencies:<\/strong> To standardize onboarding audits (seed quality, event mapping, exclusions) and deliver consistent outcomes.<\/li>\n
                                            • Business owners and founders:<\/strong> To understand how to grow beyond retargeting without wasting budget on low-quality traffic.<\/li>\n
                                            • Developers and data teams:<\/strong> To implement clean event tracking, server-side integrations, and data pipelines that make lookalikes reliable.<\/li>\n<\/ul>\n\n\n\n

                                              Summary of Lookalike Audience<\/h2>\n\n\n\n

                                              A Lookalike Audience<\/strong> is a Paid Marketing<\/strong> targeting method that finds new prospects similar to your best existing customers or users. It matters because it enables scalable acquisition with stronger relevance than many broad prospecting methods. In Paid Social<\/strong>, it\u2019s most effective when built from high-quality seeds, paired with correct conversion optimization, and measured against downstream business outcomes\u2014not just clicks or cheap leads.<\/p>\n\n\n\n

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

                                              1) What is a Lookalike Audience and when should I use it?<\/h3>\n\n\n\n

                                              A Lookalike Audience<\/strong> is a modeled audience built from a seed group (like purchasers or qualified leads) to find similar new prospects. Use it when you want to scale prospecting beyond retargeting while keeping relevance and efficiency.<\/p>\n\n\n\n

                                              2) What makes a good seed audience for Lookalike Audience creation?<\/h3>\n\n\n\n

                                              The best seeds reflect real business value: purchasers, repeat buyers, high-LTV customers, retained users, or sales-qualified leads. Avoid seeds based only on low-intent engagement unless you have no better signal.<\/p>\n\n\n\n

                                              3) How big should my Lookalike Audience be?<\/h3>\n\n\n\n

                                              Start tighter (more similar, smaller reach) for efficiency testing, then expand to larger sizes for scale once performance and creative are stable. The \u201cright\u201d size depends on budget, market size, and conversion volume.<\/p>\n\n\n\n

                                              4) Does Lookalike Audience work in Paid Social if tracking is limited?<\/h3>\n\n\n\n

                                              Yes, but results depend more heavily on seed quality, event reliability, and measurement strategy. In privacy-constrained environments, validate performance with stronger down-funnel metrics and, when possible, incrementality tests.<\/p>\n\n\n\n

                                              5) Should I exclude existing customers when using Lookalike Audience?<\/h3>\n\n\n\n

                                              Usually yes for acquisition campaigns, especially in Paid Marketing<\/strong> where you want incremental new customers. Exceptions exist (e.g., cross-sell to existing customers), but manage overlap intentionally.<\/p>\n\n\n\n

                                              6) Why did my Lookalike Audience performance drop over time?<\/h3>\n\n\n\n

                                              Common causes include seed drift (your customer mix changed), creative fatigue, market competition, saturation (small audience), tracking changes, or overlapping ad sets competing in the same auctions.<\/p>\n\n\n\n

                                              7) Is a Lookalike Audience better than broad targeting?<\/h3>\n\n\n\n

                                              Not always. A Lookalike Audience<\/strong> can be a strong starting point for scaling, while broad targeting can outperform once you have strong creative and conversion signals. Many mature Paid Social<\/strong> programs test both and allocate budget based on measured outcomes.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                              A **Lookalike Audience** is a targeting method in **Paid Marketing** that helps you reach new people who \u201clook like\u201d your best existing customers or users. Instead of guessing interests or relying purely on broad demographics, you start with a high-quality source audience (such as purchasers, subscribers, or high-LTV customers) and let an ad platform find similar profiles at scale.<\/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":[1910],"tags":[],"class_list":["post-10499","post","type-post","status-publish","format-standard","hentry","category-paid-social"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/10499","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=10499"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/10499\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=10499"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=10499"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=10499"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}