{"id":2436,"date":"2026-09-25T08:46:07","date_gmt":"2026-09-25T08:46:07","guid":{"rendered":"https:\/\/www.heliosz.ai\/blog\/?p=2436"},"modified":"2026-09-25T08:49:38","modified_gmt":"2026-09-25T08:49:38","slug":"predictive-analytics-in-marketing","status":"publish","type":"post","link":"https:\/\/www.heliosz.ai\/blog\/predictive-analytics-in-marketing\/","title":{"rendered":"Predictive Analytics in Marketing: The Shift from Measurement to Commercial Intelligence"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Every quarter, leadership teams ask marketing some version of the same question: if we move a million dollars from one channel to another, what happens to revenue? Most marketing organizations can explain in detail what last quarter&#8217;s spend delivered. Far fewer can say with confidence what next quarter&#8217;s spend will deliver.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That gap is becoming expensive. <a href=\"https:\/\/www.gartner.com\/en\/newsroom\/press-releases\/2026-05-11-gartner-2026-cmo-spend-survey-finds-cmos-allocate-15-point-3-percent-of-marketing-budgets-to-ai-but-only-30-percent-are-ready-to-scale-ai-capabilities\" target=\"_blank\" rel=\"noopener nofollow\" title=\"\"><strong>Gartner&#8217;s 2026 CMO Spend Survey<\/strong><\/a> found that marketing budgets average <strong>7.8% of company revenue,<\/strong> essentially flat, and <strong>56% of CMOs<\/strong> say they lack the budget to deliver their 2026 strategy. At the same time, CMOs are directing an average of <strong>15.3% of <\/strong><a href=\"\/blog\/marketing-budget-optimization-guide-to-maximum-roi\/\" target=\"_blank\" rel=\"noopener nofollow\" title=\"\"><strong>marketing budgets<\/strong><\/a> to AI initiatives, yet only <strong>30% report mature AI readiness.<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">When every dollar is contested, looking backward is no longer enough. <strong>Predictive analytics in marketing<\/strong> is shifting the role of analytics from explaining what happened to estimating what will happen next, giving leaders a sounder basis for deciding where to invest.<\/p>\n\n\n\n<style>\n    .custom-bg {\n        background-image: url('https:\/\/www.heliosz.ai\/blog\/wp-content\/uploads\/2026\/04\/cta-bnr-bg.png');\n        background-repeat: no-repeat;\n        background-position: center center;\n        background-size: cover;\n        border-radius: 12px;\n    }\n    .custom-section {\n        display: flex;\n        align-items: center;\n        justify-content: center;\n        gap: 24px;\n        padding: 24px;\n    }\n    .custom-section .text-box a {\n        color: white;\n        text-decoration: unset;\n        font-size: 28px;\n    }\n\n     .custom-section .text-box a:hover {\n        cursor: pointer!important;\n        color: white!important;\n        text-decoration: underline!important;\n    }\n    @media(max-width:576px){\n        .modeling-img{\n            max-width: 100px;\n        }\n          .custom-section .text-box a {\n        color: white;\n        text-decoration: unset;\n        font-size: 18px;\n    }\n    }\n<\/style>\n<div class=\"custom-bg\">\n    <div class=\"custom-section\">\n        <img decoding=\"async\" src=\"https:\/\/www.heliosz.ai\/blog\/wp-content\/uploads\/2026\/07\/optivos-cta2.png\" alt=\"optivos\" class=\"modeling-img\">\n        <p class=\"text-box\"> <a target=\"_blank\" href=\"https:\/\/www.heliosz.ai\/platform\">Optimizing Commercial Decisions with Optivos<\/a> <\/p>\n    <\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Why Measurement Alone No Longer Answers the Board&#8217;s Questions<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Traditional <strong>marketing measurement <\/strong>was built to report, and dashboards do that well. But budget decisions are about the future, and a report on the past can only guide them indirectly.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The foundations of that measurement are also weakening. Privacy regulation, browser changes, and platform restrictions have reduced the signals available for tracking customer journeys, leaving click-based attribution less complete than it once appeared. Confidence has not kept pace with investment: a late-2025 study of US marketers by TransUnion and EMARKETER found that 54.1% saw no improvement in measurement confidence over the previous year, and <strong>14.3% said it had declined.<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The consequences show up in the boardroom. Reallocation is slow because teams wait for results to accumulate. Planning defaults to last year&#8217;s split, adjusted by a few points. And when marketing and finance hold different views of performance, the discussion becomes a negotiation rather than a decision.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Predictive marketing analytics<\/strong> addresses the core limitation by letting leaders evaluate options before committing the money.<\/p>\n\n\n\n<p class=\"has-ast-global-color-1-color has-ast-global-color-4-background-color has-text-color has-background has-link-color wp-elements-1 wp-block-paragraph\"><strong>Did you know?<\/strong> <br><strong>Predictive analytics in marketing or predictive marketing,<\/strong> uses data mining, artificial intelligence, and statistical modeling to analyze data and predict outcomes such as campaign performance, customer behavior, and industry trends. These insights help marketers make more informed and proactive decisions.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What Is Predictive Modeling?<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">At its simplest, <strong>predictive modeling<\/strong> uses historical and current data to estimate the probability of a future outcome. It is a form of predictive data analysis that finds patterns in past behavior and applies them to decisions that haven&#8217;t been made yet.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Most marketing applications rely on a small set of predictive analytics models:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Propensity models<\/strong> estimate which customers or prospects are most likely to buy.<\/li>\n\n\n\n<li><strong>Churn models<\/strong> flag customers likely to leave before they do.<\/li>\n\n\n\n<li><strong>Customer lifetime value models<\/strong> forecast how much a customer is likely to be worth over time.<\/li>\n\n\n\n<li><strong>Forecasting and marketing mix models<\/strong> estimate how changes in channel spend are likely to affect sales.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Two points matter for leaders. First, predictions are probabilities, not guarantees, and a good model states its uncertainty openly. Second, predictive analytics tells you what is likely to happen, while prescriptive analytics goes a step further and recommends what to do about it. The most useful systems connect the two.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Types of Predictive Models in Marketing<\/strong><\/h3>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"600\" src=\"https:\/\/www.heliosz.ai\/blog\/wp-content\/uploads\/2026\/09\/types-of-predictive-models-in-marketing-1024x600.jpg\" alt=\"Types of Predictive Models in Marketing\" class=\"wp-image-2438\" srcset=\"https:\/\/www.heliosz.ai\/blog\/wp-content\/uploads\/2026\/09\/types-of-predictive-models-in-marketing-1024x600.jpg 1024w, https:\/\/www.heliosz.ai\/blog\/wp-content\/uploads\/2026\/09\/types-of-predictive-models-in-marketing-300x176.jpg 300w, https:\/\/www.heliosz.ai\/blog\/wp-content\/uploads\/2026\/09\/types-of-predictive-models-in-marketing-768x450.jpg 768w, https:\/\/www.heliosz.ai\/blog\/wp-content\/uploads\/2026\/09\/types-of-predictive-models-in-marketing-1536x901.jpg 1536w, https:\/\/www.heliosz.ai\/blog\/wp-content\/uploads\/2026\/09\/types-of-predictive-models-in-marketing-2048x1201.jpg 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Different predictive models answer different business questions. Some focus on individual customer behavior, while others help marketing leaders forecast demand, revenue, or the potential impact of budget changes.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Model Type<\/strong><\/td><td><strong>Primary Use Case<\/strong><\/td><td><strong>How It Works<\/strong><\/td><td><strong>Marketing Example<\/strong><\/td><\/tr><tr><td><strong>Classification Models<\/strong><\/td><td>Predicting predefined outcomes<\/td><td>Uses historical data and known categories to classify customers, leads, or other records into specific groups.<\/td><td>Predicting whether a prospect is likely to convert or unlikely to convert based on past interactions and customer characteristics.<\/td><\/tr><tr><td><strong>Clustering Models<\/strong><\/td><td>Finding meaningful customer segments<\/td><td>Analyzes similarities across data points to identify natural groups without requiring predefined categories.<\/td><td>Grouping customers based on purchase frequency, spending patterns, engagement, or product preferences.<\/td><\/tr><tr><td><strong>Regression Models<\/strong><\/td><td>Predicting measurable business outcomes<\/td><td>Examines relationships between variables to estimate a numerical result based on changes in one or more factors.<\/td><td>Estimating how changes in marketing investment could influence revenue, conversions, or customer demand.<\/td><\/tr><tr><td><strong>Time Series Models<\/strong><\/td><td>Forecasting future trends and demand<\/td><td>Analyzes historical data over time to identify trends, seasonality, and recurring patterns that can inform future forecasts.<\/td><td>Forecasting seasonal sales, campaign demand, website traffic, or changes in customer engagement.<\/td><\/tr><tr><td><strong>Propensity Models<\/strong><\/td><td>Predicting the likelihood of a specific customer action<\/td><td>Assigns a probability to a future behavior by analyzing historical customer characteristics and interactions.<\/td><td>Identifying customers most likely to purchase, upgrade, renew, or respond to a particular campaign.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Where Predictive Analytics Creates Commercial Value<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Predictive capability matters when it changes a decision with financial consequences. Three areas deliver the clearest returns.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Budget allocation and scenario planning.<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Marketing mix models show how returns change as spend rises in each channel, including the point at which additional investment stops paying off. Leaders can compare scenarios, such as shifting budget from paid search to video, before committing funds. Annual planning moves from a debate about last year&#8217;s results to a discussion of expected outcomes.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Customer value and retention.<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">With predictive analytics, marketing teams can identify which customers are likely to churn and which are likely to grow in value. Retention offers, service interventions, and loyalty investment can then go where they will have the greatest effect.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Revenue and demand forecasting.<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">When <a href=\"\/blog\/enterprise-marketing-forecasting-tool\/\" target=\"_blank\" rel=\"noopener\" title=\"\">marketing forecasts<\/a> pipeline and revenue using the same assumptions as finance, both functions plan from a common view. Sales capacity, inventory, and cash planning all benefit from earlier, more reliable demand signals.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In each case, AI predictive analytics shortens the time between a signal appearing and a decision being made.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>From Measurement to Commercial Intelligence<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The larger shift is in what analytics is expected to do. Measurement describes marketing performance. <strong><a href=\"https:\/\/www.heliosz.ai\/connected-commercial-intelligence-platform-guide\" target=\"_blank\" rel=\"noopener\" title=\"\">Commercial intelligence platform <\/a><\/strong>connects marketing activity to business outcomes in a way leaders can act on.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Measurement<\/strong><\/td><td><strong>Commercial intelligence<\/strong><\/td><\/tr><tr><td>Primarily explains what happened and how performance was measured<\/td><td>Builds on measurement to connect performance with what is likely to happen next<\/td><\/tr><tr><td>Often focuses on channel and platform metrics<\/td><td>Focuses on revenue, margin, and growth &nbsp;<\/td><\/tr><tr><td>Produces performance reports and analysis<\/td><td>Supports specific investment and business decisions<\/td><\/tr><tr><td>Typically owned within marketing<\/td><td>Shared with finance and leadership<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">AI-powered analytics accelerates this shift. Predictive artificial intelligence can process far more signals than any analyst team and update forecasts as conditions change. That speed comes with a condition: predictions are only as reliable as the data underneath them. If customer, campaign, and revenue data are defined differently across systems, a model will produce confident forecasts built on inconsistent inputs. A well-structured data foundation is the prerequisite for commercial intelligence, not an afterthought.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>How to Evaluate Marketing Tools with Predictive Analytics<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">At <a href=\"https:\/\/blog.google\/products\/ads-commerce\/google-marketing-live-2026-collection\/\" target=\"_blank\" rel=\"noopener nofollow\" title=\"\">Google Marketing Live in May 2026<\/a>, Google announced it is building Meridian, its open-source <strong>marketing mix model,<\/strong> into Google Analytics 360, and introduced Qualified Future Conversions, a Gemini-powered metric that connects current ad spend to predicted future sales and is currently in a limited pilot. GA4 already offers built-in predictive metrics such as purchase probability, churn probability, and predicted revenue, and major marketing clouds and CRM platforms increasingly include predictive scoring and audience features.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">With so many <strong>predictive analytics tools <\/strong>available, the question for leaders is less about which platform has AI and more about which one they can trust with budget decisions. Five questions help separate strong <strong>predictive analytics solutions<\/strong> from the rest:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Can it explain its predictions?<\/strong> Leaders should be able to see which factors drive a forecast.<\/li>\n\n\n\n<li><strong>Does it predict business outcomes?<\/strong> Forecasts of revenue and margin matter more than forecasts of platform metrics.<\/li>\n\n\n\n<li><strong>Is it validated against reality?<\/strong> Predictions should be checked against actual results every cycle, ideally supported by incrementality testing.<\/li>\n\n\n\n<li><strong>Is it independent?<\/strong> When the company selling the media also predicts its value, leaders should look for independent validation.<\/li>\n\n\n\n<li><strong>Does it fit your data foundation?<\/strong> A predictive analytics platform should work with your existing definitions and systems rather than creating another isolated view.<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\">Marketing tools with predictive analytics are quickly becoming standard. The advantage will come from how rigorously leaders test and govern them.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Common Challenges of Predictive Analytics in Marketing<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Predictive analytics<\/strong> can improve marketing decisions, but its effectiveness depends on the quality of the data, systems, and models behind it.<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Data quality:<\/strong> Inconsistent, incomplete, or outdated data can lead to unreliable predictions. Clean, validate, and standardize data before using it for predictive modeling, and establish clear data governance.<\/li>\n\n\n\n<li><strong>Integration with legacy systems:<\/strong> Predictive analytics often needs data from CRM, ERP, marketing, and other business systems, which may be difficult to connect. Use APIs, middleware, or iPaaS solutions to connect data across systems.<\/li>\n\n\n\n<li><strong>Model bias and fairness:<\/strong> Biased or unrepresentative data can produce biased predictions and lead to poor decisions. Use representative datasets, review data and labels for gaps or bias, and evaluate model performance across relevant groups before adjusting or retraining the model.<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Where Leaders Should Start<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Building predictive capability doesn&#8217;t require a large transformation program. Four steps create momentum:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Choose one high-value decision<\/strong>, such as quarterly budget allocation or retention investment, and start there.<\/li>\n\n\n\n<li><strong>Make sure the data behind it is structured and trusted<\/strong>, with definitions agreed between marketing and finance.<\/li>\n\n\n\n<li><strong>Compare predictions with actual results every cycle<\/strong> and share accuracy openly, because trust in forecasts is earned.<\/li>\n\n\n\n<li><strong>Make finance a co-owner of the model<\/strong>, so forecasts inform company planning, not just marketing plans.<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Conclusion<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Measurement will always matter, but with flat budgets and rising expectations, knowing the past is not enough to direct the future.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Predictive analytics in marketing<\/strong> gives leaders a way to test decisions before making them and to connect marketing investment directly to commercial outcomes. The organizations that treat prediction as a governed, validated business capability, rather than a feature inside another tool, will allocate every dollar with greater confidence.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Stop guessing where the next dollar goes.<\/strong><br><strong>Optivos unifies your data and forecasts the revenue impact of every budget decision, before you spend.<\/strong><\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-3e41869c wp-block-buttons-is-layout-flex\">\n<div style=\"--wp--block-button--width: 34.3;\" class=\"wp-block-button has-custom-width wp-block-button__width\"><a class=\"wp-block-button__link has-background has-text-align-center has-medium-font-size has-custom-font-size wp-element-button\" href=\"https:\/\/www.heliosz.ai\/contact-us\" style=\"background-color:#229346;padding-top:var(--wp--preset--spacing--30);padding-right:0;padding-bottom:var(--wp--preset--spacing--30);padding-left:0\">Request a Demo<\/a><\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>FAQ&#8217;s About Predictive Analytics in Marketing<\/strong><\/h2>\n\n\n\n<div data-schema-only=\"false\" class=\"wp-block-aioseo-faq\"><h3 class=\"aioseo-faq-block-question\">How does predictive analytics work without third-party cookies?<\/h3><div class=\"aioseo-faq-block-answer\">\n<p class=\"wp-block-paragraph\">It relies on first-party data and privacy-safe methods like <a href=\"\/marketing-mix-modeling\" target=\"_blank\" rel=\"noopener\" title=\"\"><strong>marketing mix modeling<\/strong><\/a> and incrementality testing. These don&#8217;t track individuals across the web. As privacy rules tighten, brands with strong first-party data get the most accurate forecasts.<\/p>\n<\/div><\/div>\n\n\n\n<div data-schema-only=\"false\" class=\"wp-block-aioseo-faq\"><h3 class=\"aioseo-faq-block-question\">Why do most predictive analytics projects fail to deliver?<\/h3><div class=\"aioseo-faq-block-answer\">\n<p class=\"wp-block-paragraph\">Usually the cause is <a href=\"\/blog\/what-breaks-first-without-unified-marketing-data\/\" target=\"_blank\" rel=\"noopener\" title=\"\"><strong>siloed data<\/strong><\/a><strong>,<\/strong> not the algorithm. When sales, marketing, and customer data live in separate tools, models see only part of the picture. Predictions also fail when insights never reach the people making decisions. Fix the data foundation first, then the models.<\/p>\n<\/div><\/div>\n\n\n\n<div data-schema-only=\"false\" class=\"wp-block-aioseo-faq\"><h3 class=\"aioseo-faq-block-question\">How does AI change predictive marketing today?<\/h3><div class=\"aioseo-faq-block-answer\">\n<p class=\"wp-block-paragraph\">Modern platforms use agentic AI to go beyond a forecast. They flag a change, explain why it happened, and recommend the next action. Marketers spend less time building reports and more time acting on insights.<\/p>\n<\/div><\/div>\n\n\n\n<div data-schema-only=\"false\" class=\"wp-block-aioseo-faq\"><h3 class=\"aioseo-faq-block-question\">How is predictive analytics different from marketing measurement?<\/h3><div class=\"aioseo-faq-block-answer\">\n<p class=\"wp-block-paragraph\">Measurement reports what already happened. Predictive analytics estimates what will happen next, so leaders can compare options before committing budget. Measurement is still needed, because it provides the data that predictions are built on.<\/p>\n<\/div><\/div>\n\n\n\n<div data-schema-only=\"false\" class=\"wp-block-aioseo-faq\"><h3 class=\"aioseo-faq-block-question\">What is the difference between predictive and prescriptive analytics?<\/h3><div class=\"aioseo-faq-block-answer\">\n<p class=\"wp-block-paragraph\">Predictive analytics tells you what is likely to happen. Prescriptive analytics recommends what to do about it. The most useful platforms connect the two, so a forecast comes with a next step.<\/p>\n<\/div><\/div>\n\n\n\n<div data-schema-only=\"false\" class=\"wp-block-aioseo-faq\"><h3 class=\"aioseo-faq-block-question\">How does predictive analytics improve marketing budget allocation?<\/h3><div class=\"aioseo-faq-block-answer\">\n<p class=\"wp-block-paragraph\">Marketing mix models show how returns change as spend rises in each channel, including where extra investment stops paying off. Leaders can compare scenarios, such as moving budget from paid search to video, before spending.<\/p>\n<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Every quarter, leadership teams ask marketing some version of the same question: if we move a million dollars from one [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":2439,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"footnotes":""},"categories":[129],"tags":[],"class_list":["post-2436","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-marketing-performance-analytics"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.heliosz.ai\/blog\/wp-json\/wp\/v2\/posts\/2436","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.heliosz.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.heliosz.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.heliosz.ai\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.heliosz.ai\/blog\/wp-json\/wp\/v2\/comments?post=2436"}],"version-history":[{"count":6,"href":"https:\/\/www.heliosz.ai\/blog\/wp-json\/wp\/v2\/posts\/2436\/revisions"}],"predecessor-version":[{"id":2445,"href":"https:\/\/www.heliosz.ai\/blog\/wp-json\/wp\/v2\/posts\/2436\/revisions\/2445"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.heliosz.ai\/blog\/wp-json\/wp\/v2\/media\/2439"}],"wp:attachment":[{"href":"https:\/\/www.heliosz.ai\/blog\/wp-json\/wp\/v2\/media?parent=2436"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.heliosz.ai\/blog\/wp-json\/wp\/v2\/categories?post=2436"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.heliosz.ai\/blog\/wp-json\/wp\/v2\/tags?post=2436"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}