{"id":19869,"date":"2020-01-30T16:35:49","date_gmt":"2020-01-30T16:35:49","guid":{"rendered":"https:\/\/www.arimetrics.com\/glosario-digital\/predictive-analytics"},"modified":"2026-09-29T15:39:45","modified_gmt":"2026-09-29T15:39:45","slug":"predictive-analytics","status":"publish","type":"encyclopedia","link":"https:\/\/www.arimetrics.com\/en\/digital-glossary\/predictive-analytics","title":{"rendered":"Predictive Analytics"},"content":{"rendered":"<p><img decoding=\"async\" class=\"boxpad alignright wp-image-13882 size-full\" src=\"https:\/\/www.arimetrics.com\/wp-content\/uploads\/2021\/11\/33-3.jpg\" alt=\"Predictive Analytics\" width=\"300\" height=\"300\" \/><\/p>\n<p><strong>Definition:<\/strong><\/p>\n<p><em><strong>Predictive analytics<\/strong><\/em> is the discipline of using historical and current data, statistical techniques, and machine learning to estimate outcomes that have not yet been observed. It can produce a probability, classification, risk score, or numerical forecast for customers, transactions, equipment, or other units of analysis.<\/p>\n<p>It forms part of <strong>advanced analytics<\/strong> and can draw on data from operational systems, digital platforms, and <a href=\"https:\/\/www.arimetrics.com\/en\/digital-glossary\/big-data\">big data<\/a> environments. <a href=\"https:\/\/www.arimetrics.com\/en\/digital-glossary\/descriptive-analytics\">Descriptive analytics<\/a> summarizes what happened; predictive analytics estimates what may happen under specified conditions, while <a href=\"https:\/\/www.arimetrics.com\/en\/digital-glossary\/prescriptive-analytics\">prescriptive analytics<\/a> compares possible actions according to objectives and constraints.<\/p>\n\n<h2>Predictive models for predictive analytics<\/h2>\n<p>A <a href=\"https:\/\/www.arimetrics.com\/en\/digital-glossary\/predictive-model\"><strong>predictive model<\/strong><\/a> is one component of predictive analytics. It is trained on known examples to learn relationships between input variables and an outcome of interest. It is then applied to new data to generate estimates within the scope and conditions for which it was developed.<\/p>\n<p>Depending on the problem, the <strong>type of output<\/strong> may be a class, such as fraud or not fraud; a conversion probability; a risk score; an expected quantity, such as demand; or the estimated time until an event. Predictive analytics also covers data preparation, variable selection, model evaluation, integration into a process, and performance monitoring.<\/p>\n<p>Historical patterns express <strong>statistical relationships<\/strong>; they do not establish causality on their own. A prediction can help anticipate scenarios and prioritize analysis, while the final decision must also account for costs, objectives, constraints, and consequences.<\/p>\n<h2>Advantages of predictive analytics<\/h2>\n<p>When the problem is clearly defined and the data is suitable, predictive analytics can use scattered signals systematically. Its main contributions include:<\/p>\n<ul>\n<li><strong>Planning:<\/strong> Estimating demand, workload, or inventory needs so that resources can be prepared in advance.<\/li>\n<li><strong>Prioritization:<\/strong> Ranking cases, opportunities, or alerts by a probability or score that is relevant to the process.<\/li>\n<li><strong>Early detection:<\/strong> Identifying signals associated with fraud, churn, default, equipment failure, or other events of interest.<\/li>\n<li><strong>Personalization:<\/strong> Adapting recommendations, messages, or offers to observed behavior and preferences.<\/li>\n<\/ul>\n<p>These capabilities can improve resource allocation and <a href=\"https:\/\/www.arimetrics.com\/en\/digital-glossary\/roi\">return on investment<\/a>, but they do not guarantee either outcome. The <strong>value obtained<\/strong> depends on data quality, model validity, the cost of acting on its outputs, and subsequent measurement of the real impact.<\/p>\n<h2>Challenges of predictive analytics<\/h2>\n<p>A useful prediction requires technical and organizational controls throughout its life cycle. The most common challenges concern both the data and the way results are used:<\/p>\n<ul>\n<li><strong>Data quality:<\/strong> Errors, missing values, duplicates, or inconsistent definitions can degrade estimates.<\/li>\n<li><strong>Representativeness:<\/strong> A biased training set or one that differs from the real population can produce unequal or unreliable results.<\/li>\n<li><strong>Evaluation:<\/strong> The metric must match the objective and account for the costs of false positives, false negatives, and estimation errors.<\/li>\n<li><strong>Drift:<\/strong> Patterns change with the market, user behavior, or the process itself, so performance must be monitored.<\/li>\n<li><strong>Governance:<\/strong> Privacy, security, explainability, and human oversight require controls proportionate to the risk of each use.<\/li>\n<\/ul>\n<p>It is also necessary to prevent <strong>data leakage<\/strong>, which occurs when training includes information that would not be available at the actual time of prediction. Validation on separate data, comparison with a simple baseline, and periodic review help uncover this and other failures before predictions support decisions.<\/p>\n<h2>Predictive analytics in digital marketing<\/h2>\n<p>In <a href=\"https:\/\/www.arimetrics.com\/en\/digital-marketing-agency-spain\">digital marketing<\/a>, predictive analytics is used to estimate the <strong>likelihood of conversion<\/strong>, churn, or campaign response, forecast demand, and prioritize audiences or contacts. Data may come from advertising interactions, browsing, purchases, CRM systems, and customer support, provided there is a defined purpose and lawful basis for its use.<\/p>\n<p>A <strong>propensity score<\/strong> can rank users by their estimated likelihood of taking an action. It can guide resource allocation or campaign adaptation, but it should be evaluated against a control group or relevant baseline: targeting only people who already appeared more likely to act does not show that the intervention caused additional conversions.<\/p>\n<p><strong>Recommendation systems<\/strong> also use historical signals to rank products or content. Their quality is not limited to immediate clicks; it can be assessed through conversion, margin, diversity, satisfaction, retention, or other objectives consistent with the strategy and user experience.<\/p>\n<p>Some platforms provide ready-made <strong>predictive metrics<\/strong>, while other use cases require custom models. The choice depends on data volume and quality, the need for explanation, update frequency, and the ability to integrate an output into a measurable action.<\/p>\n<h2>Tools for predictive analytics<\/h2>\n<p>Tools support <strong>different stages<\/strong>, and not all of them build models from scratch. Their role within the process can be grouped as follows:<\/p>\n<ul>\n<li><strong>Languages and libraries:<\/strong> They support data preparation, model training, and evaluation through environments such as Python or R and their statistical ecosystems.<\/li>\n<li><strong>Modeling platforms:<\/strong> They support experiments, version control, deployment, automation, and performance monitoring.<\/li>\n<li><strong>Data warehouses:<\/strong> They run queries and, in some cases, training or scoring close to the data to reduce movement and preserve traceability.<\/li>\n<li><strong>Business systems:<\/strong> Analytics, CRM, or automation applications consume scores and turn them into segments, alerts, or operational rules.<\/li>\n<\/ul>\n<p>Selection should reflect the <strong>use case<\/strong>, not only how easily a prediction can be generated. Relevant criteria include integration with data sources, reproducibility, access control, drift monitoring, total cost, and the team&#8217;s ability to interpret and maintain the system.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Predictive analytics estimates future outcomes from data and models. Learn how it works, its uses, benefits, limitations, and tools.<\/p>\n","protected":false},"author":6,"featured_media":81119,"template":"","encyclopedia-tag":[1302],"class_list":["post-19869","encyclopedia","type-encyclopedia","status-publish","has-post-thumbnail","hentry","encyclopedia-tag-data-science"],"_links":{"self":[{"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/encyclopedia\/19869","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/encyclopedia"}],"about":[{"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/types\/encyclopedia"}],"author":[{"embeddable":true,"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/users\/6"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/media\/81119"}],"wp:attachment":[{"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/media?parent=19869"}],"wp:term":[{"taxonomy":"encyclopedia-tag","embeddable":true,"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/encyclopedia-tag?post=19869"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}