{"id":34092,"date":"2022-05-10T09:47:05","date_gmt":"2022-05-10T09:47:05","guid":{"rendered":"https:\/\/www.arimetrics.com\/glosario-digital\/behavioral-marketing"},"modified":"2026-09-30T05:32:01","modified_gmt":"2026-09-30T05:32:01","slug":"behavioral-marketing","status":"publish","type":"encyclopedia","link":"https:\/\/www.arimetrics.com\/en\/digital-glossary\/behavioral-marketing","title":{"rendered":"Behavioral Marketing"},"content":{"rendered":"<p><img decoding=\"async\" class=\"boxpad alignright wp-image-35053 size-full\" src=\"https:\/\/www.arimetrics.com\/wp-content\/uploads\/2022\/06\/Behavioral_Marketing.png\" alt=\"Representation of behavioral marketing\" width=\"300\" height=\"300\" srcset=\"https:\/\/www.arimetrics.com\/wp-content\/uploads\/2022\/06\/Behavioral_Marketing.png 300w, https:\/\/www.arimetrics.com\/wp-content\/uploads\/2022\/06\/Behavioral_Marketing-150x150.png 150w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><strong>Definition:<\/strong><\/p>\n<p><strong>Behavioral marketing<\/strong> is the use of signals about the actual actions of a person or group to segment audiences, adapt messages, and decide when or where to activate a marketing action. Those signals may come from visits, internal searches, purchases, email responses, app usage, or interactions with content.<\/p>\n<p>It does not provide complete knowledge of each user&#8217;s motives. It describes <strong>observable patterns<\/strong> and produces hypotheses that should be tested against results. It also does not always depend on <a href=\"https:\/\/www.arimetrics.com\/en\/digital-glossary\/cookie\">cookies<\/a>: it can use first-party data, aggregated events, or declared information, depending on the channel, technology, and applicable legal basis.<\/p>\n\n<h2>Signals used in behavioral marketing<\/h2>\n<p>The strategy starts with <strong>measurable events<\/strong>, not traits assigned without evidence. The value of each signal depends on its quality, recency, and relationship to the decision at hand. Common sources include:<\/p>\n<ul>\n<li><strong>Browsing:<\/strong> page views, searches, reading depth, or session sequences.<\/li>\n<li><strong>Transactions:<\/strong> purchases, returns, frequency, recency, and product categories.<\/li>\n<li><strong>Interactions:<\/strong> opens, clicks, downloads, forms, or feature usage.<\/li>\n<li><strong>Commercial relationship:<\/strong> customer stage, support history, and stated preferences.<\/li>\n<\/ul>\n<p>A single signal can support several interpretations. Viewing a product does not prove purchase intent, while cart abandonment may result from price, an interruption, or a technical problem. Teams should therefore combine <strong>context and frequency<\/strong> without turning correlation into certainty about an individual.<\/p>\n<h2>How a behavioral strategy works<\/h2>\n<p>The process converts activity data into an <strong>activation rule<\/strong>. It should begin with an objective and measurement plan before segments or automations are created. A typical workflow includes:<\/p>\n<ol>\n<li><strong>Define the objective:<\/strong> specify the decision to improve and the indicator used to assess it.<\/li>\n<li><strong>Collect signals:<\/strong> select only the necessary events and document their source.<\/li>\n<li><strong>Group patterns:<\/strong> apply <a href=\"https:\/\/www.arimetrics.com\/en\/digital-glossary\/behavioral-segmentation\">behavioral segmentation<\/a> through understandable, reviewable rules.<\/li>\n<li><strong>Activate actions:<\/strong> adapt content, frequency, recommendations, channels, or contact timing.<\/li>\n<li><strong>Evaluate results:<\/strong> compare groups, control for bias, and check whether the rule remains useful.<\/li>\n<\/ol>\n<p>Activation can occur on a website, in email, within an app, through a CRM, or on an advertising platform. Personalization need not be individual: <strong>group-level adaptation<\/strong> for people with similar needs is often more stable and less intrusive.<\/p>\n<h2>Behavioral marketing, advertising, and segmentation<\/h2>\n<p>These concepts are related but <strong>not interchangeable<\/strong>. Separating them clarifies the scope of data use and prevents imprecise claims:<\/p>\n<ul>\n<li><strong>Behavioral marketing:<\/strong> a broad approach that uses observed actions to guide multiple marketing decisions.<\/li>\n<li><strong>Behavioral advertising:<\/strong> an application focused on selecting or personalizing ads according to behavioral signals.<\/li>\n<li><strong>Behavioral segmentation:<\/strong> a method for grouping people or sessions by defined patterns; it can support campaigns without determining the message by itself.<\/li>\n<\/ul>\n<p>It also differs from analysis of the <a href=\"https:\/\/www.arimetrics.com\/en\/digital-glossary\/customer-journey\">customer journey<\/a>. A journey maps stages, touchpoints, and friction throughout a relationship; the behavioral approach uses <strong>specific signals<\/strong> from that journey to decide an intervention.<\/p>\n<h2>Benefits and limitations<\/h2>\n<p>When the data is relevant, behavioral marketing can improve the <strong>operational relevance<\/strong> of communications and reduce unnecessary impressions. Results are not automatic: they depend on the product, sample, execution, and method used to attribute effects.<\/p>\n<ul>\n<li><strong>Better prioritization:<\/strong> resources can focus on situations associated with an observable need.<\/li>\n<li><strong>Consistent experiences:<\/strong> messages can reflect recent actions and avoid obvious repetition.<\/li>\n<li><strong>Faulty inferences:<\/strong> a pattern may be incomplete, outdated, or produced by several people sharing a device.<\/li>\n<li><strong>Adverse effects:<\/strong> excessive frequency or conspicuous personalization can create discomfort and rejection.<\/li>\n<\/ul>\n<p>An increase in conversions after a campaign launches does not prove that segmentation caused it. Assessing impact requires <strong>controlled comparisons<\/strong>, metrics selected in advance, and monitoring of consequences such as unsubscribes, complaints, or fatigue.<\/p>\n<h2>Responsible implementation<\/h2>\n<p>Implementation should integrate measurement, experience, and <strong>privacy by design<\/strong>. In the European Union, the use of personal data and tracking technologies must be reviewed under the <a href=\"https:\/\/www.arimetrics.com\/en\/digital-glossary\/gdpr\">GDPR<\/a> and the applicable rules for communications and user devices.<\/p>\n<ul>\n<li><strong>Limit the data:<\/strong> collect what is necessary for an explicit purpose and retain it for a justified period.<\/li>\n<li><strong>Explain clearly:<\/strong> describe sources, uses, recipients, and control options in understandable language.<\/li>\n<li><strong>Respect choices:<\/strong> record consent when required and make withdrawal straightforward.<\/li>\n<li><strong>Protect audiences:<\/strong> avoid sensitive categories, discrimination, and unjustified exclusions.<\/li>\n<li><strong>Audit rules:<\/strong> review data quality, bias, frequency, security, and outcomes for each activation.<\/li>\n<\/ul>\n<p>A <a href=\"https:\/\/www.arimetrics.com\/en\/digital-marketing-agency-spain\">digital marketing strategy<\/a> should be able to explain which behavior activates an action, why it is relevant, and how success will be measured. That <strong>operational traceability<\/strong> allows a rule to be adjusted or stopped when it no longer provides value.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Learn what behavioral marketing is, which signals it uses, how it works, and the measurement, privacy, and segmentation limits it should respect.<\/p>\n","protected":false},"author":33,"featured_media":81193,"template":"","encyclopedia-tag":[1409,1313],"class_list":["post-34092","encyclopedia","type-encyclopedia","status-publish","has-post-thumbnail","hentry","encyclopedia-tag-audience-behavior","encyclopedia-tag-data-marketing"],"_links":{"self":[{"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/encyclopedia\/34092","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\/33"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/media\/81193"}],"wp:attachment":[{"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/media?parent=34092"}],"wp:term":[{"taxonomy":"encyclopedia-tag","embeddable":true,"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/encyclopedia-tag?post=34092"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}