{"id":3793,"date":"2025-01-23T11:56:00","date_gmt":"2025-01-23T10:56:00","guid":{"rendered":"https:\/\/trackboxx.com\/?p=3793"},"modified":"2025-07-02T21:19:46","modified_gmt":"2025-07-02T19:19:46","slug":"cohort-analysis-definition-and-relevance","status":"publish","type":"post","link":"https:\/\/trackboxx.com\/en\/kohortenanalyse-definition-und-relevanz\/","title":{"rendered":"Cohort analysis: definition and relevance"},"content":{"rendered":"<p>The&nbsp;<strong>Cohort analysis<\/strong>&nbsp;is an analysis procedure in which you&nbsp;<strong>Users based on certain characteristics or time periods<\/strong>&nbsp;into groups and observe their behaviour over a defined period of time. These groups are called \"cohorts\". They are often based on the time of an action, such as the registration date or the purchase date.<\/p>\n\n\n\n<p>The idea behind it: Instead of lumping all users and their behaviour together, you specifically highlight similar groups. This is how you determine, for example,&nbsp;<strong>whether the users who registered in January<\/strong>The behaviour of the newcomers is different to those who arrived in February or March.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Why is the cohort analysis relevant for you?<\/h2>\n\n\n\n<p>If you are in the&nbsp;<strong>B2B marketing<\/strong>If you work in product development or customer success, you want to understand as much as possible,&nbsp;<strong>Why<\/strong>&nbsp;and&nbsp;<strong>when<\/strong>&nbsp;your customers remain active or churn. Especially in long-term customer relationships (subscriptions, licence agreements, software-as-a-service), cohort analysis offers valuable insights, for example:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Customer Retention<\/strong>You can recognise whether customers remain active in the long term after onboarding or drop out after a short time.<\/li>\n\n\n\n<li><strong>Product optimisation<\/strong>: You can use different&nbsp;<strong>Product versions<\/strong>&nbsp;or&nbsp;<strong>Features<\/strong>&nbsp;by creating separate cohorts for each release date.<\/li>\n\n\n\n<li><strong>Efficient marketing<\/strong>You find out whether a particular campaign has a lasting effect or only brings short-term traffic.<\/li>\n<\/ul>\n\n\n\n<p>This level of detail distinguishes the cohort analysis from the classic overall analysis, which only provides you with average values. Instead, cohorts offer you a&nbsp;<strong>Timeline<\/strong>where you can constantly track the behaviour of your users.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">How the cohort analysis works<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Define criterion<\/strong>Firstly, you determine what the trigger is for your cohorts. You often choose the&nbsp;<strong>Registration date<\/strong>&nbsp;(e.g. the week or month in which users registered).<\/li>\n\n\n\n<li><strong>Form groups<\/strong>You then divide all users who have registered or purchased in a specified period into a common cohort.<\/li>\n\n\n\n<li><strong>Observe behaviour<\/strong>You track each cohort over several time intervals (days, weeks, months). This allows you to recognise patterns, e.g. whether many users drop out after two weeks or whether they order more products after their first purchase.<\/li>\n\n\n\n<li><strong>Interpreting and acting<\/strong>For example, if you discover that the July cohort has a particularly low retention rate, you can look for specific causes: Was it due to technical difficulties, support bottlenecks or unclear communication?<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\">Example: Cohort analysis in practice<\/h2>\n\n\n\n<p>Imagine you are running a&nbsp;<strong>Software-as-a-Service (SaaS)<\/strong>-tool. Every person who subscribes to your tool will automatically move into the cohort of their starting month. If you offer a 30-day trial, you can then see how many of these newcomers switch to a paid plan.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>January cohort<\/strong>100 registrations, 20 paying subscribers after the trial subscription<\/li>\n\n\n\n<li><strong>February cohort<\/strong>120 registrations, 35 paying subscribers<\/li>\n\n\n\n<li><strong>March cohort<\/strong>80 registrations, 15 paying subscribers<\/li>\n<\/ul>\n\n\n\n<p>Now you can see at a glance which month or which marketing campaign was particularly successful and can learn from this which factors favour a subscription upgrade.<\/p>\n\n\n\n<p><strong>Tools<\/strong>Many marketers use Google Analytics (in GA4, however, the cohort analysis is more limited than in the Universal Analytics version). Alternatively, specialised platforms or&nbsp;<strong>GDPR-compliant analysis tools<\/strong>&nbsp;like&nbsp;<a href=\"https:\/\/trackboxx.com\/en\/\">Trackboxx<\/a>&nbsp;also offer the option of creating cohort-based reports - often even more data protection-friendly.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Important key figures and interpretation aids<\/h2>\n\n\n\n<p>The following key figures are particularly important in a cohort analysis:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Retention rate<\/strong>Percentage of users who remain active or paying over a certain period of time.<\/li>\n\n\n\n<li><strong>Churn rate<\/strong>Percentage of users who leave the service (cancellations or inactivity).<\/li>\n\n\n\n<li><strong>Lifetime value (LTV)<\/strong>Calculates the average turnover generated by a customer over the entire period.<\/li>\n<\/ul>\n\n\n\n<p>For example, if you observe that the churn rate in a certain cohort rises sharply after the second month, you can take proactive measures - such as personalised emails or improved onboarding.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Data protection and GDPR aspects<\/h2>\n\n\n\n<p>Since a cohort analysis analyses user data over longer periods of time, the question arises:&nbsp;<strong>What data do I collect exactly?<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Minimise personal references<\/strong>Make sure that you only collect the information that you actually need - ideally pseudonymised data.<\/li>\n\n\n\n<li><strong>Legal basis<\/strong>: If you process marketing data, you will need a&nbsp;<strong>Consent<\/strong>&nbsp;(opt-in) or must prove a legitimate interest.<\/li>\n\n\n\n<li><strong>GDPR-compliant tools<\/strong>Server-side tracking or solutions such as Trackboxx give you more control over data flows and ensure that less personal data is collected.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion and outlook<\/h2>\n\n\n\n<p>A cohort analysis enables you to&nbsp;<strong>precise view<\/strong>&nbsp;on the behaviour of your users, far beyond mere average values. With this knowledge, you can make informed decisions, optimise your offering and improve your customer loyalty in the long term. Always pay attention to the following during implementation&nbsp;<strong>Data protection<\/strong>&nbsp;and&nbsp;<strong>Transparency<\/strong>so that you can act with legal certainty.<\/p>\n\n\n\n<p>If you want to gain more control over your data or are looking for a solution that is particularly&nbsp;<strong>GDPR-compliant<\/strong>&nbsp;it is worth taking a look at alternatives to Google Analytics. Tools like&nbsp;<strong>Trackboxx<\/strong>&nbsp;focus on the protection of personal data and still offer you extensive analysis options.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Further links &amp; sources<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a>Cohort analysis in Google Analytics 4 (official documentation)<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/trackboxx.com\/en\/\">GDPR-compliant web analytics tools<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/blog.hubspot.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">Guide: Improve customer retention rate (HubSpot blog, EN)<\/a><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">FAQ on cohort analysis<\/h2>\n\n\n<div id=\"rank-math-faq\" class=\"rank-math-block\">\n<div class=\"rank-math-list\">\n<div id=\"faq-question-1745402050239\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question\">How granular should my cohort analysis be?<\/h3>\n<div class=\"rank-math-answer\">\n\n<p>This depends on your business model and your goals. If you launch new products every week in an e-commerce shop, a weekly division may make sense. For a SaaS offering with monthly subscriptions, on the other hand, monthly cohorts are a good idea. It is important that your time intervals realistically reflect user behaviour.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1745402065696\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question\">Cohort analysis vs. segmentation: What's the difference?<\/h3>\n<div class=\"rank-math-answer\">\n\n<p>With segmentation, you often divide your users according to demographic or behaviour-based characteristics (e.g. end device, region). Cohort analysis, on the other hand, looks at groups based on a\u00a0<strong>same event at the same time<\/strong>\u00a0(e.g. month of registration). Both methods can complement each other, but provide different perspectives on user behaviour.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1745402092134\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question\">Can I use cohort analysis for email marketing?<\/h3>\n<div class=\"rank-math-answer\">\n\n<p>Yes, definitely. For example, you could consider all subscribers to your mailing list who signed up during a certain period (e.g. in April) as a separate cohort. Then observe how long they remain active, how often they open your newsletters or whether they respond better to certain campaigns than other groups.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1745402125721\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question\">Which time period should I choose for tracking a cohort?<\/h3>\n<div class=\"rank-math-answer\">\n\n<p>This depends entirely on the typical life cycle of your users. If your product is usually used for several weeks or months, you should also cover this period. For short sales cycles, a daily or weekly analysis makes sense. The closer you are to the natural usage cycle, the more meaningful the data will be.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1745402149939\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question\">Do I always need a special tool for the cohort analysis?<\/h3>\n<div class=\"rank-math-answer\">\n\n<p>Many common web analytics tools (such as Google Analytics or Mixpanel) already offer integrated cohort functions. If you are looking for a particularly GDPR-compliant variant, you can also find solutions such as Trackboxx, in which you can analyse cohort data. A manual approach with tables is also possible if you have smaller amounts of data - but this is more time-consuming and error-prone.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n<details class=\"wp-block-stackable-accordion stk-block-accordion stk-inner-blocks stk-block-content stk-block stk-91f8c7f is-style-default\" data-block-id=\"91f8c7f\">\n<div class=\"wp-block-stackable-column stk-block-column stk-column stk-block stk-47fcfcf stk-block-accordion__content\" data-v=\"4\" data-block-id=\"47fcfcf\"><div class=\"stk-column-wrapper stk-block-column__content stk-container stk-47fcfcf-container stk--no-background stk--no-padding\"><div class=\"stk-block-content stk-inner-blocks stk-47fcfcf-inner-blocks\">\n<div class=\"wp-block-stackable-text stk-block-text stk-block stk-a60bf4d\" data-block-id=\"a60bf4d\"><p class=\"stk-block-text__text\">Description for this block. Use this space for describing your blck. Any text will do. Description for this block. You can use this space for describing your block.<\/p><\/div>\n<\/div><\/div><\/div>\n\n\n\n<summary class=\"wp-block-stackable-column stk-block-column stk-column stk-block stk-c7bfc46 stk--container-small stk-block-accordion__heading\" data-v=\"4\" data-block-id=\"c7bfc46\"><div class=\"stk-column-wrapper stk-block-column__content stk-container stk-c7bfc46-container stk-hover-parent\"><div class=\"stk-block-content stk-inner-blocks stk-c7bfc46-inner-blocks\">\n<div class=\"wp-block-stackable-icon-label stk-block-icon-label stk-block stk-80ca184\" data-block-id=\"80ca184\"><div class=\"stk-row stk-inner-blocks stk-block-content\">\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block-heading--v2 stk-block stk-2d414e5\" id=\"title-for-this-block\" data-block-id=\"2d414e5\"><h4 class=\"stk-block-heading__text\">Title for This Block<\/h4><\/div>\n\n\n\n<div class=\"wp-block-stackable-icon stk-block-icon stk-block stk-5c85c2d\" data-block-id=\"5c85c2d\"><span class=\"stk--svg-wrapper\"><div class=\"stk--inner-svg\"><svg style=\"height:0;width:0\"><defs><lineargradient id=\"linear-gradient-5c85c2d\" x1=\"0\" x2=\"100%\" y1=\"0\" y2=\"0\"><stop offset=\"0%\" style=\"stop-opacity:1;stop-color:var(--linear-gradient-5-c-85-c-2-d-color-1)\"><\/stop><stop offset=\"100%\" style=\"stop-opacity:1;stop-color:var(--linear-gradient-5-c-85-c-2-d-color-2)\"><\/stop><\/lineargradient><\/defs><\/svg><svg data-prefix=\"fas\" data-icon=\"chevron-down\" class=\"svg-inline--fa fa-chevron-down fa-w-14\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewbox=\"0 0 448 512\" aria-hidden=\"true\" width=\"32\" height=\"32\"><path fill=\"currentColor\" d=\"M207.029 381.476L12.686 187.132c-9.373-9.373-9.373-24.569 0-33.941l22.667-22.667c9.357-9.357 24.522-9.375 33.901-.04L224 284.505l154.745-154.021c9.379-9.335 24.544-9.317 33.901.04l22.667 22.667c9.373 9.373 9.373 24.569 0 33.941L240.971 381.476c-9.373 9.372-24.569 9.372-33.942 0z\"><\/path><\/svg><\/div><\/span><\/div>\n<\/div><\/div>\n<\/div><\/div><\/summary>\n<\/details>\n\n\n\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>Die&nbsp;Kohortenanalyse&nbsp;ist ein Analyseverfahren, bei dem du&nbsp;Nutzer:innen anhand bestimmter Merkmale oder Zeitr\u00e4ume&nbsp;in Gruppen einteilst und ihr Verhalten \u00fcber einen definierten Zeitraum beobachtest. Diese Gruppen nennt man \u201eKohorten\u201c. H\u00e4ufig basieren sie auf dem Zeitpunkt einer Aktion, etwa dem Registrierungsdatum oder dem Kaufdatum. Die Idee dahinter: Anstatt alle Nutzer:innen und deren Verhalten in einen Topf zu werfen, beleuchtest [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":3796,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1,53],"tags":[],"class_list":["post-3793","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blogboxx","category-web-analytics"],"acf":[],"_links":{"self":[{"href":"https:\/\/trackboxx.com\/en\/wp-json\/wp\/v2\/posts\/3793","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/trackboxx.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/trackboxx.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/trackboxx.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/trackboxx.com\/en\/wp-json\/wp\/v2\/comments?post=3793"}],"version-history":[{"count":0,"href":"https:\/\/trackboxx.com\/en\/wp-json\/wp\/v2\/posts\/3793\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/trackboxx.com\/en\/wp-json\/wp\/v2\/media\/3796"}],"wp:attachment":[{"href":"https:\/\/trackboxx.com\/en\/wp-json\/wp\/v2\/media?parent=3793"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/trackboxx.com\/en\/wp-json\/wp\/v2\/categories?post=3793"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/trackboxx.com\/en\/wp-json\/wp\/v2\/tags?post=3793"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}