The Cohort analysis is an analysis procedure in which you Users based on certain characteristics or time periods 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.
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, whether the users who registered in JanuaryThe behaviour of the newcomers is different to those who arrived in February or March.
Why is the cohort analysis relevant for you?
If you are in the B2B marketingIf you work in product development or customer success, you want to understand as much as possible, Why and when 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:
- Customer RetentionYou can recognise whether customers remain active in the long term after onboarding or drop out after a short time.
- Product optimisation: You can use different Product versions or Features by creating separate cohorts for each release date.
- Efficient marketingYou find out whether a particular campaign has a lasting effect or only brings short-term traffic.
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 Timelinewhere you can constantly track the behaviour of your users.
How the cohort analysis works
- Define criterionFirstly, you determine what the trigger is for your cohorts. You often choose the Registration date (e.g. the week or month in which users registered).
- Form groupsYou then divide all users who have registered or purchased in a specified period into a common cohort.
- Observe behaviourYou 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.
- Interpreting and actingFor 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?
Example: Cohort analysis in practice
Imagine you are running a Software-as-a-Service (SaaS)-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.
- January cohort100 registrations, 20 paying subscribers after the trial subscription
- February cohort120 registrations, 35 paying subscribers
- March cohort80 registrations, 15 paying subscribers
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.
ToolsMany marketers use Google Analytics (in GA4, however, the cohort analysis is more limited than in the Universal Analytics version). Alternatively, specialised platforms or GDPR-compliant analysis tools like Trackboxx also offer the option of creating cohort-based reports - often even more data protection-friendly.
Important key figures and interpretation aids
The following key figures are particularly important in a cohort analysis:
- Retention ratePercentage of users who remain active or paying over a certain period of time.
- Churn ratePercentage of users who leave the service (cancellations or inactivity).
- Lifetime value (LTV)Calculates the average turnover generated by a customer over the entire period.
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.
Data protection and GDPR aspects
Since a cohort analysis analyses user data over longer periods of time, the question arises: What data do I collect exactly?
- Minimise personal referencesMake sure that you only collect the information that you actually need - ideally pseudonymised data.
- Legal basis: If you process marketing data, you will need a Consent (opt-in) or must prove a legitimate interest.
- GDPR-compliant toolsServer-side tracking or solutions such as Trackboxx give you more control over data flows and ensure that less personal data is collected.
Conclusion and outlook
A cohort analysis enables you to precise view 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 Data protection and Transparencyso that you can act with legal certainty.
If you want to gain more control over your data or are looking for a solution that is particularly GDPR-compliant it is worth taking a look at alternatives to Google Analytics. Tools like Trackboxx focus on the protection of personal data and still offer you extensive analysis options.
Further links & sources
- Cohort analysis in Google Analytics 4 (official documentation)
- GDPR-compliant web analytics tools
- Guide: Improve customer retention rate (HubSpot blog, EN)
FAQ on cohort analysis
How granular should my cohort analysis be?
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.
Cohort analysis vs. segmentation: What's the difference?
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 same event at the same time (e.g. month of registration). Both methods can complement each other, but provide different perspectives on user behaviour.
Can I use cohort analysis for email marketing?
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.
Which time period should I choose for tracking a cohort?
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.
Do I always need a special tool for the cohort analysis?
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.
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.
