Cohort modeling

Cohort analysis (also known as a cohort model) is a behavioral analytics process that involves analyzing groups of people with a shared characteristic, experience, or event in a specific time frame.

In business, cohort analysis is often used to track customer behavior and measure the success of sales and marketing campaigns. For example, you might want to know how many people from your recent email campaign converted into customers. Cohort analysis can also identify customer trends and preferences and provide a clearer view of your company's data.

What is cohort modeling?

Cohort modeling is a statistical technique used to analyze data from a population divided into related groups or cohorts with similar characteristics. This approach can examine how different cohorts respond to treatments or interventions or compare outcomes between groups of people with other characteristics.

Cohort data can be collected in many ways but is typically gathered through surveys or other research studies. Once collected, this data can be used to track the progress of a cohort over time or to compare outcomes between different groups.

When should cohort tracking be used?

Cohort tracking is useful for tracking progress or retention over time. It could be helpful for a SaaS company, where you want to see how many people are still using the product after a particular time.

Cohort tracking can also help understand how different groups of people interact with your product. It could be beneficial for marketing or product development purposes. For example, you could track how other age groups use your app.

Finally, cohort tracking can help troubleshoot issues that arise. If you notice that a particular group of people is having trouble using your product, you can use cohort tracking to investigate further.

Limitations of cohort modeling

Cohort modeling can be a powerful tool for understanding population-level data, but it is essential to remember that this approach has limitations. Cohort models can only provide insights into group-level trends and cannot be used to predict individual behavior. In addition, cohort models are only as good as the data used to create them. If a cohort is poorly defined or the data is of poor quality, then the model's results may not be accurate.

Despite these limitations, cohort modeling can be a valuable tool for understanding population-level data and trends. When used correctly, it can provide insights that would not be possible to obtain from other types of data.

How do you measure cohorts?

There are a few ways to measure cohorts, but the most common method is by looking at customer retention. To do this, you need to track how many customers from each cohort remain active after a certain period. For example, if you have a group of 100 new users who signed up in January, track how many are still active in February, March, April, and so on.

Measuring cohorts is a great way to gauge customer retention because it allows you to see how well your business retains customers over time. It also allows you to compare different cohorts against each other to see which groups perform best (and worst).

There are a few other methods of measuring cohorts, but customer retention is the most common and essential metric. So if you're not tracking it already, start doing so! It could give you some valuable insights into your business.

How to interpret cohort data

Cohort data can be a great way to track the progress of your project over time. By tracking cohorts, you can see how they are doing compared to other cohorts. Interpreting cohort data can be challenging, but a few tips can help when examining the cohort data.

  • Start by identifying the baseline cohort. You will use this group to compare all subsequent cohorts. For example, if you are tracking the progress of a cohort, the baseline cohort would be the first group that you track.
  • Track progress over time by comparing the performance of each cohort to the baseline.
  • Look for patterns in the data.
  • Use cohort data to identify areas of need. For example, if you see a particular group of users struggling, you can target your customer success resources to help them succeed, lower churn and generate more revenue.


Cohort analysis is a powerful tool that data analysts can use to understand user behavior. By tracking cohorts over time, analysts can see how groups of users interact with a product or service and identify trends and patterns. Additionally, cohort analysis can help control for confounding factors and study rare events

If you're not already tracking cohorts in your business, start doing so! It could give you some valuable insights into your customers' behavior. And if you're unsure how to interpret cohort data, don't worry - there are a few tips in this article that can help. 

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