Cohort Analysis

Definition

Cohort Analysis is a subset of behavioral analytics that takes data from a given dataset and rather than looking at all users as one unit, it breaks them into related groups for analysis. These related groups, or cohorts, usually share common characteristics or experiences within a defined time-span. Cohort analysis allows a company to 'see patterns clearly across the life-cycle of a customer (or user), rather than slicing across all customers blindly without accounting for the natural behavior that comes with time.

Usage and Context

Cohort Analysis is used in various industries and can be significantly beneficial in areas like e-commerce, mobile apps, web apps, and online gaming. It helps businesses to know customer behavior over time, and also see how product changes influence user engagement. It's also used in medicine, social, and life sciences to conduct studies that span across many years.

Frequently Asked Questions (FAQ)

What is a cohort?

A cohort refers to a group of people who share a common characteristic over a certain period of time. In cohort analysis, a cohort could be users who signed up for a product during a particular month, or it could be customers who purchased a particular product.

Why is cohort analysis important?

Cohort analysis helps businesses to understand how behaviors and habits change over time. It also helps a company to see how strategy changes affect user behavior.

Related Software

There are several software and tools available that can help in performing cohort analysis. Some of these include Google Analytics, Mixpanel, Amplitude, and Kissmetrics.

Benefits

Cohort analysis provides numerous benefits. It helps in identifying trends over time, understanding customer lifecycle, reducing churn rate, and enhancing customer retention. It also provides insights into which product or feature contributes to better customer engagement.

Conclusion

In conclusion, Cohort Analysis is an essential analytical tool that helps businesses understand customer behavior, improve customer engagement, and make data-driven decisions.