Cohort Analysis

It is a type of analysis that allows for to examination of the behavior of two or more customer groups. This analytical method guides companies on how to improve their customer behavioral experience.

The term “cohort” refers to a group of people who have something in common, such as their age, the date they signed up for a service, or the type of product they purchased.

Cohort analysis involves studying how the behavior of these groups changes over time, with the goal of identifying trends and patterns that can inform business decisions. It is commonly used in marketing and customer analytics to measure customer retention, lifetime value, and other key performance indicators.

For example, a cohort analysis of a subscription-based business might group customers by the month in which they first subscribed. By tracking the behavior of each cohort over time, the company can determine how long customers tend to stay subscribed, how often they renew their subscriptions, and how much revenue each cohort generates.

Cohort analysis typically involves visualizing data using a cohort table or chart, which shows how each cohort performs over time relative to other cohorts. This can help identify trends and patterns that might not be visible in aggregate data.

Cohort analysis can provide valuable insights into customer behavior and help businesses make data-driven decisions. By understanding how different cohorts behave over time, companies can develop strategies to improve customer retention, increase revenue, and grow their business.

Steps to conduct a cohort analysis:

1. Determine the cohort: Start by defining the cohort you want to analyze, such as customers who signed up in a particular month, users who made their first purchase on a certain date, or employees who started in a specific year.
2. Gather data: Collect the relevant data on the cohort, such as purchase history, subscription status, website activity, or other relevant metrics. This data should be organized by cohort and by time periods, such as weeks, months, or quarters.
3. Calculate metrics: Calculate the key metrics for each cohort over time, such as retention rate, churn rate, average revenue per user (ARPU), or customer lifetime value (CLV). This can be done using formulas or a business intelligence tool.
4. Visualize the data: Create a cohort table or chart to visualize the data and identify trends over time. A cohort table shows the performance of each cohort by time period, while a cohort chart shows the trend of a specific metric for each cohort over time.
5. Interpret the results: Analyze the results of the cohort analysis to identify patterns and insights. Look for trends, such as whether retention rates are improving or declining over time, or whether certain cohorts are more valuable than others.
6. Take action: Based on the insights gained from the analysis, take action to improve the performance of the business.  This could involve optimizing marketing campaigns, improving customer onboarding, or changing product features to better meet customer needs.