How do you use cohort analysis to measure customer retention and churn?

Cohort analysis is a method for measuring customer retention and churn by grouping users based on shared characteristics or timeframes: 1. Group customers according to the likes of new customers, customers who registered or purchased product in the same month. 2. It would be important to follow these cohorts in the future to see how the behaviour of the clients change where the retention and churn rate are the main focus of interest. 3. Categorize the data by groups of students and using current and previous years’ data, one can compare the current situation with the past. 4. This data can be used towards determining where in the aspect of customer loyalty is impacted and where improvements could be made.

In our project, we create cohorts based on channels and sprints, but our product team insists that we split cohorts by releases. With a B2C app, it's extremely difficult to divide by releases if they happen no more than once a month.

Define Cohorts: Identify meaningful cohorts based on a common characteristic, such as the month of customer acquisition, the source of acquisition, or a specific feature usage. Each cohort represents a group of customers who share a common starting point. Select Relevant Metrics: Determine the key metrics you want to analyze, typically focusing on customer retention and churn. Retention metrics could include the percentage of customers retained over specific periods. Set Time Periods: Choose appropriate time intervals for analysis, such as monthly cohorts over a year. This helps in tracking how cohorts evolve over time.

We've set up cohort retention analysis in Tableau. Since this is a grocery project, we look at it across timeframes (weeks and months) and orders. This allows us to break down retention by period and purchase frequency. Tableau gives us flexibility to slice the data however we need to surface insights. We can analyze user drop-off across cohorts over time and identify any correlations to order volume. This helps inform whether our retention initiatives are having an impact on purchase behavior.

Calculate Retention Rates: Calculate retention rates for each cohort by dividing the number of customers retained over subsequent periods by the initial cohort size. Retention Rate = (Number of Customers Retained / Initial Cohort Size) * 100 Visualize the Data: Create visual representations of the cohort analysis, such as line graphs or heatmaps. This allows for easy identification of patterns and trends in retention and churn. Compare Cohorts: Compare the retention rates of different cohorts to identify patterns or anomalies. Understand how changes in acquisition sources or product features impact customer retention.

Identify Churn Rates: Calculate churn rates by subtracting retention rates from 100%. Churn Rate = 100% - Retention Rate Analyze Behavior: Dive deeper into the behavior of specific cohorts to understand why some cohorts may exhibit higher or lower retention rates. Analyze customer interactions, engagement patterns, or feedback. Adjust Strategies: Based on the insights gained, adjust customer acquisition, onboarding, and retention strategies. Identify areas for improvement and test hypotheses to enhance customer retention. Monitor Long-Term Trends: Track cohorts over extended periods to observe long-term trends. This helps in understanding the sustainability of customer retention efforts.

Consider Segmentation: Segment cohorts further based on additional characteristics like demographics or usage patterns. This provides more granular insights into specific customer groups. Use Cohort Analysis Tools: Utilize analytics tools or platforms that offer cohort analysis features. These tools can automate calculations and visualize data, making the process more efficient. Iterate and Refine: Cohort analysis is an iterative process. Regularly revisit and refine your analysis based on new data and changing business conditions. Continuously optimize strategies for better customer retention.

The most interesting aspect is to create CLTV cohorts. Segment your customers based on value and analyse comunalities. Use the generated insights to inform you acquisition strategy and acquire more high value customers.

A good segmentation is actionable! Don't get lost in data analysis if you can't operationalize the results. That also means that differences between the groups must be sufficiently pronounced.