Retention Cohort Analysis – How to set it up

Retention cohort analysis is a powerful analytical tool used to study the behaviours and outcomes of groups of users segmented by shared characteristics over time. It is particularly crucial in understanding customer retention and lifetime value, making it an essential methodology for startups and established companies alike. In this guide, we’ll delve into what cohort analysis is, why it’s important, and how you can use it to extract valuable insights about your customers.

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What is a Retention Cohort Analysis?

Retention cohort analysis groups customers based on their shared characteristics, typically starting from their first engagement with your product or service. These cohorts are then tracked over different periods, providing insights into customer behaviour, retention, and value over time.

Why Retention Cohort Analysis Matters

For businesses, especially in the fast-paced startup ecosystem, understanding how different groups of customers interact with your product can provide invaluable insights into customer loyalty, product value, and revenue potential. It helps answer critical questions such as:

  • How long do customers stay with your product?
  • Which features or services are retaining customers?
  • How does the customer lifetime value evolve over time?

By answering these questions, cohort analysis allows you to tailor your marketing efforts, improve product features, and ultimately increase customer satisfaction and retention.

How to Conduct a Cohort Analysis

The real beauty of cohort analysis lies in its ability to reveal the retention and value of customers beyond their initial purchase. Here’s how to set up and read a cohort analysis table, using a simple example to illustrate the process.

Setting Up a Retention Cohort Analysis Table

  1. Organise Your Data by Cohort: Begin by arranging customer data by the month of their first purchase. Each row in your table will represent a cohort defined by the month of first purchase.
  2. Track Subsequent Purchases: For each cohort, track the number of subsequent purchases month by month. For instance, if 20 customers made their first purchase in January, how many made a second purchase in February, a third in March, and so on.
  3. Visualise the Data: Typically, your table will look diagonal as it moves from top left (oldest cohort) to bottom right (most recent cohort), reflecting the aging of each cohort.

Here’s an example to clarify:

CohortMonth 0Month 1Month 2Month 3
Jan ’2420181715
Feb ’24252321
Mar ’243027
Apr ’2415

In this table:

  • Month 0 shows the initial number of customers in each cohort.
  • The following months show how many customers from the original cohort made subsequent purchases.

Analysing the Table

  • Customer Retention: Observe how numbers decrease over time in each row. This decline illustrates customer drop-off rates.
  • Customer Lifetime Value (CLV): To calculate CLV, you’d add up all purchases (not just the number of customers) made by each cohort over time.

Calculating Key Metrics from Cohort Data

  1. Retention Rate: This is calculated by dividing the number of customers in a subsequent month by the initial number of customers in the cohort. For example, if 18 out of 20 customers from the January cohort made a purchase in February, the retention rate for that month is 90%.
  2. Net Revenue Retention (NRR): This measures the revenue retained from existing customers over time, accounting for upgrades, downgrades, and churn. Calculate NRR by dividing the total revenue in a subsequent month by the initial revenue from that cohort.
  3. Customer Lifetime Revenue: Sum the total revenue generated by each cohort over their lifetime to get a sense of how much revenue a typical customer generates.
  4. Cumulative Lifetime Revenue: This builds on the previous metric by adding the revenue from each month cumulatively, providing insights into how customer value accumulates over time.

Practical Tips for Using Cohort Analysis

  • Use a Suitable Tool: Excel can be a starting point for cohort analysis, but as your data grows, consider specialised tools or platforms that can automate and handle larger datasets.
  • Monitor Regularly: Update your cohort tables regularly to keep track of trends and shifts in customer behaviour.
  • Combine with Qualitative Data: For deeper insights, combine cohort analysis with qualitative feedback from customers to understand the reasons behind the trends you observe.

Conclusion

Cohort analysis is not just a reporting metric but a lens through which you can view the effectiveness of your business strategies over time. Whether you’re a startup founder, a data analyst, or a product manager, mastering cohort analysis can significantly impact your strategic decisions and help you build a more sustainable business model.

Check out this article to learn more about this topic: Core Metrics You Need To Track – Top 10 Startup KPIs

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