by Trey Pruitt
Whether you are preparing a cohort analysis or interpreting data presented to you, here are some good questions to ask.
Before diving into the implications of a cohort analysis, it's worth clarifying what the cohort analysis is measuring.
What are we measuring? Are the percentage values in the chart measuring active customer count, customers purchasing count, sales, revenue, MRR, or some other metric?
If the measure is customer count, what does the percentage for a particular period mean? An active subscription at the end of the period, at least one purchase during the period, at least one session during the period?
How are the periods defined? Is period 0 the first transaction or the first month? Is period 1 the next 30 days after first transaction? Or is period 1 the next calendar month after the first transaction? Are periods normalized (e.g., 30 day increments) or is each period a calendar month?
(If cohort size is not displayed) What is the size of each cohort? Is the size of each cohort changing over time? For example, if cohorts are increasing in size, the mix (segment, acquisition source, etc.) may be changing as well, which could affect retention rates.
How are "reactivations" measured? If a customer cancels a subscription but reactivates 6 months later, is that customer counted in the original cohort or in a new cohort?
What is the shape of the "survival curve"? That is, how quickly does retention drop off in terms of the percentage of the original cohort? Often the shape of the curve is not linear. For example, we might see a quick drop off in periods 1 to 3, then flattening out in periods 6 to 12.
Is there any seasonality in the business that affects customer tenure? If so, has this been normalized in some way? For example, an e-commerce business has higher repeat purchase rates during the holidays (November and December). June cohorts would experience higher repeat purchase percentages in cohort tenure periods 6 and 7. January cohorts would experience higher repeat purchase percentages in cohort tenure periods 6 and 7.
Once the basics of the data are understood, here are good questions to dive deeper into the cohort analysis.
Cohort Segmentation: Are there significant differences in retention rates when cohorts are segmented by different variables (e.g., acquisition channel, customer demographics, product types)? This can reveal which segments are more valuable or require different retention strategies.
Cross-Cohort Trends: How does the retention of newer cohorts compare to older cohorts? How is period-normalized retention changing over time? For example, as we read down each column are the percentages increasing or decreasing? Insights into cross-cohort behavior can inform product development and cross-selling strategies.
Churn Reason Analysis: For customers who do not retain, is there data on why they churned? Understanding the reasons behind customer churn can provide critical insights into areas for improvement.
Impact of Product or Service Changes: Have there been any product or service changes that correlate with changes in cohort retention rates? Identifying the impact of specific features, pricing adjustments, or service improvements can guide future product development and marketing strategies.
Cohort Behavior Over Significant Events: How do cohorts behave during significant events or crises (e.g., economic downturns, global pandemics)? Analyzing retention in these contexts can reveal customer loyalty and the resilience of the business model.
Correlation with Other KPIs: How does the retention data correlate with other key performance indicators (KPIs) such as acquisition cost, revenue growth, and profit margins? A holistic view of the business's health is essential for assessing sustainability and growth prospects.
Predictive Modeling and Future Projections: Are predictive analytics being used to forecast future retention rates based on current data? Understanding projected retention trends can inform strategic planning and investment decisions.
Customer Lifetime Value (CLV) Integration: How does the retention rate impact the customer lifetime value across different cohorts? Understanding the relationship between retention and CLV can help assess the long-term profitability of retaining customers versus acquiring new ones.
Retention Efforts and Costs: What resources are being invested in customer retention, and how do these investments correlate with changes in retention rates? Evaluating the cost-effectiveness of retention strategies is key to optimizing marketing and operational expenditures.
Feedback Loops and Customer Satisfaction: What mechanisms are in place for collecting and analyzing customer feedback, and how does this feedback correlate with retention data? Insights into customer satisfaction and areas of dissatisfaction can provide actionable steps to improve retention.
Operational Impacts on Retention: Are there operational factors (e.g., customer support, delivery times) that significantly impact retention rates? Identifying operational efficiencies or deficiencies can be crucial for improving customer experience and retention.
Comparison with Industry Benchmarks: How do these retention rates compare with industry benchmarks or competitors? Understanding where the company stands relative to the market can highlight strengths to build on and areas for improvement.
Do you know what cohort analysis implies about your business? Book a call with me to discuss.
