Quickly diagnose where, when, and why you’re retaining customers.
Like churn, you need to continuously analyze customer retention rate if you want to keep up with how quickly customer behavior changes.
However, the real challenge in analyzing customer retention is in the changing nature of customer behavior. To uncover the key drivers behind changes in retention, you need to be able to quickly diagnose the trends across operational, behavioral, acquisition, price, product usage, and more.
With the sheer number of factors impacting retention, you’ll need to test to determine not only that they stayed or left the platform, but why, making it difficult to diagnose the reason for changes quickly.
Retention rate measures the percentage of customers retained by the end of a given time period.
Retention = # Continued active users / Total # users at start of time period
As you’d do with customer churn, customer retention rate should be tracked at the account or customer level. Each row should represent a unique customer, with one record per time period.
To diagnose the driving factors impacting your retention rate, make your dataset as wide as possible. Start with the recommended fields below and add as many descriptive variables as you can to augment your analysis.
Status = (Active/Churn)
Account Creation Date
Customer Acquisition Date
Customer Tenure (Days)
New Customer (T/F)
Days Since Last Log In
Customer City, State, Zip
# of Users on Account
Email Subscribed (Y/N)
Account Deleted (Y/N)
Account Deleted Reason
# Customer Success Tickets