Customer churn is a measure of the company's tendency to lose customers. Our user story speaks of the need to detect at-risk customers and prevent them from becoming a lost customer.
Surely, there are many variables that we may use to predict customer churn. In this case we expect customers to consistently make a purchase every so many days, so we will use a variable called customer purchase frequency to detect those that we are at risk of losing.
We could calculate the average number of days between purchases and warn sales representatives when the number of days since a customer's last purchase exceeds that average.
However, a simple average may not always be an accurate measure of a customer's true purchasing behavior. If we assume that their purchase frequency is normally distributed then we use the t-test to determine within what range the average is likely to fall. Moreover, we prefer the t-test because it can be used for customers that have made less than thirty or so...