Allastair--What have you learned about the relationship between seasonal usage (compared with non-seasonal usage) and resulting customer behaviors? If seasonal usage is not a factor, (i.e. churn is not dependent on seasonal usage) then it should have no bearing on your customer health score. If it is a factor, then you would condition on whether the usage is seasonal and then include that information in your health score calculation. If logo or product churn is your outcome of interest, then perhaps the simplest way to test for dependence is to select homogeneous customer groups and compare churn rates vs. seasonal usage in a 2x2 contingency table. This would require classifying usage as 'seasonal' or 'non-seasonal' and tallying what customers decided (renew or churn) at that time or within a reasonable preceding time period. You would then test your hypothesis using Pearson's Chi-Squared analysis, which is easily done in Excel or Google Sheets. It would look something like this: You would generate a table of expected frequencies and then use the CHISQ.TEST() function which returns a p-value. If p<0.05, then you would reject the null hypothesis and conclude churn depends on seasonal usage. Note that the larger the number of samples you have in each group, the greater the power of the test, and if any cell has less than 10 observations, you would use Fisher's Exact Test which uses the hypergeometric distribution instead of the chi-square. Of course, deriving your health scores directly from your data may not be the approach you originally used--most CS teams do it subjectively and have poor predictive accuracy as a result--but this could be a first step in walking down a path to having more reliable customer health scores. I'm happy to talk this and other data-driven approaches in more detail if you'd like. Shoot me an email at ed@se-partners.com and we can set up a time.
Ed
