[paulstovell]

Absolutely not an expert here, but I do obsess about our churn numbers.

It seems this post is trying to make a model to predict churn or guess what future churn might be. But the main case for understanding churn to me is historical.

Last month we had 10 customers paying us $10 each. Two of them cancelled. I have gross churn of $20. That's not a model, just a historical fact. What's "wrong" with that calculation that these models solve?

I will definitely get different churn numbers if I look at groups of customers in cohorts based on when they first signed up, or enterprise-vs-SMB, or whatever. So I have reports to create all of those cohorts and show me the churn for each. I look at that data - with all the historical context - and make a decision about what to do next in the business. "Selling to SMB's was a good idea, but wow they churn a lot more. Let's focus our marketing on enterprises this month."

To me the churn calculation at the top of the article is plenty useful. I'm not sure what value I'd get from these advanced models. If they just exist to predict future churn or LTV, that doesn't seem particularly useful.

[paulstovell]

Also if the problem is "churn as a single number" is wrong because it misleads people (the example being churn is up because the company is growing) surely the answer is to show a cohort analysis or historical data - not a forward looking or "best fit" model?

[onlyrealcuzzo]

Predicting LTV is quite useful for marketing and advertising.