Is it readily possible to do predictive churn analysis (i.e., associating a churn risk with every individual/customer) using statistical tools (e.g. in Excel) not involving the use of machine learning or AI or deep learning? Is such a thing commonplace at all?
My guess was that people would be doing some sort of predictive modeling (for instance using some sort of cohort analysis and/or multiple regression) in this area before ML came on to the scene, but my online search, including on these forums, yields me nothing of the sort.
Does there exist a relatively simple churn risk analysis without ML that is as simple as practical while still having actual business utility? If such a thing is described anywhere, could you please point me in the right direction? The same thing could be applied to similar scenarios such as employee attrition. Any guidance will be appreciated. Thanks a bunch.
[CLARIFICATION IN RESPONSE TO BOT COMMENT]
Let's say we have 200 to 2000 records that tell us about customers who have subscribed to our services. It tells us when they subscribed, when they left (if they did), what particular services they are subscribed to, what they are paying, etc. It also tells us how old they are, roughly how much they earn, and perhaps some other demographic info that can help us classify them into groups of cohorts of some sorts (e.g. young single urban males, married female business owners, etc.)
With this size of data (certainly not enough to "train" a machine learning model - nor is that my goal), do there exist analytical models that help approximate the churn risk for particular individuals - at any level of granularity? Are examples/illustrations of this available online? I am willing to use statistical tools such as multiple regression if really required but I want to do it purely in Excel, without deploying any machine learning algorithms. Hope this clarifies.