As a physicist, I was originally trained to describe the world in terms of exact equations. Later, as an experimental high-energy particle physicist, I learned to deal with vast amounts of data with errors and with evaluating competing models to describe the data. Business data, taken in bulk, is often messier and harder to model than the physics data on which I cut my teeth. Simply put, human behavior is complicated, inconsistent, and not well understood, and it’s affected by many variables.
If your intention is to predict which previous customers are most likely to subscribe to a new offer, based on historical patterns, you may discover there are nonobvious correlations in addition to obvious ones, as well as quite a bit of randomness. When graphing the data and doing exploratory statistical analyses don’t point you at a model that explains what’s happening, it might be time for machine learning.