I typically work in quantitative ecology and molecular epidemiology, where we use statistical models to predict species distributions or disease transmission patterns. Recently though, I had an interesting conversation with a data science PhD student who mentioned they were applying GAMs to predict Customer Lifetime Value at a SaaS startup. This caught my attention because CLV prediction, as it turns out, faces remarkably similar statistical challenges to ecological forecasting: nonlinear relationships that saturate at biological or business limits, hierarchical structures where groups behave differently, and the need to balance model flexibility with interpretability for stakeholders who need to understand why the model makes certain predictions.
This is an interesting article and I had not thought of using a GAM for calculating Customer Lifetime Value. I used a much simpler technique the one time I calculated CLV in earnest. H/T R-Bloggers.