Prediction is the easy part. In a large dataset of SaaS cancellation conversations, login frequency alone predicts churn with ~70% accuracy at 60 days out. Add feature usage depth and support ticket sentiment and you're at ~85%.
The hard part is the next step: what do you do with the prediction? A "this customer is at risk" alert is worthless without understanding why they're at risk. The same risk score means completely different things. One customer needs a feature that exists but they haven't found, another is comparing you to a competitor, another had a payment fail silently.
The SHAP explanations in your approach are a step in the right direction since they surface which features drove the prediction. But behavioral features still only tell you what happened, not why. "Usage dropped 40%" doesn't tell you if the customer is evaluating competitors, hit a bug they never reported, or just went on vacation.
Most churn prediction tools stop at the score and leave the "why" to CSMs who don't have time. The companies with the lowest churn in the dataset automate the "why": structured conversations triggered by behavioral signals, not just alerts.