January 6, 2026

Case Study: iGaming Churn Intelligence from Minimal Data

This case study examines how churn risk in iGaming can be identified using only Player ID and Activity Date, based on real data from nearly 500,000 players. By progressively applying heuristics, probabilistic modelling and machine learning, we evaluate how much actionable signal can be extracted from minimal data under an empirically validated churn definition.