A breakdown of Next Bet & Player State Detection
The Next Bet and Player State Detection uses player behaviours insights, including betting activity and engagement metrics to categorise player accounts into actionable states. . By integrating player state information and next bet predictions into existing workflows, operators can optimise retention efforts, drive player engagement, and ultimately enhance business performance.
Player State Detection:
Player State Detection utilises the RFM (Recency, Frequency, Monetory) model to assign specific actionable states to each player based on their betting history and engagement.
Each player's state is determined by their "alive" score (e.g., Churned, Active), which is based on predefined thresholds derived from RFM metrics.
The RFM model is calculated as follows:
- Recency (R): This measures the time since the player's last bet. Any recent activity indicates higher engagement.
- Frequency (F): This quantifies the number of betting days a player has had within a defined period. Players that bet consistently are suggested as active users.
- Monetary (M): Reflects the player's lifetime value based on previous betting activity. Higher monetary value indicates valuable players.
- Algorithmic Analysis: Employs machine learning techniques to compute alive scores and predict player behaviour
Next Bet Prediction:
The Next Bet Prediction forecasts the date of the next bet for each player by analaysing historical betting patterns. This predictive capability empowers operators to tailor their campaigns and communications based on each player's expected betting behavior.