Churn Prediction Model Using Weighted Exponential Moving Average
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Solution Overview
Problem
Online social games face challenges in retaining players, as existing methods fail to effectively predict and prevent churn, leading to decreased player engagement and game success.
Innovation Solution
A method and system for predicting player churn in online social games by monitoring online engagements, computing exponential moving averages of engagement scores, and applying a churn prediction model to determine a player's likelihood of churning, allowing for targeted retention strategies such as messages and social network assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing player retention methods are used, then implementation is simple, but churn prediction accuracy is insufficient
Solution Approach 1:
The player base is segmented into different churn risk groups based on engagement metrics and behavioral patterns. The system divides players into high-risk, medium-risk, and low-risk categories, allowing targeted retention strategies for each segment rather than treating all players uniformly.
Solution Approach 2:
The system performs preliminary analysis of player engagement patterns and computes churn probability before players actually churn. By predicting churn risk in advance using historical engagement data and behavioral indicators, the system enables proactive retention interventions rather than reactive measures after player loss.
2Reliability
If comprehensive player monitoring is implemented, then retention prediction improves, but data processing complexity increases
Solution Approach 1:
The system extracts only the most relevant engagement metrics and behavioral indicators from extensive player data for churn prediction. Instead of analyzing all available data, it focuses on key parameters such as login frequency, session duration, and engagement trends that have the highest predictive value for churn risk.
Solution Approach 2:
The system continuously monitors player engagement and provides feedback loops where prediction results inform further data collection and analysis. The churn probability estimates are updated dynamically as new engagement data becomes available, creating a self-improving system that refines predictions over time based on actual player behavior patterns.
Data Source
AI summary
A method and a system for predicting churn of a player of an online game is described. Online engagements of a group of players of the online game are monitored during a churn prediction model training period. Online engagement scores for the group of players are computed within a periodic number of days within the churn model training period. A weighted exponential moving average of the online engagement scores of the group of players of the online game is computed during the churn model training period. The weighted exponential moving average is used to determine an online engagement threshold value of a churn prediction model for the online game. The online engagement threshold value is applied to a weighted exponential moving average of a player during an observation period to determine a churn probability of the player within a prediction period.