Our Prediction System provided churn player evaluation based on player metrics and statistics.
Our customer was a game development company. The company wanted to leverage the players’ in-game behavior to proactively predict player churn.
Implement a solution that could periodically evaluate the player base and single out the profiles that were likely to quit playing the game within a specific time frame.
- Define the features that were significant for predicting churn
- Segment the existing audience according to those features
- Train the model to calculate the probability of quitting the game within the following 7 days for a given player profile
- Implement a periodical job that re-evaluated player profiles and saved potential leavers to a database table.
- Get access to an anonymous dataset of player profiles and their in-game activities
- Mark profiles of the players who quit playing the game
- Look for correlations between the profiles and the fact that they quit playing
- Implement and train the predictive model
- Validate the model
- Implement the model as a Hadoop job
Geomotiv’s solution was able to accumulate the required in-game statistics and identify individual player churn risk in 75% of the cases. This enabled the client to proactively address high churn risk players thus reducing the attrition rate by 45%.
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