Predicting and improving player retention is crucial to the success of mobile Free-to-Play games. This paper explores the problem of rapid retention prediction in this context. Heuristic modeling approaches are introduced as a way of building simple rules for predicting short-term retention. Compared to common classifi…
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7 results for “free-to-play”
This study evaluates methods for clustering mobile game player behavior data.
problem Clustering time series data of player behavior in free-to-play games.
method Evaluation of various similarity measures and dimensionality reduction techniques.
result Identification and validation of temporal patterns of player behavior.
Machine learning predicts video game purchases for better player experience.
problem Predicting in-game purchases for free-to-play video games.
method Evaluated and compared two machine learning models: Extremely Randomized Trees and Deep Neural Networks.
result Deep Neural Networks outperformed Extremely Randomized Trees in accuracy and speed for operational settings.
Deep neural networks outperform parametric models in predicting customer lifetime value in video games.
problem Predicting the economic value of individual players in free-to-play video games.
method Exploration of deep neural networks and parametric models (Pareto/NBD) for predicting customer lifetime value.
result Convolutional neural networks are the most efficient in predicting the economic value of individual players.
Study uses time series analysis to predict player churn and conversion in games.
problem Predicting player churn and conversion in free-to-play games.
method State Space time series approach with Autoregressive Integrated Moving Average and Unobserved Components models.
result Unobserved Components approach fails to detect marketing campaigns and predicts abandonment poorly.
Predicting which players will convert to paying users in video games.
problem Retaining premium players in free-to-play games.
method Survival analysis techniques, Cox regression, random survival forest, conditional inference survival ensembles.
result Conditional inference survival ensembles method corrects bias in RSF models and predicts conversion.
Winning solution for predicting player churn in a video game.
problem Predicting when players will stop playing a game.
method Long Short-Term Memory (LSTM) approach and conditional inference survival ensemble model.
result Models accurately predicted player churn and were robust to changing business models.