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Understanding Player Engagement and In-Game Purchasing Behavior with Ensemble Learning
Machine Learning · Submitted 9 Jul 2019
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our results suggest that discarding certain combinations of "zombies" (players whose activity is extremely sporadic) and false churners has a significant positive impact in all models considered.
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The abstract, sentence by sentence
As video games attract more and more players, the major challenge for game studios is to retain them. We present a deep behavioral analysis of churn (game abandonment) and what we called "purchase churn" (the transition from paying to non-paying user). A series of churning behavior profiles are identified, which allows a classification of churners in terms of whether they eventually return to the game (false churners)--or start purchasing again (false purchase churners)--and their subsequent behavior. The impact of excluding some or all of these churners from the training sample is then explored in several churn and purchase churn prediction models. Our results suggest that discarding certain combinations of "zombies" (players whose activity is extremely sporadic) and false churners has a significant positive impact in all models considered.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | As video games attract more and more players, the major challenge for game studios is to retain them. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | We present a deep behavioral analysis of churn (game abandonment) and what we called "purchase churn" (the transition from paying to non-paying user). | contribution 1.00 | 0.93 | 0.04 | asserted 0.96 |
| s2 | A series of churning behavior profiles are identified, which allows a classification of churners in terms of whether they eventually return to the game (false churners)--or start purchasing again (false purchase churners)--and their subsequent behavior. | contribution 0.70 | 0.91 | 0.07 | asserted 1.00 |
| s3 | The impact of excluding some or all of these churners from the training sample is then explored in several churn and purchase churn prediction models. | method 0.94 | 0.86 | 0.07 | unclear 0.50 |
| s4 | Our results suggest that discarding certain combinations of "zombies" (players whose activity is extremely sporadic) and false churners has a significant positive impact in all models considered. | result 0.97 | 0.95 | 0.80 | hedged possibility 0.96 |
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The paper
Original abstract
As video games attract more and more players, the major challenge for game studios is to retain them. We present a deep behavioral analysis of churn (game abandonment) and what we called "purchase churn" (the transition from paying to non-paying user). A series of churning behavior profiles are identified, which allows a classification of churners in terms of whether they eventually return to the game (false churners)--or start purchasing again (false purchase churners)--and their subsequent behavior. The impact of excluding some or all of these churners from the training sample is then explored in several churn and purchase churn prediction models. Our results suggest that discarding certain combinations of "zombies" (players whose activity is extremely sporadic) and false churners has a significant positive impact in all models considered.
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How the literature uses it
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Papers citing it
Cites it as background 1 paper
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Recent efforts on churn prediction in the game industry have employed various methods and models, such as binary predictions, survival ensembles, and the Cox model, and have utilized different features, including playtime, login frequency, player in-game state, and player in-game activity, such as purchases; see, e.g., [ref] .
Utilizing Players' Playtime Records for Churn Prediction: Mining Playtime Regularity · Introduction · 1.00
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