Refereed journal article or data article (A1)
Measuring Player Retention and Monetization Using the Mean Cumulative Function
List of Authors: Markus Viljanen, Antti Airola, Anne-Maarit Majanoja, Jukka Heikkonen, Tapio Pahikkala
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication year: 2020
Journal: IEEE Transactions on Games
Journal name in source: IEEE TRANSACTIONS ON GAMES
Journal acronym: IEEE T GAMES
Volume number: 12
Issue number: 1
Start page: 101
End page: 114
Number of pages: 14
ISSN: 2475-1502
eISSN: 2475-1510
DOI: http://dx.doi.org/10.1109/TG.2020.2964120
Abstract
Game analytics supports game development by providing direct quantitative feedback about player experience. Player retention and monetization have become central business statistics in free-to-play game development. Total playtime and lifetime value in particular are central benchmarks, but many metrics have been used for this purpose. However, game developers often want to perform analytics in a timely manner before all users have churned from the game. This causes data censoring, which makes many metrics biased. In this article, we introduce how the mean cumulative function (MCF) can be used to measure metrics from censored data. Statistical tools based on the MCF allow game developers to determine whether a given change improves a game or whether a game is good enough for public release. The MCF is a general tool that estimates the expected value of a metric for any data set and does not rely on a model for the data. We demonstrate the advantages of this approach on a real in-development free-to-play mobile game Hipster Sheep.
Game analytics supports game development by providing direct quantitative feedback about player experience. Player retention and monetization have become central business statistics in free-to-play game development. Total playtime and lifetime value in particular are central benchmarks, but many metrics have been used for this purpose. However, game developers often want to perform analytics in a timely manner before all users have churned from the game. This causes data censoring, which makes many metrics biased. In this article, we introduce how the mean cumulative function (MCF) can be used to measure metrics from censored data. Statistical tools based on the MCF allow game developers to determine whether a given change improves a game or whether a game is good enough for public release. The MCF is a general tool that estimates the expected value of a metric for any data set and does not rely on a model for the data. We demonstrate the advantages of this approach on a real in-development free-to-play mobile game Hipster Sheep.
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