A1 Journal article – refereed

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

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.


Last updated on 2021-24-06 at 11:19