The study of indicators affecting customer churn in MMORPG games with machine learning models

Kaan Arik, Murat Gezer and Seda Tolun Tayali
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Kaan Arik: Sakarya Applied Science University, Sakarya, Turkey
Murat Gezer: Istanbul University, Istanbul, Turkey
Seda Tolun Tayali: Istanbul University, Istanbul, Turkey

Upravlenets, 2022, vol. 13, issue 6, 70-85

Abstract: Over the past two decades, the gaming industry has rapidly increased its popularity and gained a top spot in the entertainment sector. With this rise, customer relations and churn analysis have become even more prominent in gaming, as digital games are quickly integrated into the industry and fetch high revenues. The esteem for gaming and the revenue of companies has increased, which has made the concept of customer churn more critical. The behavior of most customers in the gaming industry also shows that it is a structure worth analyzing. This study focuses on the player log data of Blade and Soul game over specific periods with machine learning algorithms and tries to answer two business problems. These problems are to predict churners and player survival time, which are modeled as a classification and a regression problem, respectively. Blade and Soul is an MMORPG (massively multiplayer online role-playing game) developed by NCSOFT and widely played in the Far East. The theoretical basis of the study is the provisions of behavioral economics and churn management. For research purposes, the methods and algorithms of machine learning were used. Player log data was collected in 2016 and 2017, then anonymized and released for researchers. The research scope follows the CRISP-DM methodology and explains the process in detail by adhering to this methodology. A set of data consisting of 10,000 players with Test 1, Test 2, and Train were released separately by NCSOFT. Considering player churn and survival times, XGBoost demonstrates the highest effectiveness for classification problem and MLP and GBR for regression problem. The results show churn analysis can help businesses identify trends and patterns in customer behavior, such as when customers are most likely to leave, which features are causing them to leave, and which customer segments are most at risk of churning identifying trends and patterns.

Keywords: churn prediction; business intelligence; digital games; machine learning; artificial intelligence (search for similar items in EconPapers)
JEL-codes: C53 D18 M21 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:url:upravl:v:13:y:2022:i:6:p:70-85

DOI: 10.29141/2218-5003-2022-13-6-6

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