Level Up: Leveraging Skill and Engagement to Maximize Player Game-Play in Online Video Games

• 成果类型: Article
• 署名作者: Huang, Yan; Jasin, Stefanus; Manchanda, Puneet
• 署名单位: Carnegie Mellon University; University of Michigan System; University of Michigan
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2019.0839
• 发表日期: 2019
• 页码: 927-947
• 关键词: Hidden Markov model; product; DYNAMICS; sales

摘要

We propose a novel two-stage data-analytic modeling approach combining theories, statistical analysis, and optimization techniques to model player engagement as a function of motivation to maximize customer game-play via matching in the large and growing online video game industry. In the first stage, we build a hidden Markov model (HMM) based on theories of customer engagement and gamer motivation to capture the evolution of gamers' latent engagement state and state-dependent participation behavior. We then calibrate the HMM using a longitudinal data set, obtained from a major international video gaming company, that contains detailed information on 1,309 randomly sampled gamers' playing histories over a period of 29 months comprising more than 700,000 unique game rounds. We find that high-, medium-, and low-engagement-state gamers respond differently to motivations, such as feelings of effectance and need for challenge. In the second stage, we use the results from the first stage to develop a matching algorithm that learns (infers) the gamer's current engagement state on the fly and exploits that learning to match the gamer to a round to maximize game-play. Our algorithm increases gamer game-play volume and frequency by 4%-8% conservatively, leading to economically significant revenue gains for the company.