How the Business Model of Customisable Card Games Influences Player Engagement

Hodge, V. J., Sephton, N., Devlin, S., Cowling, P., Goumagias, N. D., Shao, J., Purvis, K., Cabras, I., Fernandes, K. & Li, F. (2018). How the Business Model of Customisable Card Games Influences Player Engagement. IEEE Transactions on Computational Intelligence and AI in Games,

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Abstract

In this article, we analyse the game play data of three popular customisable card games where players build decks prior to game play. We analyse the data from a player engagement perspective, how the business model affects players, how players influence the business model and provide strategic insights for players themselves. Sifa et al. found a lack of crossgame analytics while Marchand and Hennig-Thurau identified a lack of understanding of how a game’s business model and strategies affect players. We address both issues. The three games have similar business models but differ in one aspect: the distribution model for the cards used in the game. Our longitudinal analysis highlights this variation’s impact. A uniform distribution creates a spread of decks with slowly emerging trends while a random distribution creates stripes of deck building activity that switch suddenly each update. Our method is simple, easily understandable, independent of the specific game’s structure and able to compare multiple games. It is applicable to games that release updates and enables comparison across games. Optimising a game’s updates strategy is key as it affects player engagement and retention which directly influence businesses’ revenues and profitability in the $95 billion global games market.

Item Type: Article
Additional Information: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: Business Intelligence, Clustering Algorithms, Data Analysis, Game Analytics, Machine Learning
Divisions: Cass Business School > Faculty of Management
URI: http://openaccess.city.ac.uk/id/eprint/19049

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