Abstract | ||
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In this paper, we analyze the gameplay data of three popular customizable card games where players build decks prior to gameplay. We analyze 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
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found a lack of cross-game analytics, whereas 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. Optimizing a game's updates strategy is the key, as it affects player engagement and retention, which directly influence businesses’ revenues and profitability in the $95 billion global games market. |
Year | DOI | Venue |
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2019 | 10.1109/TG.2018.2803843 | IEEE Transactions on Games |
Keywords | DocType | Volume |
Games,Business,Androids,Humanoid robots,Analytical models,Data models,Buildings | Journal | 11 |
Issue | ISSN | Citations |
4 | 2475-1502 | 1 |
PageRank | References | Authors |
0.37 | 0 | 10 |
Name | Order | Citations | PageRank |
---|---|---|---|
Victoria J. Hodge | 1 | 977 | 46.90 |
Nick Sephton | 2 | 6 | 2.28 |
Sam Devlin | 3 | 189 | 26.86 |
Peter Cowling | 4 | 1255 | 78.35 |
Nikolaos D. Goumagias | 5 | 2 | 0.71 |
Jianhua Shao | 6 | 1 | 0.37 |
Kieran Purvis | 7 | 1 | 0.37 |
Ignazio Cabras | 8 | 1 | 0.37 |
Kiran Jude Fernandes | 9 | 43 | 5.36 |
Feng Li | 10 | 54 | 6.69 |