Summary: Study examines how video game players engage in their chosen game and how they respond to different motivations. For games with in-game purchasing features, the more rounds or levels a person plays, the more likely they are to spend on purchases.
Source: INFORMS
In the video game industry, the ability for gaming companies to track and respond to gamers’ post-purchase play opens up new opportunities to enhance gamer engagement and retention and increase video game revenue.
New research in the INFORMS journal Information Systems Research looks at gamer behavior and how to match their engagement level with different games to ensure they play more often and for longer periods of time.
The study, “‘Level Up’: Leveraging Skill and Engagement to Maximize Player Game-Play in Online Video Games,” conducted by Yan Huang of Carnegie Mellon University, and Stefanus Jasin and Puneet Manchanda, both of the University of Michigan, works to better match players with games to achieve these goals.
For games with in-game purchase features, the more rounds a player plays, the more the player is likely to spend on in-game purchases, leading to better revenue generation; for games with in-game display ads, the more rounds a player plays the more ads the player sees, which leads to higher click through numbers and higher revenue. For games without either, revenue is boosted because engaged players are likely to upgrade to the premium or next version of the game.
The researchers looked at data from 1,309 gamers’ playing history over 29 months from a major international video gaming company.
“We find that high, medium, and low engagement state gamers respond differently and have different motivators such as feelings of achievement or the need for a challenge,” said Huang, a professor in the Tepper School of Business at Carnegie Mellon University.
“Using our model, we learn the gamers’ current engagement state on the fly and exploit that to match the gamer to a round to maximize gameplay. By doing this we see an increase in game play between 4 and 8%,” said Jasin, a professor in the Ross School of Business at the University of Michigan.
What the researchers have found is that by considering player engagement state and their state-dependent motivation, it not only increases use, but also leads to increased revenue for gaming companies.
Challenges in the game positively impact gamers with low or medium engagement levels, but negatively affects players with high engagement. Players’ curiosity decreases over time, and therefore, tends to move toward the low engagement state. This happens faster for less engaged players.
“Our findings suggest varying game difficulty based on players’ engagement level. For multiplayer video games match players to game rounds with stronger or weaker players; single player games can track players’ game play activity to infer players’ engagement state and adjust the level of difficulty,” said Manchanda, a professor in the Ross School of Business at the University of Michigan. “We also suggest introducing surprises, such as new maps or game modes, to offset the decrease in player curiosity as players become familiar with the game.”
Source:
INFORMS
Media Contacts:
Ashley Smith – INFORMS
Image Source:
The image is in the public domain.
Original Research: Closed access
““Level Up”: Leveraging Skill and Engagement to Maximize Player Game-Play in Online Video Games”. Yan Huang, Stefanus Jasin, Puneet Manchanda.
Information Systems Research doi:10.1287/isre.2019.0839.
Abstract
“Level Up”: Leveraging Skill and Engagement to Maximize Player Game-Play in Online Video Games
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.
