In the May issue of CEM, we introduced a “game flow” framework for evaluating game performance. We proposed this framework as a means by which to bring quantitative and qualitative research together when making decisions regarding game performance. In that article, we described the various steps that a player goes through with a game: identifying that it has appeal, deciding it deserves a trial wager, experiencing game flow, and ultimately developing a sense of loyalty to the game that leads to at least one more session of game play. (See Figure 1.)
In this article, we’ll show how various research methods can provide insight into a game’s performance and improve decision making in regard to a portfolio of games.
Figure 1
Figure 2A and Figure 2B
Decision Making in the Game Flow Framework
To simplify the discussion of quantitative metrics and their application to decision making, we combine game appeal and trial wagers into a category called attractiveness. The two matrices in Figure 2—the upper (a) relating attractiveness and game flow, the lower (b) relating game flow and loyalty—show how our framework can be used. Metrics such as numbers of first-time and return players and coin-in reflect a game’s performance along these dimensions.
Games that capture much intense initial play and continue to be played for long periods with high wagers in successive sessions are placed in the upper right quadrant of Figure 2A. If these games that are played intensely also experience much return play, they are placed in the upper right quadrant of Figure 2B as well. We expect these games to enjoy long and profitable life cycles. These games have “legs.”
High play of even low intensity may lead to high WPU early in a game’s life cycle, placing it in the upper quadrants of Figure 2A. Repeat play should be considered, however, before an operator places more of the same game on the floor. The intensity of repeat play can provide insight into the quality of WPU performance and what might be done to improve it. A game that falls in the lower right quadrant of Figure 2B (little repeat play of high intensity), for example, signals that those who play have a positive experience and may call for aggressive marketing to improve awareness by other players.
Figure 3: Quantitative Metrics
The basic measures of game performance along the four dimensions are straightforward (see Figure 3). Game appeal is measured by the number of first-time player sessions at the game. For these first-time sessions, the trial wager is measured by coin-in or by more detailed measures such as handle pulls, amount wagered and theoretical or actual win for the session. Game flow is similarly measured by session activity. A player’s game loyalty is measured by her number of sessions at the game. These first-cut metrics can paint a rough picture of game performance. From this raw data, an analyst familiar with a slot floor’s overall performance during the period studied can judge which games are strong and which are weak along each dimension of game performance.
In order to place the game into our framework, the raw data should be processed to reflect a game’s relative performance. The raw measure of game appeal, for example, should be adjusted for a game’s location on the slot floor. Games in high-traffic areas can be expected to attract more first-time players irrespective of their inherent appeal. A game’s relative performance will generally be summarized by ratios of its metrics to those of other games. A next step would be to boil multiple relative metrics for a game down to a single index along each dimension. A last step might be to characterize the game as a high or low performer along the attractiveness, loyalty and game flow dimensions described in Figure 2.
Qualitative Input
Suppose player intercepts are performed early in the life cycles of two games, Game A and Game B, both of which are performing well from a WPU perspective. Leveraging the cultural consensus model used in ethnographic research, a statistically large sample is not needed to derive insights once you have interviewed enough players so that you are no longer learning anything new.
Within the game flow framework, suppose both games receive positive comments regarding game appeal (see Figure 4). Players’ comments on their trial wagers continue to be positive for both games: Game A is easy to understand and creates the perception that the bonus is easy to achieve; Game B’s music is entertaining, its characters are easy to identify with, and its bonus game is intriguing.
Along the game flow dimension, however, the two games begin to tell different stories. Players start expressing frustration that Game A is taking their money too quickly, that the game teases them with large payouts that never hit, and that they generally feel the game is beyond their skills. Game B players, on the other hand, show a quick transition into game flow based on a clear understanding of the challenges of the game and a perception that their budget is being well used in pursuit of these challenges. Players who win the bonus round are well entertained and rewarded for their effort, reinforcing the concept of “flow.”
In the May issue of CEM, we defined “flow” as matching a player’s “skill” with the “challenges” associated with the game. One element of skill is a player’s perception that she has wisely chosen to play a game that gives maximum value for her budget. Examples of a game’s challenges include winning the bonus round, winning enough prizes that the gambling roller coaster is eventful, and/or playing until either the time or the money budget runs out.
Figure 4
Game Example A
Game Example B
As we think about the players’ comments about game flow and review their comments associated with game loyalty in Figure 4, the contrast between Games A and B becomes starker. Game A is perceived as unwinnable and expensive. Translation: The challenge is beyond players’ skills, and they don’t intend to play it again. On the other hand, players find Game B to be fun and they have a perception of winning. Translation: The challenge is within players’ skills. Players therefore intend to look for Game B during their next gambling adventure but not Game A.
Since both games showed tremendous potential from a WPU perspective, without further analysis, operators would have been led to purchase or add more of both games. In hindsight, however, Game A, doesn’t have legs. Players play it once but refuse to play it again. Game B shows high game loyalty and potentially strong WPU performance into the future. We are confident that the player rating analysis described in the game flow framework will provide keen insights into the quantity and quality of players’ willingness to play a game again. It will give suppliers and operators insights into whether a game has “legs.”
Leveraging Quantitative Analysis
It is best to use quantitative data to guide and prioritize qualitative research. Qualitative research can be expensive and time consuming. The information gathered in the game flow framework will help improve the evaluation of new games and lead to better decisions on where to spend precious capital when adding games. In addition, it may provide insights into how to market games to players. This simple framework helps you avoid several unfortunate decisions, such as spending too much capital on games without “legs;” dismissing games that may have tremendous loyalty among players but may not be lighting your performance sheet on fire; and dismissing games that are not high on game appeal but have a high level of game flow. These games’ performance may improve with a little help from marketing.
The game flow framework can leverage player ratings to evaluate game performance, providing a road map to improving decisions by people who earn a living designing, creating, analyzing and evaluating the revenue performance of slot machines.
Jeff Jordan most recently served IGT as a Director in several roles—product strategy and marketing research, corporate strategic planning and strategic business development. Prior to IGT, Jordan worked at Bellagio as the Executive Director of Slot Operations and Marketing. He is a Principal at Jordan Gaming Consulting Group and can be reached at jjordanlv[at]gmail.com.
Dr. Bernard Malamud, a graduate of Polytechnic University, Carnegie-Mellon University and New School University, has taught a wide variety of courses since joining the UNLV faculty in 1968. He recently served on the Clark County Planning Commission and consults to the legal profession and the gaming industry.
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