Home Leveraging a Game Flow Framework to Evaluate Game Performance, Part 1

Leveraging a Game Flow Framework to Evaluate Game Performance, Part 1

If you are using win per unit (WPU) as the only metric to measure game performance, you may be missing out on important insights that could help you improve your game selection and configuration, identify short life-cycle games earlier and better understand your players’ experience.

The framework below is proposed to help guide quantitative and qualitative research. The framework combines these research techniques to give you new insights into game performance and player experiences. The framework also makes it easier to catalog the data gathered to describe the performance of a particular game type and enables you to compare that game to other games.

The Framework
If we think about how a player finds and decides on playing a slot machine, we can visualize her becoming aware of the slot machine based upon the game’s appeal. Appeal is driven by both awareness and consideration. The game may be appealing and drive consideration due to a number of design features, some of which might be the cabinet design, artwork, game mechanics or a progressive. Once the player has decided that the game is appealing, the next step is a trial wager. The player places an initial wager to see if she should continuing playing, which would require her settling down into the chair. Her decision might be triggered via feedback by any number of design features in the game, such as game animation, bonus rounds, a lucky win and/or sounds.


Figure 1
Once a player commits to playing the game for some extended period of time, she places a bet, starts the game, watches the game mechanics and decides whether the actions should be repeated. This process gets repeated many times and, if the player is using a loyalty card, the series of point-of-sale transactions is recorded. If a player repeats this process for some time, this is referred to in the framework as “game flow.” Game flow is detailed beneath the overall framework.


Figure 2
Upon completing the session of play, the player decides whether the experience was positive, negative or neutral, and she files this information away. She also decides if she wants to play the game again in the future. At this point, the player will evaluate the overall session experience. This component of the framework is important in considering the potential life cycle of the game and the amount of repeat play that might sustain the game’s performance over time.

Figure 3
Are Your Games Inducing Flow? 
Whether a player has a positive attitude about her first gameplay experience has less to do with winning or losing and more to do with a concept called “flow.” Flow is defined for purposes of this framework as the mental state in which a player is absorbed in a feeling of energized focus, full involvement, and sensation in the play characteristics of the game, as illustrated in Figure 1.  Flow is the in-the-moment experience that brings about positive feelings.

Research has proven that people enjoy experiences in which they voluntarily choose to test their skills against identified challenges. It is important to clarify that challenges are not only “real” challenges presented by the situation but also those challenges we perceive. Skills are not just the ones we actually have, but also the ones we think we have. Our feelings at any moment are strongly influenced by objective conditions; our consciousness, however, is still free to create its own assessment of the case. A simple way of thinking about managing flow is getting a player on the right slot machine—one that fits her unique gambling profile. In the framework, game flow assumes a player has reached a state of flow in the act of playing.

To illustrate how flow works, let’s examine the impact of volatility and wager amount in several simple scenarios, where other factors such as game content, location on the casino floor, etc., are held constant.

Example 1: Volatility
First, a player chooses the game. The game volatility will either be too high, just right or too low. If it is too high, the player will have high anxiety—volatility can dramatically impact emotions related to losses and time on device, thus moving the experience up and outside the flow zone shown in Figure 1. The player may even describe the game as being too tight even if there is no difference between the hold percent of this and her favorite game. If the volatility is just right, the player will enjoy the experience and will be in the flow zone. She will have just the right amount of wins and losses, and perhaps she will experience a bonus game. Ultimately, she believes that the game was a good value. If the game has too little volatility for the player, she will call the game boring, moving down and outside the flow zone. The risk-reward relationship is insufficient to excite her.

Example 2: Wager Amount
A player chooses to make a wager on the game during a session already begun. She either chooses to bet too much, just the right amount, or too little relative to her gambling budget. If she bets too much, she is more likely to run out of money versus her allocated time, which may move her experience up to the right and outside of the flow zone, giving her an anxious view of the game and the session. If she bets just the right amount, she maximizes her money budget and time budget and feels that her experience was a good value, keeping her in the flow zone. If she bets too little, the player may describe the game as being boring; just like in the volatility example, she is not getting enough balance in the risk-reward relationship and gets bored with the game, moving down and out of the flow zone.

By using qualitative and quantitative techniques, you can identify the unique reasons why players are playing particular games. The following are outcomes of leveraging this framework when accompanied by strong technical research skills:

• Classify games that appear great but, by digging deep, you would predicate to have a short life cycle.
• Find games that are under-performing house averages but have a loyal following.
• Identify high-performing games with a loyal following.
• Distinguish unique player segments that play unique clusters of games.

In future articles, we will describe various quantitative and qualitative analyses that an operator or supplier can use to better understand the performance of their games beyond the one-dimensional WPU metric.

Author’s Note: Special thanks to Dr. Bernard Malamud for his editorial contribution to this article. Dr. Malamud will be helping in future articles as we discuss quantitative approaches within the framework.

1 Figure 1 was developed from the Mihaly Scikszentmihalyi national bestseller, FLOW.

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