Beyond their demonstrated effectiveness as a medium for communications and community-building, serious games have great potential to reveal variables that influence audience retention, engagement and loss as well as the impact of various demographic data on those influences. By examining player data, learning organizations and news organizations can gain new insight into the behaviors, motivations and needs of their target audiences.
Fundamentally, a game is a system built around a series of interactions. Sure, the conceit of the game, the storyline, the characters, the graphical treatment, and social components all have a huge influence on a game’s acceptance and popularity. But at the heart of a game’s attraction are its gameplay and its mechanics (aka, dynamics), i.e., the activities a player engages in while playing the game. Activities, of course, are a predetermined set of interactions between the player, the content of the game, and (in the case of multi-player and social games) other players.
As a player progresses through an online game, the choices she makes at each moment of interaction determine her experience of the game and, ultimately, the game’s outcome for her. The game’s internal infrastructure includes a mechanism for tracking her interactions so that the game responds appropriately to her input. Players who make different choices will get different responses from the system, have different experiences and, likely, different outcomes.
Within a game system, players generate data in two categories: gameplay (the log of player interactions) and demography. Generally, gameplay data enable game designers to tune a game, while demographic data inform marketing decisions.
In the world of serious games, where interactions are designed to yield a particular behavioral or cognitive outcome, player data are important in some key ways. They provide an assessment of a player’s overall performance. They point to areas of difficulty (diagnosis) for a player as she progresses through a game. They can also help to predict how a player will perform in similar activities, whether those activities are embedded in another game or take place in the real world.
When gameplay and demographic data from all players are aggregated, we have the opportunity to look for patterns. They might indicate patterns of interaction – desired or not, intended or not – with especially high occurrence rates. Those patterns may, for example, demonstrate the inclination of a particular demographic subset to make the same wrong choice within an activity.
For publishers of multiple games, data aggregation across all games and all players can be particularly informative. Perhaps players who respond positively to one game type are also frequent players of another game type. Players who are successful with one game mechanic or activity type may be completely unsuccessful with a second type.
Ultimately, the value of trapping, aggregating, and analyzing these kinds of data will be extremely significant. Serious game developers will gain a much better understanding of what “works” with their target audiences. Even better, players will have more successful and fulfilling game experiences.
