There is surprisingly little information available about the way people play games, and fewer still audience models. In this field, our DGD1 model (which is talked about extensively in our forthcoming book, 21st Century Game Design, which I am contractually obligated to plug) is practically the only game in town.
True, there is the Bartle type system, but this is a model that applies only to online play. There is Nicole Lazarro's four emotional keys - but this is a model of means of generating an emotional response in a player without use of narrative. I must say, I found Nicole's work hugely satisfying to read - I wish I had a link I could provide for it, and it was certainly influential when we were interpreting the clusters that came out of the DGD1 research. I wish we had more researchers doing this kind of work.
I'm going to talk about how the DGD1 came about, specifically with the goal of explaining where we are now, where we'd like to be, and why we can't get there yet.
The original motivation for the DGD1 came out of an earlier audience model we at International Hobo had cobbled together from very scratchy survey data. We wanted to see what patterns we could find between the axis of the Myers-Briggs inventory and the way people played games, as our informal observation suggested certain hypotheses (I won't go into detail - it's all in the book). There were some issues... firstly, the tendencies that correspond to each axis in Myers-Briggs are just that - tendencies - and all individuals express all these tendencies under certain situations. The notion of a 'type' in Myers-Briggs terms refers to a preferred pattern, and as such, most conventional tests are inadequate to measure this.
This didn't greatly bother me when I was setting up the original survey. I figured, if we got enough results, we'd be able to look at statistical correlations, as individual deviations would smooth themselves out.
What we were looking for were clusters of related behaviours - specifically, was there a way of dividing the results we got back such that they appeared to show patterns of related behaviour. The best clustering result we found was when we used a high score in certain Myers-Briggs axes to group the results. This lead us to clusters which predominantly showed similar patterns of game playing styles - and so we followed up with a set of case studies, which showed the patterns were robust, and statistically linked to Myers-Briggs types, albeit imperfectly.
Briefly, here are the four play styles we found:
- Type 1 Conqueror play style is associated with challenge and the emotional payoff of Fiero - triumph over adversity. This correlates with what Nicole Lazarro has called "Hard fun". We associate Type 1 play with players who aim to utterly defeat games they play - they finish games they start.
- Type 2 Manager play style is associated with mastery and systems. Victory for people preferring this play style seems to be the sign that they have acquired the necessary skills, not a goal in and of itself. They may not finish many games that they start playing.
- Type 3 Wanderer play style is associated with experience and identity. This correlates somewhat with what Nicole Lazarro has called "Easy fun". Challenge is not especially desired, but may be tolerated - what they enjoy is unique and interesting experiences. Stories and mimicry are key draws.
- Type 4 Participant play style is associated with emotions and involvement. It connects with what Nicole Lazarro calls "The People Factor". Participants seem happiest when they are playing with people, but they also enjoy play which is rooted in emotion. Any game which allows the player an emotional stake is a potential Type 4 game.
Unexpectedly, we found these patterns spread across the Hardcore and the Casual market segments - that is, those players who buy and play many games versus those who buy and play few games. This, we had not anticipated. The Hardcore clusters were universally more Introverted and Intuitive (in Myers-Briggs terms), while the Casual clusters were generally more biased towards Sensing and (to some relative extent) Extroversion.
The two elements combine to provide eight clusters - H1, H2, H3, H4 and C1, C2, C3 and C4.
The case studies demonstrated that the play styles were a robust model, but that Myers-Briggs type alone could not be used to assign a play style to an individual (not that surprising, really).
Furthermore, we did a follow up study in which we did a survey of people who belonged to online communities that identified with a specific type in Myers-Briggs typology. We found exactly what we expected - an approximately Gaussian distribution with the peak in each case centred upon the expected play style. However, this data was laughably incomplete, as not every type had an online community we could survey - and some of the communities refused to allow us to access their members, seeing what we were doing as attempting to sell to them, rather than attempting to understand their play needs.
I was hoping we would go forward and get to work on a DGD2 model - and indeed, our work looking at Temperament Theory and the related skillsets (Logistical, Tactical, Strategic and Diplomatic) suggests a possible way forward, as this connects in an interesting fashion with our current model. But one key ingredient is currently missing: feedback. I feel it inappropriate to begin a new survey until we've had useful peer review and feedback on the work we've done so far, and thus far, this hasn't really happened. We've received a lot of praise and support for what we've done (I think largely because no-one else has done something similar yet), but no feedback that would help guide and inform a next step. I'm hoping that this will come, and that in particular getting the book out to a wider audience will help.
In the long term, I'd hate to think that the DGD1 would still be the only audience model in town, say in twenty years time. That would be very disappointing. What I'd like, personally, is for other people to take forward the motivations that lead to this model and come back with better models. Nothing would please me more than, like Sigmund Freud, to be proved utterly wrong, but in doing so to inspire other people to produce something better along the same lines.
I believe we need audience models if the games industry is going to grow, and I sincerely hope that we inspire somebody to go out and prove us wrong, and come up with an audience model which blows ours out of the water. If that doesn't happen, I guess we'll be forced to do it ourselves.