Engagement and Content Volume
This is part two of the four part series on value creation in games. Part 1: Value Creation in Games; Part 2:Engagement and Content Volume; Part 3:Engagement and Personalization; Part 4: The Game Engagement Landscape.
In my last post I talked about how value creation in games falls into two large buckets: access and engagement. This time I want to focus on the engagement portion, and more specifically, how we increase it. I’ll give the usual disclaimer about how there’s many ways to slice this, but in my view there are two ways to increase engagement:
- Increase the volume of content
- Increase the personal meaning of content
Increasing the volume of content – the sheer amount of it that can be consumed – increases the possibility space and therefore the likelihood that the consumer will find something that engages them. That may evolve over time – i.e. initial bits of content may grow dull but, due to the large amount available, new bits are available that may extend interest.
Increasing personalization reduces the possibility space in a way that’s meaningful to the individual player. It does not reduce the possibility space for the game’s audience as a whole. Think of it as the percentage of interactions an individual has in the game (relative to the total interactions they have) that are interesting to them. What’s interesting to one user may not be interesting to another, of course.
In general we use three methods to increase the volume of content:
- Author a lot
- Re-use content
- Emergence
Authoring can come from developers or consumers. In the hands of skilled developers the content is often extremely well-made and balanced, and difficult to pattern match. But it runs out quickly, a lot goes unused, it doesn’t adapt well to varied player interests, and it’s expensive and economically hard to sustain except at very high sales volumes.
Letting consumers author the content (i.e. UGC, or what I like to call the infinite monkeys solution) generates an almost unlimited supply and the cost of creation is very cheap. But it has its own challenges, including a terrible signal-to-noise ratio, difficulty maintaining cohesion and consistency with the overall product, and a dependency on some level of creative or technical expertise to generate interesting content (the burden of creation, at least for some portion of the audience).
Another alternative is to simply re-use content. Far less expensive than developer authoring, it’s also relatively easy to balance. For example, we might use meta-structures like high scores, scenarios or levels, difficulty settings, quests and so forth to package what is essentially the same core game loop in a larger play mechanic. That can generate more long term interest and extend play, but it doesn’t actually solve the pattern matching problem since the core game loop remains the same (potentially leading to boredom fairly quickly). Procedural content generation is another variation on this theme but tends to produce undifferentiated content.
That leads us to emergence. In emergent play, core components are recombined to produce novel new play dynamics (in the MDA sense). In the mid-90s, the colleagues at my first company often mocked my constant preaching about “complex combinations of simple, distinct elements”. Emergence might occur at the systems level, or it might come from adding other people to the game (but not necessarily friends). As with simply re-using content, emergence is inexpensive. And it’s hard to pattern match, making it difficult for players to optimize play and get bored. But it’s terribly difficult to balance.
Next time I’ll talk about the personalization side.
January 17th, 2012 at 5:42 pm
This sounds about right. Although, since I’m taking the time to comment, I bet you can guess that I have something to add. :)
You’ve touched on emergent game play, and I think that’s the key for any game with real longevity. Having a game’s value expire as the content is consumed is a money-losing proposition in most cases.
Look at the examples to see the truth of this. Emergent game play is at the core of any consistent replay experience. Algorithmic content, player-generated content and multi-player experiences help capture this in an engaging way.
Artfully meshing those three elements will give you an engaging and compelling replay experience. And, if the core actions of your game are addicting, you’ll capture a dedicated audience.
I’m unsure that I’d characterize it as “terribly difficult” to balance emergent game play elements. The balance of your emergent systems is directly related to the algorithmic fabric that you use to author them.
I love curve tables! They allow you to abstract away a given dimension of your technical game design. Let me explain that with a concrete example. Look at the concept of damage vs. health. Getting that right is a core component of any combat-oriented design. Instead of plugging in real values for damage and health, abstract them with curve tables. Essentially say that this monster has “high” health and “medium” damage.
This simplifies the balance decisions a great deal. To tweak the balance of the game, you need only modify the curve tables. All the instance data (with their relative strengths) are preserved.
I find it interesting that you see emergent game play as breaking up the pattern-matching capabilities of the player. I’m not seeing it that way. I see it as adding a level of novelty to the game. The patterns are recognizable but the exact configuration isn’t the same twice.
The Sims is a great example of this. The pattern of play is pretty much the same for each game. But, the exact way things unfold is novel each time. Heck, this applies to Chess or Checkers as well.
I guess you could call that some kind of confounding of pattern matching, but I definitely see it as approaching infinite novelty within a well-known and comfortable pattern.
January 24th, 2012 at 8:15 pm
Hey Stephen, I tend to agree that emergent play is the key to maintaining long term player interest. Indeed, my latest venture is premised on that observation.
When I speak of pattern matching, I’m using the term the way Raph Koster does: where the act of learning and understanding how a system works is what makes it fun and that mastery of all those patterns leads to boredom (or to other states of reward).
Learning all the mechanics of a system is not the same as learning all the patterns, and while most people may get comfortable with a certain pattern of play they are not necessarily exhausting the possibility space (it’s worth nothing that a game as emergent as The Sims is probably more prone to this as a solitary experience that doesn’t push the player very hard toward new challenges; games that put players in contact with other players or have systems that internally adapt likely expose the player to more of the possibility space).
Emergent play, by its very nature, is hard to balance because of the massive possibility space that even the designer cannot predict. Things like curve tables are a great tool, but they require either an understanding in advance of the entire possibility space or such broad application that they’ll simply reduce that space (and thus reduce the amount of emergence). That doesn’t mean emergent systems are impossible to balance: we have games like Go and Chess that are ridiculously emergent but well balanced and incredibly simple in their mechanics (albeit with the ‘mastery problem’ per Koster again). It’s every game designer’s fantasy to produce something so simple, elegant and deep, but it’s a very rare occurrence.
Of greater concern to me is how we get the player to the bits of content that interest them the most regardless of the source. That’s the subject of my next post.