Hello and welcome back. Since several of you have expressed interest in the final path to long-term behavior change (i.e. intrinsic motivation), I’m going to return to the topic of gamification today. For this post, I’d like to introduce a framework I developed for organizing existing gamification tools—which are essentially implementations of some game mechanics or game dynamics.
Common gamification tools are points, badges, leaderboards, but there are many more (e.g. ranks, goals, missions, level unlock, team reputation, etc.). By now there are probably hundreds and thousands of gamification tools out in the market. Moreover, there are many variants of a gamification tool. For example, there are different kinds of leaderboards with different scope. Some only compare you against your friends, whereas others compare you against strangers that are similar to you in some ways.
How can we systematically organize and understand these tools? That is exactly what we will discuss today. This will require us to look deeper and look beneath the surface of those tools in order to find the common organizing principles.
The Common Ground—a Universal Property of Gamification Tools
Despite the fact that there are nearly a hundred game mechanics/dynamics, which give rise to thousands of gamification tools, there is one function that is common among these seemingly unrelated tools—feedback. All gamification tools give some kind of feedback to the players. They can be very subtle (e.g. incrementing some metrics in the background) or very obvious (e.g. rewarding the user with a badge).
The precise mechanism of how a particular tool gives feedback to the users is different for every tool. It could be tactile (e.g. a vibration on your mobile device), auditory (e.g. a transient sound or music), visual (e.g. a pop-up notification, etc.), or other sensory modalities. Regardless of the mechanism, the feedback is there to tell the user something about his past actions or behaviors (i.e. his progress, his performance).
The Feedback Timescale
An important property of any feedback is its timescale—roughly how fast it feeds progress information back to the user. The feedback timescale is critically important for us, because every gamification tools has a characteristic feedback timescale. That means we can organize these tools on a spectrum from really short (immediate) to very long (many years) timescale.
I must emphasize that the feedback timescale is NOT the feedback time of the gamification tool. The feedback timescale is actually a rough time range—it is a rather imprecise ballpark (order-of-magnitude) estimate.
The feedback timescale for any tool is player dependent, because different people have access to different skills and resources. They will participate at a different rate and thus receive feedback at a different rate. As an illustrative example, let’s examine the feedback time (not timescale) of a common gamification tool—a leaderboard for community contributions. A superfan in a community may get on this leaderboard in 2 weeks, so the feedback time for this particular superfan is 2 weeks. However, an ordinary contributor may take a year before getting on that leaderboard, so the feedback time for him would be 1 year. And if you are a community lurker who never contributes, you may never get on the leaderboard, so the feedback time there would be infinite.
So what’s the feedback timescale (not time) for this leaderboard? It’s roughly on the order of a month (see the gamification spectrum below for further clarification).
The feedback timescale for any gamification tool is also behavior dependent. If we are using this community contribution leaderboard to drive a simple behavior (e.g. a top tweeter leaderboard use to gamify tweeting of community content), then its feedback timescale will be relatively short. On the other hand, if we are using this leaderboard to drive a harder behavior (e.g. a top blogger leaderboard use to gamify writing good blogs that drive interactions and comments), then its feedback timescale will be longer.
The Gamification Spectrum
If we organize all existing tools using their feedback timescale, we get a spectrum of tools ranging from those with short feedback timescales to those with very long feedback timescales.
I must point out that the tools shown above the spectrum are merely representatives having the respective feedback timescale when driving a particular behavior. There are thousands of gamification tools, and any one of those tools may be used to encourage the behavior you want. Since the feedback timescale of every gamification tool depends on both the players and the behaviors (described in the previous section), the spectrum will also be player dependent and behavior dependent.
Behavior dependence: It is important to recognize that the spectrum consists of gamification tools, and these tools can be used to drive different behavior. However, the feedback timescale of every tool is behavior dependent, so the spectrum will also change when we are using the tools to drive different behaviors.
We’ve already discussed the fact that feedback timescale is directly related to the difficulty of behavior you are trying to encourage—the harder (easier) the behavior, the longer (shorter) the feedback timescale, respectively. So if we are trying to encourage a simple behavior (e.g. tweet once a day), the feedback timescale of every tool on the spectrum will decrease. Consequently, the whole spectrum compresses.
Alternatively, if we are trying to gamify a very challenging behavior (e.g. run 10 miles a day), the entire spectrum stretches as a result of the increased feedback timescale of the tools.
Player dependence: If we place that community contribution leaderboard (described in the previous section) in a very vibrant community, where many users are contributing a lot of content, then its feedback timescale may be reduced (e.g. from months to weeks). However, the feedback timescale for other tools on the spectrum also shortens when they are used to gamify the same behavior—community contribution. Again, the whole spectrum compresses.
In contrary, the feedback timescale of that leaderboard may increase in a community with less active users. Therefore when the players are less active, whether it’s because they are less motivated, have less ability (i.e. less access to resources, skills, time, etc.) or not triggered properly, the spectrum stretches again.
Although the gamification spectrum can stretch and compress depending on the context (i.e. the players and the behavior we want to encourage), the relative positions of the tools on the spectrum generally do not change under the same context. That means, although it is possible to have badges with a longer feedback timescale than a leaderboard (under different context), leaderboards generally have a longer feedback timescale than badges when the context is fixed.
By looking deeper into the world of gamification tools, we discover a common property—the presence of feedback. This means we can use the feedback timescale as an organizing principle to catalog all gamification tools. However, the feedback timescale varies as these tools are used in different context—either to drive different behaviors or used to motivate a different group of players. This only changes the spectrum by stretching it or compressing it—the relative positions of the tools on the spectrum is fairly stable under the same context.
Now we have the gamification spectrum, next time I will go further into its organizing power as a framework. This will reveal a profound insight about how the various gamification tools operate. We can then use this insight to better drive the behaviors we want to solve more challenging problems. Stay tuned for a deeper dive into the power and applications of the gamification spectrum.
Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.
Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on Lithosphere, and you can follow him @mich8elwu or Google+.
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