Welcome back to my blog and welcome back to the topic of gamification. Although big data and analytics continue to be very important to me, a little gamification adds variety in my writing and gives you a more complete picture of all the stuff I work on. So rather than staying on a single topic for an extended period, I like to play a little musical chair and switch between topics as I blog. You can let me know if that’s more interesting.
Gamification has been a subject of interest in the industry because it has followed the Gartner Hype Cycle pretty closely over the past few years. If you’ve been following the Hype Cycle reports, you will see that Gamification is entering the trough of disillusionment as I type! This is both good and bad. It’s bad because there are many naysayers challenging the over-hyped promise of gamification. But it’s also good, because after that comes the slope of enlightenment, and eventually the plateau of productivity.
As a scientist who has researched on behavior design, I thought perhaps I could help accelerate the trajectory of gamification through the Hype Cycle by offering this community some of the insights I have learned over the years. Last year I wrote a whitepaper with the Incentive Research Foundation (IRF) on the Do’s and Don’ts of Gamification with applications to the incentive industry. I will expand on that over the next series of short posts and make these practical tips more applicable to a wider audience. And I hope these advices could help gamification practitioners move faster into the slope of enlightenment and not learn these lessons the hard way—through other failed initiatives.
So where do you start?
Have a Granular Understanding of Your Desired Behavior
One of the most important success criteria of gamification is a clear, granular understanding of the behaviors you are trying to drive. The granularity is critical here, because people generally know what they want to drive at the high level, but not in specific detail. If you ask a client “what behaviors do you want to drive with your gamification program?” you will typically get the following types of answers:
The problem is that customer engagement is not a single human behavior, neither is community vibrancy, employee collaboration, or team productivity/performance. In fact, each of these high-level results consists of a long list of granular behaviors. For example, customer engagement may consist of (but not limited to) the following behaviors:
Likewise, community vibrancy is not a single behavior either. It is a series of behaviors, which may include (but again, not limited to) the following granular behaviors:
Similarly, team productivity actually consists of many behaviors, possibly hundreds. Think of all the different activities that people employ to gain productivity (education, adopting new tools, etc.). Employee collaboration is also a result of many (possibly hundreds) behaviors. Think of all the different ways that people can collaborate. You must know all these constituent behaviors well enough to list them all in complete detail. Because it is these detailed behaviors that gamification is able to drive effectively, not the high level results, which you will get only as a consequence.
If you cannot list the detailed constituent behaviors, then you don’t actually understand what it takes to drive the high-level results you want. Consequently, you won’t be able to design the proper gamification to drive them. You will need to take the time at the beginning of the design process to understand the desired results and all the granular behaviors in order to design your gamification strategy effectively.
For the rest of this year, each time I revisit the topic of gamification, I’ll share one more tenet of success gamification with you.
Ready? Then game on!
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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