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The True Power of the Gamification Spectrum

Lithium Alumni (Retired) Lithium Alumni (Retired)
Lithium Alumni (Retired)

In my last post, I introduced the gamification spectrum and discussed its basic properties. We learned that the feedback timescale of any tool is context dependent, but the spectrum will only stretch or compress under different context. That means the spectrum maintains its order, and the relative positions of the tools don’t change when the context doesn’t change. However the gamification spectrum is more than just an organizing framework. It allows us to learn about certain working properties of existing and future tools.


trends to insights.pngToday, we will look deeper into this spectrum of tools to discover several interesting patterns and trends hidden within these seemingly unrelated tools. If we examine the representative tools above the gamification spectrum, we can start to see some patterns as we move from the left (tools with short feedback timescale) to the right (tools with long feedback timescale) of the spectrum.


Since this post builds on the foundation knowledge established in the previous post, I recommend making sure you are familiar with the introductory post on the gamification spectrum before moving on.


Pattern 1: Gamified Behavior

On the left side of the spectrum, the behavior we are trying to gamify is typically one simple action from a single player. For example, points are awarded immediately to players for simple actions, such as a tweet, a share, a kudo, a download, etc. As we move to the right, the behavior typically becomes more involved. Rather than one single action, the player must repeat the same action a number of times before he gets a badge. Thus, the feedback timescale of badges will be longer than that of points—precisely how much longer is going to depend on the behavior and the player.


If we move further to the right, the gamified behavior will require even more effort from the player. Not only does he have to repeat one action, he has to outperform his peers in order to get on the leaderboard. Naturally, the feedback timescale for leaderboards will be even longer.

g-spectrum pat1.png


As we get to the middle of the spectrum, the behavior we want to drive is usually something that requires more than one type of action. The behavior may consist of 2 actions (e.g. watch a video and share it with a friend), 3 actions (e.g. download a trial software, use it, and write a review for it), or even more. Although the user needs to accomplish more than one action, the actions are still from a single player.


Finally, on the right side of the spectrum, the behavior we want to encourage is even more complex and involves actions from multiple players. These are typically reciprocal actions from other players, or collaborative actions with other players.


Pattern 2: Underlying Metrics

Gamification relies heavily on the tracking of player actions/behaviors through metrics and behavior data. As the behavior becomes more complex when we move from left to right along the spectrum, the metrics and data that reflect these behaviors also become more sophisticated.


Towards the left, the metrics that underlie the short feedback timescale tools are usually simple counters that accumulate over time as the player carries out the desired action. Moving to the right, we reach tools like leaderboards that use time-bounded frequency metrics. Since medals and trophies start to reward players for multiple actions, these tools must use multiple metrics and conjunction.

g-spectrum pat2.png


In fact, tools on the right half of the spectrum can use conjunctions of metrics from any tools with shorter feedback timescales. For example, you can get a community trail blazer trophy when you are on the community contribution leaderboard for 5 weeks in a row. In this case, the trophy is using a conjunction of metrics from the leaderboard (a tool with shorter feedback timescale). Finally, tools on the far right of the spectrum leverage reciprocity metrics and team metrics that are even more complex, because reciprocity and collaborative behavior is the goal.


Pattern 3: Susceptibility to Cheating (Gaming the System)

Tools on the far left of the gamification spectrum are highly susceptible to gaming (i.e. cheating), because the behavior we are trying to drive is so simple—a single action from the player. This means the player has full control over the gamified action. So he can easily repeat that action to his heart’s content and get all the points and badges he want, thus gaming the gamification system. On the contrary, tools on the far right are much more immune to gaming (though I believe no system is truly un-gamable), because those tools encourages behaviors that depend on many actions of many players. This makes it much more challenging to game the system as it would require a coordinated effort to do so.

g-spectrum pat3.png



By organizing gamification tools with their feedback timescale on a continuum, we created a spectrum of gamification tools—the gamification spectrum. Through this spectrum, we can start to see some interesting patterns and trends in the operational properties of gamification tools. This post described 3 useful patterns discovered by analyzing the spectrum, but there are many more, and we will examine 4 more in the next post.


As you can see, the gamification spectrum is a very useful organizing framework. However, its power and utility goes far beyond mere organization. It allows us to clearly see the relationship between different gamification tools. Moreover, the spectrum allows us to identify patterns and trends that give us a deeper understanding of some working properties of each gamification tool with respect to the others.


What patterns and trends are you seeing in the gamification spectrum? What insights do they reveal? And how do these insights help you? Let me know, so we can discuss and learn from each other here.


Stay tuned till the next post.



Michael Wu, Ph.D.mwu_whiteKangolHat_blog.jpg is CRM2010MKTAWRD_influentials.pngLithium'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+.

About the Author
Dr. Michael Wu was the Chief Scientist at Lithium Technologies from 2008 until 2018, where he applied data-driven methodologies to investigate and understand the social web. Michael developed many predictive social analytics with actionable insights. His R&D work won him the recognition as a 2010 Influential Leader by CRM Magazine. His insights are made accessible through “The Science of Social,” and “The Science of Social 2”—two easy-reading e-books for business audience. Prior to industry, Michael received his Ph.D. from UC Berkeley’s Biophysics program, where he also received his triple major undergraduate degree in Applied Math, Physics, and Molecular & Cell Biology.