Thursday 20 February 2014

Flappy Birds – A new inspiration for A/B testing experts? (appiterate.com)

We have all witnessed the success story of Flappy Birds, a game created by a indie developer based out of Vietnam. It’s quite interesting and intriguing to see how this game got such a huge traction in such a short time. This whole thing opens new door of knowledge, not only for marketers but also for various scientists looking to unravel mysteries of mind. Well, I tried playing the game myself and couldn’t get beyond 5 bars. But guess what, even to get to this level, I spent hours playing it. I am still wondering what kept me engaged.

Author at this blog post, did a great job finding out the reference to Mihaly Csikszentmihalyi’s research. Mihaly Csikszentmihalyi in his book, “The Psychology of Optimal Experience” explains the concept of “flow”, where he explains how challenge/skill ratio should be at an optimal level to maintain the flow of an experience. Flow is a mental state where you are completely immersed in what you are doing. It’s the state of complete focus. Well, I believe if you look deeper into it, this region of optimal flow itself might be clustered into various zones of different genres of “flow”. I have tried to draw these theoretically potential genres in the second photo. The different colors with different intensity shows the different nature of a “flow”. For example, the one with high dense violet color could be your “addictive” zone. A lighter zone with blue color could be a zone where your users loves to play your game every once in a while.

Flappy birds interest graph-1Flappy birds interest graph-2

Having said that, boundaries of these zones are again heavily dependent on the nature of your users. Now, “Challenge” is a controllable parameter. If you can properly segment your users based on skill level, a very structured scientific approach of A/B testing can be taken to fine-tune your challenge parameters to take the experience to “addictive” level. The science in this area is yet to provide better and clearer demarcation and definition of these zones, however, until then we will have to heavily rely on our own data-driven approaches to identify these zones, particular to our games.

In the case of flappy birds for example, concept is very old. However, challenge/skill ratio was too high. Had the same game come from a reputed game studio, they would have rejected it for sure. Come on, who would play a game when hardly anyone is able to play one session more than 30 seconds ? It’s very counter-intuitive to accept this. However, the data showed something different. It probably hit a very sweet spot in the gradient shown above. Now, many such zones, could be counter-intuitiuve in the beginning like this case, however, taken the right iterative approach, even you could find your own game’s “addictive zone”.

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