The Gaming Analytics Summit held in San Francisco brought together a nice crowd of headliner video games such as Minecraft, Call of Duty, Destiny, Angry Birds, and Candy Crush. In attendance were the big gaming giants such as Sony PlayStation, Xbox, Activision, and Electronic Arts. Being an avid gamer and data analyst made this conference extremely informative. The topics ranged from in-game analytics to building a company structure that best handles big data. My focus at this conference was to see how the user’s voice was being heard in the video game development pipeline. Qualitative interviews meant very little to this group who focus more on big data and analytics, but some companies set themselves apart by emphasizing the importance of the user in maximizing their earning potential.
With so much data available from in-game selections, purchases, and behaviors; capturing and analyzing data in such volume has to be highly efficient, lightweight, and funneled into a visualization that is simple enough to consume and draw conclusions. Sega’s entire presentation was about the importance of simplicity and consistency in analysis and visualizations. It clearly demonstrated the challenges of presenting huge bar graphs in reports that are difficult to digest. Following Sega’s presentation, I noticed a theme: Big Data, Big Results, Now What? Attention was placed on displaying data, but not on determining the next course of action.Candy Crush’s presentation also grabbed my attention. The presenter offered one listener a choice between a mobile power pack and a Rubix cube. The listener chose the Rubix and the speaker said, “now that we know what he chose we can determine some things.” I spoke up during Q&A. "My question throughout the presentation was 'Why did he choose the Rubix? Doesn't understanding ‘why’ make your content delivery algorithms more relevant?" He was a bit perplexed and said they just try to do their best to analyze the data they have to learn about users. I responded, “Wouldn't it be easier just to ask?” It seemed that there was little attention paid towards why users behave the way they do. All focus was placed on A/B testing to determine the best conversion rates. While this method may work, it also presented a very wasteful practice of blind A/B testing.
