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With over 2 billion players worldwide and an annual revenue of $20 billion in the U.S. alone, the gaming industry can no longer be overlooked as a niche sector. In America, the gaming industry is over twice as large as the movie industry ($8 billion revenue) and is expected to continue growing for the foreseeable future. This growth has resulted in a flood of player and usage data, such as:
The parallel growth of data analytics has resulted in a convergence between both industries, with Big Data playing an increasingly larger role in how the gaming industry collects and analyzes data. This intersection between gaming and analytics has resulted in the ability of gaming companies, such as Electronic Arts, to increase advertising revenue, improve gameplay, and efficiently manage the user experience.
Players interact with games in multiple ways, such as the device used, amount of time spent, playing style, dedication level, social media gaming usage, and in-game product purchases. The wide variety of gaming styles, coupled with the popularity of subscription & in-game revenue-earning opportunities, means that gaming companies need to customize and tailor their advertising content in order to maximize revenue.
Data analytics enables gaming companies to collect, cleanse, format, and model data in order to get a clear picture of how their users interact with their games. Over time, a holistic profile for each player emerges that conveys critical usage data, which enables companies to offer highly-specific products based on gameplay. For example, player characters who show an inclination towards armor customization will be offered in-game armor enhancement offers. Mobile device players who link their social media accounts will be offered in-game bonuses based on the number of social media friends they can recruit to the game, and so on.
Insights from gaming analytics also enable companies to improve the gameplay itself. For example, millions of player records could be analyzed to pinpoint the most likely in-game moments when players quit the game entirely; perhaps a series of quests are too boring or the challenges are too hard/easy based on character level. Identifying these gaming “bottlenecks” is critical to understanding the reasoning & timing behind a game’s churn rate. Gaming Designers and Developers can then re-examine the game’s storylines, quests, and challenges in order to refine the gaming experience and, hopefully, reduce the number of lost subscribers.
Analyzing the devices used by players also helps developers to create gaming experiences that work effectively for their user base. Exploring a dungeon via an iPhone is quite different than doing it using a widescreen attached to a laptop, so developers need to address issues such as screen size, available functionality, navigation, and character interactions. Data analytics empowers companies to address this challenge by modelling and visualizing massive amounts of heterogenous data.
Today, games sometimes have global player bases… so the architecture supporting those users needs to be configured and implemented correctly. Online games are particularly prone to network-related metrics, such as ping and lag rates — these issues are exacerbated during peak gaming times. Again, Big Data analytics enables gaming companies to use server and network data to understand exactly when, and how, their infrastructure is being pushed to its limits. This knowledge enables companies to scale up or down according to player need; in today’s world of cloud-based PaaS/IaaS architectures (where cost is tied to usage), this information can have a dramatic impact on a company’s bottom line.
Dataiku's Data Science Studio (DSS) is a powerful advanced analytics solution that enables companies to take advantage of Big Data in an accessible and easy-to-use environment. Some key features of DSS in terms of gaming data analysis include:
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