Dmitri Williams (PhD, University of Michigan) is the CEO, Sensei, and Co-Founder of Dmitri is a 15-year veteran of games and community research, and a world-recognized leader in the science of
online metrics and analysis. The author of more than 40 peer-reviewed articles on gamer psychology and large-scale data analysis, Dmitri’s work has been featured on CNN, Fox, the Economist, the New York Times, and most major news outlets.
He has testified as an expert on video games and gamers before the U.S. Senate, and is a regular speaker at industry and academic conferences. Dmitri moonlights as a healer and raid leader, plays a wicked Ashe in League of Legends. He loves data, and believes more of it, used intelligently, makes the world a better place.
Gaming Analytics: How to Get the Most Out of Your Data
By Dr. Dmitri Williams, CEO of Ninja Metrics
Analytics. Understood by few, used well by even fewer, and incredibly useful to those who are willing to devote some cycles to it. After all, decisions without data are blind, while data without context are equally stupid. So, developers should take analytics seriously, but need to be thoughtful about the system they implement. It’s not enough to pick one at random. It has to be married to the developer’s process and culture, and to be truly actionable–not just interesting.
Like any business decision, analytics strategy has to be just that — strategic. To help game developers make the most out of their data, this article will cover the basics of analytics and big data, some of the different forms of analytics and what this all means for gaming companies today.
Big Data, Used Better
Before we dive into poker analytics, it’s helpful to understand why all this is happening now. Why the sudden rush? The answer is another buzzword: big data. In simple terms, big data is a term that encompasses datasets so large and complicated that they require an advanced processing application. This term includes almost any type of data, from large amounts of patient information in the medical field to massive amounts of photos shared on social networking sites. Sometimes “big data” aren’t really that “big”. The term also covers things that are simply complex, e.g. you may have 80 million players, or you may have 80 who do a thousand interrelated things.
Big data capabilities mean that game developers have the ability to store more information than ever before, and can now record every move a player makes, every transaction they complete, every social connection they add — essentially, every interaction with the game. That’s a lot of information, to say the least. Data has become so valuable, in fact, that the world produces over 2.5 billion gigabytes of it per day, which is equal to about 531 million DVDs worth of data.
Now onto the analytics portion of the equation. As a game developer, you have all this information at your disposal, and big data technologies give you the capacity to store it. However, you still need a way to sort through it and derive meaning. You can record every player interaction, but that information is useless unless you have a way to process it and use it to inform your strategies.
Where Do I Start?
When breaking in to analytics, the first thing for game developers to do is set objectives for what they want to achieve with their data. As we’ve already established, there’s a lot of information, so we need to make sure we have the right tools to start analyzing. If you want to use this data to inform marketing decisions, for example, you might only need simple analytics tools to analyze your user base. If you’re looking at how to increase your in-game revenues, you might want to start by looking at using metrics that examine what players are doing in your game. In other words, you need to understand your questions before picking the tool. If you start with the tool, you’ll miss opportunities to match up with your particular issues and team.
After finalizing your goals with analytics, then you can start diving in. Here are a few basic types of metrics that game developers need to be aware of when making analytics decisions:
As the name suggests, user analytics simply analyzes your player data based on their behavior in games. This is entry-level analysis: who is playing your game and what they’re doing once they’re in there. To get the most out of your player data, you need to look through the lens of KPIs, or key performance indicators. These metrics include Daily Active Users, retention rates, Average Revenue Per User and Average Session Length, and are relatively straightforward for an analytics company to calculate once you’ve collected the data.
Social analytics takes player behavior one step further, and looks at how players interact with each other in the game. This can be related to social network connections, or just in-game data. A subset, social value analytics, measures how influential your players are to their peers, and how that influence affects your bottom line. Each time one of your players talks about or engages with your game, for example, there’s a possibility that their friends are watching — and the more they play and spend, the higher chance they have of getting their friends to do the same.
Social value analytics are especially helpful when trying to figure out who your most influential player, also known as social whales, are. User analytics will show you your highest spenders, but those players are almost never the most influential, and therefore the most valuable. Common traits of a social whale include positive engagement among the community and a knack for high-social connectedness — all of which can be tracked and measured.
Predictive analytics is a field where other types of analytics combine to help game developers see the future. Well, that might be a bit of an exaggeration, but predictive analytics takes other data, like social connections, and uses it to predict how players will behave in the future. If you have enough data, it can do this with a relatively high degree of certainty, so in a way it is like peering into a crystal ball for your game.
How? It tracks patterns, and uses statistics to predict how likely it is that pattern will happen again in the future. Applying social predictive analytics, for example, can tell you the worth of your players in the future based on social connections. Other types of predictive analytics can tell you how long a player is expected to stay in the game until they churn out, or how much a player is expected to spend before churning. The
farther out into the future you go, the less reliable the prediction will be. Think of it like the weather forecast for a hurricane. They’ll tell you where it’s likely to hit tomorrow, but if you want to see the next day and the next, the big funnel of where it will go gets wider.
So What Does This All Mean?
All this means that analytics for game developers isn’t just hype: when the correct analytics program is applied, it can make sense of data and provide actionable insights for game developers. These insights include how to lower churn rates, increase player spending and engage social whales, among other monetization opportunities. The metrics described above just scratch the surface on what’s available; the insights you can glean all depend on your business goals, the player data you are collecting, and the amount you’re willing to invest in analytics programs.
The best advice I can give? Take the first small step. Figure out what you want to improve, set your goals and your data collection systems, then start looking in to analytics programs. Happy analyzing!