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Intro to User Analytics

May 30, 2013 Article Start Page 1 of 6 Next

A reprint from the May 2013 issue of Gamasutra's sister publication Game Developer magazine, this article polls developers to find out about the challenges and opportunities around developing for Android in 2013. Purchase the May 2013 issue here.

The science of game analytics has gained a tremendous amount of attention in recent years. Introducing analytics into the game development cycle was driven by a need for better knowledge about the players, which benefits many divisions of a game company, including business, design, etc. Game analytics is, therefore, becoming an increasingly important area of business intelligence for the industry. Quantitative data obtained via telemetry, market reports, QA systems, benchmark tests, and numerous other sources all feed into business intelligence management, informing decision-making.

Two of the most important questions when integrating analytics into the development process are what to track, and how to analyze the data. The process of choosing what to collect is called feature selection. Feature selection is a challenge, perhaps especially when it comes to user behavior. There is no single right answer or standard model we can apply to decide what behaviors to track; there are instead several strategies that vary in goals: e.g., improve the user experience or increase monetization. In this article, we will attempt to outline some of the fundamental concerns in user-oriented game analytics, with feature selection as an overall theme. First, we'll walk through the types of trackable user data, and then introduce the feature selection process, where you select how and what to measure. Importantly, this article is not focused on F2P and online games -- analytics is useful for all games.

Data for Analytics

The three main sources of data for game analytics are:

Performance data: These are related to the performance of the technical- and software-based infrastructure behind a game, notably relevant for online or persistent games. Common performance metrics include the frame rate at which a game executes on a client hardware platform, or in the case of a game server, its stability.

Process data: These are related to the actual process of developing games. Game development is to a smaller or greater degree a creative process, but still requires monitoring, e.g., via task-size estimation and the use of burndown charts.

User data: By far the most common source of data, these are derived from the users who play our games. We view users either as customers (sources of revenue) or players, who behave in a particular way when interacting with games. The first perspective is used when calculating metrics related to revenue -- average revenue per user (ARPU), daily active users (DAU) -- or when performing analyses related to revenue (churn analysis, customer support performance analysis, or microtransaction analysis).

The second perspective is used for investigating how people interact with the actual game system and the components of it and with other players, by focusing on in-game behavior (average playtime, damage dealt per session, and so forth). This is the type of data we will focus on here. These three categories do not cover general business data, e.g., company value, company revenue, etc. We do not consider such data the specific domain of game analytics, but rather as falling within the general domain of business analytics. 

Figure 1: Hierarchical diagram of sources of data for game analytics emphasizing user metrics.

Developing Metrics From User Data

Many people have proposed different methods of classifying user data over the past few years. From a top-down perspective, a development-oriented classification system is useful, as it serves to funnel user metrics in the direction of three different classes of stakeholders -- for example, as follows.

  • Customer metrics: Covers all aspects of the user as a customer -- for example, cost of customer acquisition and retention. These types of metrics are notably interesting to professionals working with marketing and management of games and game development.
  • Community metrics: Covers the movements of the user community at all levels of resolution, such as forum activity. These types of metrics are useful to community managers.
  • Gameplay metrics: Any variable related to the actual behavior of the user as a player inside the game (object interaction, object trade, and navigation in the environment, for example). Gameplay metrics are the most important for evaluating game design and user experience, but are furthest from the traditional perspective of the revenue chain in game development, and hence are generally underprioritized. These metrics are useful to professionals working with design, user research, quality assurance, or any other position where the actual behavior of the users is of interest. 

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Taylor Stallman
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Great article. After just graduating from college last December with a focus on database marketing. This article is spot on. It even taught me a number of things! Thanks a lot for the article, I'll be sure to use this the next time I need to analyze data.

Henrik Strandberg
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Excellent article! I'd also be very curious to learn about your approach to and experience from QAing the data sets; after all, if you haven't verified (via QA) that the data is correctly generated, aggregated, transformed and exposed, how can you trust the analysis?

In my experience that's one of the main constraints in game play related feature selection; QAing thousands of data points is simply unrealistic.

Lukasz Twardowski
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Henrik, that's a really good question. Most of initial instrumentations of analytics generate false numbers. One, because people who instrument analytics are not the same people who use analytics. They rarely understand how to collect data, what for and how to check its integrity. Two, because games are complex and usually have plenty of small glitches or shortcuts that might not impact players experience at all but can totally corrupt your data sets.

Developers who are aware that their analytics may produce false numbers trust data only if results are in line with their assumptions. That makes any analytics kind of pointless. Those who are not aware of that and trust their data usually end up making expensive mistakes. It's really way better to go blind without data and trust your team experience in both cases.

Of course there are ways to deal with that problem and get reliable results from analytics:

1. make sure that engineer instrumenting analytics service is working directly with someone experienced with that specific service - either someone in-house or a support guy from analytics vendor who will explain the process and audit the integration.

2. having a real time analytics during integration really helps as you can record your session and check results instantly. It's also important that you have ability to clean up database (or filter out your most recent activity) to make sure that you are checking the data from the last session only. If your analytics doesn't give you that comfort, log every outgoing data point on your side and do the math by yourself.

3. Even if you are very diligent about the integration, chances that you will get it right from the beginning are low. If you don't want to get into troubles due to data misinterpretation double check it using qualitative approach. Some of analytics services allows you to export data points by session id to excel and some others* have full set of features to analyze individual sessions or users. This will help you identify mistakes in data collection but also better understand correctly collected data before you jump into conclusions.

*I know only UseItBetter Analytics (disclosure: I'm co-founder) that does that for games but I might be totally wrong about it. Maybe Mixpanel or Playnomics?

Dylan Jones
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Great stuff. Went out and bought Game Analytics by the same authors. Even after getting certified in Google Analytics, there is plenty of very applicable stuff in those 800 pages. They could probably shave the book down just a little bit, but I recommend! Thanks!