Anders Drachen

Anders Drachen, Ph.D. is a veteran Data Scientist, Game Analytics consultant and Professor at the DC Labs, University of York (UK).

Analytics is much more than a tool for generating revenue in Free-to-Play games. Analytics is a process of discovering and communicating patterns in data with the express purpose of solving problems. This can be as simple as calculating the average time players spend on a game, to a complete analysis of a company´s internal behavior. In this post we describe what game analytics is and what the fundamental benefits of integrating analytics in game development are.


Analytics is a process of discovering and communicating patterns in data with the express purpose of solving… Click To Tweet


One of the beautiful aspects of game development is that it does not require much to develop games – any parent can attest to the awe-inspiring inventiveness of small children and their constantly evolving games and play behavior. Of course, commercial-grade digital games require knowledge, skill and tools, and there is a huge span from student projects in Gamemaker to World of Warcraft.

A variety of tools, techniques, and knowledge are required to develop games, some necessary, others optional. Game analytics is a tool that has a tendency to become more and more necessary as projects and team sizes increase, but which is valuable at any level of development.

Analytics is the process of discovering and communicating patterns in data, towards solving problems in business, or conversely to make predictions for supporting enterprise decision management, driving action and/or improving performance. What this means is that analytics is the process of dealing with any kind of relevant information or data, and putting it to good use. This can be as simple as asking a few buddies for feedback on a game and modifying a design, as a result, to large-scale enterprise level project support. Irrespective of your game, analytics has something to offer.

In terms of methods, analytics relies on statistics, data mining, mathematics, programming, and operations research and data visualization. The goal is to analyze data to find insights that can be communicated to the relevant stakeholder towards driving action. For example, finding that sprint productivity is down and communicating this to a producer, or finding that players tend to give up and leave a game around the 3rd boss encounter, and communicating this to the design team.

Analytics generally relies on computational modeling, although the complexity can vary. There are a lot of branches or domains in analytics, e.g. marketing analytics, web analytics, risk analytics – and game analytics.

Analytics forms an important subset and source of business intelligence (BI), across all levels of a company or organization irrespective of its size. BI is a broad concept, but the basic goal is to acquire data and turn it into useful information. BI is usually computer-based but not always, and focuses on identifying, registering, extracting and analyzing business-relevant data for strategic and operational purposes. The goal is to support decision-making – to make it data-driven rather than reliant on gut instinct.


The goal of a game analysis is to support decision-making Click To Tweet


There are three general sources of data for BI:

1)    Market: for example benchmark reports, white papers, market reports, and other kinds of information about what games that are out there, how well they do, target audience descriptions, etc.

2)    Company: This is internally generated data, e.g. QA reports, production updated, budgets, business plans, burndown data, etc. Anything generated in-house.

3)    Users: By far the most investigated source of data in game analytics, the users (players, customers) have a natural center place in games-related BI. Data sources includes user test reports, user research, customer support, behavioral telemetry, purchasing behavior, and so forth.

All of these sources of BI operate across temporal, geographical and product distances (or dimensions). For example, we might know the purchasing history of a player, the geographical location where the player lives, and the amount of time spent playing different games. Connecting user data across dimensions is incredibly useful to gain an in-depth understanding of players.

Game analytics is the specific application domain of analytics to game development and –research. As outlined above, it is important to know that game analytics is directed both at games as products – e.g. do they monetize well, do users have fun playing them – and games as projects, i.e. the process of developing a game in a company or private context. While the first perspective has gained pre-eminence in game analytics due to the central role users play in games, the latter is as least as important for the success of a company.

In the next post in the series, we will start with defining and explaining some of the key concepts of game analytics and how they are applied 


Anders Drachen

Anders Drachen, Ph.D. is a veteran Data Scientist, Game Analytics consultant and Professor at the DC Labs, University of York (UK).

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