Game Data Analysis – Tools and Methods — Save 50%
A data-driven approach to video game production with this book and ebook
In this article by Thibault Coupart, the author of Game Data Analysis – Tools and Methods, we will see some general recommendations about game analytics and the kinds of limits they have. The first section will detail the philosophy and objectives that should guide the use of game analytics, and the second section will outline their limits.
(For more resources related to this topic, see here.)
Which game analytics should be used
This section will focus on the role data that should take in your production process. As a studio, the first step is to identify your needs and to choose the goals you will attribute to game analytics.
Game analytics as a tool
Firstly, it is important to understand that game analytics are a tool, which means they can serve several purposes. You can use them for marketing, science, sociological studies, and so on. Following this statement, you will need different tools and different approaches to reach your goal. As this article has tried to highlight it, tools are chosen according to problems, regardless if the choice is technique or analysis. You must not choose a tool because it is said to be the best performing tool ever made, or because it is fashionable. Instead, you must choose a tool because it is said to be the most efficient tool for your needs. Try to answer the following questions:
What are the long-term uses I plan to do with game analytics? Is it simply reporting the Key Performance Indicators or is it the building a user-centric framework for deep analysis?
What are the types and the level of skills of the people who will work on it?Do I have all of the skills, from data scientists to game analysts, or do I need to choose a solution which will offset some lacks in a particular field?
How much data will be collected? How do I plan to deal with possible peaks of frequentation?
How do I adapt temporalities of reporting and analysis with the rhythmof production I have on my project? Do I split them weekly or monthly?
What are the main goals of my process? Do I want to build a predictive model (for example, based on correlations) in order to define the next acquisition campaign I will run? Do I want to increase the monetization rate on the current player base? Do I want to perform A/B testing? And the list goes on.
Game analytics must serve your team
Secondly, it is important to ensure that the use of game analytics must serve your team as a whole. They should not have any disagreements about the long-term objectives that you have chosen.
They must accompany it and especially improve it, but the general objective should remain the same. Given the current state of the field, withdrawing the "human touch" from the design process entirely and listening only to data would be a mistake. That's why the game analytics process should be thought through the prism of your own team; and therefore, should be presented as a new tool. This will help them to make good decisions for the game.
The best example for the democratization of "game analytics way of thinking" inside your team is certainly the A/B testing aspect. If you experience debates about particular features in the game, instead of taking part you can propose to use A/B tests for some of those features.
Following this, there are no particular limits to the use of the tool. A game designer can test different balancing on the virtual economy of a game and an artist can experience different graphic styles.
When starting, focus your attention on simple practices
If you are new to the field, the following list may help you to start defining your first objectives. It contains most of the typical use for online games, especially free-to-play games:
Producing KPIs on a weekly or monthly basis, according to your needs. These KPIs will help you to orient the upcoming development of your game and to anticipate the return on investment of your acquisition campaigns.
Identifying if some of the steps of your tutorial phase are poorly designed; for example, if you have a sudden player loss at a particular step of your tutorial.
On the same idea, having the loss of players at each level is also very useful to improve the general balancing of your game, especially the progress curve and the difficulty. This topic is more important if you have a part of your business model based on purchasable goods, which can increase the progression rate of the player.
You can evaluate which area and which purchasable goods of your game are generating the best income.
You can perform A/B testing on particular key features of your game in order to see which ones are the most efficient.
What game analytics should not be used for
On the other hand, there are a few limits that you need to know before using methods and processes from game analytics.
Keep away from numbers
You must always be careful about the fact that numbers are used to represent a given situation during a "T" instant. From this statement, the predictive models must always be revised and improved. they should never be considered as the perfect truth.
In order for the process to be efficient, it is quite important to keep research on the data inside the structure defined by the initial goals. Otherwise, you might split your efforts and no actionable insights would be identified.
In other words, numbers must remain at their place. They are a tool in the hands of a human subject, and they should not become an obsession. Try to reason if they make any sense and if you are asking the right question.
Practices that need to be avoided
As mentioned in the the previous section, if you are new to this field, be aware of the following situations:
Data cannot dictate the full content of your next update. If it is the case, you may first re-evaluate the general intention behind your product and talk with the game designer.
When starting, try to avoid complex questions that involve external factors in the game, even if they seem crucial for you. For example, trying to understand why people stopped playing your game over a long period of time is usually impossible. Old players might stop playing because another game came out or they just got bored. Data cannot make miracles at this point of the engagement.
Data must not take too much ampleness in the creative process. There are some human intentions and ideas, and only then the data comes in order to verify and improve the potential success of those intentions.
Data must not slow down the performances of the game. One of the common methods to avoid this is to send the data when the player logs in or logs out and not at each click or each action.
This is the end of this article, and the most important thing you need to remember about game analytics in general is the importance of the definition of your objectives. The reason why you choose this tool instead of another (and this article has tried to list a maximum of them, from data mining to pure analysis) is because it fits your needs as much as possible. This statement is true at every stage of the refiection process which surrounds game analytics, from the choice of the storage solution to the type of analysis you want to perform.
The rising of a fully-connected state in the video game industry offers developers the opportunity to change the way they create games, but there is no doubt that the level of maturation related to this tool has not reached its maximum yet. Therefore, even if the benefits of game analytics are great, be prepared to make mistakes as well; and keep your own process open to various criticisms from your team.
Resources for Article:
- Flash 10 Multiplayer Game: Game Interface Design [Article]
- GNU Octave: Data Analysis Examples [Article]
- HTML5 Games Development: Using Local Storage to Store Game Data [Article]
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About the Author :
Coupart Thibault had been studying town planning and statistics for years before he developed an in in-game data analysis. Switching from the real world to the virtual world, he studied game design and completed his transformation with his first professional experience at Corexpert. Working on a database of more than 20,000 players, he has experience with the study and analysis of data in order to deliver various insights for the company.