Data is like windows through which we can gain insight into many secrets inside the game world. These secrets are like treasures hidden deep in the room. Only by truly understanding and making good use of them can we create more attractive games and form more successful business models.
A key question is: what windows are there, and how can we open them?
The first window is called "User Behavior". From this window, we can see the secrets of user-related behavior patterns.
Common indicators such as daily active users (DAU), average revenue per user (ARPU), retention rate, and return on investment (ROI) in the usual sense, or analysis such as novice funnel and activity funnel, are all obtained through this part of the data. The core of this part of data collection is a reasonable embedded point in the application, and the design of events and attributes is the top priority.
The buried point collection in the game is usually divided into two parts, one part comes from the client side and the other part comes from the server side. As far as the server-side buried point collection is concerned, its data is not easy to lose and is more accurate. Therefore, behaviors such as "payment" that require more accurate statistics will be collected through the server-side buried point. As for the buried point collection of the client, it is the top priority of the game industry.
Because for other industries, the buried point data on the client side is usually a supplement to the buried point data on the server side. For example, in e-commerce apps, we analyze the user’s operation behavior path on the interface, which may not be as meaningful as analyzing the user’s final transaction behavior. In the game industry, the entire experience of a game is in every step of the player's operation. If the player has a poor experience in a certain detail, it may be lost, and if the player obtains a huge sense of pleasure in a certain detail, it is possible to create greater value.
Taking "Hearthstone" as an example, after playing a card, clicking on the interface can speed up the animation, so this kind of operation of clicking to speed up or skip animation is not suitable for burying points on the server side. For other industries, whether the interface animation effect in an APP is accelerated is not the core of optimization, but in the game industry, you can even distinguish between two types of users, one is Buddhist, to enjoy the process, and the other is aggressive, to enjoy the results. With this understanding, the function design and operation methods for segmented groups will be different.
User behavior data is very large, highly flexible, and can be analyzed in various ways. If used well, it is the most objective and subtle embodiment of the entire game. Therefore, relatively powerful tool support is required, such as Sensory. But user behavior data is not omnipotent. First of all, it must be based on the statistical analysis of the behavior that has occurred in the APP, and there is nothing that can be done about the fact that no behavior has occurred in the APP.
Secondly, from the perspective of analyzing user behavior data, it may be difficult to give an intuitive view of the changes in the system itself. That is to say, we can intuitively see user behavior through user behavior data, but the game is a complex system, and user behavior itself will cause chain reactions in the system itself, and there will be no behavior embedded in these chain reactions. Therefore, it is difficult to track these changes with pure user behavior data.
But wait, we have four other windows.
The second window, called "game snapshot", mainly refers to the running snapshot of the game at a certain moment, through which we can see the balance and design of each system of the game under a certain time slice. This part of the data is of great significance to self-evolving complex systems.
Let's take the game "Civilization" as an example. From a certain point of view, "Civilization" is building an engine. Once the engine is built, it will continuously generate resources, and the behavior of the player is only transforming the engine. The running of the engine is driven by time. At this time, regular running snapshots based on the server are more important for the evolution analysis of the game system itself. For example, we want to see how the resources in the game will change over time? Whether resource A is related to changes in resource B, etc.
For example, the quantity fluctuation of wood and the quantity fluctuation of gold coins are always in a trigonometric function relationship, and they just fill up each other after being added. Then it shows that in game design, the two resources may be interchangeable, which also means that the total amount of resources may be used as a standard to measure the balance of the game. However, the collection methods for this part of data in the market are usually different. The more common method is to directly save snapshots of the Sever database on a regular basis.
The third window, called "Performance Data", is similar to snapshots, but it is more technical, because the experience of the game is very important, and in the experience, performance is the most intuitive to affect the experience. In fact, in order to achieve the required performance, many games have made a lot of technical innovations. For example, the client prediction technology of games such as "Overwatch", and a series of rendering technologies such as texture compression, LOD, deferred rendering, lighting precomputation, Occlusion Culling, etc., although it costs a huge price, they are all for players to "enjoy silky smoothness" when operating.
This part of data is usually not recorded in user behavior tracking points, such as FPS, loading time, memory usage, PING, crash rate, etc. In order to collect these performance data, developers usually use various tools, mainly in the following categories:
1. Analysis tools that come with the game engine: such as Unity's Profiler tool, Unreal Engine's Performance Profiler.
2. Tools provided by device manufacturers: such as Apple's Instruments, Android's Profiler, etc.
3. Third-party performance analysis tools: such as GameBench, RenderDoc, Pix, etc.
4. Customized DEBUG logs: Add custom logs in the code to collect and analyze specific performance data.
The fourth window is called "Community Feedback". This window will take us to see the data of the player's non-behavior outside the game. This part of the data is particularly valuable and effective, because it can reflect the real overall emotions of the players. For example, in the reviews on Steam, we often see that a game has been given a bad review, but the comment is indeed "We need Chinese!" These are the most authentic feedback from the players.
In the process of foreign game development, this area is particularly important. For example, in the development process of "Slaying the Spire", the development team first used slack to collect feedback on a small scale. After EA, it collected a large amount of feedback on discord.
For the collection of this part of data, first of all, there must be similar community tools or platforms, such as slack, discord, steam, taptap, etc., or Appstore reviews and direct feedback from customer service systems are also important sources.
The second is to have tools and techniques for analyzing data. Whether it is manual viewing, machine learning technology for keyword collection, sentiment analysis, or LLM for summary, the purpose is to tap the value of this part of the data.
In fact, this part of data is easier to obtain in the early stage, and it is also more valuable data. However, as the game becomes more popular and the amount of data increases in the later stage, the technical threshold for analysis rises accordingly. This can be a happy annoyance, and game developers seem to need this kind of happy annoyance.
The fifth window is called "Market Data", and this part of the data is also divided into two parts.
One is the market data related to the game itself, such as using advertisements for promotion, so the cost and effect of advertisements are very important. For advertising data, accurate effect tracking is a compulsory homework. Sensors’ advertising full-link analysis supports the full link between advertising and users, and links market data and user behavior data to achieve more refined effect evaluation. The second part is the market as a whole, as well as the situation of competitors, as well as the current advertising materials. After all, knowing yourself and your enemy can win every battle.
Each of these five types of data, from user behavior data to market data, provides a different perspective for game data analysis. When new technologies continue to emerge, the process of game development and operation will continue to iterate and change, and new windows and methods will also continue to emerge. Sensing will continue to work with game manufacturers to gain insights into the secrets of games and obtain real treasures through the window of data.
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