These 15 key statistics are what all game developers need to know

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Sometimes, mobile game analytics can feel very complex. When it comes to metrics, there are hundreds of them to track. On the simple side, there are metrics like downloads, play time, and DAU, which are very intuitive and measure specific activities. More complex metrics include churn rate, average revenue per paying user (ARPPU), and DAU/MAU ratios, which are not so intuitive and sometimes bring more questions than answers.

For example, “Am I counting users as lost over the correct time period?” “What is a normal ARPPU?”

We haven’t even introduced more advanced data such as user classification, user source, user activity, etc. Here, the author does not intend to say which data is the most important, but systematically introduces what information each data can tell you about the game, because the data that one developer should pay attention to may be very different from that of another team, and there has never been a perfect game analysis solution on the market, but by understanding some data, you can better understand your own game.

Daily Active Users (DAU)

Let's start with the simplest one. DAU is the number of unique users who open the game at least once in a given day. Usually, DAU, like other high-level data, can't tell you how well your game is performing. However, for analysis, it is still very helpful to know these simple data.

Let's look at an example: let's say a hardcore game has 10,000 active users who enter the game several times a day and are actively paying; the second is a news or communication app with 1 million users but no monetization; the third app is running a user acquisition campaign, but the retention rate is very low, with 500,000 DAU today, but it may drop to 100,000 tomorrow. Through these three apps, you can see that DAU data refers to the number at a specific time, and the context adjacent to it is also important information, sometimes more important than how many users you have.

Number of games

Every time a user opens your game, it is counted as a game session. It is especially important to focus on the average number of games played per DAU user, as these numbers can tell you how engaged your players are with your game.

The content of the game is directly related to the number of times each DAU user plays the game, because some game types themselves will cause players to enter the game more times. If a user only opens the game once or twice a day, then their long-term retention will not be particularly high.

DAU/MAU Rate

The ratio of daily active users to monthly active users shows a game's ability to retain users and is often used as one of the criteria for judging game stickiness. This data shows how often players log in to the game. For example, if a game has 100,000 MAU and 15,000 DAU, then the DAU/MAU ratio is 15%, which means that users only log in to the game 15% of the time in that month.

Since this is just a ratio, DAU/MAU is only in the range of 0-1. The closer the value is to 1, the more times users open the game that month. Social applications like Facebook are said to have a DAU/MAU ratio close to 50%, but most successful games are only close to 20%.

Retention rate

Retention rate is undoubtedly the most important data for a free game. Successful free games can establish long-term relationships with users. If users like the game content enough, they will be willing to pay to gain certain advantages. A game needs to have a high retention rate to have enough time to establish this relationship with players.

To improve retention, you need to classify them based on the date they downloaded the game. The day they downloaded the game is counted as Day 0. If your users enter the game the next day, they can be counted as next-day retained users. If they don’t open the game, they are not counted. Commonly used retention data includes next-day, 7-day and 30-day retention.

(Paid) conversion rate

Revenue may be the topic that everyone is most concerned about. The above data are all about the relationship between your game and users. But for many independent developers, the most important data is whether their game has earned enough money (to maintain life or continue development).

The paid conversion rate refers to the ratio of users who have made purchases in the game to the total number of users in a specific period of time. Of course, you can also calculate the conversion rate of advertising in free games.

Getting players to pay for a free game is difficult for most developers, but similar to other industries, repeat purchases from a small number of players generate the vast majority of revenue for free games, and you can encourage players to make their first purchase by providing very valuable items or promotions.

Revenue per Active User (ARPDAU)

Average revenue per active user (ARPDAU) is the most discussed statistic in the mobile gaming space, and it’s important because it tells you how your game is performing on a daily basis. This number is especially important to watch before and after a user acquisition campaign. During a campaign, make sure you know your game’s ARPDAU range and the normal dropoff. In a campaign, categorize new users by source and understand which networks or gaming platforms contribute the most to your game.

Revenue per Paying User (ARPPU)

The average revenue per paying user is only calculated for users who have paid in the game, and this number will vary greatly depending on the game content. Core games have relatively high monetization capabilities, so the ARPPU value is generally higher, but compared with more casual games, their user base is not comparable.

User churn rate

User churn is basically the opposite of retention, i.e. how many of the users who downloaded the game no longer play it? Churn is the most influential data for paid subscription games, but it is more subtle when applied to free games.

This data is mainly about how users play games. In paid subscription services, churn is black and white. Users either pay or they don't. In a free game, some players play the game many times a day, while some casual players only log in once or twice a week. In order to adapt to different types of games, we define churn as players who have not opened the game within 28 days.

In-game data

In addition to understanding user engagement, retention, and monetization, it is also very important to measure and balance the game economy. If the way to obtain virtual currency is too easy, users will not have to pay. However, users still need enough currency to continue to enjoy and explore the game. There is a middle number in between. The following data can help you find the right data:

Resources, consumption and circulation

Resources refer to the places in the game where players can obtain virtual currency. Resource data measures the amount of in-game currency a user obtains, of course, including free currency and game coins distributed through promotions.

Consumption data is the opposite of resources. At certain times in the game, players need to spend in-game currency. Resources and consumption can sometimes also be used for premium currency and secondary game currency. During the analysis process, you need to track different currencies separately.

Combining resources and consumption, you can get circulation data, which refers to the overall balance of players' acquisition and consumption of game coins. Generally speaking, circulation data should be stable. If the data suddenly rises, it means that players have too many game coins and there is no need to spend them. If it suddenly drops, it means that players do not have enough resources to continue playing the game.

Number of starts, failures and passes

Finally, let's look at some data about game progress. Many games have a leveling element, whether or not the user has to start a new level. Starts measures how many times players start a new level. Failures refers to how many times players fail to complete a specific level. And of course, Passes is how many times players complete a level. These three data points combined can help you better analyze game levels.

Are your levels too difficult? Are users getting stuck unexpectedly at certain levels? Which levels do users like and repeat the most? Number of starts, failures, and completions can answer these questions.

Of course, there is no so-called "secret" to game analysis. The above data is just to help you better understand game analysis. The most important game analysis data needs to be based on the type of game you are making. Once you understand user behavior, you can know the impact of a game update or user acquisition strategy adjustment.