Detailed analysis of mobile games essential data analysis indicators

Based on the TalkingData AARRR model and the characteristics of mobile game industry, this paper gives the key data indicators that mobile game operators should pay attention to at each stage of business operation.

User Acquisition

The AARRR model points out two core points for mobile game operations:

1) User-centric, with a complete user life cycle as a clue

2) Controlling the cost / revenue relationship of the entire product, and user lifetime value (LTV) much greater than the user acquisition cost (CAC) means successful product operation

Mobile game operations will go through the following cycle from input to output:

Acquisition User Acquisition (input)

Activation & Retention User active and retained

Revenue User Conversion (Output)

1. User Acquires Key Facts for Acquisition

This stage is the investment period of the business. Operators through a variety of promotional channels (Channels), in various ways to obtain the target user.

At this stage, the most important part of data analysis is to evaluate the effectiveness of various marketing channels by combining various dimensions (such as time, region and channel) so as to further optimize the reasonable investment strategy and minimize the user acquisition cost (CAC)

Key data:

1. The number of users (in different dimensions of time, region, version, promotion channels, etc.) to disassemble and analyze new, total and growth rates, and to combine various dimensions to analyze user acquisition results and target user distribution for various marketing channels.

Click number of users (Click)

Install the number of users (Install)

Registered users (Sign-Up)

Online users (Login):

The highest online (PCU)

Average online (ACU)

Day Activity (DAU)

Weekly active (WAU)

Monthly active (MAU)

Effective users: different types of products have different definitions (may be registered users or users or pay users logged in)

2. Channel Conversion Rate: Click -> Install -> Register -> Login Conversion Rate (by Channel)

3. Natural Users Organic Users: Non-promotional users, if this data growth rate relative to the growth rate of Marketing Users, or that the product has entered a mature period of stability, or that marketing needs to be strengthened.

Marketing users: Marketing channel users, including channel labels, for the macro evaluation channel promotion effect.

4. False users (One Session / Day User): as the name implies, a session of the user. Mainly used to monitor channel brush cheating. At the same time can also reflect the target user's habits, to determine whether the channel access to the user is valid, so as to evaluate the quality of channel promotion

5. Channel growth rate: the long-term evaluation of channel health

Channel share: Channel comparison

7. Finally talk about CAC (Consumer Acquisition Cost)

CAC = Input Cost / Effective Users, presented as CPX (Cost per X, for example, for each logged in user)

The dismantling of CAC by channel, we can draw the cost of channel promotion.

User activity and user retention (Activation and Retention)

Traditional more rugged data operations usually only focus on the number of users. In fact, in addition to focusing on the number of users, the quality of users is actually more critical to the operators. The AARRR model shows us the law of fine data operation, which is LTV (Consumer Life Cycle Value)>> CAC. That is, focusing on and increasing the real revenue value that users create over their entire life-cycle after cost-taking their users ensures the highest ROI.

This article will continue along the AARRR model system, focusing on the cost from the aspects of value, focusing on mobile games in enhancing the value of the user life cycle should pay attention to important indicators.

Mobile game user life cycle operations can be summarized as follows this conversion process:

Get users (download and install) -> into active users (login) -> retain users (return visit retention) -> into paid users (in-app payments).

First, the user is active (Activation)

User activity is the first step in the process of user value conversion.

Active user

Indicator definition:

Active users: Users who have started / logged in to the mobile game for a period of time

Daily Active Users (DAU)

Monthly active users (MAU)

Percentage of active users: Number of active users over a period of time / Total number of users over a period of time

Daily activity rate

Weekly active rate

Month activity rate

One-Day User: Based on the current time, no user of the app has been used since its introduction. Only when the new start / login, then no start / login.

One-time user ratio: one-time users / total number of users.

Reaction problem:

Game user quality. The absolute number of active users is low, or the proportion of the total number of users is low, indicating that the quality of users is not high. Should be in-depth analysis of whether the target user base is accurate or in-depth analysis of product usage whether the problem exists or not. On the contrary does not mean that users have high quality, product use does not exist problems, but also with other indicators in-depth analysis and judgment.

One-time users. Although by definition this part of users also belong to active users, but should be given special attention. The vast majority of one-time users are invalid, can not create any value. Such as channel brush volume cheating will bring a large number of one-time users. While observing the number of active users, please pay attention to observe this indicator at the same time to objectively evaluate the quality of users in different groups (such as channels). For mobile games, the proportion of healthy one-time users should not exceed 15%

Product Status: Activeness can effectively reflect the user's first game experience. Game interface effects, start loading time, interactive experience, user guidance and other factors will have a direct impact on the user's activity.

Health performance:

Mature and healthy game operations MAU from the long-term trend of development, should show a stable trend curve (Figure)

A successful campaign or release on-line should result in a noticeable growth curve for active users, with one-time users remaining in healthy proportions. (Figure)

The following indicators focus on the response of active users to participate in the use of the game is also an effective manifestation of product quality. In doing user activity analysis can be integrated when the various indicators for analysis, in order to find problems in product operation, to guide product optimization.

2. The number of starts

Indicator definition:

A user of the mobile game once recorded as a start. The number of starts is the total amount of user-initiated games. Statistics can be based on different time intervals. When doing data tracking statistics, it is generally recommended to open the record within 30 seconds for a complete record, not separate measurement.

The number of daily start

The number of weekly starts

Months started

Average Daily Starts: Average Daily Starts per user. Number of daily start / daily start users

Reaction problem:

The number of starts reflects the user's usage frequency of the game. Can be used as an indicator of game product quality.

Health performance:

Different types of mobile games have different levels of activation order of magnitude. The indicator should be combined with the user distribution dimension point of view, the main user should be distributed in a higher number of starts. (Figure)

3. Use time

Indicator definition:

Average single-use duration: The average length of time the user spends each time during a certain period of time = the total number of users during the time / number of starts

Average Daily Use Duration: The average daily average user-played game time count

Reaction problem:

The use of the length of time reflects the user stays in the game status, is the embodiment of the user to participate in the use of the game. Can be used as an indicator of game product quality. At the same time can also be combined with the user distribution dimension to analyze the quality of game users.

Health performance:

Different types of mobile games have different levels of usage duration. A good game should have a longer duration of use. The indicator should be combined with the user distribution dimension point of view, the main user should be distributed in the higher use of time. If there is a large number of users with short duration of existence, the exclusion of the main factors beyond the product description of the target user groups there may be problems such as channel cheating and other anomalies. This indicator can be used as an indicator to monitor the quality of channel users.

4.DAU / MAU

Indicator definition:

The ratio of daily active users to active active users on the 30th

Reaction problem:

DAU / MAU is a commonly used evaluation indicator for social games and online applications, which is used to analyze user's viscosity. The closer the ratio to 1, the higher the user activity, and the lower the ratio, the less communicative and interactive the application will be. The industry also commonly uses DAU / MAU multiplied by 30 to calculate the average number of active users per month.

Health performance:

A good game will have a higher DAU / MAU ratio. Normal healthy Freemium game DAU / MAU is not less than 0.15, and the long-term trend shows a smooth curve. If the long-term trend curve sharp increase or decrease, it is necessary to combine the other indicators of a comprehensive analysis of the causes of the problem.

Second, the user retention Retention

The user's retention tells you how much the user is loyal to the game. Simply put, is to retain active users. User retention is the most crucial stage in the process of the user finally converting to pay and creating real income value.

Indicator definition:

The user starts to use the game for a certain period of time. After a certain period of time, the user who continues to use the game is regarded as a retained user. This part of user accounts for the percentage of newly added users at that time.

Day retention (1Day Retention)

7Lay Retention

Month retention (30Day Retention)

Reaction problem:

Retaining has always been the best indicator of user viscosity and literally a good understanding of "how many users are left behind," which is the most intuitive explanation of your overall game app quality. The higher the retention rate, indicating that the higher the quality of the game application, the better the user's loyalty.

Watch how many new users at a particular day / week are still using them at different times in the future, and see how easily your application can drain you when it's used. Identify the most vulnerable users of the time period, by adjusting the application of strategies, incentives and other activities to reduce the user's loss.

In the industry, many applications value the 1 Day Retention, which is a direct reflection of the quality of the application. This indicator can also explain the satisfaction of users for the first time to some extent.

Health performance:

The user's stay in the promotion channels, the product version of the established circumstances should show a certain trend of development. In general, the user retention will show the following trend curve:

From the perspective of indicators, the user's retention on the 1st, 7th and 30th, there is a certain conversion relationship. Healthy mobile games 1, 7, 30, the user retention rate should not be less than 50% - 25% - 10% level. In other words, a good first-day mobile game application user retention rate should be maintained at about 50% level, weekly retention rate at 25% level, the monthly retention rate at 10% level.

More detailed analysis of user retention analysis Please move TalkingData blog to see "How to read the user retention"

Third, the user life cycle

The life cycle of a user refers to the whole process from the moment a user starts to use a game application to uninstall an application. Because a mobile application can hardly capture a user's uninstalling action, the user loss is usually determined according to the usage frequency of the user being lower than a certain limit value.

LTV (Lifetime Value) is the sum of a user's lifetime value. In terms of mobile games is a user in the life cycle to create revenue integration.

Revenue

The previous articles talked about metrics that should be taken into account when assessing user acquisition costs (CACs), and what should be the focus of users in the process of creating value-added conversions. The value created by mobile game users will ultimately be reflected in the operating revenue (Revenue). The focus of this article will be on the mobile game revenue-related indicators, and finally gives the measure of game users to create value of the key indicators of concept: the value of the user life cycle (LTV, Lifetime value)

At present, mobile games mainly generate revenue through the following three modes:

Paid downloads

In-app advertising

In-app payment

Revenue per paid downloads is relatively simple, Revenue = per download price * downloads (Installation)

For in-app advertising models (primarily stand-alone games), the value of an app's advertising can be measured by an indicator of "user lifetime ad value." For a more detailed explanation, go to the TalkingData blog and read "How do I evaluate the ad's worth for free mobile apps?" )

In-app fees (IAPs) are now the major trends in the future profit model of mobile games. More and more games adopt the profit model of F2P (Free to play) + IAP. The following indicators are also mainly for mobile games in-app mode.

Macro indicators of income:

1. ARPPU

Indicator definition:

ARPPU, Average Revenue per Paying User, that is, the average per-user revenue. Generally calculated on a monthly basis, calculated as follows: Total monthly gaming revenue / monthly number of paid users.

ARPPU reflects the average per-pay player's payment limit. For F2P games, most players are not spending money, ARPPU calculates that part of the spending of users.

ARPPU will be significantly different due to the type of mobile games, geographical factors and other factors. Here are some reference cases:

Virtual World: Habbo Hotel: $ 30 ARPPU (Sulake)

Online Game: Puzzle Pirates, Three Rings: $ 50 ARPPU (Gamasutra)

Social Game: Playdom: $ 20 ARPPU (Lightspeed Venture Partners)

Germany Sci-Fi MMO ARPPU: $ 58.77

France Sci-Fi MMO ARPPU: $ 14.83

For paid users, the distribution of payouts is not even. Generally, the revenue generated by a small number of whales and whales will account for the vast majority of the total paid income. Therefore, in doing revenue analysis should focus on this part of the user income changes focus analysis, and according to the actual situation to take appropriate action strategies (such as enhancing VIP customer service, etc.).

ARPU

Indicator definition:

ARPU, Average Revenue per User, which is the average per user (active users) revenue. Generally calculated on a monthly basis, calculated as follows: Monthly total gaming revenue / monthly active users.

ARPU reflects the overall revenue sharing in the overall user situation, usually the value will be much smaller than ARPPU. ARPU can be used to assess the quality of each user access to channels.

References:

Casual Social Games: $ 0.10 - $ 0.20

Card game

For example Zynga Poker, Slotomania: $ 0.25 - $ 1.25

Virtual Worlds Games: eg Habbo Hotel, Club Penguin, Runescape, and Puzzle Pirates: $ 0.84 - $ 1.62

3. Pay conversion rate (Conversion Rate)

Indicator definition:

Paid users as a percentage of the total active users. Generally calculated on a monthly basis.

Calculated as follows: monthly paid users / monthly active users

reflecting on issues:

How well does the game product lead the player to pay?

How is the player's payment preferences and wishes?

Revenue, ARPPU, and pay-for-conversion have the following relationships: Revenue = ARPPU * MAU * Pay conversion rate

Operators should develop revenue-enhancing strategies by monitoring for paid conversion rates, combined with metrics for other product operating metrics such as game application incarnation.

Health performance:

Paid conversion rates do not directly reflect changes in revenue. A low pay-for-conversion rate does not necessarily mean a reduction in paid subscribers. It may be that a large number of new users have been involved in the game during a certain period of time (for example, after a promotional campaign). Other factors such as first-time payouts should be taken into account. influences. Conversely, higher pay-to-conversion rates do not necessarily mean more pay for users.

Different types of games also have different levels of paid conversion rates. For example, for social games, paid conversion rates vary from 1% to 5% depending on the type. Pay-per-conversion rates for hardcore games such as MMOs will vary depending on the route of transmission and geographical factors (10% -50%).

4. User Life Cycle Value (LTV)

A user's life cycle is the time between the first launch of a game application by a user and the time the game application was last started. LTV is the total revenue generated by a user for the game application during the life cycle, which can be regarded as a long-term accumulated ARPU value.

LTV per user average = monthly ARPU * average lifecycle of users by month.

For example, if the game ARPU = $ 0.5, the average game user life cycle of 3 months, then LTV = $ 0.5 * 3 = $ 1.5

LTV helps operators understand how long the average player will stay in the game and how much they will spend. In combination with the CAC and LTV-CAC differences mentioned earlier, this can be viewed as the profit the game app receives from each user. So to maximize profits, it becomes how to reduce the CAC at the same time, improve the LTV, making the difference between the two maximized. Combined with analysis methods such as segmentation and cohort, LTV and CAC can be calculated for specific groups or channels to assess the profitability of specific groups and channels.