We understand that ARPDUA , i.e. average revenue / daily active user, is important metric to measure financial health ofa mobile game or an application. However as a marketer we need to predict DAU i.e. Daily Active User, before launching the game. This is important for planning marketing activities for the mobile game. In short, considering ARPDAU will remain constant, we can measure expected revenue by predicting DAU.
DAU is abbreviation for daily active users. Daily active users is calculated by adding new users and returning user for the particular day. Often DAU is calculating by averaging DAU over certain days to calculate moving average termed as average DAU.
A user who installs the application and opens the game again later is termed as returning user.
Day1 retention is defined as percentage of users who installed the game yesterday and came back to play the game today. Similarly Day 7 retention is defined as users who came to play the game on a particular day, considering they installed the application 7 days ago. Retention indicates stickiness of the user.
ARPDU is defined as average revenue per daily active user. This is usually averaged over 7 to 21 days to get more reliable number.
Table of Contents
Importance of DAU
Estimating DAU is extremely important for performance marketers. This can help them answer few very basic questions
- How much money and time is required to build certain DAU?
- Measure marketability of the game
- Identifying top performing games based on retentions
DAU is based on 2 factors, news users and returning users. New users can be estimated based on marketing spend and user acquisition efforts. Whereas returning users is a function of retentions. So on a particular day
DAU = New Users + R1 + R2 + R3 + …. R30 + … R90
Where R1 is returning users who installed the game yesterday, and R2 are returning users who installed the game 2 days ago. Based on retentions for few days, we can predict DAU by applying regression models.
DAU Calculator Tool
DAU can be calculated based on retentions for few days and predicting remaining days retention. The model was first discussed by Digital Limbo. For the tool be used effectively you need following inputs
- Retentions for Day 1, Day 3, Day 7, Day 14 and Day 30.
- Identify the regression model that fits the data points
For most of our games, power functions worked better, however few people proposed logarithmic. You can download the tool from the following link
Using the tool
- Gather the data for Day 1, Day 3, Day 7, Day 14 and Day 30 retention and update cells from B2 to B7 accordingly.
- Now click on the regression line and find regression model that best covers your data.
- Copy the forecast model (Y), which is returning users for a particular by pasting the formula in A9. Separate co-efficient and power; or logarithmic functions.
- Update the formula for J2 to J31.
- Update the user acquired to experiment with number users required.
This can help you understand users retained within your game after 30 days. You can calculate New Users / Total Users to understand stickiness of your game. Lower the number, higher will be game stickiness. If you thought gaming was all fun, marketing is all about number crunching.
Significance
If we can predict DAU based on our user acquisition target, this can help us answer few important questions
Target DAU
Product team usually require certain DAU to validate and tweak game mechanics. For data analytics team to give reliable results, product teams usually require 1,000 DAU to formulate conclusive results. This tool can help predict DAU and required marketing spend for the user acquisition.
Profitability
Mobile games or application do not become profitable immediately. This tool can help marketers plan and schedule their marketing budget and identify date when marketing spend would be equal to daily earning. This can also assist in revenue forecasting.
Future hits
Marketers need to shortlist games that can be big hits. Hence it’s important to tag and identify stars earlier. The tool helps us compare data with previous games. E.g. if a game with lesser retention and ARPDAU, was able to go profitable in 45 days, can this game go into profitability earlier ? Can this game contribute more to gross margins? Can we increase marketing budget to increase our net margins? These are important questions that needs to be answered earlier.
Caution
This models isn’t perfect as it only factors in retention for first 30 days. Ideally the model needs to be expanded over 90 days for games with better long term retentions. However this does serve as good benchmarking tool.
Conclusion
GenITeam is full stack game development agency and can help in developing and marketing games. Successful Game studios derive lot of information based on data. Either you are planning next feature or determining marketing spend, reliable data can help in improving profitability.
