I fully agree with the statement “If you can’t measure it, you can’t manage it”. Every game producer must define and measure matrices that gauge game performance. There are few matrices that we are already familiar with, including daily downloads and revenues earned, however there are some additional matrices that can help us measure game performance better. At GenITeam, we have been using GameAnalytics for tracking game performance but you can use any other available tool as well. As a matter of fact, by closely monitoring matrices we were able to increase our revenues threefold as well.
While analytics are part of post-production stage in game development process, however installing and configuring analytics earlier can help us experiment and measure game design elements earlier.
For the sake of discussion, we have categorised the matrices into four broad categories.
- Progression / Funnels
This category includes matrices that relate to downloads or acquiring new users. These numbers can provide us intersting insights.
Daily New Users
Defined as daily new installs, the number helps us analyse trends of game downloads. The number is almost same as downloads recorded on our app store account. A quick trend and pattern can help us analyse visibility of application at app stores.
The trends can also help analyse impact of experiments, e.g. trying a new icon, changed description or localised content. Also If a game is gaining traction in a specific country, it worths localising game for that market.
There are few more ratio’s that can help us understand new users little better
Organic Vs. Paid User
Usually measured through analytics platform, the fraction allows us to attribute downloads to organic vs. users acquired through paid marketing. This is practically useful if you are running marketing campaign and want to review its impact on organic downloads.
Downloads based on Build
Analytics platform allows us to divide downloads based on build number. If you are running a/b testing on builds with various features, it can help you categories downloads, that later can be used for user segmentation for other matrices including retention and monitization.
Downloads based on Country
This helps us analyse downloads within a specific country. If you have higher downloads in a specific country then you can plan for localised store listing and content localisation for specific country. In our case, one our game was doing well in Saudia and we launched Arabic version of the game. The arabic version game quickly hit top free as well as top grossing charts in Arab countries.
This category contains one of the most important matrices that usually define user’s liking for a game. We will start with retention
Retention is definitely the most important metric in a fermium game. It defines how many people are coming back to your game, after installing the game. Higher the retention, higher the engagement, and hence higher the revenues.
Within retention, most commonly measured ratio’s are Day 1, Day 7, Day 14 and Day 30 retention. Day 1 retention is defined as percentage of users that opened game one day after installing the game. Similarly day 7 retention is defined as percentage of users who came back to the game on day 7 after installing the game. Similarly day X retention is defined as percentage of the users who came back to the game on day X after installing the game.
So, what are a good or a bad retention number and what these numbers indicate about your game? Though benchmark ratio will vary based on game price, genre, downloads however we need to aim for: Day 1 Retention: 35%+; Day 7 Retention: 15%+; Day 14 Retention: 10%+; Day 30 Retention: 5%+
Improving Game app retention
If you are trying to improve retention, day 1 retention is always great place to start. You should review user flows and progression funnels to understand why are people not coming back to your game. Tutorials and level progression funnels can definitely help in understanding user flows and provide hints for improving game.
Also, if you make adjustments to your game flow or level design, you must keep watch on impact of your changes on user retention. Infact, each build retention matrices can serve as benchmark to next builds.
Another important number is Daily Active Users (DAU). The count indicates the total number of user who opened and interacted with game on a given day. It includes new users who download the game as well as returning users.
The number is effectively used in calculating other important ratios for revenues e.g. Average Revenue per DAU and Average Ad Impressions / DAU.
Another important number is MAU, stands for Monthly Active Users. The count indicates the total number of people who opened and interacted with your game on a given month.
New Downloads / DAU
This is extremely important ratio and measures the stickiness of the game for a shorter duration. It defines the percentage of your daily users who were new on that day. Though there isn’t a benchmark but we expect a number around 20% is good to start with.
DAU / MAU
Often referred to as measure of stickiness of the game for a longer duration. There are few industry averages around this ratio and a number of 0.15 or 15 % is considered as good target. Industry sources suggest that angry bird had 10 % ratio and Parallel Kingdom had 30 %
Monetisation of a Game App
Finally to everyone’s favourite topic: money! While other metrics measure general health of the game, but most indie developers are interested in weather their game is making enough money or not. Following matrices help in understanding monitiztion strategy of the game
Conversion Rate in game app sphere
It tells us the percentage of users who are converting from free to paying users. This is usually recorded when a user makes an in-app purchase within a game for the first time.
Industry conversion rates vary from genre to genre, but anything between 0.5 to 1% is decent, and 1-2 % is regarded as good and above 2 % is considered great.
Defined as average revenue earned by daily active users. This is most important metric used in analysing games ability to monazite. The number is often used in forecasting game revenues as well benchmarking to other games. What number is good or bad, is based on game genre, but here is what we define as average
|ARPDAU Range ( $ )||
|<0.01||Expect for only ads driven games|
|Between 0.01 & 0.02||Preforming better then advertisement|
|Between 0.04 & 0.08||Average|
|Between 0.08 & 1.2||Good|
|Between 1.2 & 1.8||Very Good|
|Above 1.8 +||Great|
The impact of your changes within games can also have impact on ARPDUA.
It is the average revenue generated from each paying user. This is calculated by dividing total revenues by the number of paying players. It helps you analyse effectiveness of pricing strategies and game economy. There are always fewer users who are willing to pay but we must design game economy appropriately to command maximum returns.
Impressions / DAU
Though they aren’t part of game analytics but games that rely heavily on advertisements use Total Impressions / Daily active users as a measure of health of their app. Higher the number, higher the impressions served and possibly higher the revenues. Remember, impression count is one factor in calculating advertisement revenue, so higher impressions doesn’t necessarily means higher revenues.
Though, funnels aren’t part of matrices but they help us analyze user flow within the game. There are few funnels that we regularly use and build other funnels for detailed analysis.
How users are flowing through various steps of tutorial can help us analyse effectiveness of tutorial designed. Level drops during various tutorial steps heavily contribute towards user churn. At GenITeam, we aim for 97-97 % success rate at each individual tutorial step.
Level Progression Funnel
If we have a level based game, this can help us analyse quality of levels designed. You can always update your levels based on analytics and compare results amongst builds. At one instance we saw a huge drop in usage at level 3, as our level was very tough for users and irritated our users. We eased the level and it helped us improve our level progression, retention and monitzation.
In short, funnels help us design changes based on user interaction with the game.
At GenITeam, we love to measure performance of games and assist in developing games. Reach out to GenITeam Solutions if you are interested in building your next game or using analytics to measure game performance.
ARPDAU is defined as average revenue per daily active user. It’s calculated by dividing daily revenue by daily active users.
Before you market the game, you must measure how well your game is performing. There are various matrices that measure performance of the app, and defines its marketability.
Matrices are a mean to measure and benchmark our game with industry standard, as well as past version. So if we released our hyper casual game and its ARPDAU is $ 0.06 in US, we can reliably say that our game is performing lower then industry average where average ARPDAU is $0.1. Similarly, if we released a new update and our retention increased by 10%, we can reliably state that updated version is performing better on retention matrices.