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Monetization Based Game Design: ARPDAU Contribution

A very useful tool to help game designers understand 1. Game monetization potential and 2. Potential weaknesses or areas of improvement, is an ARPDAU contribution table. This tool will help both with analysis and prediction of potential game success.

I initially published this post in my personal blog Check it out to learn more mobile gaming design techniques, analysis, and industry opinions.


In today's mobile game market the only viable games on a long term basis are those that have extremely high LTV (Life Time Value). As LTV is fairly difficult to project accurately (at least in the short term), shorter term metrics around monetization and retention are more typically used as an audit tool.

The standard thinking around the key target metrics and metric objectives has been a simple view around monetization and retention as follows: 

Typical * Or lower if better retention/social/viral; ** Event based ARPDAU often > $1.00

Note: This data is from early 2013.

These heuristics obviously miss some key important factors/information such as:

  1. User acquisition costs (which may fluctuate and thereby modify the metric objectives) 
  2. Game categorization (as Casual/Mid-Core/Hard-Core) is too crude/general
    • E.g., Retention for Subway Surfer and Candy Crush is much higher than the typical "Casual" game reducing monetization objective for a successful game
  3. Game's average user lifetime or content progression depth which in turn impact LTV

Having said that, this guideline is the best I've seen so far on a rough level. There are also other metrics and metric objectives used, but outside the scope of this post (and often confidential to the companies that utilize those methods). Some examples include metrics such as cohort LTV/30-day ARPU, first day buyer conversion, stuff like that.

So What Does This Have To Do With Game Design?:

In short, everything.

So long as costs to acquire users remain high, mobile games must be designed with the ability to monetize over the cost to acquire users:

LTV > Effective CPI 

Ok, Then What Do I Do?:

Well, one thing we have observed in the industry and why retention rates are typically used as a key metric is that various genres/types of games show amazingly similar characteristics. Hence, if I make a "runner" game or "tower defense" game, then I can be reasonably assured of what the retention metrics will look like given similar mechanics.

Given that, with information about what the retention should look like I can then try to determine what my ARPDAU will need to be to have a successful game.

ARPDAU Contribution Table:

For the games that I design, one tool that I developed to help me understand whether we can actually make it or not is something that I call an "ARPDAU Contribution Table".

This table breaks down a game's design components and attempts to project the game monetization based on each component.

Example for a game similar to Modern War by Funzio/GREE before they introduced GVG and events in Feb 2013 (Data purposely left blank):

Example ARPDAU Contribution Table (Modern War - Pre-GVG 2/2013)

This is an art not a science.

Having said that, this tool is extremely useful at the beginning of a game during the design phase to give the game designer some confidence that the game design has the ability to generate a positive return on invested capital/resources.

During more than a few monetization consulting projects I have participated in we found that it was often the case that even professional game designers had no idea about how well a game would monetize and made extremely poor decisions about monetizion mechanics. Using this tool can highlight some of the mistakes that can cause a lot of wasted time building a game that never has a shot from day 1.

Even after a game has moved to its live phase it is useful to track game monetization by contribution type to see and compare how the game is performing against other games and relative to projected values. For example, if you see that certain game design components are monetizing less than expected (or by comps) you can try to isolate game design/content/flow bugs that may be causing monetization loss.

In summary, I highly recommend the use fo this analysis for all mobile games and especially given the extremely harsh world of mobile app discovery and high UA costs that we live in today.

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