Monetization-based Game Design: ARPDAU Contribution

Context:

In today’s mobile game market, the most successful games must 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 (usually ARPDAU) and retention (usually D1/7/30) are more typically used to estimate LTV potential.

The table below depicts the simple view for monetization and retention as follows:

	Casual	Mid-Core	Hard-Core
Retention	D1/7/30 = 40%/20%/10%	D1/7/30 = 30%/15%/7%	D1/7/30 = 20%/10%/3%
Monetization	ARPDAU* = $0.06 - $0.08	ARPDAU = $0.30	ARPDAU** = $0.80

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

Note: This data is from early 2013.

These heuristics miss some key factors such as:

1. User acquisition costs which may fluctuate and thereby modify the metric objectives
2. Game categorization should be granular than Casual/Mid-Core/Hard-Core
   1. E.g., Retention for games such as Subway Surfers and Candy Crush likely much higher than the typical “Casual” game reducing monetization objective to be successful
3. User lifetime or content progression depth (often seen with good extended long term retention) can expand LTV

Having said that, the guideline above serves as a decent high level data point. There are also other metrics and metric objectives used, but outside the scope of this post and some of which is confidential to the companies that utilize those methods. Examples of which 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 (Data purposely left blank):

Category	Sub-Category	ARPDAU Contribution	Contribution %
Virtual Goods	Units - Hard
	Units - Soft
	Buildings - Money
	Buildings - Unit
	Buildings - Defense
	Buildings - Boost
	Buildings - Cosmetic
	Additional Allies
Home	Speedups
	Expansions
Resource	Energy/Stamina Refill
TOTAL		$.15	100%

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.

At Playviews, during more than a few monetization consulting projects I participated in we found that it was typically the case that professional game designers had no clue what they were doing and made extremely poor decisions about how a game monetizes. 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.

UPDATE (10/14/2013): Thanks to @Poippels (Twitter) to remind me to update the typical monetization and retention metrics.