Mobile Game Industry Points of View

Monetization-based Game Design: ARPDAU Contribution

Context:

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:

 CasualMid-CoreHard-Core
Retention D1/7/30 = 40%/20%/10% D1/7/30 = 30%/15%/7%D1/7/30 = 20%/10%/3%
MonetizationARPDAU* = $0.06 - $0.08ARPDAU = $0.30ARPDAU** = $0.80

* 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
    1. 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 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 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):

CategorySub-CategoryARPDAU ContributionContribution %
Virtual GoodsUnits - Hard
Units - Soft
Buildings - Money
Buildings - Unit
Buildings - Defense
Buildings - Boost
Buildings - Cosmetic
Additional Allies
HomeSpeedups
Expansions
ResourceEnergy/Stamina Refill
TOTAL$.15100%

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.

About Joseph Kim

Mobile game philosopher and designer. Aspiring artist. Founder of Playviews, Inc. Follow me @jokim1 or send me a note

Comments

  1. Hi Joseph, thanks to share your knowledge, but How do you calculate how much money/cents/dollar each item is? Thinking in %% for me colud be easier.

    I’m designing a freemium farm video game and I must do this kind of table. I think that the best way it is to develope a table like this, define KPI, observe them, wait for results and then act.

  2. Joseph Kim says:

    Hi Cesar. Your comments are exactly right.

    This is a hypothesis driven exercise. You don’t really know from the outset but the more numbers you see the better you get at prediction. When you start doing this a lot you get pretty good at it. In fact, I visited a publisher and guessed the ARPDAU on 6 of their games within $0.01 over a year ago. It’s not to say I’m special, it’s to say that when you start seeing a lot of the same mechanics and genre types of games then you begin to get a feeling for how well they monetize and then extrapolating e.g., this feature is worth about $0.02 but bc of X which is different from what you’ve seen before you believe it will be slightly higher and you adjust up.

    The point is not to be exact but to 1. characterize specifically what you believe will monetize in your game and to continue to track and adjust as you state (e.g., you should know what you’re betting on), 2. understand which bets were right and what wasn’t right – does that change your ability to compete?, 3. once you start seeing more and more numbers you will get better at designing your next games and having a better feeling of whether the games can back out or not.

    The other thing you can do is to try to schmooze numbers out of guys who have made similar games… it’s actually pretty hard to keep a secret here in SV.

  3. joseph, this is incredibly helpful. thanks.

  4. Hi Joseph,

    A quick question: are the retention figures rolling retention (%age of players active after Day N), as opposed to fxed retention (%age of players active on a given day)?

    Thanks,
    Baptiste

  5. Joseph Kim says:

    Hi Baptiste! Fixed retention. I’m not a big fan of rolling retention as it seems rolling is computed differently by various vendors.

    • got it, that’s what I thought too (or the numbers would have been very low :-))
      I really like more the rolling one as it reflects better the reality to me: people can still be active even if they didn’t play on a given day…

      but all in all, it’s all about definition and being able to compare games against other ones

      thanks!

Leave a Reply

%d bloggers like this: