Better Revenue Forecasting for Social Games

One of the great things about social games is that there continues to be a wealth of information available that makes their business case much less uncertain than for other kinds of games.

As one example, Lisa Marino, Chief Revenue Officer at RockYou, gave a presentation at GDC on Monetizing Social Games that’s well worth reviewing.  Lots of good stuff in her slides, and in particular she provides some fascinating data on daily revenue per DAU.  This is a much more precise set of numbers than monthly revenue per MAU.  Measuring per DAU allows for better accounting of the initial fast rise of most successful social games (when DAU can be a very high percentage of MAU), ongoing engagement (how often your players return), churn, long-term drop-off, etc.  

In her slides, Lisa provides a few very helpful numbers.  First is what she identifies as a key threshold, a ratio of 0.2 DAU/MAU.  This means that on any given day, 20% of your active players are returning to the game (and per my earlier posts, this enables you to consider the rhythms in your game design — are you designing your game around how often you want people to come back, or is this haphazard?).

She also forecasts an overall expectable K factor, the measure of virality, at less than 0.7, with changes to how Facebook allows notifications.  This means that on average, each player invites 0.7 new players (meaning that not everyone actually invites someone).  I think this is a solid number to plan on, and one that keeps customer acquisition costs low, but personally I hope for more: again, how much are you designing for virality, without relying on external forcing functions like spammy notifications?  In Lisa’s slides you’ll find some additional detail regarding viability thresholds in a few common acquisition channels.

On slide #11 of her presentation, Lisa provides some extremely helpful revenue detail.  She measures revenue in units of 1000 DAU: according to her figures the best social apps are monetizing at more than $100 per 1K DAU; the average is around $10-30 per DAU.  Now, no time baseline is given on these slides, but having done the math I have to believe that she is talking about daily revenue per 1000 DAU, as nothing else makes sense (Lisa later confirmed this in email).

In an earlier post, I referred to sources (since confirmed in various conversations at GDC) that support a typical revenue figure of $0.20 to $0.25 per MAU per month.  Here’s how the math works out between these figures and those Lisa provided:

1M MAU @ $0.25 equals $250K revenue per month.  If we assume a 25% DAU/MAU ratio (above the 0.2 threshold, and a bit below the ~0.3 that appears to be a long-term number for many successful games), that means we have on average 250,000 DAU.  $250,000 divided by 250,000/1000 [1000 DAU, Lisa’s primary unit], gives $1000 revenue per 1K DAU per month.  Dividing by 30 (approximate days per month) creates a final answer of $33.33 revenue per 1K DAU/day — right at the high end of average in Lisa’s range.

Going the other way, if the average app in Lisa’s experience is making about$20 per 1K DAU per day (midline between $10 and $30), this equates to $5000 per day at 250,000 DAU, or $150,000 per month.  Assuming the same 25% DAU/MAU ratio as above, this equates to about $0.15 per MAU per month.  That’s a little lower than the $0.20-$0.25 cited elsewhere, but close enough that we may be looking at differences based on the sample populations in each case.

It’s notable too that if highly successful apps are monetizing at more than $100 per 1K DAU/day, that equates to a monthly ARPU of $0.75 (based on the same calculations as above) — or right in the area that closes the revenue gap I wrote about earlier.

The best part about this additional data is the precision it gives social game developers in forecasting revenues.  We now have several dials to turn in financial models with a lot less hand-waving required:

  • Virality: how well and how fast does your game grow based on word-of-mouth marketing?  Figure 0.7 as a useful mid-case number.  If you can move above 1.0 you have a serious explosive hit on your hands; below about 0.4 and your cost of customer acquisition goes up considerably.
  • Engagement: How often do your active users come back?  You can assume about a 20%-30% ratio of DAU to MAU.
  • Daily revenue: Given your growth due to virality, and your daily engagement rates, you can now forecast revenue growth on a daily, not monthly basis (though averaging across a month still makes sense unless you just love swimming in big spreadsheets).  Assuming adequate (perhaps not even “good”) monetization design, you can plan on about $0.02 per user per day, or $20 per 1000 DAU per day.  Two cents may not sound like a lot, but as you can see it adds up very quickly.

One figure that’s still missing from this equation is customer life-span, which incorporates churn.  One way to express this is as a cohort: for every 1000 people or (more commonly) every group of people coming in on a given day, how many will never come back as days and months go on?  By overlaying cohorts across time (these typically have a fast drop and then a long tail) you can build up a highly detailed model of growth, engagement, and monetization of a player population, and thus more accurately forecast the probable (and high and low) cases for your social game or other app.

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5 Comments on “Better Revenue Forecasting for Social Games”

  1. […] Curves and Social Games 2.0 I’ve written here a few times about the business model for social games and why I think this is a very good area to be working in.  I continue to […]


  2. We all love social games, thaks.


  3. is the MAU/DAU revenue include advertising revenue?


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