Facebook DAU and MAU: what they tell you (and what they don’t)

DAU (daily active users) and MAU (monthly active users) data for Facebook application usage is easy to come by:  InsideNetwork publish daily leaderboards of DAU and MAU online, on Appdata.  But what does knowing DAU and MAU tell you?  

Is the DAU metric more like the “DAU-Jones index”, “The DAU of Pooh”, or “The DAU te Ching”?  

Shanti Bergel, in Designing For Monetization: How To Apply THE Key Metric In Social Gaming,  argues that DAU “has emerged as the key metric determining the popularity and potential of a social game”,  and the ratio of DAU to MAU, looked at over time, is important too.  As I understand it, part of Bergel’s argument is that since commercially interesting metrics such as ARPU (average revenue per user), or LTV (lifetime customer value) are difficult if not impossible to come by, on a comparative basis, we should enthusiastically squeeze whatever  meaning we can out of the data we can get, which is DAU and MAU.  In other words: love the one you’re with.  For Bergel, DAU and MAU are useful proxies (i.e. measures which stand in for measurements we are more interested in, but can’t get at directly).  Bergel interprets the DAU/MAU ratio as something that gives you a sense of “how well a game retains its users”, and says it “is an indicator of potential” which “demonstrates that the game is compelling and can successfully drive engagement”, although “it but does not speak directly to sales or earnings”.   (Since writing that, he’s joined Playfish.)  

Another interpreter of the DAU, Eric von Coelln,  has trademarked his own special name for the DAU/MAU ratio – he calls it “The Social Game Sticky Factor”.  In his post in Inside SocialGames, he compares a number of leading apps in terms of “stickiness”, and says that the sticky factor allows you to benchmark applications’ abilities to retain their users.   (Since writing that, he’s joined OMGPop, a social game developer, as VP.)

Yeah but.   Both Bergel’s and von Coelln’s interpretation of these metrics have serious flaws.    And I should add that both articles are worth reading, despite this, as they have lots of interesting stuff to say about design determinants of in-game user behaviour.  On the subject of metrics, though,  they are are misleading.

DAU is certainly an index of popularity, as Bergel says.   But for sure it isn’t a determinant.  (Or at least not in an obvious way.)      DAU is not really an indication of the potential of a game, either.     (Or at least not in an obvious way.)

The way DAU changes, from day to day, and how this rate of change changes, is perhaps a better determinant of popularity, and a better  illustration of potential, in the sense that this dynamic momentum information can give a hint of where an app might be in its lifecycle: on the up, peaking, or starting to lag.     But even here, there is an interpretation challenge.   One problem is that the standard model you might be tempted to use (if you are into reading tea leaves based on 2nd derivatives)  is probably inappropriate.   Another less technical problem is that many different roads lead to Rome.  

If a game is gaining DAU at an ever-accelerating rate,  this is certainly a signal worth watching.  Some light on some dashboard somewhere should be lighting up.   Depending on your taste in alerts, perhaps a siren should be honking.   What you know  is that something interesting is happening.      But here’s the catch.  You don’t know exactly what it is.  

You don’t know whether accellerating growth is due to new users joining because of massive ad spend, at the same time as old users are dropping out from boredom.   You don’t know if accelerating growth is happening because existing users have become massively incentivised with in-game rewards to recruit friends, and they, in turn, are recruiting new friends.   Both these possibilities are very interesting.  But they are interestingly different.

The same ‘devil in the detail’  gotcha is true of DAU/MAU, as a metric, which von Coelln has dubbed “The Social Games Sticky Factor”.  He says “If your application has a 33% sticky, it means that for every new user you bring in, you have a 33% shot at turning them into a daily user.”    Not so.    DAU/MAU tells you nothing whatsoever about whether any particular user ever comes back to the game, ever.  

Let’s say, as an extreme example, my app delivers a one-way-ticket-to-the-moon.  (And once you are on the moon, you can’t play the game again.   But it’s a good game, so people play it.)   As shown in the Moon Shot Game graphic below, a game in which no user ever played twice could still have a DAU/MAU of 33%.  This is the case on Day 30 of the Moon Shot game: DAU is 1, and MAU is 3, and DAU/MAU is 33%. 

When looking at active user numbers over a course of a specific period of time, whether it be a day  or a month, what you see is the additive result of two types of events:  ‘existing user plays game’, and ‘new user plays game’.    There are lots of different determinants of the probability of these two behaviours (and, as I said earlier, both Bergel and von Coelln are worth reading on this subject).    But unless you actually get explicit about modelling these two events as distinct, and unless you get specific about modelling how (and why) they are changing,  you won’t know what assumptions you are making.  And if you don’t know what assumptions you are making, you won’t be able to test whether or not your assumptions are sensible, or -even better – right. 

What would such a model look like?  Jon Radoff’s Social Application/Game Growth Model is an example of a growth model for social game use which uses customer lifecycles, in combination with viral recruitment, to model “total active customer” numbers.     His particular model doesn’t map directly onto DAU or MAU, because it doesn’t explicitly model the probability of a given customer playing, on a given day, or in a given month (or a given minute, or year).   Rather, being a customer is treated as a simple attribute which has a lifespan.  This is similar to the modelling assumptions that go into typical epidemiological models of disease spread: they don’t usually model how often you cough, just whether you get sick, and whether you get better.  To map to DAU, you would have to model the coughing.  (Also, in Radoff’s model, it might be interesting to assess the impact of modelling K as a random variable with a power law distribution.  But for that I would definitely need more cofffee. )   

Even more comprehensive customer lifecycle modelling can be found in Andrew Chen’s blog post, How to Create a profitable Freemium startup (spreadsheet model included!)  in which he drags cost and revenue into app use metrics, and comes up with some interesting conclusions about which differences make a difference.    Amongst other things, he demonstrates how customer LTV (life time value) is immensely sensitive to changes in retention rate.    Not surprising, but important.  However, much as you might like to to believe it, retention rate just isn’t something you can really tell for sure about from MAU, or from DAU, or from their ratio.  It would be nice if you could.  But you can’t.  That’s not the DAU’s fault.  It’s its nature.

When, then, of the true nature of the DAU?  Is it more like the Dow-Jones, The Tao of Pooh, or the Tao te Ching? 

My vote is that it is like a cross between the Dow-Jones and the Tao te Ching.:  it’s a hard number that needs creative interpretation.   But this interpretation process has many pitfalls for the unwary.  There many routes to the same DAU, and you have to try to figure out which one you think you’re on, to understand what it might mean.

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18 thoughts on “Facebook DAU and MAU: what they tell you (and what they don’t)

    • Thanks for stopping by. I think the reason we never met must have been that I was on the 1st floor (=UK 2nd floor) and you were on the 5th….! (WordPress is a great leveller….;-)

  1. Crisp style really helps out stat analyses. Very well written.

    At our shop, we continually parse this topic. Andrew C’s quant analysis certainly is a good comparison to enrich Dubit’s suggestion of a method by which to calculate the best blend of viral, word of mouth new user recruitment and paid marketing efforts.

    Our CPP is a rather more a hybrid than either a straight ahead Facebook/MySpace/Hi5, etc. soc net or a pure game like Moshi, Webkinz, ClubPenguin, yada yada. Our membership seems to enjoy both synchronous and asynchronous play and then, just like Bartle says, some want to hang around and chat endlessly about what they have just done, etc.

    Tim Chang at Norwest also has some handy estimate figures:

    “The future( of this whole space) is a hybrid tiered subscription model.
    About 85 percent of your users won’t be monetized. They won’t pay. But you can show them ads, give them lead-generation surveys, and you can view them as icing on the cake. They are not the basis of the business.
    Some 10 percent of the users can upgrade to micro-transactions.
    And if you’re lucky, maybe 1 percent will pay for a monthly subscription.”

    I think that for across the board, age agnostic sites, his #’s are spot on. For 13<'s, I'd say the subscription percentage will skew much higher due to kids' inability to authorize purchases.

    • Good to hear about the view from the shop floor, thanks. Could you give me a link to Dubit? Sounds interesting but name’s not familiar (And… CPP? Cost per play/purchase/pony? ;-)

  2. Catching up a bit late on this one, still a very nice reading.
    I am writing some articles about the subject for our new blog, very useful stuff.
    WIll follow your work from now on.

  3. Heather, I think you “extreme example” calculation is wrong. You say the MAU would be three. You’re off by a factor of 10 right? If one new person comes to the app daily, that would add up to 30 people for the month, not three. And so in your example, the DAU/MAU would be 3.33%, not 33.3%. Right?

  4. Oh wait, upon re-reading I realized your assumption is that just three users would come to the app in 30 days, not once a day. Well, in that case your logic is flawed as well. The DAU would be 1 for just three days of the month and 0 for the other 27 days. And so the DAU/MAU would be 0% for 27 days and 33% for 3 days. Hardly a realistic comparison to typical stats seen on an app. Your point is well taken though.

    • Yes, my ‘test case’ assumes 3 users in 30 days, as shown in the graphic. And I agree that my numbers are not typical stats. They are an extreme example am using to try to dramatise some of the interesting issues that DAU/MAU doesn’t help you with.

      [I have deleted what I originally wrote in my next paragraph as I now realise it was confusing. Or even wrong. But I’m trying again! HS 9 Oct 2011]

      One of the vital issues that DAU/MAU totally muddles up has to do with retention. DAU/MAU sounds like it might have to do with the number of days on which an individual player comes back during a given month. That’s a sensible measure of stickiness. The trouble is – DAU/MAU just doesn’t do that. You could have a really great DAU/MAU ratio even if your app was fatal to use, and it killed off every user the first time they tried it. The only excuse for using DAU/MAU is that it’s readily available. This makes it tempting to use it as a benchmark vis a vis your competitors. Don’t bother. Without knowing about marketing spend and retention, a DAU/MAU ratio could mean anything at all about the underlying business. For your own data, particularly, you deserve better. Measure what’s important.

  5. Hi Heather. I know it’s well after you wrote this but I just thought I’d say thanks for the insight. I’m a designer who has recently made the move from traditional games into the fully social gaming model and it’s nice to see that people aren’t resting on their laurels with regards to the metrics analysis and questioning what seems to have become the standard.

    I’ve always been a big fan of stickiness as a measure of success but I hate it being thrown around like it’s the be-all and end-all of a social game. It’s also nice to remind people that it’s not a measure of how good your game is, it’s a measure of how popular your game is, at best! :)

  6. Pingback: Measuring The Facebook Effect: Are Content Services Gaining From New Apps? — paidContent

  7. Here’s my big question. This notion of ‘Active’ or “Interaction’ of a DAU or MAU has never been fully defined. My understanding is that if a device(such as a smart phone or tablet) has an app installed, and is powered on, this means, from FB standpoint, that this ‘user’ is accessing the app, and there fore is active. Kind of like having a phone instrument on a wireless network, and while network pulses the phone, the device is considered ‘Active’. Seems to me that a clearer set of information is needed to re define levels of ‘Activity’ so that the casual ‘app active’ user is parsed from the ‘interactive’ actual app user. Thoughts on this?

    • Hi, Phil – From a FB metrics point of view, being installed doesn’t mean that it’s active. Active means that as a user you have actively selected it. At least it did last time I worked with FB metrics. In addition to the stats that FB provides you can also get more detailed “data exhaust” either by crafting your own solution or, more likely if you are starting out, using one of the many good off the shelf cloud-based solutions. Then you can know what people are doing when they are using the app, which is very interesting. cheers, Heather

    • p.s. there was a change in the FB stats from counting someone when they touched the canvas page to counting when they actually did something in the app – but that was a while back, water under the bridge.

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