Paul O’Rorke

Flurry’s Mobile App Analytics

by on Oct.21, 2010, under Meeting Notes

Peter Farago and Sean Byrnes gave a juicy and surprising presentation about Flurry‘s mobile app analytics at the SDForum Business Intelligence Special Interest Group meeting on 10/19/2010 in Palo Alto.  The title of their presentation was:  ”Your Company’s Mobile App Blind Spot” and it provided both business and technical insights.

Flurry made a big splash in the news when Steve Jobs got pissed off at them and called them out by name in an interview because they outed Apple’s iPad when it was still a closely guarded secret. (See a short video outtake of the interview at VentureBeat.)  Apple responded by changing legal agreements to exclude some third party analytics and some advertising.

Flurry changed how they collect device data.  The Department of Justice and the Federal Trade Commission began to investigate Apple around this time due to a collection of legal restrictions and potentially anti-competitive business practices which may have come to include the analytics proscription.  Apple later relaxed the analytics ban.

Flurry is inside about a fifth of all apps and they cover 150 million devices:  about 90% of all Android devices and 80% of all iPhones.

Flurry’s analytics works as follows:

  1. Developers download Flurry’s SDK and wire it into their app (~ 5 minutes).
  2. The instrumented app sends data via the internet to Flurry’s servers.
  3. The data is processed using Hadoop on HBase and  stored in “giant cubes.”
  4. Clients can then get metrics from a data warehouse thru a web service at Flurry.com.

Flurry provides a free service and then makes money with a product called AppCircle.  AppCircle combines an app recommender with rewards that use virtual currency.  AppCircle recommends another app to someone who already has a different app just as Amazon and others recommend a new purchase given that you have already bought or you are about to buy a given item.  Flurry makes money by splitting sales commissions with publishers roughly evenly if the app is installed after they recommend it.  One of the advantages they have is that they know which apps you have from the same publisher so they  will not recommend something you already have.

Flurry’s analytics service has accumulated 30TB of data since the company was launched.  They compress data on the client prior to sending it to their servers.  The core IP for this was developed earlier when the founders had to do compression to reduce data sizes on earlier handsets that were much less powerful than today’s handsets.  Flurry has one of the largest HBase databases.  Six servers receive reports from devices and store and forward them so they can be processed into their HBase db.

Perhaps the most interesting observation that was made with Flurry analytics is that the level of “engagement” (defined as frequency of use x the length of session) is far higher for iPhones than it is for other handsets.  iPhone apps are used far more often and for much longer periods of time (for example, people with iPhones use them to read far more than others do).

Perhaps the most important observation Flurry made is that there are 5k Android app developers and 100k apps in Android market versus 35k AppStore app developers and 300k apps in the AppStore. Apple is making money for developers in part by rewarding them with previously unheard of revenue sharing (70%).  For the Android marketplace to become viable, catch up, and surpass Apple it must attract app developers by making money for them.  Currently, the Android market is broken and it must be fixed.  Credit card scams occur in the Android store and they only go away when people vote them out (the customers are the only police) and there are a lot of other problems with the store that make it harder for app publishers to make money there.  One issue is that Android marketplace does not have a large paying customer base.  Apple has 150 million consumer credit cards (only Amazon and eBay have more).  Android is trying to work on this by working a deal with PayPal.

Currently, app developers and publishers “monetize” (make money) in several ways:

  • customers pay up front;
  • apps are free but they get you later:
    • advertising
    • micro payments
    • virtual currency
  • app stores do not support subscriptions

Carrier’s stores failed in part because of the ways they tried to make money.  For example, typically nothing was free.

Flurry observed that Apple’s AppStore achieved the adaption levels achieved by the iTune store in 1/3 the time. Also, currently, the percentage of game apps is much higher in the AppStore as compared to Android market.  It may be that this is partly due to the fact that Apple regulates categories in the AppStore while Android allows developers or publishers to self-report the categories of their apps.  This is a data quality issue for analytics.

Apple provides basic analytics for example a top downloads list and monthly store statistics.  Android apps can only see how many downloads occur if they have Flurry inside.

Flurry pointed out that it is important to have users login with a userid and password so that you can tie their activity together (rather than trying to use the handset’s Unique Device Identification Number).  They also noted that “phones lie” about the current time:  10% had “bad clocks” reporting dates in the 1970′s or 2200AD.  Flurry also deals with duplicate data coming in and de-duplicates it.  And they use some form of machine learning to identify devices, carriers, and countries.

In addition to analytics and recommendation, Flurry does clustering (especially for Games).

Some interesting opinions about Google surfaced.  The Nexus One was a commercial flop because you had to pay $500 to buy it online and then you still had to sign up with a carrier (the carrier didn’t “come with it”).  Further, Google doesn’t know how to “cuddle” a consumer.  They are engineering-oriented to a fault.  Andy Rubin’s response to Steve Job’s comments about Android (about it not being “open”) was to tweet Unix code for downloading Android, a perfect example.

To show how Flurry can be used, examples were given of ad network performance and tracking cross selling.  In the case of cross selling multi-dimensional scaling was used to highlight differences and similarities between items (apps in a portfolio).

A member of the audience asked whether Jobs can or might do the same things Flurry is doing.  The response was that any platform provider can build something up and cut out earlier third parties.  So there is some risk of that happening.

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