Mobile Analytics: like playing horseshoes or bocce ball? (When close is “good enough”)
A recent post on Flurry’s “industry insight” blog caught my eye. The post, The iOS and Android Two-Horse Race: A Deeper Look into Market Share, called out the fact that iOS app users spend more time inside applications than their Android counterparts and then posited three potential underlying causes (condensed here – visit their post for the full narrative):
- One was that the two dominant operating systems have tended to attract different types of users (we’ll get back to this shortly – this is close).
- A second possible reason was that the fragmented nature of the Android ecosystem creates greater obstacles to app development and therefore limits availability of app content (suggesting app quality is the driving force).
- The third possible explanation offered by Flurry was that iOS device owners use apps so developers create apps for iOS users and that in turn generates positive experiences, word-of-mouth, and further increases in app use (combining the two reasons above I suppose).
What struck me in this post was that, while there’s no disputing Flurry’s observation about “time spent in apps” across platforms, the lack of precision within the “2.8 billion app sessions” they track every day made genuine root cause analysis virtually impossible – and led to, in my view, an erroneous conclusion (or, more precisely, a false set of options where the real mechanics were all but invisible).
Back in January, I published the blog post Marketplaces Matter and I’ve got the analytics to prove it where I compared two versions of one of my apps, Yoga-pedia, published through Google Play and Amazon marketplaces. What’s noteworthy here is that the apps are genuinely identical – functionality, UX, everything - …and yet, the total time spent inside the app distributed through the Amazon marketplace was 40% higher than from Google Play. Which, if you pivot the ratio, total time spent inside the app sourced from Google Play was 72% of the time spent inside the (identical) app sourced from Amazon.
Now, if I’m interpreting Flurry’s graph in the above blog for January 2013 properly (when my earlier stats were generated), it shows a nearly identical ratio (the total time in “Android apps” was ~75-80% of total time in iOS). So what does that suggest?
- iOS users and Android users clearly use different marketplaces – but marketplace source is not something tracked.
- iOS apps themselves are of course always different from Android apps (I have an iOS version of Yoga-pedia that is close to my Android flavors – but even these are different). This is a major variable that Flurry analytics cannot separate out – they are looking at the roll-up of all iOS apps and comparing them to all Android apps.
- Treating all Android apps as a single data set (which includes multiple marketplaces) – further obscures what may be one of the key drivers of user behavior – the marketplace community.
So – going back to the first hypothesis, that Android attracts a different class of user than does iOS, I think that is as close as they could come given the kind of data available – the real answer is most likely that the Apple marketplace attracts a different kind of user than does Google Play (and the mix of Amazon Android app users is probably not significant enough to move the big needle).
…And so that begs my original question – is this kind of imprecise (but still accurate) intelligence “good enough” (like horseshoes, bocce ball, and nuclear war)? If this was as far as true application analytics could take me – then maybe…
BUT, once I had identified the potential role that marketplaces can have – I was able to drill down even deeper to identify the other marketplace delta’s that were (at least to me), extremely valuable including:
- Amazon click through rate (CTR) was 164% higher than the Google Play CTR
- Google Play Ad Delivery Failure rate (ADFR) was 199% higher than the Amazon ADFR
- Amazon user upgrade rate was 54% higher than the Google Play upgrade rate (from free to paid app version).
So, in my case, owning my own data and having an instrumentation and analytics platform able to capture data points specific to my needs (precision) turns out to be very important indeed.
So why would anyone use technology like Flurry’s? LOTS OF REASONS relating to ad revenue and all of the other monetization services they offer app developers (that’s why they’re in business) – and that’s I guess the big point. Services and technologies like Flurry’s are built for app monetization – and to the extent that some analytics are an important ingredient in their recipe – you can bet that they’ll nail it – but to do more would be over engineering at best and, more likely, pose a material risk to their entire business model.
For advertising across huge inventories of mobile apps, analytics should be a bit like playing horseshoes – knowing that I can expect iOS to generally perform better than Android is useful.
On the other hand, as a development organization, if I really want to fine tune my app and optimize for adoption, specific behaviors, and operational/development ROI – I need an application analytics solution built with that use case in mind – not only are alternative analytics solutions missing key capabilities, there are solid business reasons that say those alternative technologies should actively avoid adding those very capabilities for all time.