In an earlier post, I talked about the State of Second Screen Apps on Android. No discussion can be complete without an iOS equivalent (after all, they were first off the starting block, and still the first target of many app developers). Not surprisingly, measuring TV App activity on iOS is a different set of challenges than Android.
- On the ‘iTunes wins here’ category – unlike the Android marketplace API tediousness, iTunes provides a simple market query interface and a no-muss, no-fuss JSON structure in return.
- Conversely in the ‘ah, typical Apple’ category – getting iTunes download counts is like pulling teeth, where Google Play was happy enough to give one qualitative Android download numbers without much ado. ..
Given that the download numbers are kinda important (in fact, kinda the point) – one is left with two choices on inferring the download numbers.
The first alternative is to use this marketing heuristic of estimating the number of downloads as 30 times the number of user ratings. In addition to my skepticism of any such one-size-fits-all formulas (Occam’s Razor notwithstanding), this has been verified only for Paid Apps. And the number of TV Apps that are paid is not large enough for this inference to be useful for that population.
The second alternative is to combine iTunes metadata about Apps with a bit of web scraping from app search engines such as Xyologic. This idea has legs, but the effort is significant, especially as Xyologic is only one of several App search engines and likely not the gospel truth.
Here, I settle on the third alternative – a hybrid of the previous two. As with the first approach, I apply a static multiplier from # user ratings to #downloads for any TV App. But as with the second approach, I sample Xyologic data to calculate this multiplier for each of the following quantized range of downloads : <50, 50-10K, 10K-50K, 50K-250K and >250K downloads. The quantization is just as well – as it turns out that this multipler is a) signficantly dependent on the download range and b) different for each download range.
The table below shows what multiplier needs to be used on the number of user ratings returned by iTunes for a particular download category, to arrive at a download number resembling what Xyologic provides.
Eyeballing the multipliers :
- It’s intuitive that the multiplier should decrease with more popular apps (as it does here). A lower multiplier means more comments per 1000 downloads, and popular Apps are likely to have more engaged users and therefore a greater proportion of user reviews.
- The multiplier of 180 for the [10 to 50K] download range is roughly equivalent to 5 reviews per 100 downloads, which is also the average reviews:download ratio on the Android TV App Marketplace (as I described here).
Combining this model of calculating downloads with the iTunes ‘TV App’ data, yields an App population (with clean records) of about 633 Apps. The App distribution looks something like the below.
Somewhat surprisingly (or not depending on your p.o.v), this looks almost identical to the distribution I published earlier on Android TV Apps (and re-included below for visual convenience).
The similarity of the two datasets might arguably increase the credibility of both datasets (unless they are erroneous in remarkably similar ways). A intuition around for the similarity across platforms is that:
- most serious App developers have both Android and iOS offerings
- they take the platforms equally seriously and do about as good an execution job on both platforms.
- but it could also be – that App popularity is a function of marketing budget, and not platform – as the predominant way of finding TV Apps (as opposed to regular apps) is still via the program, not via search engines such as Xyologic, Hunch or Play.
- it could also be that the quality of a TV App experience is driven heavily by access to supplementary TV content (all of which is a walled garden). And the range of app developer access to TV content is somewhat agnostic to the App development platform
These and other related conundrums will be the topic of a future set of musings.