Other Attractors in the App Store Market
Sean Moore
Last week I highlighted some of the more interesting states that can exist for a product in the App Store market, and pricing decisions that may arise due to those circumstances: the volume driven high-visibility found in the top charts, and the more stable need-driven markets, where directed searches are the main input. I’d like to round out the discussion by considering where else points of attraction may lie in this space.
In a dynamic systems class, there’s typically some smart-ass kid (that’s typically me) who goes out of way to point out the most uninteresting part of the system: the null case, where there is no dynamism, and there never was or will be. If you’ve ever taken a differential equations course, where they taught you about the foxes and rabbits, you’ll know this as the one where there never were any foxes or rabbits ever.
Leave it to me then, smart-ass that I am, that this null case fully exists in the App Store market as well: it’s entirely possible, and maybe it’s even extremely likely, that an app will find itself where there are few sales at a low price. There are certainly a whole number of reasons why an app would end up in this graveyard, and few of them are interesting, and even fewer still are worthwhile to discuss. What is interesting however, is determining whether this is a final resting place for an app, and if not, what exactly can be done to escape.
The other interesting point is a combination of the two discussed previously: does there exist a place in the market where apps can command a high price and also drive volume purchases from exposure in the charts? Certainly marquee apps in the store come to mind: Apple’s own iWork and iLife suite, to be sure, but others as well.
What’s particularly interesting, beyond the literal piles of money that these lucky few undoubtedly make, is what properties does this point exhibit from the low- and high-volume positions.
Allow me to get a little technical for a moment: suppose this point consists of a simple addition of the stable, predictable sales of the search-driven state and the more volatile popularity boosts of the rankings. What would we expect to see? Something engineers commonly do to analyze black-box systems is to characterize their frequency response. If indeed our assumption is the case, our analysis would highlight a good deal of power in the signal, corresponding to those stable purchasing behaviors, and less power spread among higher frequencies, due to the volatility in purchasing from the top charts rankings.
Even if our assumptions did not hold true, performing this type of analysis would shed some light on the behaviors of the customers buying the app – whether their purchases are inherently cyclic (perhaps right after a paycheck), or some more erratic behavior is at hand. What you give up in temporal identity – the ability to correlate changes in purchasing due to specific events, you gain in understanding in seasonality.
Certainly worthwhile, if you intend to make a living out of your sales.