# Distribution of Prices on the Apple Application Store

Once iOS publishers have developed their application, paid $100 for having it distributed on the Apple App Store, gone through the long approval process, they finally have to chose a price.

But before chosing a price, they could ask themselves:

**“How did my competitors price their applications?”**

This is really important. If 99% of the competitors priced above $4.99, you might set your price at $3.99, and potential buyers would be happy to buy from you (for the time being, we do not take quality in consideration.)

The ubiquitous Gaussian, bell-shaped curve is of no use in the realm of prices: There is nothing like a bell shaped price distribution. The chart below shows how the prices of 172,083 applications sold on the Apple App Store are distributed (if you wonder how to scrape this information from iTunes, have a look at this article):

About 85,000 applications are priced at $0.99. The number of applications priced $1.99 is approximately 1/4 of those priced $0.99. The ones at $2.99 are approximately 1/9, and so on. In other words, the **number of applications sold at a certain price looks inversely proportional to the price squared**. If you want to know more about power law distributions, see the Pareto-Zipf-Mandelbrot distribution on Wikipedia. Enough to know that the measured average changes when there are more data –in our case the** average price of applications is $3.5**.

Power law distributions are better displayed on a logarithm-logarithm scale*, i.e. plotting the logarithm of the number of applications versus the logarithm of their price. The logarithmic-logarithmic plot of the distribution of prices on the Apple App Store looks like this:

A few points are placed close to a straight line: These points correspond to the number of applications sold at 1$ and multiples of 5$ (one cent is almost always subtracted to the round price: actual prices are 0.99$, 4.99$, 9.99$ etc). Interestingly, the number of application selling for odd prices, like, for example, $7.00, is much lower then the number of application selling for $4.99 or $9.99. Therefore the point corresponding to $6.99 lies well below the straight line.Although prices can be freely chosen, most sellers price their application at multiple of $5 (minus 1 cent).

Let us imagine a publisher of a very innovative application, which, at launch, has no competitors. The marketing manager tries different prices, and notices that two prices generate the highest revenues: $3.99 and $4.99. When the price is $3.99, the publisher sells 50 units a day, while 40 units are sold if the price is $4.99. The total revenue is $200 a day in both cases. So, if there is no competition, both prices will do.

As we know, competition is alert and after a few months, other publishers enter the market with similar applications, which are also priced at $3.99 or $4.99. A few months later, the publisher discovers that 90% of the competitors prefer, for some reason, to price their application at $4.99, and only 10% at $3.99. By setting the price to $3.99, our publisher can price-differentiate its product and confront less competition.

The take-home message is the following: By analyzing the distribution of prices, it is possible to spot price regions that are relatively empty, that’s what we call “price-gaps”. Price gaps offer the possibility to diversify a product by setting its price in an uncommon range. In a market where competition is hard, a handful of cents can make the difference. Our simple analysis provides a reference pricing on which to base marketing decisions.