>> I attached two charts to illustrate that. I recently lowered the price of the iPad app (http://bit.ly/92xWv1) from 5 to 1 Dollars.

Try $3. I think it was a coincidence $5 and $1 had the same revenue. The maximum revenue price point should be in between. // guy who studied first year microeconomics.

So, of the people paying $1 for the product, you know that half of them would gladly pay $2?

Sounds like it's time for 2 versions of the product : (i) The almost-full-featured version ($1) and (ii) The super-duper gold-plated version ($2). The removed feature doesn't even have to be very useful : You might lose 10% of the $1 value people, but snag 25% of the $2 value people (who just want some justification for paying the full value to them).

  iSomething   = $X
  iSomething+  = $X+100
  iSomething++ = $X+200

mostly unrelated but: could the parenthetical after the title of a post be modified to include subdomains? Seeing (google.com) made me think Google was saying "No matter what price we choose...", whereas if it said (plus.google.com) I'd realize it's likely just a "blog" post by somebody.

edit: or is there a good reason we shouldn't do this?

A fine example of the price elasticity of demand. Not surprising in the slightest, but a helpful real-world illustration.

Its easy then. Keep the price high and it will lower your support costs. Make more profit.

The author is falling victim to the Confirmation Bias. There are only two experiments, yet the author is content to over-generalize and conclude that "no matter what price we choose, we always make the same revenue." Why not reduce the price 3/4? Increase it by 50%? Increase it by a 100%?

Is this why products often enter market at a high price, which is then lowered gradually?

I think Writer is a pretty successful app that is in the charts (and Apple may have also promoted it at various times). Maybe the dynamics are slightly different for less successful apps that are less exposed in the App Store?

For one of our app, which was selling well, we eventually doubled the price.

Revenues started growing, month after month, and stabilized after about 6 months.

Conclusion: revenues more than doubled, units sold slightly increased.

A question that's important to consider is support costs: Does the amount of support a customer requests scale twice as they pay twice the amount for a certain app, or no? My guess is "no".

This is why in-app purchases are so effective. Charging everyone the same price all the time doesn't make sense with digital goods

This seems to disagree with what I've heard of Valve and Steam sales. That the bigger the discount, the more (total) revenue is made, almost across the board.

$50 game -> 50% off, 100 sales $50 game -> 75% off, 500 sales etc

Interesting data points, but you need more before you say "no matter what price we choose, we make the same revenue."

I agree with some of the posters who suggest splitting into 2 levels of the app at different price points. (Usually, it's better to have 3 levels, but it seems in the App Store, most companies go with 2 to make it easier on customers. I'd love to find some data with evidence that one way or another is better for the App Store.)

From a comment on the Google+ discussion came this corroboration, of sorts, from Gabe Newell.

http://www.geekwire.com/2011/experiments-video-game-economic...

Too many details to sum up nicely, but one is that when they did not advertise prices changes for CS they say almost perfect elasticity.

I guess people dont weigh in the utilitarian point of view. Why not give the joy of your app to more people. After all you are getting the same. And as some people have mentioned it might eventually be helpful.

There is no mention about competition. Most economic models assumes some kind of competition. I think he should survey the prices of similar apps and then decide where to price his app.

I wonder what happens when he sells for $0. An infinite number of buyers?

In some sense it is irrelevant whether or not revenue is independent of price...if you're extrapolating from that dataset, then you really need to give yourself a few good whacks on the head with a stats text. I mean, N=2? Give me a break.

In his position I might stick with a higher price. I'll sell fewer units meaning there's not as many clients to support while making the same revenue.

I remember an example similar to this from high school algebra-- when we had started learning about conic sections and polynomials. A barber's profit was modeled as a function of the price he charged per haircut. It turned out that the graph was an upside-down parabola. If he charged too much, no one would come and he wouldn't make any profit. If he charged too little, he wouldn't be able to cover his expenses, and he wouldn't make any profit. Our job was to find the function maximum in order to find what he should charge in order to make the most money. This article surprised me be cause it suggests the price-profit function is not only linear but horizontal over an interval. One possible explanation is that we don't have continuous data, and the actual function is some kind of complicated polynomial that has a lot of waves, which would allow a horizontal line to intersect the function at several points.