Economics tells is that there is an optimal price for maximizing revenues. Set the price too high and demand falls and with it, revenues. Set the price too low and you're leaving money on the table. In his blog, Richard Rodgers
demonstrates this phenomenon with live data from his own sales data.
The most critical decision you face for a new product is how to price it. There's all sorts of stuff written about this. And all sorts of names for the various strategies: skimming, penetration, cost-plus, etc. Go read Joel for a nice introduction.
In this case the components Richard is selling are CVS Manager
and XML Manager
. The numbers he uses to graph both the demand curve and revenues have been obscured however the graphics do highlight the expected trends. Charging more for the product lowered demand but the increased revenue more than made up for that until the price reached a level where revenues started falling.
What Richard's graphics demonstrate is that $120 is about the price which would maximize his revenues. His question is, should he lower his price from the current level of $170? This is where theory and practice part. First point is that this study is very flawed. For one thing it makes the assumption that the data is perfect which we know it cannot be. However it doesn’t mean that we cannot draw useful conclusions from it.
You can tell that the $47 price point was a disaster. I stuck with it for too long. The data does not lie. It may have generated higher sales volumes, but overall revenue was significantly down.
Richards reasons for not lowering the price follows practice and not theory. As his product gets better he believes that it will be more valuable and as the value increases the current price will become more attractive. He recognized that he could have larger volumes but since maximizing revenues is his goal, the data shows that having a higher price is a better strategy. Richard’s final request is that others that have this type of data should also publish it.