Average Sale Price (ASP): the average of all net sale prices, for a given product, over a period of time.
The ASP is a very common performance metric for many types of businesses, where the prevailing notion is that higher ASP is better. If the unit economics stay the same this is born out: selling 10 cars at $20K is better than selling 10 cars at $15K, for the revenue of the business. Finding ways to drive ASP higher and higher seems like an obvious conclusion, if you want to increase your overall revenue. However, the “average” is meant to describe a random set of observations (originally, the position of a star) to assess the most likely center of the thing being observed.
But is it actually “average”? Are sale prices really random?
Problem 1
Raise the average by dropping lower selling points
Raising the average of a group of numbers presents 2 straight forward methods:
Add more numbers (more sales) to the high end, or
Remove numbers from the low end.
The second is the most problematic because the ASP will go up (yay!) but the rate in increase over all sales will decline (boo!). Selling 7 cars at $20K is not as good as 10 cars at $15K, in terms of total sales. See the previous image, where the average of the red curve (dotted line) is higher than that of the original black curve, but the area under the curves (representing total sales) is much smaller.
Problem 2: Outliers
Mean Sale Price vs Average Sale Price drift apart when one or more sales happen at extremely different values.
Another way to fool oneself into thinking an average is moving is to have a few values that are really, really far away from the mean. This would be selling 9 cars at $15K and 1 at $150K (ASP = $28.5K). The single outlier drags the average up, but by definition “outliers” are rare and not a regular observation in the typical population. This is a case where calculating an average is not appropriate, because the outlier (the Ferrari) isn’t part of the usual set (a lot of Toyotas).
The clue that would tip us off is that the Mean Sale Price (MSP) would stay at $15K, while the Average Sale Price would jump to $28.5K. Relying on the MSP will be more reflective of what’s actually in the data.
Problem 3: Multi-modal data
Perhaps sales are not ‘normally’ distributed, meaning they don’t stack up in a nice bell-curve. At Boardable we sell 3 different levels of access at different price points, which can then be increased (buy more licenses) or decreased (discounts or special offers). This causes the spread of sale prices to clump around the 3 entry price points, making a tri-modal distribution. Fitting these sale points into a single average gives us misleading results, because the data is not normally distributed:
Three different price points, resulting in three groups, are fit into a single ASP [simulated data]
In ‘averaging’ terms we are looking at 3 different populations, the entry level buyers, the mid-tier buyers, and the premium buyers. In many cases this basic split also reflects the types of buyers and what they’re looking for in our product, and gives us more insight into how each ‘package’ is performing. See the next image, where each group is fit with its own average:
Three different price points fit with three different ASPs [simulated data]
Solutions
Test for normality. At its simplest, load all your values into a spread sheet and look for a histogram chart. Eyeball it. If it looks like a bell or a mountain (see images on this post): go for it, ASP will be reflective of what’s actually happening.
If it looks like multiple hills: cool! Figure out how to split it out into separate groups (populations) and determine what causes the clumping. Did they buy at a different time? Different packages? More or fewer add-ons or services? Is discount strategy identifying price sensitive populations? This is where insights will be uncovered.
Then, it may be appropriate to have multiple ASP values that you track. Does each package move differently, based on business decisions or market conditions?
January, 2022
Dr. Ben Smith is a Data Scientist and thinker, fascinated by the appearance of computers in our daily lives, creativity, and human struggles. He has had the privilege to think, learn, and write at the University of Illinois, the National Center for Supercomputing Applications, the Cleveland Institute of Art, Case Western Reserve U., IUPUI, and at Boardable: Board Management Software, Inc.
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