Dear Account Manager: Please Don't Ask Me

What is the average {fill in your favorite event here}?

Average is the most dangerous word in marketing for three reasons.

• First, our goal is to satisfy a need in a differentiated manner such that consumers make a connection with us.  There is no individual who believes she truly is average, so why should we think that way?  
• Second, our job is to change history - new products, new markets, and better growth all succeed when we focus our attention away from the mushy middle.  
• Third, as a measure of central tendency, it is either technically inappropriate because the underlying data doesn't behave normally and / or a single number masks too much useful information.

Consider the following plot of a typical event - there are some who do it once, a bunch who lump together at some low level and then a long tail out to super-consumers who do whatever this represents a lot.   Consumer behavior often looks this way - product trial, application usage and email opens all take this form.

The bars are the data, the lines are two different ways of smoothing the data so that we can draw conclusions or possibly make predictions.

• Red is what we were all taught in class and produces an average of 42, which is almost on top of a big dip in the event count - as well as the meaning of life. Are we missing something important? Notice that it assumes we do less than zero things, an impossibility.
• Blue is a better overall fit and shifts the curve to the left where it appears more logical, but like the normal curve it still misses the post-fifty dip.

Statistics, even as simple as average, work on a set of assumptions.  The above picture suggests that the red ones aren't quite right and the blue ones are probably much better.   There are other ways to describe those events, so I need more information to help you.

Instead of asking what the average is, ask me the following questions:

• What does the distribution of {event} tell us about our customers?
• Are there gaps or lumps that present opportunities to adjust our marketing?
• What is different about consumers on one end versus the other?
• How many events should we expect over what time frame?

And I promise I won't answer gamma, Pareto-NBD, or Weibull....

Post inspired by "Doing Data Science" by Cathy O'Neil and Rachel Shutt as well as  Eric Cai - the chemical statistician - and his series on R-bloggers.