How to Identify Group Variation in Data Driven Marketing
It’s important to be able to identify variation in groups in data driven marketing. Imagine there are 20 vehicles in a parking lot. You decide to count the passenger doors on each vehicle. You find there are seven pickups with two doors, three two-door coupes, and ten four-door sedans. Adding all that up gives you 60 doors. Divided by 20 means a mean of 3 doors per car.
Clearly, this average is not reflective of the vehicles in the parking lot — in fact, not a single vehicle has three doors.
A more useful way of looking at that data is to graph it. The figure is a graph showing how many cars have two, three, and four doors. This graph is much more informative and useful than the average. It clearly shows that the cars fall into two distinct groups.
The figure is an example of a histogram. A histogram is a type of graph that shows you the distribution of a variable across its various values. It gives you a much better sense of what’s really going on in your data than a mean does.
In your explorations of your customer data, you won’t find a variable whose values are grouped symmetrically around the mean. The famous bell curve is a fundamental part of statistical theory, but you will never run across one in your marketing database. Always look at how the data is actually distributed.
The car door example is completely made up. But it’s typical of a pattern that you’ll frequently see in your data. This may be refer to this pattern as a bi-modal distribution. Bi-modal means there are two bumps in the distribution where the data is congregated. Distributions can sometimes have more than two such bumps.
Bi-modal distributions are often signals that you’re dealing with two distinct customer behaviors or motivations. You may find that customers buying a particular product are largely grouped in the 20-something and 60-something age groups. In this case, you clearly don’t want to be targeting the average — that is, 40-somethings. But you can develop separate marketing strategies for the two groups that reflect their differences.