In e-commerce, I like to take my customer file, and split it into six segments. Any customer without a purchase in the last twelve months, but at least one life-to-date purchase is put into segment "Z".
Then, I use dollar cutoff points to rank customers based on twelve month activity. For any customer who purchased in the past twelve months, I create five equal cutpoints. Here's an example:
- Spend $500+ = "A"
- Spend $300 to $499 = "B"
- Spend $200 to $299 = "C"
- Spend $100 to $199 = "D"
- Spend $1 to $99 = "F"
Now that you've done this exercise, look at all mobile app purchasers in the past twelve months, and look at total dollars in the past year. Compare the distribution to the entire file:
|Grade = "A"||50,000||394||2.58|
|Grade = "B"||50,000||176||1.15|
|Grade = "C"||50,000||88||0.58|
|Grade = "D"||50,000||65||0.42|
|Grade = "F"||50,000||42||0.27|
In this example, it is obvious that your mobile shoppers are disproportionately skewed to the top portion of your twelve month buyer file. This means that your mobile shoppers are likely to be disproportionately loyal to your business.
When this happens, the question of "incrementality" comes into play. Your most loyal buyers are often the ones most likely to try new channels, and when they try a new channel, they are not necessarily interested in purchasing more, they are simply trying a new channel.
So that's not so hard, is it? Give the methodology a try. It's basic, simple, and it allows you to clearly communicate results to management without your leaders becoming paralyzed by methodology issues.