White Paper: The Butterfly Effect: Estimating Faux-New Customers
The effect of his work has ranged far beyond evolutionary science -- eventually, as we'll see, even forming a foundation for statistical analysis of the retail business sector.
How did an early 20th-century statistician come to influence the analysis of retail customer data? The answer lies in the counting of species of butterflies. How do you estimate counts of separate butterfly species that you don't yet know exist?
As we'll show, with the help of some brain-numbing formulas that will make all but the most hardened statisticians lightheaded, it is possible to do so. More importantly, you can use the same math to estimate new customers becoming visible through your loyalty or database marketing program, customers we like to call "faux-new" customers.
Here, we've used the word "faux" to mean "fake" or "false" -- customers who look new in the next month (because we didn't observe them in the first month), but are indeed customers (because they made a purchase before the first month, a month for which we don't have data).
That is, there are "faux-new" customers and "actual-new" customers whom we'll observe in the second month.