I have a data set of a list of invoices each of which have a date. I'm trying to detect when I might consider that a customer has stopped ordering a part.
The way I'm approaching this might be completely wrong. I'm taking the invoices, ordering by date, and then counting the difference between dates of consecutive invoices. I then produce a nice graph and it lets me say something like "95% of all orders were within 15 days of each other". I don't know where to go next :) Is this concept at all related to the Pareto distribution?
Reading up on the Poisson distribution I can take the same data and produce the probability of N invoices per day. I get something like 57% of days have 0 invoices, 23% have 1 invoice, 12% have 2 invoices, etc. At this point I get to the not knowing what I'm doing part :)
The end goal is to be able to notice stopped orders, ideally with some kind of tweakable 'confidence' cutoff. I'm afraid of using the 'confidence' word because I assume to be using it wrong.