RFM for RSS feeds: Recency, Frequency, Momentary Value

I’ve been throwing round an idea in my head for a while: how the RFM method for analyzing and prediction customer behaviour could be applied to RSS feeds (blogs, podcasts, …).

Recency, Frequency, Monetary Value – customer segmentation

What does RFM do: it analyses 3 parameters for each customer:

It then does a cluster analysis of the numbers (or in the simple version: a marketing guy decides based on gut feeling) and defines boundaries for each parameter, in order to split them up into categories.

Recency: R1 is everyone who purchased in the last 2 months, R2 is everyone who bought in the last year and R3 is the rest.
Frequency: F1 is every customer that purchased on average 3 or more times per quarter, F2 purchased at least 1 time per quarter and F3 is the rest.
Monetary Value: M1 are those who purchased more than �500 per visit and M2 are the rest.

In this scenario you have split up your heterogeneous customer group into 18 (3x3x2) more or less homogeneous subgroups that you can address in different ways. Your supercustomers R1-F1-M1 don’t need the same approach as the R3-F2-M1 (the big spenders that haven’t been around to your shop in the last year). And you hope you can predict the behaviour of each customer by analyzing his past behaviour.
(Side note: I learned this stuff while working in Sopres for Stefaan Vermeiren, who’s now teaching the Kiwis to do online banking)

RFM for RSS – feed segmentation


Now how would this work for RSS feeds?
RFM analysis for RSS feeds

What can you do with this kind of statistic? Well, I see some applications:

This RFM analysis could be done by a company like Technorati, Bloglines or Feedburner, and they could combine it with language, location, topic and popularity stats to create an excellent segmentation of blogs. Or if someone feels tempted to set it up?

💬 podcast