Toward a linear RFM

In addition to the many challenges of RFM already discussed, the segmentation puts up artificial barriers between segments.  Some of these include:

• Let’s say someone is one of the people we talked about yesterday that gives every November or December.  If s/he gave in November 2014, then again in December 2015, are you really going to consider them having “lapsed” in the middle?
• The distinction between frequency groups is artificial. As we discussed on Tuesday, Sandy, who gave you 100 gifts, and Miriam, who gave you two, are both considered multidonors for most RFM segmentations.
• The distinction between monetary value segments is artificial. Which donor would you prefer – a donor who donates \$10 ten times per year or a donor who donates \$50 once a year?  RFM prefers the latter; I’m guessing you would prefer the former.

But how to do you create equivalencies among every different segment?  Would you rather have a donor who gave \$100 to an acquisition package six months ago or a loyal semi-frequent \$20 donor?

The ideal would be to run a model with lifetime value as the dependent variable and your traditional RFM variables, plus as many of the ones that we’ve talked about this week, and determine what your actual drivers of value are.

But lifetime value, as you can tell from the name, takes a long time.

So let’s steal a rule-of-thumb model from the for-profit world.  Connie Bauer first (at least first to my knowledge) proposed this in an influential 1988 Journal of Direct Marketing article called “A Direct Mail Customer Purchase Model.”  Here, I’ve replaced purchases with donations; I think it works in our world with this replacement.  To get the RFM score, you multiply these three things together:

1. The reciprocal of recency of the last donation in months.
2. Number of donations
3. The square root of the total amount of donations the person has made.