I read your post on the other board about RFM analysis. This is something I learned about in college and tucked WAY back in my mind until about 3 months ago. I started using it on my database of customers in my effort to increase frequency (which I’ve posted for help recently) and advertising ROI.
I don’t want to post a link to the other board because it might not be considered appropriate, but you think you can post your explanation again here? I think a lot of operators would benefit; it’s actually worked very well for me. Even if your POS doesn’t do it for you automatically, it should be pretty easy for anybody to accomplish by exporting their database into Excel (which is what I do.)
I’d like to mention that I added one attribute to the traditional RFM analysis. This attribute attempts to define a customer’s “coupon elasticity”. You may have customers that are a “555” (using your example from the other board) but are coupon junkies. The only way to keep them a “555” is to keep sending coupons.
On the other hand, you may have customers that NEVER use coupons. These people may remain “555” customers without needing a coupon. No point in sending anything to them.
Using your example scales, a person in my database that never uses a coupon would be “xxx5” and a person that always uses a coupon would be “xxx1”.
A “5555” and a “5551” have the same value to me, but I know the latter needs some prodding.