What are the implications of using MFR binning instead of RFM binning?

[AvinashRdy]

Hi,

I'm dealing with data from a hotel where customers visit occasionally. Here, the recency wouldn't be much higher compared to any retail store. Hence, we decided to go with binning the data starting with Monetary value first, then binning Frequency finally binning Recency. The final index would be MFR index rather than RFM.

Please suggest / comment on using MFR instead of RFM in this scenario. mi

Thank you in advance for your help.

Regards,

Avinash

[ChrisHemedinger · Accepted solution]

RFM is all about segmenting customers according to their value to your business, for the purpose of creating the next offer or some other interaction with the customer.  If you want to weight the "M" more than the "R" because you feel that's a better indication of value or likelihood to respond, you can do that with proper data preparation.

Are you using the RFM task in SAS Enterprise Guide?  If so, you can simulate a greater "M" value by multiplying the actual monetary amount by some index (you decide the weight) and using that as a proxy for the Monetary bin assignment.  Also, if you allocate more bins (or fewer bins) you can control the granularity of the segmentation for any or all of the three aspects of value.

Chris

[AvinashRdy]

Thank you for the response. This is very helpful for my research

[AnnaBrown]

Thank you for joining the SAS Data Mining Community, AvinashRdy!

Feel free to mark answers as "correct" or "helpful" so that other community members can see what worked for you.

Thanks!

Anna