I have an
RFM model that I use to segment customers based on
RFM score. What I would like to do is:
- Understand more about the charateristics of my customers than just their
- Be able to predict which the RFM segment a non-customer is likely to fall into.
To do this I am planning to overlay other data I have about the customer (demographics, how much they use our other services etc) as indepenent variables in a supervised classification problem
RFM segments as the dependent variables. I'd then look to use some sort of classification technique (random forrest etc) to build a predictive model that would give me:
- The combination of independent variables that correlate with a customer being in any specific
- The probability of a non-customer being in any given
RFM segmentgiven the independent variables (demongraphic information etc)
I am also thinking about using
PCA to determine which of the independent variables seem to have the greatest effects on which segmeent a customer falls in before starting the classification.
I have had a look around and I don't see many examples of people using RFM segments as dependent variables in a classification model.
Is this a worthwhile/scientifically sound approach or am I missing something that makes this approach unsuitable?