RFM can misbehave and here’s why –Part 1

  • 18 Mar 2015
  • Posted by Anand Sambasivam

Having got through the data wiring aspects of connecting real time transactions into the repository, it is tempting for Marketers to put in a Customer Value indicator in place – something like the RFM model.   This can give tangible quick wins like

  1. Easy Customer segmentation.
  2. Formulation of a Customer specific Engagement Strategy.

RFM really is a context sensitive framework and is most effective when the computational dynamics and interpretation are mapped to business realities.

Computation: RFM can be computed using value or median based methods. Median based approaches are simple. They order customers based on say Recency, frequency or monetary value and allocate the ranks for each quantile. They really force an equal number of customers in each “rank”.

Value based approaches are slightly more evolved and they rank the unique  values for example, the total purchase value and allocate the customers having the top 20% values as 1, the next 20% as 2 and so on.

Interpretation: For instance, a customer having a preference to high value products and purchasing at a lower average frequency, will possibly be ranked at the bottom of the pile when compared to the rest of the population. RFM assumes that the driving need for buying behavior is innately similar and that can lead to a “one size fits all” approach – aka disaster.

Another instance where RFM simply cannot help is with new customers. Since RFM needs demonstrable, recorded transactions, it will by default categorize fresh and new acquisitions customers at the bottom of the pile until they work their way up the rankings – which could result in marketeers not encouraging new customers to move up the value chain if RFM is a critical piece of your marketing strategy.

Weightages:  RFM considers each of the R, F and M as equally important i.e. a weightage factor of 1 each. Though it is simple enough to allocate relative weights for each of these dimensions,

Marketers can struggle to find out the optimal combination of weightages that fits the business and represents reality. The only way really is to continuously tweak different weightages and closely monitor marketing ROI.

It’s not All Bad!!  However, there are other avenues to improve RFM efficiency like considering additional behavioural aspects to supplement purchase behavior and using Product Affinities to drive RFM Analysis that can yield much better results.