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 [VR2] 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.
Median Based Approach
|
Value Based Approach
|
|
Computation
|
Rank All & Split
|
Rank Unique and Split
|
Distribution
|
Uniform
|
Staggered
|
Repeatability
|
Inconsistent
|
Consistent
|
Interpretation
|
Uniformity
|
Outliers
|
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 ie 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 [VR3] 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.
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