RValue: was a trivial calculation, used here to document the transition from the client domain into RFM calculation. For Recency it was seconds since 1/1/2000. The other values were number of transactions, and the total sales in the time period.
Z of R: was the Z Score for the measure. That is how many Standard Deviations the customer’s value is from the mean. This required a table calculation because it changed, depending on how we filtered or grouped. This was:
(MAX[RValue] - WINDOW_AVG(MAX([RValue]))) / WINDOW_STDEVP(MAX([RValue])).
RValue was the only measure aggregated using MAX. The other measures are summed. Also, if you try this, it is important to make sure it, or dependent calculated fields, are computed using the relevant dimension(s) for a given visualization.
RScore: reflected the quintile (in our case) in which RValue would lie in a normalized distribution. This was a calculated measure:
IF([ZofR]< -0.84) THEN 1
ELSEIF([ZofR]< -0.25) THEN 2
ELSEIF([ZofR]< 0.25) THEN 3
ELSEIF([ZofR]< .84 ) THEN 4
Those 0.84 and 0.25 values are the fractions of Standard Deviation which delimit the quintiles in a normal distribution. So, for example, 20% of the members of a population with a normal distribution have a Z Score less than Mean – (0.84 * the Standard Deviation).
RWeighting: was a Tableau Parameter used to weight the R Score to control its relative contribution to the final RFM score. As mentioned we de-emphasized R because most recencies were 1 day or less. There were parameter controls to our dashboards to allow users to experiment with the weighting.
Weighted R: was the contribution of the R score to the composite RFM value. The calculation was (RWeighting * RScore).
RFMScore: was the sum of Weighted R, Weighted F and Weighted M. This wasthe primary measure we used for visualization.
Having a single measure combining several measures of customer value in a meaningful manner allowed us to make visualizations that were powerful, yet visually straightforward. For example, we could map the composite measure in a single map, which I felt was faster to grasp visually than using the 3 component measures individually.
It was helpful to have separate weighted measures. For some visualizations, we showed the contribution of the weighted R, F, and M to the composite score. This answered some follow-up questions in our visualizations.
It was also valuable to be able to alter the weightings of each score. Experimenting with this showed the composite score did not vary in a surprising way as the weightings were changed, within reason.