Recency, Frequency, Monetary analysis in Tableau

RFM is commonly used in marketing, retail and professional services industries to assess customer value. The general idea behind the analysis can be summarized as

Recency : People who have purchased recently from you are much more likely to respond to a new offer than someone who you haven’t sold to in a long time.

Frequency : People who shop frequently at your store are more likely to respond to new offers than less frequent buyers.

Monetary : People who spend more money at your store are more likely to show interest in new offers.

in descending order of importance. There are a few different ways to calculate this metric but I will use the method outlined HERE with the sample Superstore database that comes with Tableau.

Quintiles needed for the analysis can be calculated using Tableau’s percentile rank function. For recency the formula would look like the following:

Percentile rank for recency

For frequency we can use Number of Records since each purchase is a record in the Superstore dataset or count OrderIDs.

Percentile rank for frequency

And monetary is the simplest of all

Percentile rank for monetary

Now let’s convert them to quintiles. I will just show the calculation for recency here but they’re the same for all of the above.

Converting to quintiles

And finally combine the results into a single score to get the RFM metric :

Adding up to RFM

One comment on “Recency, Frequency, Monetary analysis in Tableau

  1. Doug says:


    I’m new to tableau so apologies if this is not the right forum for questions. Thanks for sharing the steps to create the RFM metrics. Question: if i want to use the RFM Metric to create customer segments (example If RFM Metric = 555, THEN “Best Customer”) – how would I use the calculated field as a dimension? Ultimately i would like to count customers, sum sales, etc by segment. I’ve tried using LOD expression but keep getting errors. Hope you can help. Thanks!

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