Understanding RFM and how it works

-Basically, RFM (recency, frequency, monetary) analysis is a marketing technique mainly used to determine quantitatively which of your customers are the top ones by identifying how recently a customer has purchased (recency), how often they purchase (frequency), and how much the customer spends (monetary). RFM analysis is based on the marketing axiom

 What you don't know is that 80% of your sales come from 20% of your customers! interesting huh.. the Pareto Principle, you know this rule of thumb intuitively. You’re in business largely because of the support of a fraction of your customer base: your Best Customers.

Actually, Some people might not understand why it's important to understand RFM however they are considered important mainly for 2 reasons

1) to continue to provide the loyal group with what they’re looking for and keep them as frequent & recent customers.

 2) to target your marketing efforts toward prospects who resemble your best customers.

let's Define the Terms of RFM individually:

Just what are “recency,“frequency,” and “monetary” measures? 

Basically, turning them into measures that you can use to produce RFM scores can be somehow tricky. but keep in mind that the measures you use to rank your list are not the same numbers as the 5-4-3-2-1 score that you assign to each customer. 

  • For recency, you’ll figure out how long it’s been since each customer interacted, in days, weeks, or months. You then use those time-based measures to rank your list in order, from most recent to the long-lapsed. The recency score comes from that ranked list, with the 20 percent who gave most recently assigned a score of 5.
  • For frequency, a measure is a number of interactions in a given period.
  • For monetary, the measure is total transaction value.

Calculating RFM scores

To calculate RFM scores, you first need the values of three attributes for each customer:

 1) most recent purchase date

 2) number of transactions within the period (often a year), 

 3) total or average sales attributed to the customer (total or average margin works even better).

You then have to decide the number of categories for each RFM attribute. The number is typically 3 or 5. If you decide to code each RFM attribute into 3 categories, you’ll end up with 27 different coding combinations ranging from a high of 333 to a low of 111. Generally speaking, the higher the RFM score, the more valuable the customer.

You can assign customers to categories by sorting on RFM attributes or by applying business rules. For example, customers can be assigned to frequency category 3 if they have made 10 or more purchases in the past year, category 2 if they have made 3-9 purchases, and category 1 if they have made 1 or 2 purchases. Determining meaningful rules often requires a bit of data mining, however, so it’s common for RFM users to simply sort the customer file on each attribute and assign customers to categories from top to bottom (i.e., the top third of customers on frequency are assigned to category 3 and so on).

You also have to remember to sort your customers on recency first, then sort on frequency in each recency category, and, finally, sort of monetary value in each combination of recency and frequency categories. This way, you end up with an equal number of customers for each RFM score.