RFM Analysis#

Stage 4 · 🧩 Sampling, Partitioning & Segmentation · Lesson 29 of 56 · intermediate

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Important

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From raw values to scores#

RFM analysis turns the three raw numbers into a comparable score. Raw values are awkward to compare directly — is 30 days “recent”? is $500 “high”? — because the answer depends on your business. The fix is to score each customer relative to the others, so the numbers become ranks on a common 1–5 scale.

Quintile scoring#

The standard method is quintiles. For each dimension, sort all customers and split them into five equal groups, then assign a score from 1 to 5. The top 20% on Frequency and Monetary score 5; for Recency the scale is inverted — the most recent 20% (fewest days) score 5. Because quintiles are defined by your own data distribution, the scores adapt automatically to any business, with each score level holding about 20% of customers.

The RFM code#

The three scores are combined into a single RFM code — usually just concatenated, like $R{=}5,\ F{=}4,\ M{=}5 \rightarrow$ “545”. This gives $5 \times 5 \times 5 = 125$ possible codes, from 555 (the best — recent, frequent, high-spending) down to 111 (the worst). Sorting customers by their code, or by a weighted combination if one dimension matters more, ranks the whole base at a glance. Because behaviour drifts, the scores are recomputed regularly.

In Python#

The computation is a short pandas routine: group the orders by customer, then aggregate to the three values — recency from the latest InvoiceDate, frequency from a count of orders, monetary from the sum of spend. pandas.qcut cuts each into quintile scores in one call. The result is a tidy table, one row per customer with an R, F and M score — ready to be grouped into the segments of the next lesson.

Hint