The Mathematics of RFM Segmentation & Customer Lifetime Value

RFM Segmentation: Turn Transaction History into Customer Strategy Imagine you run an online store with thousands of customers. Some buy every week, some bought once and vanished, some spent a fortune months ago and never returned. Who do you email a loyalty reward? Who needs a win-back discount? Who is about to churn? RFM answers all three — using only three numbers computed from your existing orders table. No machine learning, no extra data — just Recency, Frequency, and Monetary.

1. The Business Problem RFM Solves Ranking customers by total spend alone is misleading. A customer who spent $2,000 two years ago and never returned is less valuable than one who spent $400 last week and buys monthly. RFM fixes this by scoring when they last bought, how often they buy, and how much they spend — then combining the three into a single actionable segment like Champions or At-Risk .

2. Your Starting Point: The Raw Orders Table In Project 35 (and the notebook assignment) you already have this table as df_orders after ingesting from SQLite. Here is a miniature version with 8 rows across 3 customers — everything RFM needs is already here:

transaction_id customer_id transaction_date quantity unit_price total_amount 1001 C001 2024-06-10 2 120.00 240.00 1002 C001 2024-06-22 1 899.00 899.00 1003 C001 2024-06-28 3 45.00 135.00 1004 C002 2024-02-12 5 80.00 400.00 1005 C002 2024-03-18 2 250.00 500.00 1006 C002 2024-04-01 4 120.00 480.00 1007 C003 2024-06-25 1 45.00 45.00 1008 C003 2024-04-10 1 45.00 45.00 You have seen total_amount = quantity * unit_price since Module 3. RFM simply rolls this table up to one row per customer .

3. The Three Pillars — Plain English + Formula Pillar Plain English How to compute (per customer) Higher is better? R — Recency How many days ago was their last purchase? (snapshot_date − max(transaction_date)).days — pick a single snapshot (we use 2024-07-01 ) and subtract the customer's most recent date Lower days = better (3 days ago beats 140 days a