Customer Segmentation in Python

The sequence is the point: each technique answers a different question, and seeing them side by side teaches you which to reach for. Cohort analysis tracks how groups behave over time, RFM scores customers on recency, frequency and spend (the workhorse of retention marketing), and clustering finds patterns you didn't already suspect. Crucially it stays interpretable — segments a marketer can actually act on, not a black box.

It's hands-on in Python and best for marketers and analysts with some Python who want applied skills, not a no-code approach. Access is via DataCamp's subscription (about $14/month billed annually, first chapter free), and the completion certificate documents the learning rather than certifying it. These segmentation methods are long-standing staples (as of 2026).

Customer Segmentation in Python covers three real-world segmentation techniques in sequence: cohort analysis to track customer trends over time, RFM (recency, frequency, monetary value) scoring to build interpretable segments, and k-means clustering to make those segments more powerful using a real anonymized retailer transaction dataset.