.. _dpa-creating-segments-of-observations-for-business-reasons-rfm: ======================================================================== Creating Segments of Observations for Business Reasons (RFM) ======================================================================== **Stage 4 · 🧩 Sampling, Partitioning & Segmentation** · Lesson 30 of 56 · *intermediate* :doc:`◀ Previous · RFM Analysis <29-rfm-analysis>` · :doc:`Next · Least Squares Regression ▶ <31-least-squares-regression>` · :doc:`↑ Section ` .. important:: **✨ AI-generated content.** This page was written with the assistance of an AI language model and is provided as a learning aid. Despite careful review, it may still contain mistakes, omissions, or out-of-date information. Whether you are new to the topic, a team lead, or a senior practitioner, treat it as a starting point rather than an authoritative reference: read it critically and independently verify anything you act on (code, commands, figures, and factual claims) against official documentation and primary sources before relying on it. From codes to segments ------------------------ 125 RFM codes are too many to act on. The final step is to group them into a handful of **named segments** — typically **six to ten** — each describing a recognisable kind of customer and, crucially, each calling for a **different business response**. This is where scores become **decisions**. A common taxonomy ------------------- A widely used starting taxonomy names segments by their RFM profile: * **Champions** — high on all three (555, 554): the best customers. * **Loyal Customers** — buy consistently and often. * **Potential Loyalists** — recent buyers with growing frequency. * **New Customers** — recent, but few purchases so far. * **At Risk** — once frequent and high-spending, but **lapsing** (low recency). * **Can't Lose Them** — high past value, gone quiet. * **Hibernating / Lost** — low on everything, long inactive. Each segment, an action ------------------------- The point of naming segments is the **"so what"**. **Champions** get rewards, early access and referral asks — **not** blanket discounts that erode margin. **At Risk** and **Can't Lose Them** get **win-back** campaigns and personal outreach, ideally *before* they fully churn. **Hibernating / Lost** get a re-permission push or are **suppressed** to save budget. One message for a new buyer and a ten-year loyalist would waste both; segmentation lets each be treated for **who they are**. Segments as clusters ---------------------- Notice this is the **clustering** idea from earlier in the stage, made concrete: customers are grouped by similarity — here, similarity in RFM space — so that each group can be understood and served differently. Whether the groups come from a rule-based RFM taxonomy or from an algorithm like k-means, the goal is the one this stage began with: turn a mass of observations into **meaningful segments** a business can act on. With customers understood, the course turns next to **predicting** outcomes — starting with regression. .. hint:: **Related lessons:** :doc:`Recency, Frequency, and Monetary Value (RFM) <28-recency-frequency-and-monetary-value-rfm>` · :doc:`RFM Analysis <29-rfm-analysis>` · :doc:`Clustering <27-clustering>` · :doc:`Cluster Profiling Using Decision Trees <48-cluster-profiling-using-decision-trees>` .. seealso:: **Source article** Adapted (context, re-expressed) in our own words from: `https://insightful-data-lab.com/2026/01/14/creating-segments-of-observations-for-business-reasons-rfm/ `__ (insightful-data-lab.com). .. tags:: purpose: reference, topic: data analysis, topic: data preparation, level: intermediate