How to segment customers by behavior

business analytics

slug: recipe-business-analytics-how-to-segment-customers-by-behavior

Recipe: How to segment customers by behavior

category: business analytics

Problem

You want to understand customer behavior and prioritize engagement by identifying which customers are most valuable, most active, or at risk.

Examples:

• Identify top customers for retention campaigns
• Target high-potential but infrequent buyers for promotions
• Recognize low-value or at-risk customers to optimize resources
• Segment customers for loyalty programs or tiered services

Solution

Compute Recency, Frequency, and Monetary (RFM) metrics and use them to classify customers into actionable segments:

• rfm step — Calculate recency, frequency, and monetary metrics
• cube step — Aggregate metrics by segment to summarize patterns
• chart step — Visualize RFM segments for interpretation and decision-making

Step Sequence

rfm step -> cube step -> chart step

Input Datasets

• Customer transaction history (purchase date, amount, customer ID)
• Optional: customer demographics or campaign response data

Output Dataset

• rfm_segments — customer-level RFM scores and segment assignments
• Key columns: customer_id, recency_score, frequency_score, monetary_score, rfm_score, segment_label

Step-By-Step Explanation

Step	Purpose	Notes
rfm step	Compute Recency, Frequency, Monetary metrics	Standardized scoring (e.g., 1–5 per metric)
cube step	Summarize metrics by segment	Aggregates metrics for visualization and reporting
chart step	Visualize RFM segments	Optional heatmaps, bar charts, or dashboards

Variations & Extensions

• Combine with filter step to analyze specific regions, products, or time periods
• Feed RFM segments into classify step for predictive modeling
• Integrate with [[step-sendEmail]] for targeted campaigns
• Adjust scoring methodology for different business contexts

Concepts Demonstrated

• Customer segmentation
• Behavioral analytics
• Prioritization of high-value customers
• Integration of analytics into reporting and campaigns

Related Recipes

• How to evaluate segments relative to a benchmark (Index analysis)
• Understand what drives change and what matters most (Contribution + Pareto)
• How to classify customers for targeted campaigns

Notes & Best Practices

• Use at least 6–12 months of transaction data for meaningful recency/frequency patterns
• Consider normalization or weighting if transaction amounts vary widely
• Segment thresholds can be business-specific; test and iterate for optimal targeting
• Visualizations help executives quickly grasp customer distribution

Metadata

title: "How to segment customers by behavior"
category: "business analytics"
difficulty: "Intermediate"
tags: [rfm, segmentation, customer analytics, marketing]
inputs: [customer transaction history]
outputs: [rfm_segments]
steps: [step-rfm, step-cube, step-chart]
author: "Tom Argiro"
last_updated: "2025-10-25"
doc_type: "recipe"