Customer Segmentation
PROSegment customers using RFM analysis, K-Means clustering, and behavioral data to identify high-value groups, predict churn, and build targeted acquisition strategies.
Example Usage
“Analyze our customer transaction data from the past 2 years with 25,000 customers. Segment using RFM analysis and K-Means clustering on purchase frequency, average order value, and recency. Identify the top 3 highest-value segments and for each, develop: (1) a detailed customer persona, (2) targeted acquisition strategy with CAC projections, (3) retention playbook, and (4) specific marketing messages and channels that would resonate with each segment.”
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Suggested Customization
| Description | Default | Your Value |
|---|---|---|
| Days to look back for calculating recency score (90 for fast-moving retail, 730 for B2B) | 365 | |
| Number of clusters for K-Means algorithm (determine via Elbow Method, typically 3-8) | 5 | |
| Percentile threshold above which customers are considered high monetary value | 75 | |
| Days of inactivity defining a customer as churned (30 for SaaS, 365 for annual subscription) | 180 | |
| Minimum purchase frequency to classify as frequent buyer (varies by industry) | 5 | |
| Target Customer Acquisition Cost payback period in months | 12 |
Research Sources
This skill was built using research from these authoritative sources:
- Comarch Customer Segmentation Guide Comprehensive 7-step segmentation strategy covering geographic, demographic, psychographic, behavioral, needs-based, and value-based approaches
- 8 Proven Customer Segmentation Frameworks Details 8 frameworks including 4A Model, 3C Framework, STP, RFM Analysis, 5W Framework, Persona Framework
- How to Perform Customer Segmentation: 5-Step Strategy Step-by-step implementation guide with real examples of demographic, geographic, psychographic, and behavioral approaches
- Ultimate Framework for Segmenting Customers Practical framework covering behavioral, psychographic, RFM segmentation across CRM, success, sales, and product teams
- Customer Segmentation in Python with Machine Learning Hands-on Python tutorial using K-Means clustering with EDA, feature scaling, and segment interpretation
- Customer Segmentation via Cluster Analysis Technical guide on K-Means, hierarchical, and density-based clustering methods
- Customer Segmentation Using K-Means Clustering Project-based tutorial covering EDA, feature transformation, elbow method, and cluster interpretation
- Machine Learning for Customer Segmentation in Retail Academic overview of ML techniques including clustering, classification algorithms, and retail case studies
- AI-driven Customer Segmentation in E-commerce Framework covering clustering algorithms, data integration, implementation case studies, and ROI measurement
- RFM Analysis Method for Customer Segmentation Detailed guide on Recency-Frequency-Monetary analysis and segment interpretation