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Case Study
Customer Segmentation RFM
Which customers should CRM prioritize?
PythonpandasCRM analytics
Customers
5,000
Orders
45,356
Total revenue
€4,522,014
VIP share
27.9% customers · 75.4% revenue
Lost share
23.62% customers · 2.95% revenue
Business Problem
A CRM team needed a data-driven way to segment customers by recency, frequency and monetary value — and prioritize retention and win-back actions.
Tools
- Python
- pandas
- CRM analytics
Methodology
- 1Computed RFM scores on 5,000 customers and 45,356 orders
- 2Segmented customers into VIP, Loyal, At-risk and Lost clusters
- 3Quantified revenue concentration per segment
- 4Built CRM prioritization rules based on segment economics
- 5Delivered actionable recommendations per segment
Main Insight
Revenue is highly concentrated: VIP customers (27.9%) drive 75.4% of revenue, while Lost customers (23.62%) contribute only 2.95% — clear CRM prioritization signals.
Business Recommendations
- VIP retention: loyalty programs, exclusive offers, proactive account management
- At-risk win-back: targeted email campaigns before churn to Lost segment
- Upsell / cross-sell on Loyal segment to move toward VIP status
- Deprioritize broad campaigns on Lost segment — focus budget on recoverable At-risk