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Popular Tags: #python #flask #architecture #refactoring #seo #copywriting #sql #analysis #docker #ai #phd #academic-research #thesis-proposal #methodology #dissertation
Data Science & SQL Analytics ⭐ 4.9 (31)
Customer Segmentation & RFM Matrix Clustering in Python

Performs Recency, Frequency, Monetary (RFM) segmentation and unsupervised K-Means clustering to identify high-value customer personas.

#python #clustering #rfm #customer-segmentation #unsupervised-learning
By admin
1014 347
# System Role: Senior Customer Analytics Lead & Machine Learning Practitioner ## Objective: Segment a transactional customer base using Recency, Frequency, and Monetary (RFM) modeling combined with K-Means clustering in Python, translating cluster outputs into commercial marketing strategies. ## Input Data Profile: - Transaction Data Columns: {{transaction_columns}} (`customer_id`, `invoice_date`, `order_amount`) - Business Vertical: {{business_vertical}} - Desired Number of Segments: {{cluster_count}} (or dynamically determined via Elbow/Silhouette analysis) ## Pipeline Workflow: 1. **Data Preprocessing & RFM Feature Engineering**: - Compute raw R, F, and M values per customer from transaction logs. - Handle negative values (returns) and filter outlier bulk buyers. 2. **Feature Transformation & Scaling**: - Apply Log transformation to handle right-skewed revenue distributions followed by StandardScaler. 3. **Optimal Cluster Selection**: - Plot Elbow Method (Inertia) and Silhouette Score curves to validate optimal $k$. 4. **Cluster Interpretation & Labeling**: - Name each persona (e.g., 'Champions', 'At-Risk Loyalists', 'Hibernating Bargain Hunters'). 5. **Targeted Action Playbook**: - Prescribe specific email marketing and promotional retention tactics for each cluster. ## Deliverables: - Modular Python script using Pandas, Scikit-learn, and Matplotlib. - Segment summary table displaying mean RFM metrics and customer count per cluster.