Build Advanced Customer Segmentation Model

## Customer Segmentation Strategy for E-commerce Fashion Retailer ### 1. Data Preprocessing Strategy **Feature Engineering:** - Create RFM features (Recency, Frequency, Monetary value) - Calculate seasonal purchase patterns - Derive product category preferences and brand affinity scores - Engineer engagement metrics (email open rates, site session duration) **Data Transformation:** - Normalize monetary values using log transformation - Create binary flags for seasonal shoppers - Handle missing demographic data using mode/median imputation ### 2. Segmentation Approach **Recommended Algorithm:** K-means clustering with hierarchical clustering for validation - **Primary features:** RFM scores, category preferences, engagement levels, seasonality patterns - **Optimal segments:** 5-7 clusters based on elbow method and silhouette analysis ### 3. Implementation Plan ```python from sklearn.cluster import KMeans from sklearn.preprocessing import StandardScaler # Feature scaling scaler = StandardScaler() X_scaled = scaler.fit_transform(customer_features) # K-means clustering kmeans = KMeans(n_clusters=6, random_state=42) segments = kmeans.fit_predict(X_scaled) ``` ### 4. Expected Segments - **High-Value Loyalists:** Frequent, high-spend customers - **Seasonal Shoppers:** Purchase during sales/holidays - **Price-Sensitive:** Low AOV, promotion-driven - **New Customers:** Recent acquisitions needing nurturing - **At-Risk:** Declining engagement/purchases ### 5. Business Applications - Personalized email campaigns per segment - Dynamic pricing strategies - Targeted product recommendations - Customized retention programs