- Overview
This project involved implementing a customer segmentation model for a base of 3 million clients using the LRFMC approach. The segmentation aimed to better understand customer behaviors and preferences, supporting data-driven decision-making for marketing strategies and enhancing business outcomes.
- Key Metrics and Data
- 15 key metrics (such as recency, frequency, and monetary value) and 85 general metrics were analyzed.
- Data preprocessing involved cleaning, balancing, and standardizing the dataset to ensure reliability and consistency.
- Segmentation Approach
- Techniques Used:
- K-Means Clustering: For partitioning data points into clusters based on proximity to centroids.
- Spectral Clustering: Applied to reduce dimensions and identify distinct customer groups.
- DBSCAN: Used for density-based clustering to capture outliers and dense clusters.
- Hierarchical Clustering: Explored relationships within clusters using agglomerative (bottom-up) and divisive (top-down) methods.
- Process Model:
- Step 1: Define business and ML objectives, and verify data quality.
- Step 2: Select and engineer features, reduce noise, and augment data.
- Step 3: Train and optimize models using domain-specific knowledge.
- Step 4: Validate model performance with defined metrics.
- Step 5: Deploy models in production, monitor performance, and retrain as needed.
- Outcomes
- Personalized Customer Engagement: The segmentation results were instrumental in creating targeted marketing campaigns, enhancing customer satisfaction and loyalty.
- Enhanced Recommendation Systems: Insights derived from segmentation were directly applied to develop recommendation systems, aligning products and services with customer preferences.
- Operational Efficiency: The clustering analysis optimized resource allocation and marketing budgets.
- Benefits
1. Enabled personalized marketing strategies for distinct customer segments.
2. Enhanced the customer retention rate through targeted engagement.
3. Discovered new market opportunities by identifying underserved segments.
4. Improved the efficiency of marketing investments, reducing waste.
5. Delivered actionable insights for product development and improvement.
- Future Enhancements
- Incorporate real-time customer behavior tracking to dynamically update clusters.
- Expand segmentation to include advanced psychographic and behavioral metrics.
- Integrate segmentation outcomes into omnichannel marketing strategies for consistent customer experiences.
This project exemplifies the potential of data-driven approaches in understanding and predicting customer behaviors to optimize business strategies