- Overview
The project involved the development of an advanced recommendation system for a retail company catering to a diverse customer base of 100,000 clients and a product catalog comprising 500 items. The primary goal was to enhance sales productivity by delivering personalized product suggestions tailored to individual customer preferences
- Technology and Methodology
- Deep Learning Model: Implemented a two-tower neural network to understand and predict customer-product interactions.
- Feature Engineering: Extracted critical customer-related features (e.g., demographic data, purchase history) and product characteristics (e.g., price, category, sales trends).
- Ranking and Filtering: Integrated a two-phase ranking mechanism—retrieval and re-ranking—to ensure optimal product suggestions.
- Hybrid Techniques: Combined data-driven insights with rule-based methods for better coverage of edge cases and seasonal trends.
- Key Features of the System
1. Personalized Recommendations: Tailored suggestions based on customer behavior and preferences.
2. Real-Time Capabilities: Delivered recommendations instantly during customer interactions.
3. Scalable Architecture: Supported high-volume data processing without performance degradation.
- Results and Impact
- Achieved a recall rate of 83%, indicating the model's effectiveness in providing relevant recommendations.
- Boosted overall sales by an average of 31%, with notable increases in under-promoted product categories.
- Enhanced customer satisfaction and loyalty through improved shopping experiences.
- Challenges and Innovations
- Cold Start Problem: Addressed using advanced feature engineering and enriched initial customer profiles.
- Dynamic Updates: Regularly retrained the model to adapt to new customer behaviors and market changes.
- Operational Efficiency: Streamlined the recommendation process, enabling sales experts to use the system effectively in both online and offline retail environments.
This project exemplifies how advanced AI techniques like deep learning can drive business growth in the retail sector by transforming customer engagement and sales strategies.