Client Overview
Client: Confidential Fashion Retailer
Industry: E-Commerce & Apparel
Duration: 8 Weeks
Business Challenge
The client faced low product discovery rates and suboptimal conversion due to generic recommendations. They sought a scalable, AI-powered solution to personalize user journeys and increase engagement across their web and mobile platforms.
Our Solution
We implemented an end-to-end ML-based recommender system tailored for the fashion domain. The system continuously analyzed customer behavior, preferences, and product metadata to deliver hyper-personalized suggestions in real time.
- Trained collaborative filtering and hybrid models using TensorFlow & Scikit-learn
- Used customer purchase history, browsing behavior, product attributes, and trend signals
- Integrated with React Native and Shopify storefront via APIs
- Included fallback logic for cold-start users using trending and similarity-based logic
Key Outcomes
- 45% increase in product discovery via personalized carousels
- 22% uplift in conversion rates across mobile and web
- 70% reduction in manual merchandising effort
- Enabled real-time campaign-specific recommendation tuning
Technology Stack
Modeling: Python, Scikit-learn, TensorFlow
Deployment: AWS Lambda, API Gateway, Docker
Integration: React Native, Shopify, REST APIs
Client Testimonial
“Durga Analytics delivered a smart recommendation engine that elevated our user experience and directly improved our sales funnel.”
— Chief Digital Officer
Next Steps
We're helping the client evolve toward a multi-touch AI personalization engine — integrating image recognition, pricing sensitivity models, and style clustering for next-gen customer journeys.
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