AI Recommendations That Increased Revenue by $18M in 9 Months
From generic product suggestions to AI-driven hyper-personalization that understands customer intent, context, and preferences in real-time.
247%
CTR Increase
63%
Basket Size
$18M
Revenue Lift
Generic Recommendations Leaving Money on the Table
The client had $200M annual online revenue, 2.8M active customers, and 150,000 SKUs. Their existing rule-based engine had 0.8% CTR on recommendations. Pain points: generic 'customers who bought X also bought Y,' no contextual understanding, cold start problem, and inventory bias.
Challenge: Build an AI system that understands fashion, personal style, context, and intent to deliver truly personalized recommendations across all touchpoints.
Multi-Model AI Recommendation Ecosystem
We built a multi-model ensemble: Style Understanding AI (GPT-4 Vision + fashion models), Collaborative Filtering (matrix factorization + deep learning), Content-Based (product embeddings), and a GPT-4 Ranker for final ordering. Feature engineering produced user/product embeddings and context vectors.
Style Understanding AI
GPT-4 Vision + Custom Fashion Models
Visual style extraction, outfit compatibility, 'complete the look' suggestions.
Collaborative Filtering
Matrix Factorization + Deep Learning
Real-time updates, handles sparse data, temporal dynamics.
GPT-4 Ranker
OpenAI GPT-4
Final ranking, explainability, diversity and business rules.
Measurable Transformation
Recommendation CTR
Items per Order
Revenue Lift
Results as reported by Premium Fashion Retailer.
Technical Stack
AI/ML
Data
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