Unlocking AI-driven personalization at scale with unified data and ML platform

A leading retail organization transformed its approach to personalization by implementing a unified data and ML platform. By unifying behavioral, transactional, and product datasets into a governed Customer 360 foundation, the organization enabled collaborative ML workflows and scalable recommendation models. 

Instead of spending time reconciling data and rebuilding features for each model, teams can now develop and deploy AI-driven recommendations, predictive promotions, and next-best-offer campaigns from a shared, trusted platform. 

In this case study, learn how the retailer: 

• Unified customer, behavioral, and product data into a Customer 360 dataset 
• Enabled collaborative ML workflows using MLflow 
• Implemented a reusable feature engineering framework 
• Scaled recommendation engines and predictive personalization models 
• Built an AI-ready retail data platform with Databricks Lakehouse 

Discover how modern lakehouse architecture enables retailers to turn fragmented customer data into intelligent, personalized experiences. 

Download the case study