AI Personalization for E-commerce

Typical outcomes for this kind of engagement. Results depend on your data and starting point.

Rule-based merchandising shows the same recommendations to broad customer groups regardless of individual preference or recent behavior, so recommendation modules occupy prime page space while converting poorly.

• Rule-based recommendations ignoring individual preference and recent behavior
• Search returning poorly ranked results across many query patterns
• Prime page space occupied by modules that convert badly

CloudGate deploys a personalization layer using Azure Personalizer for real-time recommendations and Azure AI Search for personalized ranking, both learning continuously from customer interactions.

• Real-time product recommendations via Azure Personalizer with continuous learning
• Personalized search ranking through Azure AI Search per customer and session
• Contextual bandit approach balancing exploration against exploitation