The client tracked user activity across hundreds of configurable products but couldn't predict which SKUs would convert. Marketing and product teams used manual reports and isolated tests to guide decisions. Without predictive signals tied to real-time sessions, personalization efforts missed the moment when customers were actively browsing and comparing options. 

Solution 

Calsoft built a machine learning pipeline that analyzes user behavior and product attributes to forecast add-to-cart probability for each configuration. 

• Train models on clickstream data and interaction patterns 
• Engineer features from pricing, specs, and session depth 
• Deploy real-time API for live personalization 
• Monitor predictions against actual conversions for continuous retraining 

The system integrates with existing platforms and scales across desktop and mobile without adding latency during peak traffic.