Case Study: Leading U.S. Retailer achieves 20.2% revenue lift with Session AI

Data-driven Predictive Engagement Leads to 20% Revenue Lift

Leading U.S. Retailer wanted to boost conversion and personalization to turn casual browsers into buyers without eroding margins. Faced with identifying “on-the-fence” visitors and engaging them with the right incentive before they left the site, the retailer partnered with Session AI and leveraged Session AI’s in-session Early Purchase Prediction (EPP) predictive engagement platform.

Session AI grouped visitors into “will not buy,” “will buy,” and “on-the-fence,” then used real-time intent prediction plus contextual signals (behavior, purchase history, location, inventory, etc.) to trigger personalized, time-limited nudges and offers only to influenceable shoppers. By targeting those visitors, Session AI helped the retailer achieve a 20.2% average lift in revenue per visitor while preserving overall margins.