Strategy and Solutions

Zymr developed a full-stack AI personalization engine to deliver targeted user experiences in real time. We implemented collaborative filtering and deep-learning recommenders using TensorFlow to generate dynamic product suggestions and pricing offers. Behavioral targeting models were trained on session-level data to trigger cart recovery emails and nudges, which were orchestrated via Braze for multi-channel delivery. A customer data platform (CDP) was built by integrating Segment to unify behavioral, CRM, and purchase data into a 360° view. Smart promotion engines were developed using demand elasticity modeling to optimize offers and A/B test campaigns by user segment.

About the Client

A rapidly growing online retailer in the fashion and lifestyle sector, serving a diverse customer base across North America and Europe. The client wanted to use AI to personalize customer journeys at scale, increase conversions, and improve retention across digital channels.

Key Outcomes

Achieved 1:7 ROI on AI investment within the first quarter.

Enabled dynamic segmentation and churn risk targeting across the customer base.

Business Challenges

The eCommerce platform delivered a generic shopping experience, resulting in low cart-to-checkout conversions and a rising volume of abandoned carts. Customer data was fragmented across marketing, CRM, and transaction systems, making personalization difficult. Promotions were manually configured, lacking dynamic targeting and performance tracking.

Business Impacts / Key Results Achieved

Cart-to-checkout conversions increased by 38% within six weeks of launch. Repeat purchases rose by 31% due to personalized recommendations, and campaign setup time dropped by 50% through automated optimization.