A leading retail organization aimed to enhance its customer engagement and drive higher sales through advanced, AI-powered personalization. The client faced challenges with static recommendation systems that failed to adapt to real-time user behavior and product dynamics.
Despite having a large product catalog and diverse customer base, the retailer struggled with:
Stagnant sales figures
Low conversion rates from product recommendations
Subpar user experience due to non-personalized journeys
Emral Technology Inc. implemented a
Graph Machine Learning (Graph ML) powered end-to-end recommendation engine. This dynamic system:Mapped user interests and interactions to product attributes in real-time
Built a graph-based structure capturing intricate user-product relationships
Continuously learned and adapted to changing user preferences
By deploying the Graph ML solution, the retailer saw measurable improvements across key metrics:
| Metric | Impact |
|---|---|
| Increased Sales | +25% |
| Improved Conversion Rates | +18% |
| Enhanced User Experience | +30% |
Traditional ML models often treat users and items independently. Graph ML empowers recommendation engines by:
Understanding contextual relationships
Connecting similar users and products through real-world interactions
Making smarter, more accurate suggestions
Graph Neural Networks (GNN)
Real-time Data Pipelines
Scalable Cloud Infrastructure
Recommendation APIs for Omnichannel Integration
With Emral's deep expertise in Graph ML and AI-driven retail solutions, the client not only improved its KPIs but also laid the foundation for long-term customer loyalty through personalized experiences.
Looking to revolutionize your retail strategy with AI? Contact Us today.
