CHALLENGE
A leading B2B eCommerce company faced significant challenges in delivering an efficient, accurate product search experience for technical buyers with complex needs. Their legacy search platform struggled to handle detailed product specifications, lacked semantic search capabilities, and offered little personalization. As a result, customers often received irrelevant search results, experienced longer search times, and faced frustration navigating the catalog. To support customer engagement and scalable growth, the company needed a smarter, AI-driven solution capable of delivering faster, more relevant, and highly personalized search results.
SOLUTION
To address these issues, we transformed the existing search platform into an intelligent discovery engine. This was achieved by integrating a sophisticated combination of Microsoft Azure Cognitive Search, Elasticsearch Enterprise, and custom ML models. The solution featured:
- Semantic search and personalization to enhance search relevancy for technical buyers.
- Multi-modal interfaces to improve user experience across various devices.
- Databricks Enterprise for data processing.
- MuleSoft for B2B integration and Snowflake for analytics, creating a robust and scalable architecture.
- Deep learning models trained on industry-specific terminology to handle complex product relationships.
- Azure Machine Learning services for real-time personalization.
RESULTS AT A GLANCE
This solution successfully enhanced the B2B eCommerce platform’s ability to meet the complex needs of technical buyers while improving both user experience and operational efficiency.
- 65% improvement in search relevancy, ensuring that technical buyers can quickly find the most relevant products.
- 40% reduction in search time, leading to improved efficiency and a faster decision-making process for users.
- A scalable and robust architecture, capable of supporting future growth and more advanced features as the business expands.
