Session Description

E-commerce search engines are often optimized for simple, product-centric queries.
In B2B contexts, however, users search differently: they describe their needs, use cases, and constraints through long, highly domain-specific queries.
The result is predictable: zero results, irrelevant products, degraded relevance—even though the right products do exist in the catalog.
In this talk, we start from a real-world, large-scale B2B e-commerce search case to challenge a common misconception: the problem is not the ranking. Instead, it lies in a combination of overly strict retrieval, poor query understanding, a single search strategy applied to multiple intents, and product data that is misaligned with real-world usage.
Using a decision-based diagnostic tree, we will show how to precisely identify where search fails (strict AND logic, stopwords, field weighting, intent-based routing, data normalization), and how to design targeted experiments to improve relevance without “rebuilding everything from scratch.”