2026-05-06 –, Main Stage
This talk uses a real-world B2B search case to show how a decision-based tree helps quickly diagnose why search fails and how to improve relevance without rebuilding the system.
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.”
Product & Operations Manager at Adelean, I specialize in search, data, and complex systems, with a particular focus on e-commerce search engines.
I work on topics that combine architecture, relevance, product data, and technical decision-making.
Alongside this role, I am an elected member of a sports federation, where I contribute to digital transformation projects, giving me the opportunity to operate across a wide range of organizational contexts.
I also run a consulting business, delivering advisory, audit, and administrative and operational structuring engagements.
This dual product and consulting background allows me to bring a strong field-oriented perspective, grounded in real-world data and actual usage patterns.