Focus:
When shoppers type queries that don’t precisely match your product catalog, such as misspellings, synonyms, or natural-language phrasing, traditional keyword-based search engines often return nothing. Each empty results page ends the session, sending customers elsewhere and eroding trust in your site’s search experience.

Problem:
Every “no-results” page is a lost sale. Even a small percentage of failed searches can translate into significant revenue leakage over time. The underlying issue is that lexical search alone depends on exact text matches and cannot infer meaning or intent behind a query.

Fix:
Adopt a hybrid lexical + vector search approach that blends the precision of keyword matching with the semantic understanding of AI-driven vector embeddings. Vector search uses machine-learned representations of text to capture meaning and context, allowing your system to find relevant results even when shoppers use unfamiliar terms or natural language. Combining both methods ensures coverage of both explicit and implicit intent—reducing zero-result outcomes, improving conversion rates, and setting the foundation for more intelligent retrieval across your eCommerce experience.