Vector Search vs Keyword Search for Ecommerce | Particular Audience
Keyword search matches words; vector search matches meaning. Why that matters for long, descriptive and conversational shopper queries.
Vector search vs keyword search: what's the difference for ecommerce?
Keyword search finds products whose text contains the shopper's words. Vector search turns queries and products into numeric representations of meaning, so it finds relevant products even when the words differ. Both handle short queries like 'milk' well; vector search keeps returning useful results on long, descriptive or conversational queries where keyword overlap breaks down.
Where keyword search struggles
Misspellings, synonyms, descriptive phrases and questions. Teams patch these with synonym rules one query at a time.
Where vector search helps
The long tail of specific queries, multi-language catalogues and conversational shopping assistants.
Best of both
Many retailers combine the two: keyword precision for exact codes and brands, vector recall for everything else.
How Particular Audience does it
In production
- Shopify bikes retailer (head-to-head A/B): One Shopify bikes retailer, 590,878 visitors: PA won every KPI head-to-head (+8.0% Average order value (A$241.82 vs A$223.89))
- Top-10 North American Grocery Retailer: Fixing grocery site search (+65% More relevant catalog surfaced (Recall@120 0.485 → 0.800))
Frequently asked questions
Does vector search replace merchandising rules?
No. It improves which products are eligible; merchandising rules still control boosts, exclusions and ordering.
Related guides
- How to reduce zero-result searches on your ecommerce site: Five steps to find, fix and prevent zero-result and near-zero-result searches, from query logs to meaning-based search.
- How to evaluate your ecommerce site search: Test site search relevance with a fixed query set, graded results and head-to-head comparisons, including LLM-as-a-judge grading.
- Site search for fashion ecommerce: Why fashion search needs vector understanding and visual similarity, and how to turn sold-out and no-result pages into the next click.