From words to meaning
A vector-based approach makes texts and product information representations that can be compared in terms of content. This allows a product to be relevant without the search query literally appearing in the product text.
AI Search
Vector Search finds substantive similarities.
Vector Search makes it possible to compare search questions and products based on meaning. That helps especially when customers use words other than the catalog or descriptive search.
Suitable for inspiration
Descriptive search questions such as “minimalistic lamp for above a round table” are more difficult for traditional keyword search. Vector Search can help recognize the substantive relationship between such wishes and product information.
More value from longer search questions
The more specific a visitor searches, the more likely it is that exact keyword matching will be inadequate. A layer of meaning can link long-tail searches to attributes, categories, and product descriptions.
Not everything has to be vector search
Exact SKUs, article numbers and brand names require precise matching. That is why Vector Search works most strongly in combination with traditional search, filters, ranking rules and commercial control.
Product data determines the quality
Good attributes and descriptions give the search layer more context. For a strong implementation, technical relevance and quality of product data are therefore considered together.
Measure what actually works
Search technology is not an end in itself. CTR, zero-result searches, product clicks and conversions show whether visitors are really being helped better.
When to deploy?
Vector Search is especially powerful when customer language varies.
Think of inspirational shops, large catalogues, multilingual environments and products with many characteristics or use cases.
Combine precision with meaning.
Findoviq can bring together different relevance signals so that exact searches and natural language are treated well.