What is vector search in e-commerce?

Answer: Vector search is a search methodology that returns more relevant results by using signals like product descriptions and user behavior to understand what shoppers mean—not only what they type.

When this is true:

• Shoppers search with broad, natural-language phrases.
• Product data contains rich text (descriptions, attributes, metadata).
• You want relevance beyond literal keyword overlap.

When it’s false:

• Your catalog is tiny and shoppers search by exact SKU/name.
• You only need strict keyword matching.

When should you adopt vector search for your online store?

Answer: Adopt vector search when your catalog is large, queries are diverse, and keyword-only search produces irrelevant results or requires heavy manual tuning.

When this is true:

• You see many broad queries and zero-result searches.
• Synonym maintenance is becoming a constant task.
• You want stronger personalization and smarter ranking.

When it’s less urgent:

• Your shoppers search mostly by exact product names.
• Your assortment and terminology are stable and limited.

Why is e-commerce search so important?

Search is a major revenue driver because searchers often have strong purchase intent and spend more than non-searchers.

The search bar is the key to directing shoppers to the products they want to buy.

People who search spend 2.6 times more money compared to non-searchers, and on for B2B sites, 92% of purchases start with search!   

What are the main issues with traditional keyword search?

Keyword search relies heavily on exact term matching, which often requires extensive manual optimization (weights, tuning, synonyms) to avoid irrelevant results and lost revenue.

When this becomes a problem:

• Shoppers use vague queries (“shoes for winter hiking”) instead of exact product terms.
• Synonyms and language changes aren’t covered (e.g., “trainer” vs “sneaker”).
• Results include “technically matching” products that aren’t what the shopper wanted.

This is where vector search enters the scene, a transformative technology that has the potential to redefine the E-commerce search experience.

Vector and keyword-based search can be combined to give you the most powerful search on the market.

But first let’s get started with an overview of vector search.

How is vector search different from keyword search?

Keyword search matches words; vector search matches meaning by modeling relationships between items and queries (concept-level similarity).

Vector search is a search methodology that provides even more relevant results by taking into account information like: product descriptions and user behavior to return better quality search results.

Unlike traditional keyword search , vector search seeks to understand the inherent qualities and relationships between items in a dataset. To put it even more simply, it’s like transitioning from looking up words in a dictionary to understanding concepts and their connections.

Vector Search goes beyond mere keywords, diving deep into the very nature of products, understanding their features and nuances. This means more relevant search results, better product recommendations, and an altogether more satisfying shopping journey.

What are the biggest benefits of vector search for e-commerce businesses?

Answer: Vector search improves relevance, UX, speed, synonym handling, personalization, adaptability to language, and ranking quality—leading to better search outcomes and revenue potential.

Below are the 7 main advantages as reorganized into snackable blocks:

1. Result Relevance Improvement

Vector search enables shoppers to search more generally, while prioritizing relevancy, which means even if they cannot exactly explain what product they need, they can search for the concept and receive matching results.

Take the following queries for example:

1. home decor items for a minimalist living room
2. engagement rings with sapphire stones
3. skincare products for sensitive skin
4. hiking boots for all-terrain trekking
5. recommend pet toys for large dogs

A search engine powered by vector search would understand these broadly described concepts, and provide the user with the following results:

1. furniture, lighting and accessories that align with the minimalist aesthetic
2. a list of rings specifically designed for engagement, featuring sapphire gemstones
3. all kinds of skincare products known to be gentle and beneficial for sensitive skin (moisturizers, cleansers, serums)
4. hiking boots known for their durability and performance on various terrains
5. a selection of pet toys specifically designed for larger breeds, taking into account durability and safety

By leveraging a technology that can understand such queries, you can boost your search coverage, which will lead to Zero Result Search Rate reduction, and Search Success Rate, Conversion Rate, and Revenue increases.

If you want to dive deeper into measuring your E-commerce store, check our comprehensive guide on the 25 most essential E-commerce KPIs.

2. Enhanced User Experience

Vector search enhances the overall shopping experience.

Besides returning more relevant results and enabling shoppers to search using natural language (no need for exact keyword matches), it supports real-time updates, so shoppers always see in-stock products and the most up-to-date product information. Of course, it also helps reduce the appearance of irrelevant products.

All this together can lead to longer sessions, a higher retention rate, and a greater sense of user satisfaction.

3.Faster Shopping Journey

4. Less Manual Synonym Editing Work

Synonym management is an important, but extremely time-consuming, way to optimize your online store’s search results. Most often, tagging products and keywords with relevant synonymous keywords is a manual process.

There are solutions for automatically mining and recommending synonyms for search keywords in order to improve synonym management productivity, but these recommendations still need to be manually reviewed for accuracy.

Vector search can handle synonyms automatically based on the language model it leverages by machine learning techniques.
By freeing up your team from this time-intensive process, imagine how much more time they’ll have for other conversion rate- and UX optimizations.

5. Improved Personalization and Recommendations

By deeply understanding product relationships, vector search powers advanced personalization and recommendation systems. This not only helps customers discover new items they might love but also increases cross-selling and upselling opportunities for E-commerce businesses, and can even be used for digital marketing purposes as well.

Besides creating vector embeddings of the search queries sent and the products in your stock, you can make a vector space of user profiles and offer an even more personalized shopping experience for your customers based on their journey on your site and their behavior in general (categories of interest, previous purchases, etc.) 

Learn more about AI-drive product recommendation from our all-inclusive guide.

6. Adaptation to Evolving Language

7. Smart Ranking

Besides the traditional keyword-based search scores, like the matching and the popularity score, vector-based search results have a so-called similarity score. This is computed based on the item’s vector distance from the query’s vector.

A search solution powered by vector databases ranks the search results based on their similarity to the query. The most similar items are retrieved and presented to the user.

What is hybrid or AI search (vector + keyword)?

Hybrid or AI search blends vector search and keyword search results to combine the strengths of both: more coverage and better relevance across more query types.

Decision rule:

• Use keyword search when exact terms matter (SKUs, brand+model, part numbers).
• Use vector search when intent/meaning matters (broad needs, natural language).
• Use hybrid search when you want consistent performance across both.

Wrap up: Is vector search “the future” of e-commerce search?

Vector search is transforming e-commerce search because it improves how shoppers discover products by understanding intent, especially when deployed as part of a hybrid search approach that also keeps keyword precision.