Vector Search in eCommerce: when semantic search increases conversion

41% of online shops have problems with their search function.¹ Is vector search the key to higher relevance and fewer drop-offs? Here’s a crash course from an eCommerce perspective.

Definition: what is vector search?

Vector search uses AI to translate complex data, like products, texts and images, into numbers (vectors). It stores this data in a vector database so that when someone searches with an image or another type of complex data, it can quickly find and show similar content based on mathematical distances.

In eCommerce, this technology is used to maximize the relevance of product listings for search results, category pages, recommendations, etc. AI models, like machine learning and natural language processing (NLP), understand the semantic connection between products and search queries, allowing them to deliver relevant results even when there’s no exact match. So, your customers will still see sneakers, whether they search for “kicks” or “trainers”.

Vector search and semantic search – what’s the difference?

• Semantic search is an umbrella term for technologies that understand the context and meaning of search queries to find matching results, often using NLP. Vector search is a type of semantic search, specifically based on calculating mathematical distances between vectors.

• Vector search compares shopper input with product data in a multidimensional space using “nearest neighbor search.” While the graphic shows three dimensions, AI models typically use hundreds or even thousands.

How does vector search work?

We’ll walk through the workflow with a typical example: intent-based search queries. These queries indicate the reason behind the search. For example, if an online shopper types in “comfortable shoes for a city trip in July,” we can assume they’re looking for shoes suitable for long walks on hard surfaces during summer temperatures. But how does vector search recognize this purchase intent and deliver fitting results? This process consists of three steps:

1. Vector embedding – An AI model translates the words into numeric expressions to capture the meaning behind the query. Now, it can understand that “comfortable” in the context of shoes refers to ergonomically designed, cushioned, lightweight, etc. And from “city trip” and “July,” it extracts, among other things, attributes related to the ground surface and season.

2. Nearest neighbor search – Next, the AI checks these numeric expressions with the product data to semantically similar items based on the mathematical distances in the vector space.

3. Response – The closest products are then returned to the shop as search results. For this search query, these might be breathable, low-cut hiking shoes with arch support.

Use cases: vector search vs. keyword search

Let’s explore different types of search queries to compare the hypothetical performance of a vector search with keyword-based search methods.

In some cases, vector search yields better results; in others, keyword search is more efficient. Our analysis of search queries from over 2,000 shops shows that online customers still mostly do not use natural language inputs — they don’t ask questions like they do in Google, but search with short, precise terms. However, user behavior is changing as we speak, and in the coming years, a reliably functioning vector search could become vital for online retailers.

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With hybrid search, you keep the customer benefit in focus

Vector search isn’t always enough on its own, especially when it comes to exact product names, brands or item numbers. Many online shoppers still expect reliable keyword search. At FactFinder, we address this with a unique hybrid approach, combining the strengths of linguistic and semantic methods:

1. Linguistic matching – The core of our solution is the Worldmatch® search algorithm, which we’ve been refining for over 20 years. The technology detects phonetic similarities between terms and ensures incorrect or imprecise inputs, like “naiki” vs. “Nike,” lead to the correct result.

2. Semantic relevance – We’ve designed Atlas AI to process information in a more intuitive, human-like way than pure vector methods. The AI model trains neurons using vectorized product data, helping it understand context and product relationships on a deeper level. This enables Atlas AI to capture search intent in real time, even if there’s no linguistic match. It can also apply insights from behavioral data to suggest not only exact product matches but related items in the same context. Hence, search results are automatically personalized and continuously improved.

3. Human expertise – Unlike some search and product discovery platforms, FactFinder is not a “black box.” You can easily track the sorting of your product and recommendation lists in an intuitive backend editor. It’s the perfect blend of automation and control: you can let the AI do the work for you, while still having the flexibility to manually adjust the configurations in line with your business goals, e.g., through ranking rules that push selected brands or sale items.

Instead of blindly following the hype around vector search, FactFinder focuses on thoughtful, step-by-step integration. We don’t view new technologies as an end in themselves but concentrate on the experience they bring to users and shoppers. Our solution brings AI to the areas where it creates real value, striking the best balance between keyword precision, semantic context and human expertise.

Conclusion: hybrid over hype

Vector search offers huge potential to increase conversion rates and AOVs, especially for intent-based search queries, synonyms and natural language inputs. However, it’s not a cure-all. The key is taking a hybrid approach that combines semantic understanding with proven keyword precision and AI-powered personalization.

If you need advice on your unique eCommerce challenges and goals or want to learn more about our search and product discovery solutions, we’d love to hear from you. Hit the link below to get in touch with our experts.

1. Deconstructing E-Commerce Search UX: The 8 Most Common Search Query Types (41% of Sites Have Issues) – Baymard ↩︎