Vector Search: The Future of E-commerce Search

What if there was a revolutionary way to help shoppers navigate the vast E-commerce universe? One where search results are not just based on keywords, but on the essence of the products themselves?

In this article, we

  • Explain what vector search is
  • Showcase 7 Main Advantages Vector Search has over traditional Keyword Search technology

Importance of E-commerce Search


In the ever-evolving landscape of E-commerce, where countless products are just a click away, the power of search cannot be overstated.

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!   

Issues with traditional keyword search


More often than not, search solutions are currently powered by traditional keyword-based search, which are usually called semantic search engines (when they have advanced capabilities like query understanding). This traditional search method that, as the name suggests, relies on keywords, has its limitations.

Solutions that only leverage keyword matches often require a lot of manual optimization efforts (think: relevance optimization, matching and tuning weight editing, and synonym management), otherwise they lead to irrelevant results, frustrating experiences, and missed revenue opportunities.

Online E-commerce store illustration

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.

Vector Search explained


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.

Benefits of Vector Search for E-commerce businesses


One of the primary challenges with traditional E-commerce search methods (keyword-based search) is the heavy reliance on keyword matching.

Until the emergence of vector search, this has been a challenge for shoppers because, in order to receive relevant results, a retailer must have a highly optimized search solution or the shopper must meticulously craft their search queries in order to see relevant results.

E-commerce vector search illustration

However, with a solution solely leveraging keyword-matching search, even the best search queries can still return irrelevant results.

For example, searching for “black leather boots” might return brown boots or other unrelated products simply because they’re black OR leather. 

This is where vector search can make a big difference, offering numerous benefits for both customers and E-commerce businesses.

Here are the 7 main  benefits you can get from using a search solution that leverages vector search.

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.

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.

Faster Shopping Journey


Vector search streamlines the search process, ensuring a quicker shopping journey for the user. Its ability to understand and interpret intent means that customers spend less time thinking about how to phrase, then rephrase their search queries, sometimes several times in a row. 

By using vector representations, the search engine narrows down the search space to focus on items that are semantically similar to the user’s query. This reduces the number of items that need to be considered during the search process, and as a consequence, it can provide results more quickly since it doesn’t need to sift through a vast number of irrelevant items.

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 synonym management illustration

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.

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.

Adaptation to Evolving Language


This is a significant challenge for search engines, as language is constantly evolving with the introduction of new words, phrases, and shifts in terminology. 

Traditional keyword-based search relies on predefined lists of keywords, which results in less adaptiveness to linguistic changes. This limitation might lead to a less satisfactory shopper experience. 

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Thanks to Natural Language Processing (NLP), vector-based search models can adapt more quickly to changes in language usage and stay current with evolving language through machine learning.

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.

Hybrid Search – Blending Vector and Keyword Search 


The emergence of vector technology does not mean that it will replace keyword search altogether. Instead, they complement each other, so that more, and more relevant results appear.

When vector search and keyword search results are blended, it’s called a Hybrid search solution.

A Hybrid Setup that combines keyword-based and vector-based approaches offers a more robust and versatile solution, thanks to the benefits mentioned above, providing highly relevant search results across a wide range of scenarios. 

But let’s cut the story here for now, we have another blog post exploring this topic.

Wrap Up


Vector search is transforming the search and online shopping experience. 

As the E-commerce landscape continues to evolve, businesses that embrace vector search are poised to lead the way. 

By adopting this transformative technology, you can revolutionize how customers discover and shop for products while optimizing your operations for the future.