How Semantic Search in NLP Powers Hyper-Relevant Product Results
Semantic search in NLP understands user intent, not just keywords, to deliver more relevant product results. It can significantly boost key business metrics like conversion rates and average order value.
Published August 29, 2025
Imagine you’re shopping online and can't remember the exact product name, so you type in a phrase like “cozy shirt for cold weather.” A traditional search might fail, but a semantic search understands your intent, showing you fleece-lined pullovers and thermal tops.
That’s the real strength of semantic search in natural language processing (NLP) — it looks past basic keywords to understand meaning and context. For online merchandising, this means products are matched to what shoppers are truly looking for. In this blog, we will explore what semantic search is, how it works, and its impact on real-world businesses.
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What Is Semantic Search?
Semantic search goes beyond keyword search—it interprets the meaning and context behind a user’s query using NLP and machine learning. Instead of matching exact words, it understands intent, synonyms, and even typos to deliver more relevant results.
Traditional keyword search focuses on literal matches in product titles or descriptions, often missing the mark if phrasing differs.
Vector search (embedding-based) adds another layer by representing queries and products as numerical vectors to find similar items.
To summarize: Semantic search combines these approaches—transforming user queries into intent-aware vectors using models like BERT—allowing the system to “understand” what the shopper is really looking for.
» Here's everything you need to know about semantic search and its impact on eCommerce
Semantic Search vs. Traditional Keyword Search
| Feature | Semantic Search | Traditional Search |
|---|---|---|
| Search method | Understands meaning and context, matching user intent. | Matches literal keywords or terms in the database. |
| Technology | NLP, Machine Learning (Transformers, Embeddings), and Vector Search. | Simple string matching, Boolean operators, or full-text indexing. |
| Query flexibility | Highly flexible. Can handle synonyms, typos, and natural language. | Very rigid. Fails if the exact keywords aren't present. |
| Relevance | High. Retrieves results based on semantic similarity. | Variable. Can return irrelevant results if keywords are out of context. |
| Example | "cozy sweater for winter" will find a "fleece-lined pullover." | "cozy sweater" may not find a "fleece-lined pullover" unless those exact words are present. |
» Read more: 7 Best AI solutions for eCommerce search
Smarter Search for Your Store
Enhance your search capabilities with Fast Simon’s AI-powered natural language search for effortless product finding.
Industries That Benefit Most from Semantic Search
Semantic search is highly valuable for any industry managing vast, complex, or specialized data where users may not have the precise vocabulary to find what they need.
eCommerce/retail: Semantic search helps online shoppers navigate massive product catalogs by understanding natural language. For example, a shopper looking for "a dress for a summer wedding" can find appropriate formal but lightweight options without needing to search for a specific item name.
Media & publishing: Semantic search transforms content discovery on websites with extensive libraries of articles, videos, or news. Instead of relying on rigid keywords, a user can search for a concept like "climate change's impact on polar bears," and the system can surface relevant content.
Legal & healthcare: These fields use highly specialized terminology. Semantic search allows professionals and patients to find relevant cases, statutes, or medical studies even when using layman's terms or complex, nuanced queries.
Research & education: For academic databases, patent offices, or educational platforms, semantic search helps users match complex queries to the right scientific papers, patents, or learning materials. A student can ask a question in their own words, and the system can connect them to the correct section of a textbook or a relevant lecture, making information more accessible.
Did you know? Fast Simon reports that adding semantic search boosted conversion rates and AOV - customers using the upgraded search saw 3× higher conversions and 2×-4× increases in mobile search conversions.
» Learn more: How to add search to your mobile app
How Semantic Search Works: A Step-by-Step Guide
Here's a breakdown of how a semantic search system works to help shoppers find products.
Step 1: Understanding the User's Query
When a user types a search query, the system first cleans it up using NLP. This includes correcting spelling errors and identifying the user's core intent. For example, it understands that "cozy shirt for cold weather" is a request for a warm shirt, not just something with the word "cozy" in its description.
Step 2: Vectorization (Turning Text into Numbers)
The cleaned-up query is fed into a special model (often called an encoder). This model transforms the text into a numerical code, or a query embedding, which represents its meaning. At the same time, every product in the store's catalog has already been turned into a similar numerical code and stored in a special index.
» Read more: How vector search AI optimizes the eCommerce search experience
Step 3: Finding the Closest Matches
The system then quickly compares the query's numerical code to the codes for all the products in the catalog. It uses a similarity search to find the closest matches—the products that are most similar in meaning to the user’s query.
Step 4: Ranking and Personalization
The initial set of results is then sorted and refined. The system might re-order products based on factors like popularity, stock availability, the user's location, and their past shopping behavior. This final re-ranking can also include filters by price or brand, ensuring the user sees the most relevant items first.
» Improve your search experience with our complete guide to the Shopify search bar
AI-Powered Natural Language Search
Synonym and antonym suggestions for accurate results
Context-aware search that understands user intent
Enhanced user experience to drive engagement
The Role of Data, Personalization, and Third-Party Tools in Semantic Search
Critical Data for Semantic Search
To get semantic search to work well for a store, the most critical data falls into two categories: product data and user data.
Product data: This includes the product's core details like titles, product descriptions, categories, and tags. When this information is detailed and accurate, the search model can learn to understand the specific terminology of a brand and its products.
User data: Data on what customers search for, click on, and buy is also crucial. This information reveals the actual language shoppers use and what terms lead to a purchase. By fine-tuning the model with this data, you can teach it that certain search terms should match specific product groups, making the results much more relevant.
Implementing Personalization in Semantic Search
You can add personalization to semantic search without sacrificing speed or accuracy by separating the initial search from the final re-ranking process.
Two-step process: First, the semantic search uses a fast, broad query to pull a large group of relevant products. Then, it quickly re-ranks those results based on user-specific data, such as a shopper's past purchases or browsing history.
Pre-computed profiles: By using pre-computed user preference profiles, platforms can apply personalization without having to re-run the entire search query. This post-processing approach ensures that the initial semantic match is fast and accurate, while a small group of products gets sorted according to the user's preferences.
» Learn the importance of using personalization in site search
The Role of Third-Party Tools
Third-party tools like Fast Simon play a key role in helping online retailers quickly deploy semantic search. Instead of building a complex system from scratch, retailers can use ready-made platforms that already include the necessary components.
Out-of-the-box functionality: Tools like Fast Simon's site search engine come with built-in features like NLP encoding, synonym handling, and vector search. This allows a merchant to integrate semantic search quickly, often via a plug-in, without the need to develop a custom machine learning pipeline.
Hidden complexity: These tools handle all the complicated technical aspects, such as managing vector databases and continuously updating models. This allows retailers to focus on selling and improving the customer experience rather than dealing with the technical hurdles of maintaining a semantic search system.
» Want to take search to the next level? Check out the best practices for eCommerce site search
Real-World Examples of Semantic Search Implementations
Canada’s Fragrance Buy
A beauty retailer specializing in fragrances, Canada's Fragrance Buy saw a massive boost after implementing Fast Simon's semantic search.
For example, on the Fragrance Buy website, if you search for a misspelled term like "Cristian Dior" instead of "Christian Dior," it still gives you the right results. This new search could handle common issues like misspellings and also effectively suggest related products, which directly led to higher average order values and click-through rates. Their search conversion rate tripled overall and quadrupled on mobile.
The significant lift in these metrics shows how a more precise and intuitive search experience can directly translate to increased sales.
» Check out these 7 ways site search optimization can increase your conversion rates
BulbAmerica
As a lighting retailer with a massive inventory, BulbAmerica saw almost immediate gains after adding AI semantic search.
For example, on the BulbAmerica website, shoppers can use improved filters to narrow results by color, base, and more. The system also displayed better product suggestions, making product discovery faster and more intuitive. Shoppers who used the search converted 10 times more often, and the average order value doubled.
» Struggling with search relevance? See our ultimate guide to internal site search
Steve Madden
On their Shopify Plus site, the footwear fashion brand Steve Madden used Fast Simon's AI search to make users twice as likely to convert when they engaged with the site search.
For example, on the Steve Madden page, when you search for "pink shoes," it shows autocomplete suggestions and even highlights popular searches. The search interface itself, with its quick visual previews and smart autocomplete, captured shoppers' attention and drove more conversions. In the months following the launch, the conversion rate from searches doubled and remained consistently higher.
» Need more help? Here's how to add natural language search to your eCommerce store
The Future of Your eCommerce Search
The evolution of eCommerce search, powered by semantic search in NLP, is no longer a complex, custom-built project. For your business, this technology is now an accessible, plug-and-play tool. Companies like Fast Simon are at the forefront, providing a sophisticated, AI-powered search engine that you can integrate seamlessly.
This solution handles all the technical heavy lifting—from managing large product databases to understanding natural language queries. By adopting semantic search, your business can quickly enhance the customer experience, boost conversion rates, and increase your average order value.
» Ready to begin? Book a demo today
FAQs
What is the difference between semantic search and traditional keyword search?
Semantic search uses Natural Language Processing (NLP) to understand the meaning and context behind your query, including synonyms and intent, to find more relevant products.
Traditional keyword search is very literal; it looks for an exact match of the words you type.
How does semantic search help my eCommerce business?
Semantic search directly benefits your business by improving the customer experience. By providing more relevant search results, it increases conversion rates and average order value.
Is semantic search only for large businesses?
No. While highly beneficial for businesses with large and complex catalogs, third-party tools like Fast Simon make it easy for businesses of all sizes to implement semantic search.
How does personalization work in semantic search?
Personalization is often implemented in two steps. First, the semantic search finds a broad range of relevant products.
Then, it quickly re-ranks those results based on user-specific data like past purchases or browsing history, ensuring the most relevant products for that specific customer appear at the top.
