In the vast world of online shopping, finding the right products amidst countless options can be a daunting task. Thankfully, semantic search is transforming the way we navigate eCommerce platforms, making the search process more efficient and personalized. In fact, a study by Baymard Institute found that 39% of users abandon a purchase due to poor site search. That’s where semantic search is making a game-changing difference.

Unlike traditional keyword-based search, semantic search understands the intent behind a query. It analyzes context, user behavior, and product data to surface more accurate and relevant results—think of it as a search engine that actually gets you. The result? More satisfied customers, higher conversion rates, and a smoother, more intuitive shopping experience.

In this article, we’ll dive into how semantic search is reshaping eCommerce. You’ll learn how it enhances product discovery, powers personalized recommendations, and ultimately helps retailers turn searches into sales.

What Is Semantic Search?

Semantic search is a type of AI-powered search that focuses on the meaning and context of your query, rather than just matching specific keywords. When you search using traditional methods like lexical search, the search engine looks for web pages that have the exact words you entered. It’s like a simple word-matching game. 

But with semantic search, it goes a step further and tries to understand what you’re actually looking for. For example, let’s say you want to find information about the tallest mountain in the world. In traditional search, you might type “tallest mountain in the world” and hope for the best. But with semantic product search, the search engine goes beyond those words. 

The semantic search algorithm recognizes that you’re interested in mountains and their heights, so it brings up relevant information about Mount Everest, which is actually the tallest mountain. Semantic search uses things like natural language processing and artificial intelligence to understand your query in a more human-like way. It looks at the context, relationships between words, and the overall meaning of what you’re asking. 

In the context of eCommerce, semantic search enables more intelligent, personalized, and friction-free product discovery. It helps users find what they actually want—even if they don’t phrase it perfectly.

And this matters more than ever: a large share of shoppers abandon their purchase journeys due to frustrating search experiences.

semantic search and cart abandonment

What Types of Semantic Search Exist?

While semantic search is a broad concept rooted in understanding the meaning behind user queries, it can be broken down into several implementation types based on the underlying techniques and use cases. These types often overlap in practice, but each contributes a unique function to a more intelligent search experience.

Here are four three types of semantic search commonly used in modern systems.

1. Natural Language Understanding (NLU)-Driven Search

This type of semantic search focuses on parsing full-sentence queries—questions phrased as users would naturally ask them. It utilizes Natural Language Processing (NLP) and semantic parsing to understand grammatical structure, relationships between words, and user intent.

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Example in Action: A user searches, “What are the best wireless earbuds for running under $100?”
Traditional search might struggle with this compound query. An NLU-driven engine breaks it down into entities (wireless earbuds), attributes (for running), and filters (under $100), returning relevant results instead of a generic list of earbuds.

2. Entity-Based Semantic Search

Entity-based search focuses on recognizing and disambiguating named entities—such as brands, people, locations, or products—within user queries. These entities are then linked to structured data sources like knowledge graphs, enabling deeper understanding and contextual relevance.

Research shows entity linking can improve product relevance scores by 22–35% when compared to keyword-only approaches (Information Processing & Management, 2020).

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Example in Action: A query like “paintings by Vincent van Gogh” is semantically parsed to recognize “Vincent van Gogh” as a named entity—specifically, a Dutch painter. The search engine then retrieves a list of his artworks such as The Starry Night, Sunflowers, and Café Terrace at Night, even if those specific titles aren’t mentioned in the user’s query.

3. Concept-based Semantic Search

Rather than relying on exact words or named entities, this form of semantic search uses vector embeddings to understand the conceptual similarity between queries and content. Queries and products are transformed into high-dimensional vectors, allowing the engine to retrieve semantically similar results—even if there’s no lexical overlap.

Semantic Search Example in Action: A customer types “snacks for weight loss.” Instead of matching only on “snacks,” a vector-based engine can retrieve products tagged “low calorie,” “keto,” or “sugar-free,” understanding the broader intent of the query.

How Does Semantic Searching Work?

Here’s how semantic search technology functions in the context of eCommerce:

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1. Understands Product Intent

Semantic searching aims to understand the intent behind user queries related to products. 

For example, if a user searches for “comfortable running shoes,” the semantic search algorithm recognizes the user’s desire for comfortable athletic footwear and displays relevant options from various brands.

2. Grasps Product Relationships

By leveraging product taxonomies, knowledge graphs, and co-purchase data, semantic search engines can detect how products are related—even if not explicitly stated.

3. Recognizes Product Attributes

Semantic searching can recognize and process specific product attributes mentioned in a query. For example, if a user searches for “black leather purse,” semantic product search understands the desired color (black) and material (leather) to display relevant black leather purse options.

4. Personalizes Product Recommendations

Semantic searching leverages user preferences and browsing history to provide personalized product recommendations. By integrating user profile vectors, browsing history, and purchase patterns, it can tailor recommendations to individual preferences in real time.

5. Improves Semantic Product Search Accuracy

Semantic searching helps improve the accuracy of product searches by understanding and correcting spelling errors or typos related to product names. 

For example, if a user types “iphon” instead of “iPhone,” semantic product search recognizes the intended product and displays relevant iPhone options.

User Benefits of Semantic Search Technology

1. Typo Correction

Imagine you’re searching for a “MacBook Pro” laptop, but you accidentally type “MackBook Pro.” 

With semantic search, it recognizes the typo and understands your intention, displaying the correct “MacBook Pro” options.

2. Personalized Recommendations

Let’s say you frequently shop for home decor items. Semantic search learns your preferences and past purchases. 

When you search for “table lamps,” the semantic search algorithm will provide personalized recommendations based on your preferred styles, colors, or brands.

3. Trending Product Insights

If you’re interested in finding popular kitchen gadgets, you might search for “top-rated kitchen appliances.” 

Semantic product search uses AI to analyze search trends and user behavior, presenting insights into the latest popular appliances like air fryers, smart coffee makers, or instant pots.

4. Voice Search

Picture yourself using voice search to find a new pair of headphones. 

You say, “Find me wireless headphones with noise cancellation.” 

A semantic search engine processes your voice command, understands your requirements, and displays options for wireless headphones known for their noise-canceling capabilities.

Implementing a Semantic Search Engine

Implementing a semantic search engine into your business can revolutionize the search experience for your customers. With Doofinder, you have an easy semantic search tool that incorporates all the essential features mentioned in this article.

Doofinder provides advanced search capabilities such as natural language processing, personalized recommendations, and seamless integration with your existing systems. 

It empowers your eCommerce site to deliver accurate and relevant search results, enhancing user satisfaction and engagement. To get started, take advantage of Doofinder’s 30-day free trial. Test out all the features and experience firsthand how they can benefit your website. 

If you need assistance in implementing Doofinder on your eCommerce site, request a free demo with one of our eCommerce search specialists.  They will guide you through the process and help you maximize the potential of the tool.

Don’t miss out on the opportunity to elevate your search capabilities and provide your customers with an exceptional shopping experience. Try Doofinder today and discover the power of a semantic search tool for your business.