7 Real-World Examples of Natural Language Search in Action

In today's digital world, we're used to searching for information using keywords. But as the shopping experience is becoming more and more convenient, eCommerce site search functionality is expanding to accommodate questions in plain English. Almost as if you're talking to a real human! That's the power of natural language search (NLS), and it is rapidly changing how we interact with technology. It is allowing us to use natural language to find exactly what we need, making search more intuitive and efficient than ever before.

If you want to further understand what this means or how it works, we will look at 7 examples of natural language search operating on real eCommerce stores.

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Natural Language Search vs. Traditional Search

Natural language search allows users to input queries in everyday language, enabling the system to interpret the intent behind the query and provide relevant results.

In contrast, traditional keyword-based search relies on matching specific terms within the query to the indexed content, which can lead to less accurate results if the exact keywords aren't used.



Key Features of Great Natural Language Search

• Understanding customer intent: A good search system doesn’t just look at the words, but it figures out what shoppers actually mean. Whether someone types “affordable smartphones” or “cheap mobile phones,” the system knows they’re after budget-friendly phones.
• Handling different words for the same thing: People use all sorts of terms for the same product. A smart site search engine connects the dots between “sneakers” and “running shoes,” giving shoppers what they’re looking for no matter how they phrase it.
• Knowing the right context: Words can mean different things depending on how they’re used. For example, “Apple” in “Apple laptops” is clearly about the tech brand, but in “apple pie recipes,” it’s about the fruit. A strong search system picks up on these differences.
• Fixing typos on the fly: A good search tool doesn’t let a small mistake ruin the experience. If someone types “blak dress,” it knows they meant “black dress” and shows the right products anyway.

» Want to implement NLS on your store? Here's how to add natural language search to your eCommerce store

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Benefits of Natural Language Search for eCommerce

• More accurate and relevant search results: One of the biggest advantages of natural language search is that it understands intent, not just keywords. Traditional search engines often require users to phrase things in a specific way, but NLS removes that barrier. Research shows that chatbot-driven searches boost eCommerce support by increasing product attribute mentions by 84% and promoting natural language formulations by 139%.
• Higher user engagement and retention: When users can easily find what they’re looking for, they stay on a site longer and return more often. Studies have found that natural language search improves the shopping experience by making it more effortless, which increases engagement and retention.
• Better conversion rates and sales performance: For eCommerce businesses, an effective search function directly impacts revenue. When customers find the right product faster, they’re more likely to buy. A case study by Team Internet Group found that retailers using natural language search saw a 24% increase in conversion rates.
• Reduced workload and increased operational efficiency: Without AI-powered natural language search, companies spend countless hours manually tagging and categorizing content to make traditional search functions work better. NLS chatbots handle up to 80% of customer queries, saving time and resources.

» Interested in other search functionality as well? Compare federated search and unified search

7 Real-World Examples of Natural Language Search in Action

1. Steve Madden

Steve Madden faced challenges with their traditional search functionality, which often failed to interpret complex customer queries and nuanced fashion-related terms effectively. This limitation led to irrelevant search results.

After integrating Fast Simon's Natural Language Search functionality, their search engine could comprehend and process everyday language, including synonyms, color variations, and specific product attributes.

For example, a customer searching for "open toe shoes" would receive accurate and relevant products and AI-powered autocomplete suggestions from different brands and styles, even though the product names don't contain any matching keywords.



» Operate a fashion store? See our guide to visual merchandising for fashion eCommerce

2. Spiceology

Spiceology, a company specializing in spices and seasonings, struggled with customers finding specific products due to the limitations of their existing search functionality. The traditional search system often failed to interpret the intent behind different colloquial terms.

By integrating Fast Simon's Natural Language Search, Spiceology enhanced its product discovery process with an internal search engine that could understand and process different queries to show customers everything that might interest them, building lasting customer relationships.

For example, searching for terms like "spicy seasoning for BBQ ribs" returns various results, from BBQ rib recipes and chef articles to various rubs and seasonings designed for BBQ.



3. Targus

Targus, known for tech accessories, often failed to interpret detailed customer queries effectively due to the varying specifications of tech products. Leveraging a natural language search system allowed them to understand and process more complex queries including specific product attributes and customer preferences.

For example, searching for "comfortable mice" on the Targus website yields various results related to computer mouses and keyboards. In addition to the search engine asking the user if they meant to search for a different term more closely related to products on the website, it also understands the intent behind "comfortable" and alters the stores merchandising to list products that are wired or wireless, ergonomic, and antimicrobial.



4. Ally Fashion

Ally Fashion often failed to interpret complex customer queries effectively due to the conversational nature of many fashion-related products. Implementing natural language search enabled their search system to comprehend and process everyday language, including specific designs and seasonal trends.

For example, a customer searching for "summer dresses with floral patterns" would receive accurate and relevant product suggestions in many different designs matching a particular theme.



» Make sure you know these fashion eCommerce strategies and how to overcome fashion eCommerce challenges

5. NRS World

NRS World, catering to the western lifestyle and rodeo communities, faced challenges with their traditional search functionality. Customers often used specific terminology related to equestrian and rodeo equipment, which the existing search system struggled to interpret accurately. Implementing natural language search enabled them to comprehend and process industry-specific terminology and natural language queries.

For example, when a customer searches for "youth rodeo gear" or "leather saddles for trail riding," they receive relevant product recommendations in varying colors and designs.



6. Satya Jewelry

Satya Jewelry, known for its spiritually inspired pieces, encountered difficulties with its search functionality as well. Customers often searched using specific spiritual or symbolic terms, and the traditional keyword-based search system failed to interpret these nuanced queries effectively. Natural language search was able to understand and process these spiritual terminology and symbolic references.

For example, when a customer searches for "chakra necklaces" or "yoga-inspired bracelets," the system accurately interprets these phrases and displays relevant products.



» Need more help? See our eCommerce site search best practices

7. CURATEUR

CURATEUR, an online shopping community offering curated luxury products often struggled with the descriptive phrases customers often used to find solutions to specific conditions. They integrated natural language search in a way that could understand and process these symptom-related queries to deliver matching products.

For example, when a customer searches for "acne and dry skin", the natural language search system understands that the keywords are symptoms related to specific conditions and recommends various personalized creams and moisturizers that might help.



» Don't miss out: Our guide to eCommerce personalization technology

Fast Simon's Natural Language Search

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Conclusion

From voice search assistants understanding dinner cravings, to business intelligence platforms revealing hidden trends, natural language search is no longer a futuristic concept—it's a present reality shaping our interactions with technology. The examples explored here only scratch the surface of what is possible, and the field of NLS continues to evolve at a rapid pace.

If you're looking to bring the power of natural language search to your eCommerce store and create a truly seamless and engaging shopping experience for your customers, look no further than Fast Simon's eCommerce Search, designed to help you unlock the full potential of conversational and social commerce.

» Ready to begin? Book a demo today