Natural Language Search
Search that understands queries written in everyday conversational language, like asking a question rather than typing keywords.
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Start Free TrialView PricingThink about how you search on Google or ask Siri a question. You probably don’t type “weather Amsterdam tomorrow” in robotic keyword format. You ask “What’s the weather going to be like in Amsterdam tomorrow?” using complete sentences, natural language.
E-commerce search is evolving the same way. Instead of typing “blue dress size 8 summer”, customers increasingly search like they talk: “I’m looking for a blue summer dress in size 8” or even “What dresses would be good for a beach wedding?”
Natural language search is technology that understands these conversational queries, extracting the meaning and intent from how people naturally express what they want.
Why natural language matters now
Several trends are pushing search toward natural language:
Voice search is exploding, especially on mobile. When you speak to your phone, you use complete sentences. You don’t say “running shoes men size 10”, you say “Find me men’s running shoes in size 10.” Voice search is inherently conversational.
AI assistants like ChatGPT have trained people to communicate with computers conversationally. If you can ask ChatGPT complex questions in natural language, why can’t you do the same with a webshop?
Mobile shopping is now dominant, and typing on mobile is tedious. Natural language lets customers express complex needs without typing long, precisely formatted queries. Saying or typing “comfortable walking shoes for all-day wear” is easier than constructing the perfect keyword query.
Younger generations especially expect technology to understand them naturally. Rigid keyword search feels outdated and frustrating.
What makes a query “natural language”
Natural language queries share certain characteristics that distinguish them from keyword searches:
They use complete sentences. “I need a jacket that’s warm but not too bulky” versus keywords “jacket warm light”.
They include context words that traditional search ignores. Words like “I”, “need”, “for”, “that’s” don’t match products directly but convey meaning.
They describe intent or use cases. “Gift for someone who loves hiking” tells you purpose and recipient characteristics, not product specifications.
They ask questions. “Which laptops are best for video editing?” or “What’s the difference between these two models?”
They’re conversational and natural-sounding, like talking to a knowledgeable shop assistant.
How natural language search understands you
When you type “I’m looking for a laptop that won’t slow down when I have lots of browser tabs open”, here’s what natural language search does:
First, it identifies the core intent. You’re shopping for a laptop, not just researching or comparing. This is a transactional query.
Second, it extracts requirements from your natural description. “Won’t slow down” + “lots of browser tabs” translates to: needs good RAM (8GB+ minimum), decent processor, handles multitasking well.
Third, it understands implicit priorities. You care about performance for everyday web browsing, not necessarily gaming or video editing. This helps rank results appropriately.
Fourth, it generates search criteria from this understanding, finding laptops with specs that match your described need, even though your products never mention “browser tabs” or “slowing down”.
This multi-step interpretation happens in milliseconds, transparently translating your natural expression into meaningful search criteria.
Examples that show the difference
Let me show you concrete examples where natural language search dramatically outperforms keyword search:
“Something nice to wear to a job interview” - Keyword search is completely lost. Natural language understands this means formal professional attire and shows business suits, formal dresses, appropriate shoes.
“Comfortable shoes for standing all day” - Keywords finds products mentioning these words. Natural language understands you need cushioning, arch support, and ergonomic design, matching products with these attributes.
“Gift for my girlfriend who loves fitness” - Keyword search looks for those literal words. Natural language recognizes gift context plus fitness interest, showing gift-appropriate athletic wear, fitness accessories, and wellness products.
“Laptop for college student under $800” - Natural language understands budget constraint, use case (schoolwork, not gaming or professional work), and finds appropriate mid-range laptops with good battery life and portability.
Each of these queries is how people naturally think and express needs. Natural language search makes them work.
Questions as searches
Natural language search enables question-based searching, which feels intuitive to customers:
“Which running shoes are best for beginners?” - The search interprets this as looking for beginner-friendly running shoes, perhaps prioritizing comfort and support over speed.
“What’s the difference between these two monitors?” - If the system has comparison capabilities, it can generate a comparison. Otherwise, it shows both products with key specs visible.
“Do you have waterproof jackets in my size?” - Extracts product type (waterproof jackets), filters by user’s known size if available, or shows size options.
“How does this shirt fit?” - Might direct to product reviews or size guide if available, showing customer feedback about fit.
Question-based search feels like having a conversation with a helpful shop assistant, making the shopping experience more natural and less transactional.
Mobile and voice integration
Natural language search becomes essential for voice shopping. Voice queries are almost always natural language:
Spoken: “Find me comfortable walking shoes under a hundred dollars” Not spoken: “shoes walking comfortable under 100”
The first is how humans talk. Natural language search handles it perfectly. Keyword-only search would struggle with the natural phrasing.
Mobile typing also benefits. Instead of painfully typing a perfectly formatted keyword query on a small keyboard, customers can type more naturally and trust the search to understand.
When keywords still work better
Natural language isn’t always better. Some queries benefit from keyword precision:
“iPhone 15 Pro Max 256GB” - This is exact and specific. Keywords handle it perfectly. Natural language isn’t needed.
“Nike Air Max 90” - Brand + model number searches work great with keywords. No interpretation required.
“SKU-12345” - Product codes are identifiers, not natural language. Keyword matching is correct here.
The best search systems recognize query type and apply the right approach. Exact-looking queries use keyword precision. Natural, descriptive queries use natural language understanding. Customers don’t choose, the system figures it out.
What this means for your webshop
Natural language search means customers can express complex needs without knowing your catalog’s exact terminology. They can ask questions, describe use cases, and search conversationally.
This dramatically reduces search friction. Instead of wondering “How do I phrase this?” customers just describe what they want. If the search understands them, they find products. If it doesn’t, they leave frustrated.
Modern search solutions like TextAtlas include natural language understanding by default. The system interprets conversational queries, extracts intent and requirements, and translates them into meaningful product matches, all automatically.
From your perspective, it means capturing more sales from customers who don’t speak your catalog’s technical language. It means voice search works. It means mobile shoppers can express needs easily. It means search that feels helpful rather than rigid.
As more customers expect to communicate with technology naturally, natural language search evolves from nice-to-have to essential. Your search should understand how customers actually talk, not require them to learn a special keyword language.
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Start Free TrialView PricingFrequently Asked Questions
What's an example of natural language search vs. keyword search?
Does natural language search work better than keywords?
Can customers ask questions in search?
Do people actually type in complete sentences when searching?
Related Terms
AI-Powered Search
Search technology that uses artificial intelligence and machine learning to understand queries, learn from behavior, and continuously improve results.
Search Personalization
Tailoring search results based on individual user context, preferences, and behavior to show more relevant products for each customer.
Semantic Search
Search technology that understands the meaning and context behind queries, rather than just matching keywords.
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