What Is Natural Language Search? A Guide for eCommerce

Natural language search (NLS) is revolutionizing how eCommerce stores interpret and respond to customer queries. By understanding the intent behind search phrases, rather than just relying on keywords, stores can offer more accurate results and an intuitive shopping experience. From improving search relevance to handling complex, conversational queries, natural language search is becoming a critical tool for online retailers looking to enhance user experience and increase conversions.

Adopting natural language search queries allows stores to stay ahead of the curve, providing seamless, personalized eCommerce site search results that improve engagement and conversion rates.

» Ready to improve your natural language search? Book a demo with us and learn about our eCommerce solutions

What Is Natural Language Search?

The Technologies Behind Natural Language Search

Natural language search is powered by technologies like machine learning, natural language processing (NLP), and artificial intelligence (AI), all of which work in tandem to improve the search experience.

How they work together:

• NLP breaks down user queries into key components, such as product types, prices, and features, enabling the system to understand the user's intent.
• AI uses contextual data and past interactions to refine the search results, ensuring relevance and personalization.
• Machine learning adapts over time by learning from user behaviors like clicks, searches, and purchases, improving search accuracy and handling more complex queries.

» Read more: 7 Best AI solutions for eCommerce search

Fast Simon's Natural Language Search

Enhance your store's search capabilities with Fast Simon's AI-driven eCommerce search and natural language processing.

Install Now



Benefits of Natural Language Search

• Enhanced search accuracy: Traditional keyword-based searches often yield misaligned results, with studies indicating that 61% of eCommerce sites display search outcomes that don't match user intent. By implementing natural language search, your algorithm can better interpret user queries, leading to more precise product matches and reducing customer frustration.
• Increased conversion rates: Accurate search results directly influence purchasing decisions. Research from Forrester shows that 43% of website visitors go directly to the search bar, and when they find relevant products quickly, conversion rates can increase significantly.
• Improved user experience: Natural language search allows customers to search using conversational terminology, making the shopping experience more intuitive. This approach reduces the learning curve associated with rigid keyword searches, leading to higher user satisfaction and prolonged site engagement.
• Higher average order value (AOV): By understanding nuanced customer queries, natural language search can suggest complementary products effectively. This personalized recommendation strategy has been shown to increase AOV, as customers are more likely to add suggested items to their carts.
• Enhanced customer retention: A seamless and intuitive search experience encourages customers to return. Businesses that have adopted natural language search have reported improved customer loyalty, as users appreciate the ease of finding products that match their needs without navigating through irrelevant results.

» Improve your search experience with our complete guide to the Shopify search bar

5 Essential Features of an Effective Natural Language Search Engine for eCommerce



1. Intent Recognition

Intent recognition helps eCommerce search engines understand what shoppers really want. For example, a query like “affordable sneakers for running” isn’t just about shoes, but about budget and activity. By identifying user intent, search results become more relevant and show products that fit the exact purpose.

2. Synonym Matching

Shoppers often use different words for the same product. One person might search for “sofa,” another for “couch.” Natural language search engines ensure that no matter the term used, the search system pulls up the right products. This is crucial in eCommerce where product descriptions may vary, but customer intent doesn’t.

» Learn more about the importance of synonyms in eCommerce search

3. Contextual Understanding

In eCommerce, the same word can have different meanings depending on context. A search for “Apple” could mean the tech brand or the fruit. Contextual understanding allows the search engine to consider the user’s behavior, product categories, and even seasonal trends to deliver accurate results.

4. Dynamic Filtering

Effective natural language search engines dynamically apply filters based on user queries. If someone searches for “black winter boots under $100,” the system automatically filters by color, season, and price without manual input. This streamlines the shopping experience, making it quick and hassle-free, which is especially important in mobile shopping where users expect speed and precision.

» Need help with filters? See our guides on adding filters to Shopify and optimizing eCommerce filters

5. Voice Search Optimization

With voice shopping on the rise, eCommerce stores need search systems that handle spoken queries effectively. Shoppers speak differently than they type, often using longer, more conversational phrases. A well-optimized natural language search engine ensures that mobile and voice users can easily find what they need, boosting both traffic and sales.

» Learn more: How voice search is changing SEO

» Implement this strategy easily with our upselling strategies and cross-selling strategies

Step-by-Step Guide to Implementing a Natural Language Search Solution for eCommerce



1. Data Preparation

Data preparation is the foundation of an effective natural language search system. It ensures that product data is structured in a way that allows the search engine to interpret and match user intent accurately. Poorly organized or inconsistent data can lead to irrelevant search results, frustrating customers and reducing conversions.

Checklist for implementation:

• Audit existing product data for consistency, accuracy, and completeness.
• Standardize product titles, categories, descriptions, and attributes across your entire catalog.
• Create a dictionary of synonyms, alternate terms, and common misspellings to account for varied customer queries.
• Ensure metadata (such as tags, SKUs, and pricing) is clean and well-defined.
• Implement tools for regular data cleaning and updating.

» Make sure you're creating irresistible eCommerce product descriptions

2. System Integration

System integration is the process of connecting your natural language searching system with your eCommerce platform to ensure smooth data flow and functionality across all components. It enables the search engine to retrieve up-to-date product information, manage inventory changes, and provide consistent search results across all user interfaces and devices.

Checklist for implementation:

• Select and configure the NLS solution that fits your platform (Shopify, Magento, custom CMS, etc.).
• Set up APIs or connectors for seamless communication between your NLS and product database.
• Implement real-time data synchronization to reflect stock changes, new products, and price updates instantly.
• Ensure cross-platform functionality (desktop, mobile, voice search compatibility).
• Perform integration testing for various query types, user loads, and edge cases.

3. Optimization and Testing

This step involves continuously improving algorithms so they better understand user intent, handle complex or ambiguous queries, and deliver more relevant and accurate search results. By analyzing data from user behavior, you can make targeted adjustments to ensure the search engine remains responsive and accurate as user needs evolve.

Checklist for implementation

• Adjust search algorithms to improve relevance by analyzing user behavior (e.g., clicks, purchases).
• Implement dynamic filtering to allow search refinement (price, color, brand, etc.).
• Conduct A/B tests comparing NLS with previous search solutions, measuring KPIs like conversion rates and bounce rates.
• Collect user feedback through surveys or heatmaps to identify pain points.
• Regularly update and retrain machine learning models based on new data and user interactions.
• Set up ongoing monitoring tools to track search performance, response time, and user satisfaction.

» Need more help? Here's how to add natural language search to your eCommerce store

AI-Powered Natural Language Search

Synonym and antonym suggestions for accurate results

Context-aware search that understands user intent

Improved user experience for higher engagement

Book a Demo



3 Examples of eCommerce Brands Successfully Using Natural Language Search

1. Steve Madden

Steve Madden, a renowned fashion brand, integrated Fast Simon's NLS to enhance their online shopping experience. By understanding customer intent through conversational queries, they provided more accurate search results.

This implementation led to a doubling of conversion rates from search compared to non-search sessions across all their brand stores. The seamless integration supported their expansion to 10 more stores globally.

For example, when a customer searches for "open toe shoes," the system will provide relevant and precise product suggestions, along with AI-driven autocomplete options featuring various brands and styles, even if the product names don't directly include the search terms.



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

2. Ally Fashion

Ally Fashion, an Australian women's clothing retailer, utilized Fast Simon's advanced filters and merchandising tools to refine their eCommerce platform. The NLS allowed customers to find products effortlessly, even with complex search terms.

This enhancement improved the user experience, leading to increased customer satisfaction and higher conversion rates.

For example, if a customer types in "summer dresses with floral" the system would present them with a range of relevant product options that fit the specific theme. Additionally, this integration delivers powerful analytics, giving valuable insights into customer preferences and how products are performing.



» Do you have a fashion store? See our guide to visual merchandising for fashion eCommerce

3. CURATEUR

CURATEUR, an eCommerce community curated by designer Rachel Zoe, implemented Fast Simon's NLS to optimize product discovery. The advanced eCommerce merchandising capabilities allowed them to tailor product collections to their business needs without extensive manual effort.

As a result, collection pages generated 65% of total site revenue, and the autocomplete feature achieved an 18% conversion rate. This strategic use of NLS enhanced the shopping experience and significantly boosted sales.

For example, if a customer searched for "Rice," the system would automatically display all products within the Rice collection.



» Learn more in our guide to the Shopify search bar

Leverage Natural Language Search Today With Fast Simon

Incorporating natural language search into your eCommerce store can significantly boost both the search experience and sales. Natural language search examples from brands like Steve Madden and Ally Fashion show how this technology can optimize product discovery, from handling complex queries to offering personalized recommendations.

Fast Simon can help your store seamlessly integrate this advanced search capability, ensuring that customers easily find exactly what they want—whether it's through voice or mobile search. Let Fast Simon streamline your search process and take your store’s performance to the next level.

» Ready to begin? Book a demo today