Leverage Natural Language Search to Drive 10x More Sales

What’s Inside
- What Is a Natural Language Search Engine?
- How Does the Natural Language Search Work?
- What Are the Benefits of Natural Language Processing Search?
- What Are the Key Challenges in Executing Natural Language Search?
Insightful Stop!
- Semantic Search vs. Natural Language Search
- How Can Experro Boost Your Website's Performance with NLS Integration?
- Conclusion
Key Takeaways
- Natural Language Search (NLS) enhances search accuracy by understanding user intent and context.
- Natural language search uses natural language processing (NLP) to interpret human language and provide accurate, context-aware results.
- NLS offers faster, more relevant results compared to traditional keyword-based search engines, improving customer engagement.
- By handling conversational queries and supporting multiple languages, NLS is ideal for global businesses.
- Experro’s AI-powered NLS continuously learns and adapts, delivering smarter, more personalized search results.
Tired of playing word games with search engines to find what you need?
With Natural Language Search (NLS), you don't have to play the keyword guessing game anymore.
It understands your questions just the way you ask them, making natural language searches faster and more accurate.
As businesses adopt AI, natural language search is becoming a key tool for improving how users interact with websites.
But how does NLS actually work, and why is it so important for your business? Let’s break it down!
What Is a Natural Language Search Engine?

A natural language search engine allows users to enter search queries in everyday language. Instead of relying on specific keywords, it interprets the intent behind the query.
You could ask, "What vegan leather handbags are trending this season?" or "Show me sustainable home decor ideas for under $50", and the search engine will display products that match your intent and preferences.
Natural language processing (NLP) technology powers this, making it possible for the product search engine to understand complex queries. The engine analyzes context, grammar, and meaning to deliver relevant results.
The goal is to reduce the effort needed to get useful information. It’s smarter than traditional search query.
How Does the Natural Language Search Work?
Natural language search relies on NLP and ML. When you enter a query, the engine breaks it down to understand the intent and context.
Advanced machine learning algorithms help the eCommerce search engine make sense of synonyms, sentence structures, and ambiguous phrases. By doing this, it can provide results that align closely with your expectations.
This approach enhances the relevance and accuracy of search results. It continuously improves as the system learns from natural-language user interface and interactions over time.
What Are the Benefits of Natural Language Processing Search?
Natural language search lexis offers several benefits, including the ability to understand complex, conversational customer queries.
Let's explore them below:

1. Improved Customer Experience
NLS simplifies the search experience for users and helps improve customer experience.
This leads to faster and correct results, making users feel more satisfied. Businesses that use NLS often see better engagement and retention rates. It creates a more intuitive interaction between users and the search engine.
2. Faster Search Results
By understanding queries more accurately, natural language search engine reduces the need for users to refine their search multiple times.
Users spend less time filtering through irrelevant results. This efficiency boosts satisfaction. Fast, accurate results are essential in today's fast-paced digital world.
3. Reduced Need for Technical Knowledge
With NLS, users don't need to know how search algorithms work. There’s no need for specific syntax or knowledge of boolean search.
The system interprets queries written in simple language, making it accessible to everyone. This levels the playing field for users with different technical expertise.
4. Enhanced Search Accuracy
NLS provides search relevance and more accurate results by understanding context. For example, a search for "apple" can differentiate between the fruit and the tech company based on surrounding words.
This contextual understanding reduces irrelevant results and helps users find exactly what they're looking for.
5. Contextual Understanding
NLS uses natural language algorithms to interpret the deeper meaning behind queries. This is particularly useful when dealing with ambiguous or vague search terms.
It considers past interactions and user preferences to fine-tune the searchandising results. This leads to more personalized, context-driven responses.
6. Support for Conversational Queries
One of the biggest strengths of NLS is its ability to handle conversational search queries. Users can phrase their questions in full sentences, similar to how they would speak in a conversation.
This opens the potential for natural language voice search and natural language querying.
7. Increased Engagement
Since users receive relevant answers quickly, they are more likely to continue natural language searching and interacting with the search engine.
By incorporating mobile search best practices, natural search queries create a more engaging user experience, which often leads to higher conversion rates for businesses.
8. Multilingual Support
NLS systems often support multiple languages, making them ideal for global businesses.
With built-in search intelligence, the engine can process and understand queries in different languages, ensuring that users from various regions can interact seamlessly.
What Are the Key Challenges in Executing Natural Language Search?

Here are the main challenges:
1. Understanding Complex User Queries
Natural language queries can be highly complexed. People often use idioms, slang, or incomplete sentences, making it harder for the engine to grasp the search intent.
Handling and answering complex queries require continuous learning and advancements in natural language models.
2. Handling Vague Language
Sometimes, users search for broad or vague terms as they are unclear about what they want. This presents a challenge for NLS, which must still deliver relevant search results.
Handling such queries requires advanced context, intent and natural language analysis.
3. Ensuring High Search Accuracy
Maintaining a high level of search accuracy is crucial. The system needs to interpret not only what the user says but what they mean. 62% of users would not return to a site with poor search experience.
Natural language search engines must evolve to ensure the most accurate results, balancing efficiency and precision.
4. Continuous Learning and Improvement of Models
NLS systems rely on machine learning to improve over time. This requires constant monitoring and updating of models to ensure the search engine stays accurate.
Without this, the system might become outdated or ineffective at handling new types of natural language search queries.
Insightful Stop!
Before going through Semantic Search vs. Natural Language Search, explore the differences between product search vs discovery.
Semantic Search vs. Natural Language Search
Both semantic search and natural language search aim to improve search accuracy. However, they work in different ways.
Here’s a quick comparison:
Basis of Distinction | Semantic Search | Natural Language Search |
---|---|---|
Focus | Focuses on understanding the relationships between concepts and entities (e.g., understanding that “New York” is a city in the USA). | Focuses on interpreting queries based on how users naturally speak or write, without needing precise keywords. |
Data Type | Relies on structured query data like knowledge graphs, taxonomies, and ontologies to map relationships between entities. | Can handle unstructured data, understanding queries based on context rather than predefined data structures. |
Use Case | Primarily used for complex data retrieval (e.g., medical databases, legal documents, product information). | Best suited for conversational searches and user-friendly interfaces where natural phrasing is expected. |
Handling Ambiguity | Less adaptable to ambiguous queries or incomplete sentences. | More flexible in handling vague or incomplete queries and conversational tones |
Data Enrichment | Typically requires data that has been semantically enriched with metadata for personalized results | Does not rely heavily on pre-existing metadata, making it more flexible in various search environments |
How Can Experro Boost Your Website's Performance with NLS Integration?
Experro’s AI-powered search engine integration can transform your website’s search capabilities.
By implementing natural language processing search, your site delivers more relevant search results to users.
For example, when Diamonds Direct integrated Experro, they saw a marked improvement in search relevance, helping customers find exactly what they needed more easily.
Experro goes beyond basic NLP search engine by continuously learning from user interactions to enhance performance.
This results in faster, more accurate search outcomes that keep users coming back. Additionally, Experro offers seamless natural language search integration that is easy to set up, ensuring a smooth transition for your website.
Conclusion
Natural language search is rapidly changing how users interact with websites. It provides a more intuitive, conversational way of finding information, reducing the need for technical knowledge.
As businesses strive to deliver better user experiences, NLS offers significant advantages, including improved accuracy and faster results.
However, it comes with challenges, such as handling vague queries and maintaining high accuracy.
With the right implementation, such as through Experro, NLS can significantly boost website performance and user satisfaction.
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Pallavi Dadhich
08 October 2024Pallavi is an ambitious author known for her expertise in crafting compelling content across various domains. Beyond her professional pursuits, Pallavi is deeply passionate about continuous learning, often immersing herself in the latest industry trends. When not weaving words, she dedicates her time to mastering graphic design.