Keyword-based search have always been one of the primary methods for delivering relevant search results to customers. It matches customer queries with product titles, descriptions, and other attributes for sharing exact matches. However, this method was successful until a certain point.
Keyword-based search is not equipped to understand customer intent, handle synonyms, or automatically recommend relevant products based on customer preferences. Rigid keyword matching can also lead to 'no search results' pages even when relevant products are available.
This is where neural search comes in. It employs AI-powered technologies like natural language processing and machine learning among other models to showcase more accurate search results to customers. It offers significant advantages over the old-school keyword search.
In this post, we will learn more about neural search, how it works, and its application in eCommerce.
What Is Neural Search?
Neural search is one of the rapidly emerging onsite search methods that leverages a network of algorithms including AI to understand and interpret natural language queries. This network, commonly called a neural network, relies on deep learning models to gain a context-rich understanding of the semantics of search queries. It analyzes the meaning behind the words rather than just their form. This functionality allows it to deliver more relevant results even when the customer's input is vague, misspelled, or conversational in nature.
As neural search is capable of deeply understanding customer intent, it can deliver highly relevant and contextually aware search results. For example, a customer looking for affordable smartphones for gaming will always be presented with affordable gaming phones because neural search engines will focus on customer intent - gaming performance and budget - when fetching the results.
Similarly, neural search is designed to handle synonyms and misspellings in search queries effectively. It can recognize synonyms, minor errors in spelling, and variations in phrasing via natural language processing and still show relevant results.
Neural search utilizes various machine learning models to track customer behavior and their preferences. It recognizes patterns in this data to dynamically suggest complementary items or relevant products. This also reduces zero-result pages as it can suggest product alternatives when exact products are not available.
Neural Search vs Keyword Search
The traditional search i.e. keyword search and neural search both analyze customer queries to deliver results. So, how exactly are they different?
The major difference between the two is the manner in which they interpret search queries. Keyword search matches exact or partial keywords from product titles, descriptions, or metadata to generate relevant results. On the other hand, neural search focuses on understanding the intent and context behind the queries to identify related terms and broader meanings for generating relevant results.
The ambiguity in search queries often causes keyword searches to produce irrelevant or incomplete search results. Neural search is designed to decipher such queries by analyzing customer intent.
Keyword search generally offers static search results that are generally the same for all customers. However, as neural search can analyze customer preferences and their behavior, it is well-equipped to deliver personalized search results.
To sum up, keyword search is simpler and serves basic search needs. However, it falls short in handling complexities like understanding intent and personalization. This makes it less suitable for modern eCommerce needs. Neural search overcomes these limitations due to its ability to interpret language naturally. It can deliver smarter, more relevant, and engaging search experiences to customers.
Why Is Neural Search Important?
Neural search plays an important role in enhancing the overall eCommerce experience for your customers.
Improved user experience - Neural search can understand the context and intent behind search queries. Thus, it can provide relevant results even for complex or vague queries. Further, it can handle typos, synonyms, and other conversational search phrases to help customers find the products they are looking for quickly. This eliminates the buying frustration among customers, leading to a more seamless shopping journey.
Increased conversions and revenue - Neural search delivers highly accurate search results and personalized product recommendations that increase the likelihood of purchases. As customers are able to find products without any difficulties, there are fewer occurrences of shopping drop-offs and abandonment caused by irrelevant search results or no search results pages. This directly improves conversion rates and increases your business revenue.
Enhanced customer satisfaction - Neural search ensures quick and accurate search results irrespective of what terms or keywords customers enter in the search box. It offers tailored product suggestions that save customers some time in shopping. This fosters a sense of trust and loyalty in their minds for your brand. It distinguishes your store from the rest and encourages them to purchase from you again and again.
That being said, let us understand how neural search actually works to deliver highly relevant search results.
How Neural Search Works
Neural search employs a neural network search engine that replaces the traditional keyword-based search system with a modern one. This search engine leverages deep learning models to understand the context and intent behind the queries. It also tries to comprehend the semantic relationships between the words rather than just going for exact wording.
For example, let us consider the query 'affordable smartphones for gaming'. The neural network search engine will associate -
- The keyword 'affordable' with terms like 'budget' or 'low-cost'
- Keyword 'smartphones for gaming' with features like 'high processing power', 'fast graphics', 'mobile gaming', 'long battery life', or 'gaming performance'
even if these terms are not explicitly stated in the original search query. It will prioritize smartphones offering the above-mentioned features and showcase them in search results. Thus, returning more accurate and personalized results.
The Neural Search Process
This entire neural search process can be summed up in four steps.
Understanding the query - The neural search engine processes customer queries using various natural language processing techniques. It automatically corrects typos, interprets conversational language, and identifies the intent behind the query.
Convert query into vector embeddings - The neural search engine converts the query into numerical values called vector embeddings so that the algorithms can rightly understand the query. This helps the search system to find content based on the context rather than exact keywords.
Match and rank products - Here, the neural search engine measures how closely the vectors designed in the previous step align with the product vectors. The results are then ranked based on relevance and displayed to the customer.
Learn and refine results - The neural systems continue to learn from customer interactions to refine results and improve their efficiency.
By understanding context, neural search engines eliminate irrelevant results and enhance overall search accuracy. It reduces frustration among customers and keeps them engaged in your online store.
Applications of Neural Search in Ecommerce
Neural search helps you deliver more intelligent and user-friendly search experiences to customers. It enhances shopping experiences by optimizing product discovery and driving conversions.
Here are some of the best applications of neural search in eCommerce.
1. Handling Complex Customer Queries Better
With neural network search, your search system can understand and respond to complex or vague customer queries better. It can interpret the meaning and context of the queries using NLP techniques to deliver relevant results without any hassles.
2. Enhancing Product Search Relevance
Neural search understands the semantic relationships between words in the search queries. It can recognize synonyms and typos very well. This feature helps it deliver what customers are looking for without including any irrelevant search outcomes in the results.
3. Delivering Personalized Search Results
Neural search can analyze customer behavior, their interests, and their previous interactions on your website to generate data patterns for delivering personalized search results. It makes sure these results resonate with the customers. Thus, improving engagement and conversion rates.
4. Showcasing Dynamic Product Recommendations
Neural search can share dynamic product recommendations based on customer interests and behavior. It can suggest products that match their requirements, and even predict their future purchases. This helps you identify ample upselling and cross-selling opportunities for selling complementary or high-priced products to customers.
5. Refining Search Results via Faceted Filtering
Neural search allows customers to refine their search results based on multiple attributes like size, color, or price. It helps narrow down the product selection to match the customer’s specific needs more efficiently. Faceted search is particularly useful for large e-commerce businesses as they have vast product catalogs.
6. Offering Customer Support via Chatbots
Neural search can also pair up with AI-driven chatbots like Bodt to offer intelligent support to customers. These chatbots can understand customer queries in natural language, helping users find products, track orders, or answer common questions. With neural search, the chatbot can enhance its efficiency, offering a more interactive and satisfying customer service experience.
Top 3 Neural Search Apps to Consider
If you are looking to implement the neural search for your online store, here are the 3 top neural search apps you might want to consider.
1. Sparq
Sparq is a powerful AI-driven neural search solution that helps you enhance product discovery on your eCommerce website. Its advanced neural search technology can understand all types of search queries including conversational ones for delivering personalized, context-based results to customers.
Key Features
- Contextual understanding to understand the intent behind customer queries, improving product relevance.
- Faceted search to help customers refine search results based on attributes like price, brand, and more.
- Product recommendations to recommend products based on customer behavior and context, enhancing upsell and cross-sell opportunities.
- Seamless integration with Shopify for seamless store and search solution implementation.
Pros
- Highly intuitive, context-driven search results improve customer experience and satisfaction.
- Provides dynamic, personalized recommendations to boost conversions.
- Offers powerful analytics to track and refine the user search experience.
- Easy to integrate with Shopify allowing you to make your store live in less than 3 days.
Pricing
Starts $19 per month.
2. Algolia
Algolia is another search engine that you can use for your eCommerce store. It offers real-time product search with AI-powered personalization for delivering excellent shopping experiences to customers.
Key Features
- Instant Search for delivering real-time search results.
- AI-powered personalization to tailor search results and recommendations based on customer behavior and preferences.
Pros
- High level of customization options, making it flexible for different industries.
- Seamless integrations with various eCommerce platforms.
Cons
- Can be complex for non-technical users to configure initially.
- Customized pricing structures can become expensive for businesses with large catalogs.
Pricing
Connect with the Algolia team for customized pricing plans.
3. Coveo
Coveo is another AI-powered search and recommendation platform for delivering a highly personalized search experience. Its advanced capabilities ensure that customers find relevant products with ease.
Key Features
- AI-powered search and recommendations to deliver relevant search results and personalized recommendations.
- Content and product relevance to optimize search results based on the specific content or product.
Pros
- Impressive search functionality delivery best search efficiency.
- Highly customizable and adaptable to a wide range of business needs.
Cons
- More suited for large enterprises.
Pricing
Connect with the Coveo team for details on pricing structures.
Optimize Your Neural Search with Sparq
Neural search technology is transforming the way eCommerce search engines deliver search results. By understanding the context behind queries and handling complex search terms, neural search engines can enhance user experience and drive higher conversion rates.
Sparq offers an exceptional neural search engine that optimizes your eCommerce store's search functionality to design seamless shopping experiences for customers. Its advanced AI-powered technology ensures your product search results are always relevant.
Schedule a demo with Sparq today and learn how you can leverage our neural search functionality to boost your eCommerce store.
FAQs
How is neural search different from the traditional keyword-based search functionality?
Traditional keyword-based search matches search terms quite literally. While, neural search uses artificial intelligence to understand the intent and context behind search queries, providing relevant results even for vague or complex queries.
Is neural search expensive to implement for small or medium-sized e-commerce stores?
Although neural search appears to be a costly technology, there are many cost-effective solutions like Sparq that offer flexible pricing plans for small and medium-sized businesses. This allows such stores to implement advanced search functionality without requiring significant investments.
Can neural search handle large inventories efficiently?
Yes, neural search can handle large inventories. It leverages advanced AI algorithms to process large volumes of data and deliver precise search results quickly.
How does neural search impact SEO for my e-commerce site?
Neural search offers accurate and relevant results for every search query, thereby delivering seamless shopping experiences to customers. This positively impacts SEO as customers tend to spend more time on your website, reducing bounce rates.
