Natural Language Search: Personalizing the Shopping Experience

To remain competitive in ecommerce, retailers must adapt to the evolving search behavior of shoppers. According to Google, there has been a 60% increase in natural language queries in their Search product from 2015 to 2022. This is where Natural Language Search becomes essential, offering a more personalized and intuitive way for customers to find what they need.

By boosting conversion rates and cultivating brand loyalty, Natural Language Search can transform digital commerce operations. In this article, we will explore the ways in which Natural Language Search can benefit online retailers.

What is Natural Language Search?

Natural Language Search (NLS) allows users to submit queries in conversational language, rather than rigid keywords. With NLS, customers can enter search queries in the same way they would communicate with a friend, using everyday language and phrases.

To illustrate, a shopper can search for "I want to buy brown boots for women" instead of "women's brown boots." NLS harnesses the power of artificial intelligence and machine learning to understand the user's intent, delivering search results that correspond to the user's query.

Unlike standard search algorithms, natural language search has the capability to comprehend language nuances, considering the wider context and meaning of the user's query. This results in more accurate and relevant results. By integrating this technology, ecommerce platforms can provide an individualized search experience, improving user engagement and customer satisfaction.

Natural Language Search is a specific application of a broader discipline called Natural Language Processing (NLP). NLP aims to create systems that allow computers to understand, interpret, generate, and respond to human language in a meaningful way.

How Natural Language Search works

In essence, NLS applies principles of NLP to make search functions more intuitive and user-friendly. NLS leverages NLP technologies to understand the intent and context behind a search item, providing more relevant and precise results than traditional keyword-based search systems.

To understand further how NLS works, here are some of its most important capabilities:

• Contextual understanding: When a user inputs "apple" within a search query, NLS has the capacity to discern whether the user wishes to look for results related to the technology company, the fruit, or perhaps even the Beatles' record label. NLS deciphers the context behind the search, taking into consideration the intent and meaning.
• Semantic analysis: NLS grasps how different words in a sentence relate to each other, enabling it to figure out the meaning of a query even if it's articulated differently than conventional search terms. Its understanding goes beyond individual words, to include the overall sentence structure and meaning.
• Sentiment analysis: NLS can comprehend the emotional tone of the search terms. For example, does it convey a positive or negative sentiment? This underlines the importance for businesses to develop digital content that appeals to users, rather than simply targeting the search keywords.
• Multilingual support: NLS can understand search queries in multiple languages. This means that a user can input a query in their native language, and the search engine will still be able to understand and return relevant results.
• Typo tolerance: NLS has the ability to recognize and correct typographical errors in a search query. Minor typing mistakes typically won’t prevent users from finding what they're looking for.
• Entity recognition: NLS is capable of identifying significant elements in a piece of content, such as names, locations, time, statistics, and more. When it comes to images and videos, which are critical in ecommerce, separate but related techniques to NLS are employed, such as image or video recognition. These can categorize visual content based on their inherent features, bypassing the need for alt text. To complement a platform’s NLS capabilities, retailers must incorporate images and videos that are contextually relevant to the content of the page.

Benefits of Natural Language Search

Enabling users to input queries in natural language offers a range of benefits and advantages to any business with a digital presence (not just retailers). Let's explore the key benefits of NLS in greater detail:

• Boost conversion rates: By accurately understanding the intent and context of user queries, NLS can deliver more personalized search results. This increased relevance can make users more likely to find what they're looking for and make a purchase.
• Enhance user experience: Since NLS understands queries phrased in everyday, conversational language, users can interact with a website or app more naturally and intuitively. This improved user experience can lead to higher satisfaction rates, driving customer loyalty and repeat business.
• Gather insights into customer behavior: NLS provides valuable data about how users phrase their queries and what they're searching for. Businesses can analyze this data to gain further insights into customer needs and behaviors, informing product development, marketing strategies, and more.
• Expand audience with multilingual support: Because NLS can understand queries in multiple languages, businesses are able to operate in diverse linguistic markets. It allows non-native speaking users to also enjoy a seamless search experience on foreign sites.
• Gain competitive advantage: Implementing NLS can give a business a significant edge over competitors that rely on traditional keyword-based search algorithms. Customers who find NLS more intuitive and effective may prefer shopping on platforms that offer it.

SEO considerations for Natural Language Search

As businesses embrace Natural Language Search on their digital platforms, they should continue to fine-tune for SEO performance. Embracing NLS isn't just about leveraging conversational queries, but also about maximizing search engine visibility. Here are a few tips to execute this effectively:

• Incorporate long-tail keywords: Content managers need to anticipate the questions users might ask and provide comprehensive, user-focused answers. This includes incorporating long-tail keywords and phrases that align with natural language queries.
• Focus on localized queries: As many voice searches are local (e.g., "best burger joints around here"), retail businesses should enhance their local SEO. This includes maintaining up-to-date listings on relevant online directories like Google My Business and optimizing content for local queries.
• Leverage featured snippets: NLS often utilizes featured snippets to directly answer user queries. It's crucial for retailers to optimize their content in a way that increases the chances of being included in these valuable snippets.
• Optimize product pages for NLS: E-commerce platforms must prioritize optimizing product pages for natural language search. This includes using natural language in product titles and descriptions, along with providing comprehensive information about product features and benefits.
• Integrate NLS with SEO best practices: NLS doesn't replace traditional SEO techniques. Businesses should view NLS as a component of a broader SEO strategy that includes keyword optimization, link building, site speed, and mobile-friendliness.

Use cases in retail: NLS in action

By understanding and interpreting user queries in a human-like manner, NLS goes beyond keyword-based searches and brings more relevance, personalization, and efficiency. Here are some compelling use cases of NLS (and NLP) in the retail and e-commerce sector:

• Voice-enabled search: Voice assistants, such as Amazon's Alexa, Google's Assistant, and Apple's Siri,allow users to perform searches by speaking to a device, offering a hands-free, convenient, and often quicker alternative to typing out search queries. Furthermore, voice searches are typically lengthier than typed searches and often take the form of full questions or commands. Users can ask questions or make commands in their natural speaking style, instead of having to use specific keywords or phrases.
• Customer feedback analysis: For large retailers, it would be inefficient to read each and every customer review and extract insights from such a massive amount of content. The capability of NLS for sentiment analysis can provide retailers with an extensive understanding of how their products and services are perceived in the market. This technique employs a machine learning algorithm to classify customer feedback as positive, negative, or neutral based on key phrases.
  
  Home Depot and ASOS have stated that they are using natural language processing to understand customer reviews of their products.
• Streamlining customer inquiries: By categorizing and prioritizing queries based on their content, NLP can streamline the handling of customer inquiries. It pinpoints keywords within each inquiry to discern the customer's intent (e.g., complaint, return, refund, payment issue, etc.).
  
  With NLP, standard inquiries can be directly addressed by bots. These bots can interpret and respond to customer queries, promoting rapid resolution of common issues. More complex or non-standard inquiries, meanwhile, are directed to live agents. This process ensures that each query receives the appropriate level of expertise and attention.
• Highly personalized product discovery: Personalized product recommendations stand at the heart of natural language search (NLS). NLP algorithms interpret customer data, such as browsing history, past purchases, search queries, and product review language, to create a detailed picture of customer preferences. This comprehensive analysis allows the system to understand the context, intent, and semantics behind a search query, offering tailored product suggestions.
  
  For instance, if a customer searches for "waterproof hiking boots for winter," the system recognizes the customer's interest in outdoor activities and may recommend related products. Moreover, NLS enhances product recommendations by assessing the language used in product reviews. If a customer favorably reviews a lightweight running shoe, the system can suggest other lightweight products, effectively catering to the customer's broader needs.
  
  Prominent retailers such as Sephora, Birkenstock, and home24 have incorporated NLS into their platform to enhance product discovery. Zalando is about to launch a fashion assistant powered by ChatGPT. Walmart has even acquired a NLP startup in 2019 to boost their e-commerce capabilities.
• Efficient processing of customer queries: Here at Netguru, we’ve begun to build various proofs of concepts for different verticals (e.g., fashion, food delivery, hospitality, and finance). For the past couple of years, we’ve seen accelerating interest from these industries and shifting trends in customer search behavior.
  
  With this in mind, we embarked on testing NLS feasibility in our R&D department. We built a fully functioning NLS capability for fashion retailers where online shoppers can search for shoes using long-tail, human-like queries. One distinctive feature is that it can display highly relevant results without the need for search filters. We’re now advancing our R&D into prototype apps on both web and mobile.

An intertwined future for retail and NLS

Natural Language Search opens up new avenues for e-commerce businesses to deliver highly personalized experiences. Its ability to understand user intent, context, and natural conversation paves the way for a search experience that aligns closely with human thinking.

As NLS matures and integrates with other AI technologies, shopping online could become as natural as asking a knowledgeable sales assistant at a physical store.

The challenge for e-commerce businesses will be in how they can best leverage this technology to foster a greater connection with their customers and, in doing so, create an unmatched shopping experience. The future of retail and e-commerce is intertwined with the evolution of Natural Language Search.