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AI-Powered Site Search: Boost Customer Satisfaction and Loyalty

Seek, and ye shall find. So goes a line from the New Testament. But when it comes to online shopping, it doesn’t always work. How many times have you searched for something and couldn’t find what you were looking for? Try to recall a moment when you entered a query into a search engine only to receive no or irrelevant results. What did you feel? Probably, frustration. That’s what your customers experience when they can’t find what they need on your site. By not providing relevant search results to user queries, you damage the user experience, losing your clients. In fact, 50% of consumers are willing to switch brands if a company doesn’t anticipate their needs. But how can you anticipate customer needs? How can you always offer exactly what your clients want? The answer is AI-powered site search. With the help of natural language processing (NLP) and machine learning (ML), an AI-driven search engine can understand the user intent and context behind a query, learn from user interactions with your site, and provide relevant content that matches user needs. Unlike a traditional search engine, it offers personalized search experiences for each customer, helping businesses boost customer satisfaction and loyalty. In the following paragraphs, we will discuss what an AI search engine is, how it works, its difference from traditional search engines, and the benefits it can bring to your business. Without further ado, let’s get started! What is AI-Powered Site Search and How it Works People who perform a search on a site are twice as likely to convert. That’s why it is critical to provide relevant content to user queries. With AI-based search, you will be able to accurately anticipate customer needs and provide them with what they want. AI-powered search engine is a website’s search functionality that utilizes artificial intelligence to enhance the search experience. Unlike traditional site search that relies on basic keyword-matching algorithms, an AI search engine leverages natural language processing, which helps it understand the intent and context behind a query while recognizing synonyms, related terms, and natural language queries. Consequently, it can provide relevant search results even when the exact keywords entered by users are not present in product listings. For example, if a user uses unconventional language or terminology to describe a product (like “Device for removing wrinkles from clothes without an iron” for clothes steamer), an AI-driven engine can still understand their intent and retrieve what they’re looking for. Another important distinction from traditional search is that AI-powered search systems can support multiple inputs, including voice search and image search. Amazon's AI-powered site search, for example, lets you search for a product with your phone’s camera: you can take or upload a photo of any item, and the platform will provide results containing the exact or similar items. Apart from NLP, AI-powered search relies on machine learning. Machine learning algorithms analyze patterns in current and historical data to make predictions about future events or trends. When it comes to online shopping, they analyze user data and their interactions with a site: what products a user viewed, their purchase history, demographics, and other relevant information. Based on these factors, ML algorithms provide personalized results. Let’s make it clearer with an example. Suppose a user purchases a Google Pixel 8 in your store. After some time, they come back and type “screen protector” into the search bar. In this case, a traditional search engine would return items based on their popularity, rating, number of purchases, and other ranking factors it is programmed to consider. The screen protector for that particular Google Pixel model may not rank high in the returned search results, so the user will have to spend a while scrolling the page down until they come across the desired item (or they will have to rephrase their initial keyword). With an AI search engine, however, the case would be different. The system would analyze the user’s purchase history, recognize that they purchased a Google Pixel 8 recently, and rank screen protectors for this model first in search results. That’s how AI-driven search can anticipate the needs of your customers and understand their intentions even when they use generic terms like “screen protector”. Traditional Search vs. AI-Powered Search To understand AI-powered search even better, let’s compare it with traditional site search. AI-powered search engine vs traditional search engine Technology Traditional Search AI-Powered Search Search Algorithms Relies on basic keyword-matching algorithms. Searches for exact matches of search queries within the website’s content. Relies on natural language processing and machine learning. Understands the intent behind a query, can work with natural language, synonyms, and related terms; provides content even if the exact keywords are not present. Input Typically supports text search only. Can support voice search and visual search. Personalization Provides the same results for all users, doesn’t consider their preferences, purchase history, and interactions with the website. Offers personalized search results based on current and historical data. Considers purchase history, interactions with the website, preferences, and other parameters to tailor search results for each individual. Contextual Understanding Doesn’t understand the context of a query. Understands the context of a query. Natural Language Understanding Doesn’t understand natural language queries. May provide irrelevant and no results for natural language queries, restricting users in expression. Understands natural language queries, allowing users to express themselves in a conversational manner and still receive relevant search results. Learning and Improvement Lacks the ability to self-improve. Requires manual adjustments to enhance search relevance. Continuously learns from user data, interactions, and feedback and self-improves over time. Can adapt to changes in user behavior.
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