When Irish rock band U2 sang “I still haven’t found what I’m looking for”, they summed up a major problem for online shoppers, one that persists into 2025. With the rise of next-day, or even same-day delivery, e-commerce is increasingly a game of instant gratification: one recent report found that three-quarters of consumers will abandon an ecommerce site entirely when they can’t find what they need quickly. In this article from keywords to conversions, you’ll learn how AI’s semantic search is transforming the e-commerce SEO industry.
That makes search and discovery one of the most important mechanisms of e-commerce. Traditional search, based on keyword association, is inherently limited because it requires consumers to accurately describe their needs. No matter how sophisticated your product categorization, there will always be gaps.
Increasingly, AI is offering a solution. AI-powered semantic search interprets the true meaning of consumer search terms, rather than connecting appropriate keywords, and allows consumers to describe their needs and desires in natural language. Recent research from AtomRadar, explored in depth in this article, found that 74% of consumers prefer to search in this fashion rather than with traditional keyword and filter methods.
For e-commerce brands, it’s a simple equation: The better you can return products that match consumer needs and desires, regardless of how they’re expressed in search, the more you’ll sell. How you integrate semantic search matters, however, and not all shoppers respond positively to the explicit integration of AI in their experience. AI-powered search is the future of e-commerce discovery, but e-commerce brands must be careful with the cutting edge.
How Semantic Search is an Upgrade on Traditional Search
Semantic search works by integrating AI-powered natural language processing (NLP) and machine learning into customer search. By analyzing the overall context and the relationship between words, alongside individual customer signals such as past behavior and location, semantic search can accurately interpret a customer’s needs no matter how they’re expressed and return a highly relevant set of products.
For example, suppose a customer is seeking comfortable shoes appropriate for formal settings. In that case, they can express their criteria in natural language rather than squeezing their desires into the relevant search terms. With lexical search, the user might have to filter for black athletic shoes or undertake a second search for supportive formal shoes — if they make it that far. Effective semantic search will return all the relevant results based on the consumer’s long-tail search terms.
Our own experience with search and discovery has had a dramatic impact on sales across Atom.com’s domain platform. In 2023, we introduced buyer intent and advanced AI categorisation to optimize lexical search and bring buyers both broader and more relevant domain names based on their search terms. Through this, we saw consumer engagement rise by 17.4% and conversions jumped 14.6%. Now, with the integration of semantic search. Founders and entrepreneurs can describe their business idea in their own words, based on what it does. What’s unique about it or how it should make their customers feel.
Whether you’re selling domains, luxury jewellery, or boutique beauty products, semantic search will better connect buyers with products they truly desire.
Paying AI-ttention: Customer Perceptions of AI-Integrated Search: AI’s Semantic Search
Wherever AI-assisted search has been implemented, it’s been a huge success. Of those who have used AI assistance in shopping, 92% of shoppers said it improved the buyer experience and would consider using it for future purchases.
Our recent research found a considerable appetite for AI search, as well as the added functionality that it can offer. 60% of consumers agree that AI search is more effective than keyword search, with just 14% disagreeing. Men and younger consumers are more drawn to AI search: 68% of men vs. 54% of women favor AI search. While 74% of 18 – 34s agree that AI search is more likely to return the products they’re looking for.
These disparities draw attention to the fact that brands must be alert to consumers’ attitudes to AI. Which are not, as of 2025, unanimously positive. For local business search, a majority of consumers still trust Google over AI results. While our research found that just 45% of 55 – 64s consider AI search more effective than traditional methods.
Notably, when AI is removed from the question, all demographics respond more positively to natural language search. In fact, 55 – 65s are more than three times as likely to agree that natural language search is preferable to keyword-based search. When AI is removed from the question.
Semantic search can support every customer interaction by better understanding their intentions, preferences, and desires. Research suggests, however, that it’s not just about what you integrate in search: how you integrate it matters too. So, for a range of demographics.
Integrating Semantic Search Effectively in E-Commerce
Semantic search is a giant leap forward in e-commerce search and discovery. Requires attention to detail in both your inventory and audience expectations. It’s not as complicated as you might think. However, and should be up by any e-commerce brand looking to increase conversions with AI’s Semantic Search.
- Think about your audience: if you’re marketing to a younger audience. Emphasizing the presence of AI integration across your site could enhance your reputation. An older audience, however, might appreciate the functionality of semantic search but respond less positively to explicit promotion of AI.
- Enhanced product classification: Semantic search works by interpreting customer intent. But it can only return appropriate products if they are effectively described and categorized. If you’re using AI to generate product descriptions, test and iterate to ensure search results match intent.
- Don’t abandon lexical search altogether: Lexical, keyword-based search remains part of e-commerce search and discovery. It’s fast, affordable, and easy to scale, so prioritize a hybrid approach to search which gives consumers options.
- Consider white label semantic search: You don’t need to be a tech expert to integrate AI-powered. So, natural language search on your e-commerce store. Many white label options allow you to add powerful AI capabilities to your e-commerce store with minimal development effort. The flexibility to tailor search behavior to your product catalog and customer needs.
The Future of Engines
The natural language processing ability of large language models AI’s Semantic Search will allow customers to search in their own words. So, turning online shopping into an intuitive experience and better returning relevant, intent-matched products to your audience. Integrating this effectively. Across hybrid search and sensitivity to customer perceptions of AI, ensures customers get what they need. Through an experience they appreciate.
The brands that win in e-commerce will be the ones that not only understand what their customers are looking for. But also how they’re trying to ask for it. Semantic search helps you listen more closely.
