Where conventional on-site search matches queries to the most relevant inventory products by keyword, AI-powered search leverages additional data about the user’s search and browser behaviour and history to return relevant results.
Unlike conventional search, AI-powered search self-learns and improves as it collates more data. As a result, it can make real-time data-driven decisions about which products most closely match the user’s intent (e.g. gift-buying Vs bargain-hunting).
Natural Language Processing (NLP)—another feature of AI search—offers a further benefit to goalless peak-season shoppers. It allows the search to respond more naturally to human search terms. NLP-driven search understands, for example, the difference between a ‘black dress’ and a ‘little black dress’ and can also serve up relevant, personalised results for vague search terms like ‘makeup’ or ‘gifts’.
Leveraging user data to contextualise queries and applying NLP to return more relevant results effectively eliminates zero-results searches. Improved functionality can result in a 98% boost in conversions from on-site searches.