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This article is brought to you by
\n Retail Technology Review:
\n Outdated search functions are costing retail trillions \u2013 this is how you fix them\u2026.<\/p>\n
By Karl Hampson, Chief Technology Officer, Data & AI, Kin + Carta.\u00a0<\/strong><\/em><\/p>\n As the brand that\u2019s synonymous with search, it\u2019s no surprise that Google is the go-to platform for the vast majority of us when we want to find something online. However long or short, simple or complex our search terms are, there\u2019s a feeling that Google just \u2018gets us\u2019 and can intuitively produce exactly what we\u2019re looking for.<\/strong><\/p>\n Google has set the benchmark. That means there are high expectations when it comes to search, and we expect this quality in all corners of the internet. But unfortunately, online retailers aren\u2019t living up to them. According to exclusive data from Kin + Carta and Google Cloud, 85% of the UK\u2019s largest retailers see \u201csearch relevancy\u201d as their biggest challenge regarding product discovery on their ecommerce sites.<\/p>\n And these difficulties with delivering relevant results are leading to search abandonment issues \u2013 a multi-million pound headache for most ecommerce retailers. In fact, Google research has shown that it’s costing retailers more than $2 trillion per annum globally.<\/p>\n What\u2019s the simplest way to go about this? By harnessing the full potential of AI. Here\u2019s how\u2026<\/p>\n When people enter long \u2018tail\u2019 search queries including three or more terms, they often do so in more of a natural language formation: \u2018date night dress in small\u2019 for example. Longer queries such as this are a growing trend, as customers become more accustomed to interacting with AI chatbots in a conversational way \u2013 so it\u2019s essential that retailers\u2019 search functions are able to deal with them effectively.<\/p>\n Generally speaking, the more terms that are included in a search query, the more specific it is. This in itself is an indication that the consumer is looking for a particular item, and that they will be less tolerant of results that don\u2019t meet their expectations.\u00a0<\/p>\n The problem with traditional search functions is that longer queries tend to be more distant from the language in the product catalogue, which makes it more difficult to deliver accurate results. But introducing large language models (LLMs) can help to bridge this gap between user intent and product information.<\/p>\n Consumers are also increasingly turning to search functions in search of inspiration, essentially looking for product inspiration with terms such as \u2018Christmas gifts for dad\u2019. This presents almost the opposite problem to long search terms: the lack of specificity makes matching the query to relevant products from the inventory extremely challenging.<\/p>\n Again, generative AI can be used to retailers\u2019 advantage here. Because they\u2019ve been trained on such vast data sets, LLMs can accurately interpret the intent behind these search queries and deliver the best results.\u00a0<\/p>\n The occasional typo or spelling mistake is part and parcel of online interaction, particularly when we\u2019re in a rush. But while a friend might be able to look past a typo in WhatsApp and still respond to your message appropriately, traditional search engines can find this more difficult.\u00a0<\/p>\n For example, a search for \u2018riped high rise skinny jeans\u2019 instead of \u2018ripped\u2019 can lead to a \u2018result not found\u2019 response and user frustration. But with LLMs in place, retailers can ensure the intent behind these misspelt queries is understood and that relevant results are delivered.\u00a0<\/p>\n The impact of a failed search function is huge. Google\u2019s research shows that:<\/p>\n
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\n<\/ul>\n \u2026and generative AI is the perfect solution for troubleshooting the vast majority of these problems. But the benefits extend beyond fixing errors and making search more intuitive.\u00a0<\/p>\n LLM tools such as Google Cloud Retail Search can improve conversion metrics further by personalising results for every user to provide a more intelligent experience \u2013 a clear differentiator vs legacy search. Using this technology, retailers are seeing up to a 20% increase in revenue per visitor against their existing search platforms.\u00a0<\/p>\n It also means a more intelligent ranking of search and browse results. This will not only establish what a customer is most likely to buy, but also what brings you, the retailer, the most revenue. And in time, it can become hyper-personalised to the individual customer, rather than just a cohort – reducing cart abandonment as well as increasing the likelihood of repeat custom.<\/p>\n With both of these elements of functionality in play, merchandising becomes more about directing search towards what you want to sell and less about trying to band-aid your search engine with hard-coded rules.<\/p>\n With generative AI implemented, retailers are able to start delivering these \u2018wow\u2019 moments very quickly, immediately removing pain points from their existing search to deliver an experience that just works.\u00a0<\/p>\n LLMs are rapidly becoming a critical component of ecommerce search systems \u2013 and one that requires little to zero human intervention once implemented. \u00a0<\/p>\n Retailers that fail to keep pace with this change are potentially leaving millions of pounds of revenue on the table, so the time to act is now.<\/p>\n<\/p><\/div>\n![\"\"](\"https:\/\/www.retailtechnologyreview.com\/assets\/image-cache\/files\/images\/12_07_23\/bigstock-warsaw-poland-february-471445859.7fa9aa46.jpg\")
<\/p>\nInterpreting \u2018tail\u2019 queries<\/h2>\n
Finding inspiration\u00a0<\/h2>\n
Handling typos<\/h2>\n
Delivering \u2018wow\u2019 moments<\/h2>\n
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