Like all big companies, Home Depot has a list of IT projects that it wants to tackle. When the COVID pandemic hit two years ago and ecommerce activity surged, it accelerated one of them in particular: the development of vector search algorithms to augment basic keyword search on its website and mobile app. Since going live, its homegrown vector search engine has yielded exceptional returns and, most importantly, a more relevant search experience for visitors.
If you’ve ever tried to search for a specific part or obscure product on a website, you know how difficult it can be to find it. Unless you are on the exact same page as the company in terms of the words it uses to name and describe its products, it’s unlikely that you’re going to find it on the first try.
The usual reaction to this common obstacle is to try different words, what is colloquially known as “Google-fu”. If you’re good at words and persistent, this will usually do the trick. But if you’re busy or just can’t come up with new words for whatever reason, you may abandon the search before finding what you’re looking for.
Huiming Qu, Home Depot’s vice president of data science and analytics, is familiar with this phenomenon. “It’s very hard to describe things,” Qu tells Datatami. “A lot of the products are so specialized.”
With more than 2 million products, Home Depot has more than its share of obscure items, and it struggles at times to help people find them. Whether it’s an angled downrod for a Hampton Bay fan or an 18-volt lithium ion battery for a Milwaukee cordless drill, Home Depot’s search engine has its work cut out for it.
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