Sites that bill themselves as fashion search engines are more sophisticated than ever, and it can sometimes feel like a new one crops up every day. One of the more recent additions to the space is Fynd.me, a new twist on the e-comm experience that revolves around customers Liking various styles of a certain item to find that elusive piece (instead of, say, having a gazillion tabs open for myriad retailers before, most likely, just abandoning the quest altogether). The site, which utilizes natural language-processing and machine-learning, launched last month.
Now, Fynd.me wants to really personalize the whole shopping experience that these start-ups strive for. Enter the Fyndbot, which is rolling out today: It’s a shopping (ro)bot that utilizes Facebook’s Messenger function to “chat” (in as human-like a manner as possible) about what you’re trying to find fashion-wise.
The Fyndbot idea first came about in March 2016, and development began in two months later, right after the launch of Fynd.me. “Fyndbot was created to mimic a real, human personal shopper that you’d find in a brick-and-mortar store,” Fynd.me founder Charese Embree told Refinery29. “Fyndbot can do more than take commands one line at a time; it can hold a conversation with you. And, like a personal shopper, Fyndbot remembers the context of a conversation.” To wit: It will recall and then dig up appropriate merch for your latest and your past shopping queries. You know, like a real person would. Specifically, like a personal shopper would. This is what Fynd.me is focused on bringing to the e-comm experience.
“Many department stores are experiencing a decline in in-store sales and an increase in e-commerce transactions. With this change comes the loss of personal conversations with in-store sales associates,” Embree said. “Most fashion sites force shoppers to tediously browse page after page.”
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Fynd.me’s (much bigger) competitor, Spring, has a chatbot, too, but Embree points out the differences between the two sites: Spring is primarily “an amazing platform for brands,” compared to Fynd.me’s focus on personal-shopper search capacities. Spring’s bot is also a vote of confidence for the kind of thing Fynd.me is trying to build, because it “shows [Facebook] sees the value in using chatbots for e-commerce, and that excites us,” Embree explained.
Like Fynd.me, Fyndbot is partnered with Nordstrom and Bloomingdale’s. The bot knows over 1,300 clothing attributes, like “off the shoulder” or “cut out,” to help navigate a reader’s dress quest; you can search by store, brand, or trend, too. At the moment, it can only scour for dresses, but in September, Fyndbot will expand to include women’s tops, skirts, and pants, plus an additional retailer partnership. Come summer 2017, it’ll also be able to look for men’s clothing. “We don't want to force people into using kludgy filters and then browsing item after item,” Embree said. “Let’s bring the joy back to shopping.”