January 22, 2013, 10:18 AM

How SamsFurniture.com squeezes more from live chat

The retailer anticipates the questions shoppers might have.

Lead Photo

SamsFurniture.com last year deployed LivePerson Inc.’s live chat program, which uses an algorithm to dig into the site’s Google Analytics and Omniture SiteCatalyst analytics programs to determine which shoppers might benefit from an invitation to chat. The algorithm uses data, such as how many pages a consumer has viewed, his navigation path and how he arrived at the site, to develop rules about when to invite a visitor to chat.

The move stands an example of a retailer using advanced live chat to offer better customer service and encourage more sales, a topic examined in depth in the upcoming February issue of Internet Retailer magazine.

Because SamsFurniture.com sells furniture and appliances, both products that consumers think carefully about before buying, shoppers often browse the site without any intention of completing a purchase online, says Seth Weissblatt, owner of the Texas-based multichannel retailer. “The furniture industry is different than a lot of other forms of e-commerce,” he says. “When you buy a shirt online, it’s easy to return. But that isn’t the case when you’re buying a sofa that is 300 pounds and 10 feet long.”

When buying something so large and expensive, consumers are going to think about it, he says. But shoppers still provide clues that they have questions, and recognizing those clues can be extremely valuable, he says. For instance, when a shopper clicks to the site’s “Lease to Own” page after spending several minutes comparing two sectional sofas, he is likely in the final stage of his decision-making process, Weissblatt says. 

Since the retailer began working with LivePerson, about half of the retailer’s chats stem from proactive chat invitations.  The retailer’s online chat sessions contribute about $50,000 in sales each month, where shoppers chat online, then complete a purchase in one of the retailer’s two stores. Weissblatt tracks the effectiveness of live chat by manually matching the chat logs, which include the name of the customer on the chat, and matching it to his sales orders.

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