Research presented today at the NRF Big Show in New York highlights 2016 holiday findings from popular retailers.
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Jelly Belly uses MyBuys Inc.'s recommendations engine that builds a profile based on the consumer's clicks throughout the site. The system then offers suggestions based on what other shoppers who have taken similar paths throughout the site viewed. The algorithm the candy retailer and manufacturer uses focuses on driving shoppers to click throughout the site because Jelly Belly uses its commerce site for branding and marketing and wants high engagement rates, as well as to drive sales. MyBuys also has algorithms that aim to drive conversions.
That means that a shopper who arrives at the site and navigates directly to berry-flavored Sport Beans, which are primarily marketed to endurance athletes like marathoners, will likely find other flavored Sport Beans recommended to him. But a shopper who looks at those Sport Beans, then clicks to look at its Cold Stone Ice Cream Parlor jelly bean collection will likely see a broader assortment of products suggested—perhaps one type of Sport Beans, a couple Cold Stone varieties and perhaps another product.
"We are known for our jelly beans but not as many people know about our gummy worms, chocolate products or any of the other brands we have," Finch says. "Recommendations are an opportunity to show people that we make more than just jelly beans."
The suggestions have worked. Finch says testing shows that JellyBelly.com sees between a 2% and 10% increase in its conversion rates after adding recommendations to product pages, search results pages and category pages throughout the site. It also saw a "slight" uptick after recently adding product reviews, in the form of stars, to those suggestions.
Those gains are why Finch says he is constantly looking for new places to show recommendations. For instance, he plans to run a test to see whether a consumer will click on recommendations that appear in the pop-up window shown when an item is added to the cart. He's also planning tests to see whether displaying four or five suggestions produce a better response. "We want to make them as useful as possible," he says.
That's the idea, too, at Build-A-Bear.
"You have to be relevant," Sawyer says. "You have to show the guest that you understand what she's looking for." By using what it knows about its customers—what the shopper has looked at, where she is located, what similar shoppers have bought—Build-A-Bear is showing shoppers what they're interested in. And that's leading more of them to click and buy.