Retailers have teased and rolled out online deals for days, even weeks, but the real Black Friday is here.
... product recommendations. But which method to choose is a matter of debate.
There’s a sweet fragrance in the air at Scentiments, and it’s not from any of the e-retailer’s myriad colognes or perfumes. It’s coming from newly implemented product recommendations technology. And this fragrance has created quite an attraction.
“These recommendations have far exceeded our expectations,” says Howard Wyner, CEO and chief of e-business. “There are astounding conversion rates in the double-digits. We scratch our heads-this is unbelievable.”
Scentiments switched on product recommendations in August. Since then, total sales are up 10% and average order value is up 40%, it reports.
“We have a pricing structure that is very competitive that aids in the ability to make a cross-sell,” Wyner says. “We’re saving customers 80% to 90% in some cases. So if they’re going in for a refill of their favorite fragrance and see that they saved $50, and then are presented with some strong recommendations, they say, ‘I’m saving so much on this bottle, I’ll roll the dice and give this other product a try.’”
Such product recommendations act like a virtual salesperson: They step in while shoppers are considering or selecting products and suggest other products the shoppers might be interested in. The more products shoppers view and buy, the better the recommendations can become because the systems coming up with the suggestions have more data to work with.
Many e-retailers rely on vendors such as Baynote Inc., ChoiceStream Inc. and MyBuys Inc. to create and maintain product recommendations systems. Though some retailers build systems in house, they are more often delivered by vendors in a software-as-a-service model, meaning data from the retailer site flows to a system hosted by the vendor, which displays recommendations on the e-commerce site.
Vendors generally divide into two camps when it comes to how they come up with recommendations. The first method builds a profile for customers based on their actions as individuals on an e-commerce site. The second creates profiles based on the activity of all shoppers, then bases suggestions on the profile the shopper seems to fit into given her current behavior. (Some systems that focus on individual profiles can also take into account group behavior at some level.) Which of the methods is best is the subject of debate among vendors and e-retailers.
E-retailers provide product recommendations for cross-selling purposes at different points during the online shopping experience. Some e-retailers use recommendations right from the start, on the home page. A returning customer hits the site and is presented with products the e-retailer thinks she will like. More commonly, e-retailers wait, displaying recommendations on category pages, product pages, search results pages, in shopping carts and on order confirmation pages.
54% of U.S. online shoppers notice product recommendations on e-commerce sites, according to a 2007 survey by Forrester Research Inc. 34% of those shoppers say they have made purchases based on recommendations.
“Consumers are often persuaded by recommendations, as recommendations help them discover products they might not have been familiar with otherwise,” says Sucharita Mulpuru, a principal analyst at Forrester Research.
Bookseller Borders considers product recommendations integral to e-commerce, and they play a prominent role in the multi-channel retailer’s new web site launched in May after the retailer moved off of the e-commerce platform of Amazon.com Inc. “Recommendations are critical to the online customer experience,” says Kevin Ertell, vice president of e-business at Borders Group Inc., which uses software-as-a-service product recommendations from ChoiceStream.
While some shoppers know exactly which book or film they want and are looking to get in and out quickly, more than half prefer to browse because they don’t know what they want, Ertell says. Borders’ web strategy is to create a bricks-and-mortar bookstore experience online, letting customers “walk around the store” and browse to find things of interest. Product recommendations are key to this strategy.
“Recommendations are a great way to find what you’re interested in and then continue along a chain of recommended products that keep appearing,” Ertell says. “You get a depth and breadth of interesting items along the way during the online experience.”
Borders displays product recommendations on product pages, the shopping cart page and the order confirmation page. The greatest opportunity is in the shopping cart, Ertell says.
“It’s the same as placing products by the cash registers in the stores. It’s a chance to see something else on the way out,” Ertell explains. “Plus, we have free shipping thresholds, and product recommendations on the shopping cart page can encourage customers to buy something else to get them over a threshold.”
More likely to buy
Borders is happy with the results to date. Shoppers are more likely to buy a product going to a product page via a recommendation than they are going to a product page on their own, Ertell reports. And shopping carts that include recommended items have a higher average order value, he adds.
“Product recommendations have proven their worth,” Ertell says. “It’s not an overly significant cost to implement them, and the return on investment is definitely there.”
This is also the case at bridal accessories retailer The Knot Inc. It cost $7,000 to implement Baynote product recommendations in March, and the retailer pays an undisclosed monthly fee. In return, The Knot credits product recommendations with lifting overall sales 15%.
Recommendations appear on product pages and search results pages. The retailer is considering adding recommendations to the home page, replacing the current list of top-selling products.
“A customer can be looking at some candy, and the recommendations system will show perhaps a place card holder and ribbons to go with that candy,” explains Kristin Savilia, vice president of e-commerce. “For all other customers who bought that particular candy, they most often were looking at or buying these other products, and the recommendations system pulls to the forefront some combination we the retailer would never even have thought of.”