Turning Browsers Into Buyers

The sad truth about electronic commerce is that although a Web site may receive millions of visitors, only about 3% actually buy anything. Consequently, the Holy Grail of e-commerce is figuring out how to turn the browsing 97% into buyers. Online retailers are making some progress in that regard: The order-conversion rate increased from 1.8% to 3.2% in 1999, according to an April 2000 study by the Boston Consulting Group and shop.org.

Analysts attribute that rise to improved Web-site design and consumers' increasing comfort with Internet shopping. But there's still a lot of room for improvement. About 65% of online shopping carts are abandoned before the purchase, representing significant lost sales, the study said.

The solution is not to offer pop-up discounts or promotions to every Web-site visitor. Some of those visitors are mere tire kickers and have no intention of buying, and some will buy even without an extra incentive. Two marketing professors have developed an online buyer-conversion model that distinguishes between committed browsers and potential buyers — a capability that has important ramifications for how retail Web traffic is managed.

The model is described in a new working paper, “Which Visits Lead to Purchases? Dynamic Conversion Behavior at E-Commerce Sites,” by Wendy W. Moe, assistant professor of marketing at the University of Texas at Austin, and Peter S. Fader, associate professor of marketing at the University of Pennsylvania's Wharton School (http://216.107.131.155/archive/papers/980.pdf).

The model builds on the understanding that Web-site visits can be valuable to a retailer even if no purchase occurs. A consumer may need to make four or five visits to a Web site — researching the options and checking the terms — before feeling comfortable enough to go through the cyber checkout line. The working paper identifies four types of online shopping visits:

• Directed-Purchase Visits. The consumer is ready to purchase right away.
• Search and Deliberation Visits. The consumer is researching the merchandise and terms — and eventually intends to buy.
• Hedonic-Browsing Visits. The consumer is doing electronic window shopping —that is, shopping for pleasure or recreation.
• Knowledge-Building Visits. The consumer is engaged in exploratory browsing to learn more about the marketplace — a pursuit that may affect long-term shopping behavior.

Moe and Fader's model analyzes visit and purchase data for a given consumer and makes a real-time prediction about whether the consumer's current visit will result in a purchase.

Using the model, an e-retailer could manage visitor traffic and redirect the likely buyers “to a server that will provide a better shopping experience,” the paper says. And what about those tire kickers? They're good targets for a pop-up promotion that will nudge them toward making a purchase.

But customizing Web visitors' buying experiences according to purchasing patterns has its risks. Last year, Amazon.com tested the waters of so-called “dynamic” pricing by varying the price it charged for the same DVDs. When consumers compared notes on Internet message boards, they were outraged, and Amazon retreated.

Moe and Fader say that loyal customers and tire kickers get different treatment from salespeople at conventional stores all the time — and will get used to it on the Internet. Victoria's Secret, for example, already redirects its frequent buyers to a faster Web server. What consumers don't like is blatant price discrimination and invasions of privacy, Moe says.

To avoid a backlash, e-tailers need to be subtle in their preferential treatment and must not spook consumers by revealing just how much personal information they know.

It may take a few years for something like the conversion model to become integrated into mainstream e-commerce systems, says Barrett Ladd, senior analyst at Gomez Advisors Inc., an e-commerce consultancy in Lincoln, Massachusetts. And it will be a while before e-tailers are brave enough to try more dynamic-pricing schemes, after the highly publicized Amazon.com fiasco. Eventually, however, real-time computer models that analyze and learn from consumers' Web visits will play an important role in turning browsers into buyers. Ironically, the models' real value may be that they make the Web site more like a human salesman, says Jody Dodson, executive vice president of cPulse LLC, a New York firm that measures online customer satisfaction.

Think of the old-time shoe-store salesman who knew his customers, knew what they had bought for years, and knew who had to try on 11 pairs before one pair would feel right. E-commerce has a long way to go to match that salesman.

“In an offline shop you interact with people and find out what they like and don't like. You wouldn't go into the back room, put on a blindfold and sit there, and at the end of the day count up the sales and ship out the products,” Dodson says, “but that's what most Web businesses do.”