Turning Browsers Into Buyers

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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 (

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.

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