Hit and Miss: Why High Traffic Streams Need not Lead to More Online Business (page 2 of 2)

They state that the most dramatic demonstration of their model's validity and usefulness is its ability to pinpoint important differences in purchasing behavior across households. Besides confirming that customers who visit a particular online store frequently tend to buy something when they do-a maxim that has long made frequent shoppers a prime target segment--their model also confirmed the researchers' complementary hypothesis: Households in the process of increasing their visiting rates over time are more likely to buy something during a visit to an online store than households that are slowing down.

When you consider these two effects together, say the authors, it becomes clear that households that combine high frequency with an upward evolutionary trend in visiting behavior have dramatically higher conversion rates (the number of purchase transactions divided by the total number of visitors) than all other households. And for e-commerce executives, to whom measuring and managing conversion rates is becoming increasingly crucial, this is an important finding.

Web stores can apply this more refined segmentation approach in many ways, says Moe. Rather than look at the entire mass of visitors who come to a site, e-commerce executives can look at individual visiting patterns and identify people who may be visiting fairly frequently, and at an increasing rate. Those segments of consumers tend to be the most promising buyers, notes Moe. Online retailers should work on getting visitors to come at a more frequent pace, perhaps by offering benefits for frequent visitors or by providing learning mechanisms that encourages them to interact more often with the website.

Web store owners could also use this more closely targeted information when trying to manage online traffic more efficiently. Moe gives the example of a method already used by a popular website. "Victoria's Secret frequently redirects visitors into either a faster server or a slower server, because they just have too much visitor volume to handle it all. People who are more serious buyers are redirected to a more efficient server. Our model can detect whether or not someone is more likely to buy, and that person can be treated a little differently in terms of how they're being served."

Moe notes that the model holds up well, too, when it comes to looking ahead: "Our model helps people forecast their web traffic in the future," she says. For both retail sites examined, the model tracks future visiting patterns extremely well, staying within 5% of the actual data during a four-month period.

Moe believes that e-business owners who heed such marketing insights will gain advantages over those who do not. "It's been easy to get a business going on the Web. The stock market has been pretty good, and investors have been supportive. But eventually--and I think it's starting now--the market will settle down. And for industries like the book market and the CD market, a shakeout is coming. In order to come out on top, you have to really understand your customers and better meet their needs. Companies will have to better manage their web traffic, and understand when customers are going to buy. Those are the ones that will come out on top."


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