Boosting the Browse-to-Buy Ratio at eBags

By Heather Baldwin

Earlier this year, with the dot-com world in the pits, online luggage and bag retailer eBags faced a challenge shared by countless other e-commerce companies: how to build revenue and profitability by increasing browse-to-buy conversion rates without increasing costs. EBags executives knew they could accomplish this mandate in one of two ways-increase traffic or convert a higher percentage of existing traffic into revenues. Anxious to keep acquisition costs as close to zero as possible, they chose the second option.

“Conversion rates are the best measure of customer satisfaction,” explains Jon Nordmark, CEO of eBags. “Improving those rates means making continuous improvements in click-throughs on emails, getting more browsers to put items in their shopping carts and getting more shopping carts all the way to the order desk. The way to improve all these rates is to understand our customers better, and the software industry is providing a wealth of tools for aggregating data sources, analyzing information on customer behavior, and putting the lessons learned into action via Website enhancements and personalized campaigns. The only question in our minds was which of those tools to use.”

Ultimately, eBags went with KANA, which was able to aggregate disparate data sources like eBags’ Oracle site database and email activity datamart, its J.D. Edwards financial system and data from customer-service email, chat and phone systems. Once it had the complete picture, eBags learned some important things about its customers. For instance, “we learned that in a list of offers in a customer email, people tend to buy the first and last items more often than the others,” says Mike Frazini, vice president of technology. “We wouldn’t know that except by examining both email data and sales data. Now we know to put our most profitable items at the beginning and the end of the list.”

The company also is able to shift content and react to buying trends more quickly now. With KANA analytics, it can analyze five sets of data in the time it used to analyze one set manually. With its increased knowledge, the company conservatively expects to increase its browse-to-buy conversion rate by 20% to 30% “just by making more good decisions about Website content,” says Nordmark.