JD.com and Alibaba create indexes to identify Chinese shoppers’ spending trends, which help retailers gain insight.
Customer segments built with analytics helps Gilt run more effective promotions.
Gilt Groupe Inc., the members-only, flash-sale retailer of designer fashions, figured it was missing the mark in terms of persuading women to buy men’s goods. “Women tend to buy stuff for men, but we were under-performing,” says chief marketing officer David Zucker.
To turn that around, Gilt worked with customer analytics data compiled by Mu Sigma, the retailer’s analytics service provider, to build predictive models of women shoppers most likely to respond to pitches for men’s products.
Mu Sigma combined such data such as demographic profiles of women known to purchase men’s products with information related to how women shop on Gilt.com. Mu Sigma then helped to identify segments of shoppers mostly likely to respond to particular promotions. A segment of female shoppers of certain age and income ranges, and from particular areas of the United States, for example, might be more likely than other women to shop for gifts such as men’s cufflinks.
When Gilt sends e-mail offers for men’s products to segments of female shoppers that it deems mostly likely to respond, the online retailer experiences a 10% to 25% increase in the rate of shoppers who respond to those offers and make a purchase, Zucker says.
Zucker adds that Gilt also knew from web site analytics data that many women shoppers do their impulse buying at noon—which turned out to be a good time to present them with offers for men’s products.
Gilt is No. 140 in the Internet Retailer Top 500 Guide.