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An e-retailer revises its holiday plans to cater to independent-minded shoppers
Monetate’s testing tools reveal ModCloth customer preferences for the unique.
Managing Editor, B2B E-commerce
Topics: 2013 Internet Retailer Top 500, global header, Megan Walsh, Mobile, modcloth, Monetate Inc., Nathan Richter, personalization, SaaS, Software-as-a-Service, targeting, test and target, wish lists
Web-only retailer ModCloth takes pride in offering a forever-changing array of unique apparel, accessories and home furnishings that it sources from more than 1,000 independent designers, like a “cosmic sea” printed dress, complementary bow-tied heels and bedspreads illustrated with images of tropical plants. And when it tested a “Most Wished For” campaign during the 2012 holiday shopping season, featuring the items customers most listed on their wish lists, it expected to see high conversion rates.
It soon learned otherwise, as conversion rates during that test dived. After further testing, ModCloth concluded it had trained its customers to expect the unexpected. “We learned our customers didn’t appreciate our suggestions,” says Megan Walsh, director of retention marketing. “They don’t shop for common ideas—they shop for unique items.”
With that lesson learned, ModCloth is planning to run a “Hidden Gems” campaign in the 2013 holiday season, enticing shoppers with the new and unexpected.
The retailer, using technology from Monetate Inc. to test and personalize merchandising displays, is also going beyond a broad understanding of its customers’ interests to dig more deeply into what moves particular segments of shoppers to buy. With about 400 promotional campaigns underway at any one time as part of ongoing testing and targeting efforts, ModCloth is learning what attracts segments of shoppers based on such criteria as whether they’re new or returning customers, their geographic location based on the IP address, the type of computing device they’re using to shop online, and their past shopping history.
In a test of different ways to feature additional items with displays of red dresses, ModCloth found that shoppers as a whole converted at the same rate whether the displays featured “Best Sellers” among an array of red dresses or featured items related to a red dress, such as a cardigan sweater or dressy shoes. But when segmenting shoppers, ModCloth found differences: returning customers preferred to see related items, for example, while new customers wanted to see best sellers.
ModCloth, No. 366 in Internet Retailer's 2013 Top 500 Guide, also has learned that shoppers on smartphones preferred to view videos of products rather than flipping through a digital style book. It found that mobile customers’ average time on site and number of pages went up “dramatically” with video as compared with the digital style book, Walsh says.
Among its tests and personalization campaigns, she notes, ModCloth has seen lifts in conversion of 15% or more along with a double-digit percentage increases in average order values.
The cost of Monetate starts at $50,000 per year on a software-as-a-service model, with the price ranging upward based on the number of products and amount of traffic to the web site, according to a company spokeswoman. There are no additional set-up fees, he adds. Under a SaaS model, client retailers rent the technology from a vendor that hosts the software, with the retailer accessing it through a web connection, saving the cost of installing the application on the retailer’s own network hardware.
The company says that deploying Monetate’s technology requires a retailer to copy and paste a single line of software code in an e-commerce site’s global header, the section of a site’s web pages that contains a company’s logo and other elements such as a main navigation bar and site search window. That can take from minutes to hours, depending on site’s size and traffic volume, he adds.
Getting a retailer set up to run test and target campaigns, Monetate says, can take from days to weeks, depending on the number databases from which a retailer is pulling such information as customer shopping history and demographic data.