ModCloth’s iPhone shows reviews based on a shopper’s size

The idea is that a consumer will trust reviews from others who have similar measurements.

Amy Dusto

Web-only women’s apparel retailer ModCloth has added a feature to its iPhone app that allows a shopper to enter her measurements and receive not only a list of products that will fit her, but also reviews for those products from customers of a similar size. Called Fit for Me, ModCloth designed the tool to help shoppers take better advantage of its thousands of customer reviews, says product manager Fontaine Foxworth. It maps to more than 900 items in the retailer’s catalog, she says.

After tapping on Fit for Me in the app’s left navigation, a shopper may enter up to four of her measurements (height, waist, bra, hips). Depending on the specificity of her criteria, she then receives anywhere from a handful to thousands of product results, along with reviews by customers that match her measurements, Foxworth says. If the results are too scanty, the shopper may select to add results for close measurements, too. That way, a 6-foot-2-inch woman, for instance, might receive a few hundred product recommendations with reviews from customers between the heights of 5 feet 11 inches and 6 feet 3 inches, rather than only five results that match her exact measurements.

“One of the toughest problems in e-commerce is always: ‘Will it fit the customer?’”Foxworth says. ModCloth already had the data—more than 80% of its products are reviewed by customers, with 55% of those including the reviewer’s measurements, she says. All her team had to do was make that data searchable.

“It definitely was a significant investment for the back end, but we verified interest with customers first,” Foxworth says. “It would have been a gamble that users would want to put in their measurements, but we already knew they would.” ModCloth in May added for a feature that lets customers write reviews directly from the app, and since then customers have been leaving 30% more reviews every day, she says.

Before fully developing the feature, Foxworth’s team also proved that adding reviews to product search results made search results more compelling to customers, she says. “We tested the exact same algorithm with an interface that just showed the pictures of the products, not with users and what they said about it,” she says. In that case, customers reported having less trust in the quality of the results because they weren’t sure about the basis for the recommendations. With reviews, however, they could see “that it’s coming from real people with real dimensions—not an abstract algorithm,” she says.

ModCloth decided to release Fit for Me on the iPhone first because more than half of its customers shop via smartphones, Foxworth says. The retailer will soon release the feature on its iPad app, with an Android version to follow. However, the merchant will wait to see how customers respond before considering whether to add it to the web site. In addition to serving ModCloth’s mobile majority, Foxworth says a mobile-first release has two other benefits: It provides incentive to download the mobile app because it offers a service the web site doesn’t have, and it fits customer expectations that mobile shopping will be more personalized than web shopping, which ModCloth discovered from customer surveys. “When we asked customers, ‘How do you use your apps?’ it kept coming up that it was about personalization,” she says.

The retailer may build on Fit for Me, perhaps by allowing customers to follow reviews from a particular “measurement twin,” or to sort reviews by quality, she says. ModCloth already allows customers to click on a review to mark it as helpful or not. “Fit for Me is our first stab at elevating those relevant reviews,” she says. “We have great plans of how to make it better, potentially even rewarding a user if she contributes really helpful reviews.”


customer reviews, Fit For Me, Fontaine Foxworth, iPhone app, m-commerce, Mobile, mobile app, mobile commerce, Mobile technology, modcloth, product recommendations