Fit Analytics has an intuitive solution that reduces the need for shoppers to deliberately over-order.
The Multi-Size Purchase Issue
Poor fit is one of the leading reasons shoppers return items they purchase online. Sizing varies across different apparel brands and even within a single retailer’s store. This has led shoppers to mistrust online sizing information and taking matters into their own hands by multi-size ordering.
In this article, we cover how to reduce returns in e-commerce by focusing on how Fit Analytics is using the power of machine learning to combat the apparel industry’s multi-size purchase problem.
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Fit Analytics’ Head of Product, Dan Mayer, explains how Fit Finder takes the guesswork out of finding the perfect size online.
Launched in 2013, Fit Finder was created to help find your fit. The intuitive size advisor tool is powered by the most advanced machine learning algorithms in the business and the industry’s biggest data set.
Bridging the gap between in-store and online shopping, Fit Finder provides customers with the requisite confidence needed to find the right size and fit every time, without ever stepping foot inside a store.
Fit Analytics’ Head of Product, Dan Mayer, gives a deeper insight into Fit Finder and reveals how the tool can evolve in the future.
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A two-month A/B test with Fit Finder showed positive results for Swedish outdoor retailer Ridestore. Customers using Fit Finder showed a 9% increase in value per visitor and a 4% increase in conversion rate.
Everyday explorers need extraordinary gear – Ridestore is where they go to get it. Founded in Sweden over 10 years ago, Ridestore is known for its impressive selection of outdoor brands. With customers at the heart of its operational strategy, the company offers regional online shops in Sweden, Finland, Norway, Denmark, Poland, Germany, Austria, Switzerland, Italy, France, Spain, The Netherlands, and the UK.
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