Icebreaker Uses Machine Learning to Power Product Recommendations

Who would have thought that a successful business could be made by selling t-shirts made of wool? Not many, but in 1994 New Zealand-based Icebreaker went out on a limb and developed base layer garments made of fine merino wool. In the years since, Icebreaker has grown to sell a wide assortment of outerwear and lifestyle clothing for men, women and children, in more than 5,000 stores across 50 countries.

The company got a handle on what customers wanted — before they even knew they wanted it. Today that continues online, as Icebreaker has successfully implemented Product Recommendations from the Salesforce Commerce Cloud, formerly Demandware, which leverages leading edge data science to suggest products for both known and anonymous shoppers across the entire shopping journey.

“People want to be offered something that’s relevant to them. I know that’s what I’m looking for when I’m shopping,” says Brian Hoven, Global Head of eCommerce at Icebreaker. “Personalization has become key to purchase decisions.”

Demandware is now the Salesforce Commerce Cloud

“If you’re not using this, you’re missing out on quite an opportunity.”Brian Hoven,Global Head of eCommerce,Icebreaker

CASE STUDY: Icebreaker Uses Machine Learning to Power Product Recommendations

Icebreaker had been using an alternative predictive recommendation engine, but when Product Recommendations were introduced as an integrated element of the Commerce Cloud, Icebreaker decided to A/B test against the incumbent.

The incumbent ran the test for Icebreaker over a two-week period in May in an apples-to-apples comparison.

Icebreaker found that its shoppers clicked on Commerce Cloud (formerly Demandware) Product Recommendations 40% more often, leading to 28% more revenue from recommended products and an 11% overall increase in average order value.

“Honestly, it’s a no brainer,” says Hoven. “There is no external integration. It was easy to set up and we were able to get in there and make edits ourselves. I’d tell other retailers, if you’re not using this you’re missing out on quite an opportunity.”

Plus, he adds, “Not only are there significant savings, everything performs better too.”

The engine powers product recommendations on the product detail page in two ways: one is “you may also like,” which shows three related items based on purchase history or additional purchases other shoppers have made; and “designed to go with,” which shows three specific items designed to complement the original. For example, if the shopper was looking at an outerlayer, the system will recommend a baselayer, first layer, and/or socks and accessories.

The recommendations are generated by the product intelligence engine, built into the Commerce Cloud, that models shopper activity and affinities in real time to predict the most relevant products to

promote to each individual shopper. With each click and interaction, the engine gets smarter.

Product Recommendations give Icebreaker a greater opportunity not only for cross-sell but also upsell, as full- priced merchandise that is actually relevant to shvers can be recommended.

Icebreaker has implemented Product Recommendations on all six of its global sites, and plans to also test Predictive Email to deliver 1:1 personalized emails.

The incumbent vendor “insisted they’d perform better than Commerce Cloud (formerly Demandware) Product Recommendations,” says Hoven. “But the Commerce Cloud results were consistently better, which made them the obvious choice as other solutions failed to make the grade.”

Salesforce Commerce Cloud5 Wall Street Burlington, MA 01803

+1 (781) 425 1400

©2016 salesforce.com, inc. All rights reserved. Various trademarks held by their respective owners.

commercecloud.com