In 1994, New Zealand-based Icebreaker 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.

Since day one, Icebreaker has understood what customers wanted — before they even knew they wanted it. Today, Icebreaker continues this approach with the successful implementation of Commerce Cloud Einstein, 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,” said Brian Hoven, Global Head of Ecommerce at Icebreaker. “Personalization has become key to purchase decisions.”

Icebreaker had been using an alternative predictive recommendation engine, but when Einstein Product Recommendations was introduced as a fully integrated element of Commerce Cloud, Icebreaker decided to A/B test it against the incumbent. The test ran for two-week period in May, following an apples-to-apples comparison.

Icebreaker found that its shoppers clicked on Commerce Cloud 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,” said 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 added, “Not only are there significant savings; everything performs better, too.”

 
 

If you’re not using this, you’re missing out on quite an opportunity.”

BRIAN HOVEN | GLOBAL HEAD OF ECOMMERCE AT ICEBREAKER

Commerce Cloud Einstein 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. The other is “designed to go with,” which shows three specific items designed to complement the original. For example, if the shopper was looking at an outer layer, the system will recommend a base layer, first layer, or socks and accessories.

Because Einstein is baked into the very fabric of the Commerce Cloud platform, Product Recommendations 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.

 
Einstein Product Recommendations gives Icebreaker a greater opportunity not only for cross-sell but also upsell, with recommendations for full-price merchandise that are actually relevant to shoppers. Building on this success, Icebreaker has implemented Einstein Product Recommendations on all six of its global sites.

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