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Adam Snyder
Global E-Commerce Sites Manager, Icebreaker
Supernova Award Category:
Artificial Intelligence and Augmented Humanity
The Company:
Icebreaker was created by a 24 year old Jeremy Moon in 1994. His aim was to provide garments for outdoor adventures with less reliance on petrochemical fibers, providing a more sustainable future for us - and the planet. But back then, synthetics were king, so convincing people was going to be far from easy. So the goal of Icebreaker was simple - to work with nature to develop performance clothing for outdoor adventurers. We knew that our fiber worked – because nature had created it to ensure that merino sheep could thrive through freezing winters and scorching summers. 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 Problem:
Icebreaker has been committed to powering engaging customer experiences on their sites for years. In fact, Brian Hoven, their Global Head of eCommerce said, “People want to be offered something that’s relevant to them. Personalization has become key to purchase decisions.”
Just like their unique shoppers, Icebreaker knew that their shopper journeys had to be equally as unique. Showing shoppers the best products for their specific needs was a key initiative for personalizing the commerce experience. To execute on this, Icebreaker had been using an alternative recommendation engine to power their product recommendations on site. That technology was expensive and added yet another tool to their technology stack, meaning that Icebreaker had to train a separate admin and ticketing system. The incumbent recommendations provider had periodic problem with feed updates, tagging and inventory awareness. It also required constant upkeep and QA checks for any new development.
The Solution:
Icebreaker was an early adopter of one of a kind, embedded Commerce AI. They eliminated both the additional cost and extra tool by implementing Commerce Cloud Einstein Product Recommendations. Einstein uses Artificial Intelligence to generate a unique predictive model for every single shopper who comes on site, regardless of whether their are known or anonymous. By embedding this AI deep within Commerce Cloud every single click from every single shopper on a retailer’s site is consumed and used to power cutting machine learning technology. With each click a shopper makes, their predictive model gets smarter and smarter, powering stronger and stronger recommendations.
Adam, global ecommerce manager, put his trust in AI and led his team through a smooth setup, working with support as necessary. By adopting this innovation technology, Adam was able to reduce development work, enabling them to get up and running quickly. Plus, freeing up resources to work on new projects!
The Results:
Product Recommendations give Icebreaker a greater opportunity not only for cross-sell but also upsell, as full- priced merchandise that is actually relevant to shoppers can be recommended. Icebreaker has implemented Product Recommendations on all six of its global sites.
Icebreaker used the flexibility of the Einstein Product Recommendations feature to test several different types of recommenders on their site. They found that the “You May Also Like” recommender on the PDP was a star performer, powering cart conversion rates of 30-40% rates globally.
Not only did Icebreaker see impacts on metrics, they also saw impact on resources. By enabling AI powered recommendations Icebreaker was able to revolutionize the way their team operated. Instead of spending hours manually merchandising or working with a third party team, Commerce Cloud Einstein provided them with automated personalization so they could free up resources and focus on strategic tasks.
Metrics:
Before going live on Einstein Product Recommendations they were tested against Icebreaker’s existing personalization vendor. 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 Einstein Product Recommendations 40% more often, leading to 28% more revenue from recommended products and an 11% overall increase in average order value.
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.”
The Technology:
Icebreaker fully embraced using an embedded AI technology within their commerce platform, right from the beginning. Instead of fearing AI or being uncomfortable with losing some of their control, they fully embraced data driven machine learning algorithms. They were early innovators with Product Recommendations, personalized product recommendations across the eCommerce experience. Plus, they continue to do so as are an early adopter of Einstein’s newest feature, Predictive Sort.
Disruptive Factor:
Icebreaker did not face any major challenges during implementation due to the ease of implementation. However, their site feel and eCommerce team workload has changed since go live. After successful testing, Icebreaker went live with recommendations throughout their entire site. This is disruptive because it eliminates the time consuming task of manual merchandising. Instead of a member of the eCommerce team having to keep up with manually selecting recommended products for every product page, Einstein now does all of the work automatically. Retailers say that this switch can save them up to 20 hours of work per week! In addition, once Icebreaker saw that Einstein technology was impactful, they continue to implement recommendations in new ways. They have also expanded beyond recommendations and have begun implementing Predictive Sort as well.
Shining Moment:
Icebreaker’s implementation for Einstein has earned them recognition at Dreamforce and Salesforce XChange as an Einstein Trailblazer.