It’s not hard for visitors to the website of engelhorn to get lost in the enormity and diversity of its product catalog. The Germany-based department store carries more than 700 brands and 35,000 products, offering customers a vast selection of fashion, luxury, and even sporting goods.

While great for shoppers, the breadth of their catalog presents big challenges for engelhorn.
Because of their expansive inventory, it is crucial to the online shopping experience that engelhorn present each customer with the products they are most interested in.

But with an online merchandising staff of just three managing a business growing between 20%–30% annually, it was almost impossible for engelhorn to deliver these tailored product recommendations — particularly with such a huge and diverse product catalog.

How could the company get the right products in front of the right customers?

According to Kristin Hesse, Director of Online Merchandising, when she first heard about Commerce Cloud’s AI-powered product recommendations, she was eager to give it a try. After all, engelhorn had been running its fast-growing online business on Commerce Cloud since 2013, and was almost always open to implementing revenue-driving growth strategies suggested by the Commerce Cloud team.

So in November 2016, engelhorn became one of the first retailers to implement Einstein Product Recommendations.

“Salesforce informs you about the latest innovations and brand-new tools, and there’s always the opportunity to communicate with other Salesforce customers to share knowledge and experience,” said Hesse. “It’s a partnership. The company talks very openly and shares information, and when you share, you gain much more than you lose.”

 

Every customer is an individual. A huge factor for us was that the recommendations should be tailored for each customer and fit the product category and price segment.”

KRISTIN HESSE | DIRECTOR OF ONLINE MERCHANDISING AT ENGELHORN

Because Einstein is built right into Commerce Cloud, implementation couldn’t have been easier. After taking part in implementation workshops run by the Commerce Cloud Retail Practice team, engelhorn’s merchandising team was able to configure and implement personalized recommendations after only four weeks.

In-house developers activated product and order feeds, while merchandisers determined placement, layout, and usability of the recommendations. The recommendation content slots sit below each product image and are based on browsing and purchasing history. There are three recommenders: products that “complement,” “alternates” to what is being viewed, and “recently viewed.”

Hesse’s team can optimize recommendations with flexible product sliders. The content slot is so easily customizable that every retailer can make its recommendation sections look and feel unique.

“The implementation was very fast and quite easy,” said Hesse. After several weeks, in a nod to the diversity and breadth of its catalog and its customers, engelhorn added several rules to meet its specific business objectives, such as only showing luxury recommendations with luxury products.

“Every customer is an individual. A huge factor for us was that the recommendations should be tailored for each customer and fit the product category and price segment,” said Hesse.

 

For engelhorn, AI-powered personalization has resulted in a 2.5% increase in conversion rate, a 1.5% increase in average order value, and a nearly 4% boost in revenue per visitor.

After seeing these results, Hesse and her team were eager to use Einstein more broadly, and implemented Einstein Predictive Sort, which personalizes category grid pages, displaying products in the order in which the customer is most likely to want to buy. They are also using Predictive Sort to personalize search result pages.

“We’ve seen our add-to-cart rates go up by more than 1%. We’re still at the beginning but figuring out how to use Einstein more and more. Someday, we may totally stop manually adding sorting rules.”

 

Einstein leverages the power of AI to automate tasks, thereby relieving merchandisers and marketers of the tedium of manually setting and resetting rules, and eliminating the need to manually merchandise full category and search pages. This fact, although attractive, actually gave engelhorn some pause.

“We were so used to telling our system exactly what to do. We had a challenge in trusting Einstein to do its own thing,” said Hesse. “We kept arguing that we needed more rules to feed to Einstein, but we didn’t, and we can see that it really works. We are totally convinced that the less you tell it to do, the better.”

She says engelhorn used to hear from irate vendors wanting to know why a luxury handbag was being recommended to a customer who was buying a pair of jeans.

“They don’t call anymore,” said Hesse, adding that Einstein has also helped them figure out which price thresholds and promotions to present to different customers.

engelhorn’s partnership with Salesforce, which dates to 2013, has not only propelled its online business by a factor of almost five, it has propelled Hesse’s career. Because she’s able to focus on meeting the needs of customers rather than maintaining technology infrastructure, she’s concentrated her efforts on innovations that enhance the customer experience.

She transferred her knowledge (gleaned through a retail practice adoption webinar and more) to others at engelhorn, teaching them best practices and proven methods she’s learning from Salesforce to drive the broader digital business.

“The way things are implemented and presented by Salesforce gives you a huge opportunity to grow and gain more insights and knowledge. You always have the opportunity to try something new.”

The whole point of Einstein is that you don’t need a large team of data scientists — or a large team at all — to achieve big results. That’s certainly the case at engelhorn.

“Our mantra,” said Hesse, “is do less, achieve more.”

 
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