Coop

  • RETAIL SEGMENT: Big Box
  • SOLUTION: Recommend™
  • CHALLENGE: Coop sought an innovative personalization partner to help create a dynamic, customized site experience that increased engagement and conversion, with minimal IT investment.
  • RESULTS:
    • 20% of turnover influenced by recommendations
    • 15% increase in basket size
    • 5 – 10% lift in sales

«The fact that the ensemble of algorithms is constantly learning and improving means that as we learn more about users, we improve and deliver better recommendations each time. It’s a positive feedback loop.» — Brian Andersen Director Coop Denmark

One-hundred fifty year old Coop Denmark is the country’s leading retailer with about 1,200 supermarkets, hypermarkets, and discount stores selling furniture, toys, baby, garden, bikes, and wine. Coop is owned by the cooperative FDB (Fællesforeningen For Danmarks Brugsforeninger), which comprises 1.4 million members of the Danish Consumers’ Cooperative Society.

As the Director of Coop Denmark, Brian Andersen manages not only Coop.dk, but also dedicated specialists in key areas of online sales, marketing, IT project management, logistics and customer service.

About 1.5 years ago, Andersen was confronted with the challenge of a fully static website. Customers had the same site experience, regardless of whether it was a first-time visit, or the 100th visit. Having written his graduate dissertation on the power of personalization, Andersen was keenly aware that personalization could improve the online experience, and investigated possible solutions.

Navigating the customer’s path to purchase

Today’s tech-savvy and sophisticated online shopper has little tolerance for browsing through endless pages of products. While navigation and site search are functional starting points, personalized product recommendations are an intuitive discovery tool that can help shoppers find what they’re looking for faster. Having recommendations present on multiple site pages not only improves navigation, but also serves the purpose that an in-store sales associate typically fulfills.

“It’s pretty clear to me that personalization delivers a more relevant customer experience, because the site changes all the time according to the users’ behavior. Customers start using recommendations as a primary navigation tool when browsing the site,” says Andersen.

RichRelevance’s personalization engine dynamically facilitates competition among over 100 independent algorithms that consider different user behavior and catalog recommend™ “ data—and decides in real time which algorithm is best matched to a particular customer’s needs at a specific place and time. As a result, the more customers interact with recommendations, the more benefit there is for Coop.

“We chose RichRelevance because it had so many different built-in algorithms. Other solutions only had shopping basket rules, or a few kinds of algorithms. The fact that the ensemble of algorithms is constantly learning and improving means that as we learn more about users, we improve and deliver better recommendations each time. It’s a positive feedback loop, which is much more powerful than other solutions,” said Andersen.

As an added bonus, Andersen can now free up the time that his merchandising team previously spent updating recommendations (usually several hours of manual upkeep), as the automation of this task now allows them to focus on more strategic tasks.

Recommendations drive increases in basket size and conversion rate

Since implementing RichRelevance on the product, category, basket, and home pages, Andersen notes that 20% of turnover has been influenced by recommendations; sales have resulted from those shoppers who have specifically engaged with recommendations.

Basket size has increased by 15% and there is a 5 – 10% lift in sales. “Shoppers who use recommendations have significantly larger baskets—both in value and number of items. This is a very important metric for us in our daily figures,” said Andersen.

Reaping the benefits of personalization without massive IT investment

Another important criterion for Andersen’s personalization investment was ensuring minimal burden to his IT department.

“I was given the mandate to improve our ecommerce experience without a significant IT investment,” said Andersen. “It was great to implement RichRelevance without having to change our backend and ecommerce system, with the end result of a fully integrated system on customer behavior with very little investment.”

Leveraging one lightweight technical integration, Coop also uses RichRelevance for personalized emails and targeted dynamic promotions, and has big plans for delivering additional initiatives, beginning with personalized product lists.

“Our current solution sorts product lists by price, but we will use Discover™ in the future to personalize each product list with the most popular items first to see how this influences order size,” said Andersen.

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