John Lewis & Partners

  • RETAIL SEGMENT: Department Store
  • PRODUCT: Recommend and Advanced Merchandising
  • CHALLENGE: Greater understanding of customers and their shopping missions. Insights and fast learning on best performing personalisation strategies
  • RESULTS: Detailed insight into customer behaviour through programme of testing and optimisation

“RichRelevance gives us the ability to easily and confidently test new ideas and strategies which is invaluable to our learning as we seek to continually improve our knowledge of what our customers engage with and what improves their experience”

– Stelios Nikolidakis
Partner and Personalisation Manager, John Lewis & Partners

John Lewis & Partners is a department store with 51 locations, known for its fashion, beauty, homeware and technology. At the heart of the success of John Lewis & Partners is their ambition to better understand their customers’ needs. John Lewis & Partners leverages data to identify shopping missions and trends to continuously drive a better customer experience.

John Lewis & Partners has been a RichRelevance customer since 2011 utilising personalisation across their desktop and mobile sites to deliver the most relevant shopping experiences possible using RichRelevance’s Xen AI and machine learning.

To ensure constant innovation and improved customer experiences, John Lewis & Partners has always sought to continually test and optimise their personalisation applications and placements. With the RichRelevance personalisation platform they are able to quickly test and uncover insights about what works and what doesn’t.

As an innovator in personalisation, John Lewis & Partners was eager to be one of the first RichRelevance customers to test and deploy image based recommendations. Via the RichRelevance Data Science Workbench, in conjunction with Clarifai, John Lewis & Partners leveraged image based AI to enhance recommendations across key categories including dresses, men’s shirts and home. The image based recommendations display suggestions based on matches in colour, patterns, etc.

The John Lewis & Partners team works closely with RichRelevance expert personalisation consultants to continually test initiatives and try new strategies aimed at improving their customer experience. John Lewis & Partners is keen to find out which personalisation strategies and placements perform best. Some of their unique tests and results have included:

  • Increase Cross-Selling: Interested in increasing accessories sales, John Lewis & Partners leveraged Advanced Merchandising to recommend a related item on the product item page immediately after a shopper adds an item to cart. This placement really resonated with John Lewis & Partners customers, it received high engagement with +4.2 % click thru rate , plus a 23 x higher propensity to purchase compared to the site average.
  • Clearance Boost: During a Clearance event John Lewis & Partners boosted Clearance items in recommendations on Offers pages to customers who previously showed affinities for offers. This resulted in increased engagement with Clearance lines, providing great insight and learning for future events.
  • New Items: In order to personalise the experience on the ‘New In’ womenswear category, John Lewis & Partners added a placement at the top of the category listing page that only displayed personalised recommendations. This placement saw higher engagement than with the top row of the category listing, increased overall basket adds and basket adds per session.

“Due to the breadth of our assortment coupled with the range of shopping missions our customers are on, we use a test and learn approach to better understand our customers and learn how we can provide them with a best in class experience with John Lewis & Partners. Having the RichRelevance personalisation platform helps automate and enhance our learning through AI and data science, enabling us to learn faster and make quicker decisions.” Commented Gabriella Frankl, Partner + CRM Team Manager at John Lewis & Partners.

More recently as part of their digital strategy for their in-store initiatives, John Lewis & Partners enhanced their Partner App with personalisation. The Partner App, which was originally launched in 2017 enables partners to better serve customers in-store through instant stock checking, ordering items, browsing the entire John Lewis assortment, doing product comparisons and sharing products by email, now also utilises the RichRelevance AI- driven personalised recommendations. Rolled out across 2000+ devices throughout their 51 shops, RichRelevance insights give partners a direct route to cross-sales during their conversations with customers, helping partners improve the customer experience by steering them towards the right products faster.

Overall John Lewis & Partners has proved to be a highly inventive retailer, at the forefront of innovation, always excited about the opportunity to test new technologies and ideas. By placing customers at the heart of everything John Lewis & Partners does, they are rewarded with great customer loyalty, trust and of course tremendous success.



  • 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.
    • 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, 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.



In a retail world with more digital touchpoints than you can count, how do you connect the dots?

Your customers are busier, more distracted and more overwhelmed then they’ve ever been before so understanding their shopping journey is crucial. Your shoppers will be more inclined to spend the time logging in if you can demonstrate that it translates to a more individualized experience from which you can both benefit.

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Founded in 2005, Bubbleroom is a pureplay ecommerce retailer specialising in women’s apparel, operating in the Nordic Countries. Bubbleroom is now the number one Scandinavian retailer for women’s party fashion.

Bubbleroom started working with RichRelevance in 2016 and have deployed both personalized recommendations, Recommend™, and personalized browse and navigation, Discover™, on their desktop and mobile sites. Personalized product recommendations enable Bubbleroom to craft highly tailored and accurate suggestions for their shoppers, with personalized browse and navigation helping shoppers locate the right products, more easily.

“The RichRelevance personalization solutions have helped us enhance the customer experience on our website. As a result of using RichRelevance we have improved our conversion rate by 11% and average order value by 9.5%” commented Ville Kangasmuukko, CEO at Bubbleroom


Towards the end of 2016 Bubbleroom turned their attention to their mobile site. Bubbleroom refused to accept their mobile conversion rate of 1.3%, half that of their website, was good enough. Rebuking the myth shoppers research and browse when on their mobile, rather than purchase, Bubbleroom sought to improve theirs through optimizing the mobile experience for shoppers. They targeted themselves with achieving a similar conversion rate to the website rate of 2.4%.

“We saw the potential mobile presented.” explained Ville, “With industry gures telling us that 4 in 10 transactions involved multiple devices, and with the number of purchases on mobile devices on the rise, we believed there was an opportunity to do much better than we were. While mobile traffic represented 50% of our traffic volume, it was only delivering 30% of revenue.”

With over 50 brands and 65,000 products, the main challenge Bubbleroom faced on mobile was the limitations in showing the wide range of products and brands they had on the smaller device. With limited options to display recommendations on mobile, Bubbleroom had to ensure they didn’t waste this opportunity to inspire shoppers with their suggestions.


Bubbleroom turned to data as the answer.

The RichRelevance User Profile Service, (UPS) builds one profile for each customer, tracking all activity as it happens across all sales channels. Every view, click and purchase is stored, building a holistic profile for each customer, which is then available within a second to drive personalized decisions on the subsequent options presented to each shopper as they travel on their shopping journey.

“Through the UPS there was a wealth of information at our fingertips, explained Ville, “From size preferences, brand afinity and price afinity, to search queries, context and location – we realised we knew a lot about our customers.”

Leveraging the RichRelevance UPS, Bubbleroom has been able to utilize what they know about their customers to improve the mobile experience. The resulting personalized product recommendations made for highly tailored and accurate suggestions, connecting shoppers with the right products straight away.


As a result, Bubbleroom have seen a 16.8% higher average order value from shoppers interacting with recommendations on their mobile device, with items per order increasing by 18.5%. Importantly Bubbleroom has successfully increased their conversion ratio on their mobile to 2% in line with that of their website. Mobile now represents 63% of traffic and 59% of revenue.

“We are delighted we have managed to turn around the performance we are seeing from mobile traffic, it now rivals our website performance. Next, we are setting our sights on taking our personalization to the next level through advanced merchandising.” Concluded Ville.