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RelevanceTV: Book People


For more information on how Book People work with RichRelevance, read their case study here.  Find out more about RichRelevance personalization solutions here or request a demo.

Blue Tomato Case Study

  • RETAIL SEGMENT: Sporting Goods
  • PRODUCT: Personalized Recommendations
  • CHALLENGE: Wide range of products. 60% Webshop in 14
    languages and nine currencies
  • RESULTS: 3x revenue from orders containing product recommendations. 20% increase in
    average shopping basket. Average of one more product per purchase. Improved revenues on mobile devices

Blue Tomato was founded in 1988 as a snowboard school by the former European Snowboard Champion Gerfried Schuller. Since then it has transformed into a successful international boardsport and fashion shop. Blue Tomato now owns more than 30 shops in Austria, Germany and Switzerland and their webshop offers more than 450,000 products from more than 500 brands and delivers to more than 40 countries. Having launched its webshop in 1997, Blue Tomato was an eCommerce pioneer. It now aims to become the leading omnichannel retailer for boardsports and freeski in Europe.

Product variety proved too challenging for existing recommendation engine

By 2016 the growing breadth of Blue Tomato’s product range became a challenge for the company’s existing recommendation engine.

“Our traditional tools weren’t working for us anymore,” explained Andreas Augustin, Head of Webshop Development at Blue Tomato. “Our existing tool had reached its capacity to make automatic recommendations. We spent a lot of time and resources trying to manually improve results and ingest products, with limited results.”

Blue Tomato therefore sought a more sophisticated recommendation engine to handle its growing complexity. “We knew of several diverse eCommerce tools that included an element of machine learning within its platform to make recommendations”, said Andreas. “We decided we did not want this type of solution, but one whose core competency was recommendations.”
During an exploratory stage Blue Tomato evaluated solutions from six different vendors and finally chose Recommend™ by RichRelevance for its advanced machine learning algorithms, ease of use and merchandising functions.

Machine learning algorithms that challenge each other

“We particularly liked RichRelevance’s ‘King of the Hill’ approach, with its different machine learning algorithms that continually challenge one another to get the best results,” said Andreas Augustin. “We also valued the extended merchandising functions that enable us to maintain specialist product areas inhouse. Finally, the personalization developed specifically for mobile devices was very important for our omnichannel strategy.”

Experienced RichRelevance consultants supported Blue Tomato through the implementation process, which ran smoothly and fast despite the webshop’s complexity. “Thanks to RichRelevance’s great support – often on short notice – we were not only able to optimize the system but could also be assured that our personalization project would be successful in real-time mode,” explained Andreas Augustin.

Personalization that customers and staff can equally identify with

Blue Tomato’s revenue created by product recommendations has grown significantly since going live with RichRelevance in Autumn 2016. Crucially for Blue Tomato, revenue resulting from product recommendations has tripled, proving the value of the investment in RichRelevance. That the personalized recommendations resonate with Blue Tomato’s customers is also rejected by the fact that they spend more. “Thanks to RichRelevance, the value of the shopping baskets resulting from the product recommendation has increased by an average of 20 percent, with an average of one more product purchased by each customer”, summarized Andreas Augustin. ”The numbers apply as well for the recommendations shown on the mobile devices, where significantly less products can be listed but thanks to RichRelevance these are the most relevant.”

Blue Tomato’s product management and marketing teams are also impressed by the the quality of the recommendations. One type of recommendation problem that has been historically difficult to manage is when customers buy separate matching products – for example bikini tops and bottoms in different sizes or from different collections. When this happens, appropriate recommendations should still appear as if the customer had chosen a matching set. “Before using RichRelevance, this common scenario had been difficult to maintain and manage,” explained Andreas Augustin. “We were able to configure Recommend™ to ensure matching parts were listed together. However, this wasn’t even necessary as the algorithms figured it out pretty fast themselves.”

More personalization, also for content

Looking to the future Blue Tomato plans to extend personalized recommendations to its various online theme parks such as “beach life,” and also for the Blue Tomato “rider crew” sites that feature snowboard, freeski, surf and skate athletes who are sponsored by 30-40 different brands. The company plans to use RichRelevance to personalize the content that visitors see, along with more interactive sites to further improve the customer experience.


Clarisonic Case Study

  • RETAIL SEGMENT: Wellness & Beauty
  • PRODUCT: Engage™
  • CHALLENGE: Clarisonic wanted to maximize revenue per session by optimizing home page content tiles for different customer segments.
  • RESULTS: Using RichRelevance Engage, Clarisonic achieved the following performance on its home page content tiles:*
    • 20% increase in clickthrough rate (CTR)
    • 24% increase in revenue per session
    • 35% increase in overall clicks

* Comparing performance from October 2014 against October 2015.

“The fact that I can keep testing content and justify why I’m showing it is amazing! I’m confident that these data-driven decisions are helping to move visitors efficiently through the purchase funnel.” Casey Davidson Director of Ecommerce and Digital Marketing , Clarisonic.

Clarisonic’s mission is to beautifully transform skin through its award-winning devices, which cleanse skin six times better than hands. Hand-assembled at its Redmond, Washington headquarters, the devices are distributed through prestige retailers, dermatologists, cosmetic surgeons, spas and online at Clarisonic is part of the L’Oreal Group.

As Director of Ecommerce and Digital Marketing for Clarisonic, Casey Davidson is in charge of managing customer website experiences to achieve the directto-consumer revenue goals for the business. In addition to optimizing traffic, conversion and revenue, the team manages all content, paid media channels, affiliates, search engine optimization and search engine marketing as well as retargeting, email and loyalty programs.

For Davidson, engaging and educating the new user is a critical objective for the website. When new visitors have their first experience with the brand on the website, she wants the site to represent the “knowledge hub of the brand” so they can learn everything—from which Clarisonic device is right for them to what skin care products and brush heads pair best with their chosen device.

Old way: multiple messages meet multiple needs

Prior to working with RichRelevance, Davidson’s team used a carousel approach for their website homepage. A rotating carousel of three to four content tiles cycled through the home page, switching to one static tile during sale periods.

Davidson considered this method to be standard but outdated. “Promoting multiple messages regardless of behavior, and hoping one sticks, was not ideal. We gained no insight into the segmentation of our customer database—who’s coming, what they’re interested in,” said Davidson.

While the approach satisfied internal stakeholders who wanted frequent home page updates and wanted to ensure that a variety of messages for different products were seen, the issue remained: It was impossible to determine which messages resonated with visitors.

Personally, Davidson felt that it could be hard for visitors to digest any message moving through a carousel, let alone decide where to click. With no analytics or data to back up the message choices being made, she knew it was time for a change.

“We spend a lot of money to drive folks to the website and my job is to make sure they convert with the highest revenue per session,” said Davidson.

New way: content and context motivate visitors through the purchase funnel

Having learned about RichRelevance through existing partnerships with sister brands at L’Oreal, Davidson decided to use the Engage™ solution to segment audiences, and test and optimize content tiles against key metrics—clickthrough rate and revenue per session.

“Everyone will always have an opinion, but our job is to move from opinions to data-based decisions that show what customers are gravitating toward,” said Davidson. “Clickthrough rate may be a function of creative; revenue per session may be function of the right message and content. A lot of times we don’t know, and that’s why it’s critical to always be testing.”

RichRelevance Engage maps individual shopper behavior against advanced targeting and audience segmentation tools so that marketers can deliver highly personalized campaigns with relevant content. Its ability to automatically target each segment, optimize the most effective creative for that segment, then pass the data back to the business to inform creative decisions for the next round of campaigns saves valuable time and eliminates the need to run hundreds of manual A/B tests to get content personalization right.

Clarisonic implemented Engage in June 2015, and has seen significant success in key metrics for users that interact with its home page content tiles, such as a 20 percent increase in clickthrough rate and a 24 percent increase in revenue per session, while also seeing a 35 percent increase in overall clicks.

“Engage gave us information on what messages and creative were resonating with customers. We tested 12-15 pieces of content for each customer segment and found two that resonated very well: ‘Find your Clarisonic’, which linked visitors to an interactive skin quiz recommendation engine, and a tile with the message ‘Great Skin Starts Here,’ which highlighted our key skin care benefits to our users,” said Davidson.

With plans to continue testing current content “winners” against new content, Davidson looks forward to uncovering more learnings and insight from using Engage.

“The fact that I can keep testing content and justify why I’m showing it is amazing! I’m confident that these data-driven decisions are helping to move visitors efficiently through the purchase funnel,” she said.


N Brown Group Case Study

  • SECTOR: Pureplay department store
  • SOLUTION: Recommend™ & Consultancy Services across web, mobile and contact center
  • CHALLENGE: N Brown Group was looking to free up time and resources from manual recommendations and implement a more dynamic and scientific solution that would enable quicker reactions to customer behavior. As the business underwent a transformational programme from traditional catalog retailer to digital first, online retailer, N Brown Group needed more insight into its customer base to deliver a more personalized experience.
  • RESULTS: 30% increase in sales

N Brown Group is one of the UK’s leading online retailers with a specialism in fashion that fits and flatters, offering customers an extensive range of products in clothing, footwear and homewares. The Group’s three power brands include JD Williams, Simply Be and Jacamo.

Back in 2013, N Brown Group was struggling with manual product recommendations on its ecommerce sites, which was proving both time-consuming and costly. Frustrated by their inability to react quickly to customer behavior and transactions, the business decided it needed a more dynamic and scientific solution, which was based on customer insight rather than someone’s personal opinion.

N Brown Group turned to the RichRelevance personalization platform as a data driven engine that made decisions quickly and dynamically with the ability to react to a mix of transactional and browsing data when making recommendations for individual customers. The fact it was mobile friendly was an additional advantage.

“The ease of integration with our existing systems and an attractive commercial model, made the decision to move to RichRelevance easy.” Explained David Jennings, Head of Development and Customer Experience for N Brown Group.

Since these early days back in 2013, N Brown Group has undertaken a number of projects to improve its understanding of the ever-changing customer and increase the sophistication of personalization initiatives across all sales channels.

Ann Steer, Chief Customer Officer at N. Brown Group, commented, “Personalization and the ability to deliver, individual relevant experiences are the single most important thing that will help us deliver business success.”

To support more traditional product recommendation capabilities, N Brown Group introduced RichRelevance’s Advanced Merchandising solution in 2016.  Advanced Merchandising has enabled N Brown Group to put more relevant products in front of online shoppers that appeal directly to individuals’ tastes and preferences. For example ’complete the look’ products or recommending products that take into account customers purchasing history or current basket items.

Data-driven Advanced Merchandising has freed up merchandisers’ time so rather than manually creating outfits for display on the sites, they can focus on building merchandising rules that support wider site trading objectives and the needs of their customers. These merchandising rules work in partnership with RichRelevance’s algorithms, combining the expertise of merchandisers with the power of machine learning.  Reporting on the performance of Advanced Merchandising is provided by RichRelevance and allows merchandisers to see which rules are attracting the most customer engagement and driving the most attributable revenues.

Another personalization initiative has seen N Brown Group bring recommendations into the contact center, which is often overlooked as a retail sales channel especially when it comes to creating a personalized, relevant experience for customers. However, N Brown Group realised its potential and implemented RichRelevance recommendations in the contact center, training 950 UK-based contact center agents in the RichRelevance product recommendations engine and rolling out the personalization solution within the contact center system’s computers.

“Before we introduced data-driven recommendations into the contact center, staff would only offer the five products we had on sale, so it wasn’t personalized for the customer at all. Now, our customer service agents can advise customers on an increased product range using the power of the online recommendations engine. This real-time solution helps agents answer product queries, upsell and cross-sell products, or recommend alternative items that will resonate with the customer. We’ve effectively created another sales channel and improved the customer experience at the same time.” Said Jennings

Having Recommend™ live in the contact centers has had the added benefit of drastically reducing the time spent doing manual merchandising for the contact center.

By 2016, N Brown Group’s customer touch-points had diversified as it transformed itself from a catalog to online retailer. As a result N Brown Group sought to better understand its new breed of customer and maximize the opportunity online shopping presents to deliver a more personalized customer experience.

To gain this customer insight, N Brown Group commissioned RichRelevance to undertake an insights campaign to map the online shopping experience and explore what levels of personalization were right for N Brown Group’s customers to create a superior customer experience.

The project involved extensive user experience and personalization tests which the N Brown Group will use over the next year to guide the personalization implementation across all channels to enhance the customer experience.

“We see investment into understanding the customer as the most valuable data in terms of driving business and personalization forward in next few years.” concluded David Jennings.

Measures of success

As a result of their work together, the RichRelevance personalization engine has grown sales by 30% in 2016. This means recommendation-driven sales now account for 7% of all N Brown Group sales.

Furthermore, the result of the extraordinarily dedicated customer analysis is that N Brown Group is poised to exceed the £5m+ sales lift that its personalization initiatives already delivered in 2016.

Overall the data-driven product recommendations are now supporting the ecommerce site and, unusually, contact center staff, benefitting customer service and supporting an overall 12% year on year increase in sales for the group.


Shop Direct Case Study

  • RETAIL SEGMENT: Pureplay Department Store
  • PRODUCT: Personalization Consultancy
  • CHALLENGE: Not satisfied with the out of the box improvements, Shop Direct sought the help of the RichRelevance personalization consultants to optimize implementation and maximize results from their personalization initiative.
  • RESULTS: 2.5% lift in revenue per visit (Electrical Category)

Shop Direct is the second largest pureplay retailer in the UK with brands including and Littlewoods.

Shop Direct has been working with RichRelevance since 2014. They selected RichRelevance as a forward thinking technology partner who understood the needs of the customer and was able to provide personalization at all parts of the customer journey.

Over the past two and a half years, RichRelevance has grown to account for just over 6% of Shop Direct’s increasing sales, which are now almost £1.9 billion.

With an experimental test and learn culture, Shop Direct wanted to get more than the out of the box improvements they were seeing from the RichRelevance personalization platform. In August 2016 they commissioned a consulting engagement with the objective to optimize placements and maximize the results from their personalization initiative. In addition they sought technical assistance on implementation methods across the Shop Direct sites and

Measures of success

RichRelevance first undertook an assessment of the site, which included an implementation health check and a review of manually created rules. As a result, enhancement opportunities were identified and outdated rules were cleaned up. Improvements were made to enable Shop Direct to update additional placements more quickly as well as adding more parameters as a default.

Another element to the project was supporting the implementation of advanced merchandising. RichRelevance data analysts built specific reports to show the value of the advanced merchandising rules. Additionally, the personalization consultant provided advice on best practice implementation.

Having an implementation free from technical issues as well as using RichRelevance best practice was the optimal foundation for moving to the next step: the optimization.

Optimization Work

The first step in the optimization plan was to test the RichRelevance best practice strategies verses the current Shop Direct strategies. Four tests were carried out on the cart and item pages. The results indicated comparable or small lifts for the best practice strategies. While it showed that the current strategies were working well, it offered alternative strategies for Shop Direct to utilize if they wanted to.

After completing the site-wide strategy tests, the next step was to narrow the scope to category level. The first test ran on the Electrical category. When certain strategies were preferred, Shop Direct saw a 2.5% lift in revenue per visit. Other category tests are still ongoing.

“We very much see the consulting engagement as a long term optimization partnership for continual improvements across the Shop Direct ecommerce sites”, commented Kathryn Jones, Findability Manager at Shop Direct. “It’s as important to Shop Direct to find out what doesn’t work as well as what does, but to do so quickly and efficiently. By utilizing the skills of the RichRelevance personalization team we were able to tap into their expertise of both personalization and the RichRelevance platform which meant we progressed quicker.”

“We work with our RichRelevance consultant on a weekly basis as a sounding board for both ideas and advice on how to best set up rules and strategies to support our business objectives. This has been very helpful and worked really well for us”, added Isobel Gerrard, Product Recommendations Analyst, Shop Direct.

The optimization efforts continue and Shop Direct is now looking at new on site placements, email implementation and ‘build your own’ strategies.

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