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Jequiti Case Study

  • RETAIL SEGMENT: Wellness and Beauty
  • PRODUCT: Personalized Recommendations
  • CHALLENGE: Brazilian perfume and cosmetics manufacturer Jequiti wanted its new eCommerce site to maximize engagement and sales with an optimized experience tailored to shoppers’ geographic location and past brand interactions.
  • RESULTS: 40% MoM increase for online sales

Jequiti is a leading Brazilian brand of perfume and cosmetics with products backed by Brazilian and international celebrities. The combination of star power and value pricing translates into high quality products to a new customer segment – Jequiti’s hallmark. In 2017, Jequiti’s sales topped R$415 million and the company set out to double that figure by 2020 through a revitalized sales strategy and the launch of its first eCommerce site.

Celebrity endorsement of Jequiti in every category, such as fragrances, makeup , hair, body, bath, man and kids, goes farther than simply connecting names with products. Stars appear in print, television and billboard advertising and make appearances at promotional events, including meetings with the brand’s more than 250,000 direct sales representatives.


As Jequiti prepared to launch its first eCommerce initiative in 2017, it sought to approach its customers to the entertainment universe backed-up Silvio Santos Group business, translating the glamour of celebrity backing into a rich online experience tailored to shoppers’ individual beauty needs. Complicating this effort was the need to adapt the new website to reflect and match different regional campaigns that runs at the same time, in different cycles, depending on the region, because it should follow the same Direct Sales door-to-door campaigns and catalog   according to a site visitor’s location.

In addition to this geographic segmentation, Jequiti sought to enrich the online experience by tailoring product assortments and content according to individual preferences and past purchasing history. By maximizing relevance with dynamically personalized content, Jequiti sought to win customers and earn loyalty in Brazil’s crowded beauty and fragrance marketplace.

Solution Overview

Jequiti built its eCommerce business around RichRelevance’s AI-based personalization platform. The brand turned to RichRelevance Engage to dynamically detect and serve location-dependent content, products, and offers. RichRelevance Recommend then applied dynamic intelligence to present the most relevant items for each individual from within the region’s assigned catalog.

How it Works

Prior to implementing RichRelevance, Jequiti embarked on an extensive search for the right technology to launch its eCommerce presence. All-in-one solutions that promised personalization alongside an eCommerce platform didn’t offer the breadth and depth of functionality to meet Jequiti’s requirements.

Instead, Jequiti opted to select and integrate two best-of-breed solutions – RichRelevance and VTex, the largest eCommerce provider in South America. VTex offered the simplicity of a cloud eCommerce implementation along with the extensive commerce functionality Jequiti sought. RichRelevance’s industry-leading dynamic personalization provided the flexibility and interaction-specific machine learning to deliver the layers of contextual relevance Jequiti required. Thanks to RichRelevance’s open and flexible architecture, integration was swift and straightforward, with a four-month timeline from selection to launch.

When a shopper first visits the Jequiti site, RichRelevance’s Engage, Recommend and Personalization AI dynamically personalizes the site experience inn real time. Content from the home page hero banner onwards (from celebrity content to product assortments and site navigation) is tailored to the shopper’s geographic location, as well as individual history, context and behavior. A shopper in Bahia might be served content and products connected with one star, while Rio de Janeiro consumers might view content showcasing a new product launch for an entirely different line headed by another spokesperson.

With each site interaction, RichRelevance’s AI dynamically applies the most relevant decisioning algorithm to deliver the products best suited for the individual shopper’s beauty needs,  continually learning and optimizing the experience in real time. Replenishment recommendations, complementary items, and new product launches are presented according to the shopper’s context within the customer lifecycle, while at the same time adhering to the business rules established for the regional product catalog.

Since launching in late 2017, Jequiti has worked hand in hand with RichRelevance to build a strong startup presence.Thanks to the content-rich, individualized shopping experience Jequiti offers, engagement is consistently high across geographic regions

Online sales have soared, with month-over-month growth since December average order sizes is increasing. The new eCommerce channel now accounts for a good portion of overall sales. And despite the short time frame since launch, Jequiti’s site is attracting return traffic and sales, thanks to personalized recommendations for replenishment items and complementary products.


The Works Case study

  • RETAIL SEGMENT: Arts and Crafts Discount Retailer
  • PRODUCT: Personalized Search
  • CHALLENGE: To improve the functionality and performance of the on-site search function
  • RESULTS: 36% attribution for online sales

The Works is a retailer that serves over 22.5 million customers each year – stocking 40,000 different products including books, toys, gifts, stationery and arts & crafts at discount prices. Selling over 1 million products each week, The Works appeals to anyone looking for a wide variety of products at great value prices.

Lack of Functionality and Performance Data with Current Solution

The Works has been working with RichRelevance since 2013, employing product recommendations and content personalisation solutions across its website As ecommerce sales have grown as a percentage of overall sales, The Works were looking to improve other areas of their website. In 2017 they started to look for a new onsite search solution, as their current solution lacked functionality as well as performance data on how it was working.

Alex Beard, Online Trading Manager at The Works takes up the story. “Previously, we’d employed an out of the box solution. It was restrictive in its functionality and performed very poorly at a subjective level. I say subjective, as we had no data to help us understand how it was performing at an objective level.”

Focus on Connecting Shoppers with Exactly what they Search for

The Works reviewed several onsite search solutions, including a thorough benchmarking analysis. In the end the decision was easy, and The Works chose the RichRelevance personalised on-site search solution, Find™, due to its ability to connect shoppers with exactly what they were looking for, as Alex explains:

“After meeting with RichRelevance, it was apparent that they’d created a solution that really focused on providing the most relevant results for the customer. We felt that RichRelevance had a better understanding of what we required from our site-search. We had no interest in all of the “fluffy” parts of search that others were pitching us (like SEO benefits) and only had an absolute interest in making sure customers found exactly what they were looking for when they came to our site. After a thorough competitor benchmarking project, we found that Find was the best solution to do this for The Works.”

Find performance is extremely positive

RichRelevance implemented Find™ on in just 12 weeks to ensure they were up and running in time for the 2017 Peak Trading Season. During the peak season, 36% of The Works online sales can be directly attributed to the implementation of Find™. Since peak trading, Find™ has continued to help The Works optimise searches for key terms over Valentine’s Day, Mother’s Day and Easter. Their Findability score has remained strong, as has their conversion rates.

“We are delighted with the performance of Find. After a strong start over peak trading, it has helped us achieve great like for likes in January and February of this year, of 20% and 45% respectively.”

“We can see customers have been finding what they want, and quickly. The results being returned are more akin to customer expectations.”

The Works are continuing to optimise their implementation of Find™ and are also now looking at personalising their listing pages with the RichRelevance Discover™ solution.


European Grocery Infographic


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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.

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