GANT
- RETAIL SEGMENT: Fashion
- PRODUCT: Personalized Recommendations
- CHALLENGE: Manual, non-personalized product recommendations. Confusing navigation. Invisible long tail products
- RESULTS:15% Growth of Online Sales. Improved navigation and discreet recommendations resulted in an enhanced customer experience. Long tail articles remain visible and don’t turn into shelf ware
Discreet Personalization for Improved Navigation
The fashion brand GANT was founded in 1949 on the American East Coast by the Ukrainian emigrant Bernard Gantmacher. Originally specializing in shirts, the company was particularly popular with students at Ivy League universities and laid the foundation for American sportswear with its casualness and sportiness. After the takeover by Swedish companies in the 1960s, GANT expanded globally. In Germany, the company now employs more than 450 people, operates over 30 of its own stores, 6 outlets, and supplies exclusive wholesale customers at over 700 points of sale.
In 2013, GANT launched its German eCommerce site, which was very well received by customers and exceeded all expectations with high double-digit growth rates. Despite this success, GANT continuously analyzed the shop and identified potential for improvement.
Product Detail Pages Were a Dead End
GANT discovered a couple of key issues. Product detail pages were only accessible via the main menu navigation, impacting a customer’s ability to locate products, often leading to bouncing and unsuccessful shopping sessions. In addition, manual product recommendations only offered seasonal looks and did not take a customers’ preferences or behaviors into account.
“With the old system, maintaining the recommendations was very time-consuming,” said Werner Hammer, Head of eCommerce at GANT. “In addition, we saw a significant advantage in providing customers with recommendations that were of interest to them. With a recommendation engine we would be able to easily show what our customers really want.”
“Invisible products“
GANT listed each of their products in every color variant, impacting the amount of different products that could be shown on the main product category page. This meant customers could miss products they may be interested in as they were pushed deeper into sub-listing pages.
“In order to improve the navigation on our website and prevent articles from getting lost in the flood of offers, we looked around for a recommendation engine,” says Werner Hammer. “We wanted the solution to be discreet and unobtrusive in showing customers recommendations that really interest them, and in no way give them the feeling of being overwhelmed”.
Performance and Integration Capabilities for Step-by-Step Optimization
Overall, the solution had to fit the company’s conservative eCommerce approach, requiring the software to gradually incorporate and optimize personalization allowing the brand to maintain some level of control and override, allowing for a unique “man + machine” capability. After an exploratory phase in which various solutions were evaluated, the German GANT team chose RichRelevance Recommend as their personalization solution for its high performance and comprehensive integration capabilities.
RichRelevance Recommend™ uses powerful machine learning and a comprehensive set of algorithms to show customers product recommendations tailored to their individual needs. Mirroring an experienced salesperson in a retail store, a “super-algorithm” evaluates the anonymous navigation data to select which of the more than 150 pre-developed recommendation algorithms is able to show customers exactly those recommendations that best matches their needs.
After a short implementation phase, during which GANT was supported by experienced RichRelevance consultants, the German eCommerce site launched in 2016 with Recommend.
Significant Sales Growth and Improved Navigation
Since the go-live of Recommend in 2016, GANT has seen a lot of improvement in the web shop and an additional growth in online sales of 15% for purchases attributed to the RichRelevance product recommendations.
“We have put our trust in the RichRelevance personalization platform from the outset and have seen a clear improvement in online sales. Customers are guided discreetly – depending on their preferences – to the products they are actually interested in – all automatically and without time-consuming manual maintenance”.
RichRelevance’s product recommendations have significantly improved navigation and thus the customer experience. By using the personalized product recommendations, GANT was also able to solve the problem of long tail items getting buried amongst other more well known, trending products.
To further enhance the customer experience, the company plans to deploy RichRelevance personalization within its email newsletter and for merchandising in the future.
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