![]() Even if customers wouldn't purchase the recommended product, they would enter a funnel to see other products on pages like "Color Swatch," "Shop the Look," and "Just for You," which generate 30% of Pomelo Fashion's revenue. Increasing the relevance of the products shown on these pages had huge potential to uplift revenue. Category pages generate the largest portion of sales for Pomelo Fashion: 38% of purchased products are discovered by customers on category pages. The rank was calculated daily and stored in a database, providing an identical experience for every user by country.īut as Pomelo Fashion grew, it recognized that enhancing the algorithm with ML would improve the quality of recommendations on category pages for customers, leading to higher digital user engagement and conversion. For years, Pomelo Fashion relied on an algorithm that ranked products on category pages-such as "Dresses," "Blouses," and "Pants & Bottoms"-based on page views and sales, blending the trends of the past 30 days with lifetime behaviors, product price, and newest releases. Its gross revenue tripled from 2017 to 2018, doubled from 2018 to 2019, and is on track to double in 2020 despite the overall global economy being down-in July 2020 alone, the company reported $7.5 million in revenue. Shipping to nearly two million customers in more than 50 countries, the company currently employs 500 staff members across its corporate offices, retail stores, and warehouses. Pomelo Fashion sells apparel online and in 18 retail locations throughout Southeast Asia. Updating a Years-Old Algorithm Using Amazon Personalize By using Amazon Personalize-and the services of AWS Advanced Technology Partners Segment and Braze-to build fresh sorting and categorizing features, Pomelo Fashion created a unique, personalized shopping experience that boosts customer engagement and more efficiently converts it into sales. Pomelo Fashion turned to Amazon Web Services (AWS) and used Amazon Personalize, which enables developers to build applications with the same machine learning (ML) technology used by for real-time personalized recommendations.
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