Carousell is using Supahands to evaluate the search relevance algorithms for its machine learning models to further enhance the search and recommendation functions on the Carousell platform.
This creates a seamless and personalised user experience by showing the customer what they need, when they need it.
Carousell has a market presence in Singapore, Hong Kong, Indonesia, Malaysia, the Philippines and Taiwan. It offers a diverse range of products across a variety of categories, including cars, lifestyle, gadgets and fashion accessories.
Since 2020, Carousell has seen significant improvement in the search relevance, which the company said was further validated by an equally significant drop in related user reports.
“In e-commerce, search is everything—if you can’t find it, you can’t buy it,” said Puneet Garg, head of data science and data engineering at Carousell.
“At Carousell, we continuously strive to provide the best possible shopping experience for our users, and creating effective search and recommendation algorithms is a critical part of it,” said Garg.
He said Supahands’ evaluations of their search results have added significant clarity and value to their search and recommendation models.
“We are looking forward to further enhancing our technology and supporting our customers—both merchants and buyers—by creating an increasingly relevant, personalised and rewarding content discovery platform,” he added.
Supahands’ flexibility and scalability as a data labeling partner has enabled Carousell to enhance their models quickly and effectively, supporting Carousell’s journey to continuously make searching, buying, and selling easier.
“Carousell’s technological achievements that have improved the marketplace landscape up-close, is a privilege,” said Greg Meehan, chief revenue officer at Supahands.
“We are looking forward to a long partnership ahead, and will continue to challenge our own processes to grow alongside Carousell’s increasing demand for quality data,” said Meehan.