CKGSB Professor Quantified Value of Personal Data in E-Commerce

The experiment uniquely assessed the significance of data assets to the digital platform economy by disabling the algorithmic recommendation system based on personal data on the platform's homepage for the test group. The results were telling: when users were denied access to information flows recommended to them based on characteristics inferred from their personal data, ecommerce platforms saw asymmetry in the products recommended, hurting the diversity of products on platforms and leading to fewer transactions and lower engagement rate.

Products recommended on the homepage dropped from 4 million to around 280,000, and the top 1,000 products from leading brands took up 90% of recommended slots for a disproportionately high exposure. Customers' click-through rate (CTR) and page views (PV) went down by 75.3% and 33.6% respectively and sellers' gross merchandise value (GMV) and transaction volume plummeted by a whopping 81.1% and 86%. Small and medium-sized sellers on the platform saw a 54% decrease in pageviews and a 90% drop in GMV, suggesting they were more impacted than leading sellers, whose PV fell by 27% and GMV by 79%. Research also found consumers with low purchasing power are negatively impacted.

This paper provides a quantified basis for how to balance the use of data assets and personal privacy protection through large-scale experiments, but also helps businesses formulate more informed digital economic policies.

Professor Sun leads CKGSB's Center for Digital Transformation, focusing on digital business, digital technology and implication for traditional businesses. This research underscores CKGSB's commitment to pioneering knowledge that drive understanding and innovation in the digital economy.

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