Abstract: Companies routinely gather consumers' personal data, including location, contact details, and online activities, via mobile apps to refine targeting capabilities. However, privacy regulations are increasingly restricting such data collection. This paper studies the impact of privacy regulations on consumers’ purchasing behavior, leveraging Apple’s App Tracking Transparency (ATT) policy as an exogenous shock. Because the ATT feature affects only iPhone users but not Android users, we collect consumer spending data from a leading Chinese coffee brand and estimate a causal forest model within a difference-in-differences framework. Our findings reveal that, following ATT implementation, iPhone users, on average, redeemed more coupons and placed more orders, leading to higher net spending with the brand compared with Android users. Furthermore, we uncover salient heterogeneity of treatment effects across individuals: the effects are particularly pronounced for female customers, customers who are previously more privacy-sensitive, and those who used few coupons in the pre-treatment period. These results indicate that improved individual control over privacy enhances consumer trust and leads to higher responsiveness to promotions. Our results suggest that privacy regulations that provide users with control over their personal data can potentially benefit both consumers and businesses.
Bio: Dr. Wei Miao is an Assistant Professor of Marketing and Analytics at the UCL School of Management, University College London. Before joining UCL, he obtained a PhD in Marketing from the National University of Singapore and a Bachelor’s in Economics from Fudan University. His research applies causal machine learning, structural modeling, and field experiment methods to investigate substantive issues in the area of sharing economy, platform design, and industrial organization. In particular, his research explores the effects of different pricing schemes (such as surge pricing and flat-rate pricing) and dispatch systems (bidding versus allocation) on ridesharing markets. He also studies optimal platform designs, such as the landing page design and platform leakage prevention. His research has been published in leading academic journals, including the Journal of Operations Management and Transportation Research Part A.