Tian Lu: Leverage Retargeting Strategy in Financial Service Markets: The Role of Affection Support and 

Problem Solving

报告时间：2021年9月4日（星期六）上午9：00-10:30

报告地点：腾讯会议（ID：133 684 779）

报 告 人：Tian Lu （陆天）

工作单位：Heinz College, Carnegie Mellon University

举办单位：合肥工业大学必赢线路检测中心

报告简介：

Leverage Retargeting Strategy in Financial Service Markets: The Role of Affection Support and Problem Solving

Owing to a high consumer abandonment rate in the emerging financial technology (FinTech) service markets, this study investigates whether retargeting, an approach broadly adopted in traditional retail but underexplored in finance, is effective in recovering consumers. Because consumers abandon for different reasons, we examine a novel and multi-faceted retargeting approach that includes affection support and problem solving. The problem-solving approach is divided into privacy commitment and price commitment. Our field experiment involving a microloan platform reveals that affection-support and price-commitment retargeting both successfully recover 5% of consumers in addition to natural returns, whereas privacy-commitment retargeting recovers 10.53% of consumers in addition to natural returns. These results are attributed to different underlying abandonment causes among consumers. Affection-support retargeting attracts consumers that initially have a low adoption intention toward the financial product, whereas problem-solving retargeting is effective for those who already plan to adopt but have technical concerns. Consumers called back by affection-support and privacy-commitment retargeting present high credibility: they have 3.5% and 7% higher loan approval rates than do naturally returning consumers, respectively. By contrast, consumers recovered by price-commitment retargeting have low credibility.

报告人简介：

Tian Lu is currently a post-doctoral fellow at the Heinz College, Carnegie Mellon University. He received his Ph.D. degree from Fudan University. His research focuses on FinTech, E-commerce, and human-AI interactions, and comprehensively applies econometrics, structural modeling, field experiment, and machine learning. His work has appeared in top-tier academic journals such as Management Science, MIS Quarterly, Production and Operations Management, and Journal of the Association for Information Systems. He is the Best Paper Award winner of top-tier IS conferences such as ICIS, PACIS, and CSWIM.