日期：2016-07-01 | 学术活动 - 学术会议
主题：Trajectory-based mobile advertising strategy
刘思源，美国宾夕法尼亚州立大学斯米尔商学院助理教授，博士生导师。获得香港科技大学计算机科学与技术博士学位，中国科学院大学工学博士学位。主要研究领域是移动数据挖掘与知识发现，社会网络分析与行为模型，移动市场营销。其研究成果荣获Google Internet of Things Technology Award 2016, Google Faculty Research Award 2015，Marketing Science Institute Award 2015, and USDOT National University Transportation Center for Safety Award 2015.
Rapid improvements in the precision of mobile technologies make it possible for advertisers to go beyond using the real-time static location and contextual information about consumers. In this study, we propose a novel “trajectory-based” mobile advertising strategy that leverages full information on consumers’ physical movement trajectories using granular behavioral information from different mobility dimensions. To analyze the effectiveness of this new trajectory-based advertising strategy, we design a large-scale randomized field experiment in a large shopping mall that involved 83,370 unique user responses for a 14-day period in June 2014. We find that trajectory-based mobile advertising can lead to higher redemption probability, faster redemption behavior, and higher transaction amount from customers compared to other baselines. It also facilitates higher revenues for the focal store as well as the overall shopping mall. However, trajectory-based ads become less effective in boosting the revenues of the shopping mall during the weekends and for those shoppers who like to explore across products. It suggests that highly targeted mobile advertising can have the inadvertent impact of reducing impulse purchase behavior by customers who are in an exploratory shopping stage. The effect of trajectory-based advertising comes not only from improvements in the efficiency of customers’ current shopping process, but also from its ability to nudge customers towards changing their future shopping patterns and generate additional revenues. Finally, trajectory-based mobile advertising is especially effective in influencing high-income consumers. On a broader note, our work can be viewed as a first step towards studying the large-scale, fine-grained digital trace of individual physical behavior, and how it can be used to predict and market to individual anticipated future behavior.