Mobile Targeting Using Customer Trajectory Patterns
Anindya Ghose, Beibei Li, and Siyuan Liu, 2017, 17-108-04
Rapid improvements in the precision of mobile technologies make it possible for advertisers to go beyond the real-time snapshot of the static location and context information about consumers.
In this study, Anindya Ghose, Beibei Li, and Siyuan Liu propose a novel “trajectory-based” targeting strategy for mobile recommendation that leverages full information on consumers’ physical movement trajectories using granular behavioral information from different mobility dimensions.
To analyze the effectiveness of this new strategy, the authors 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.
They find that trajectory-based mobile targeting 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. Moreover, the effect of trajectory-based targeting 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.
The authors find significant heterogeneity in the impact of trajectory-based targeting. It is especially effective in influencing high-income consumers. Interestingly, it becomes less effective in boosting the revenues of the shopping mall during the weekends and for those shoppers who like to explore across product categories. Their findings suggest that highly targeted mobile promotions can have the inadvertent impact of reducing impulse purchase behavior by customers who are in an exploratory shopping stage.
This 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.
Anindya Ghose is Professor of Information, Operations, and Management Sciences and Professor of Marketing at the Stern School of Business, New York University. Beibei Li is Assistant Professor of Information Systems and Management, Heinz College, Carnegie Mellon University. Siyuan Liu is Assistant Professor of Information Systems, Smeal College of Business, Penn State University.
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Anindya Ghose [Webinar] (2017)
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