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Mobile Targeting Using Customer Trajectory Patterns
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Journal Article
Mobile Targeting Using Customer Trajectory Patterns
2019
Overview
Rapid improvements in the precision of mobile technologies now make it possible for advertisers to go beyond real-time static location and contextual information on consumers. In this paper we propose a novel “trajectory-based” targeting strategy for mobile recommendation that leverages detailed information on consumers’ physical-movement trajectories using fine-grained behavioral information from different mobility dimensions. To analyze the effectiveness of this new strategy, we designed 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 found that trajectory-based mobile targeting can, as compared with other baselines, lead to higher redemption probability, faster redemption behavior, and higher transaction amounts. It can also facilitate 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 processes but also from its ability to nudge customers toward changing their future shopping patterns and, thereby, generate additional revenues. Finally, we found significant heterogeneity in the impact of trajectory-based targeting. It is especially effective in influencing high-income consumers. Interestingly, however, it becomes less effective in boosting the revenues of the shopping mall during the weekends and for those shoppers who like to explore across products categories. Our overall findings suggest that highly targeted mobile promotions can have the inadvertent impact of reducing impulse-purchasing behavior by customers who are in an exploratory shopping stage. On a broader note, our work can be viewed as a first step toward the study of large-scale, fine-grained digital traces of individual physical behavior and how they can be used to predict—and market according to—individuals’ anticipated future behavior.
This paper was accepted by Anandhi Bharadwaj, information systems.
