Distance and Local Competition in Mobile Geofencing

• 成果类型: Article
• 署名作者: Ho, Yi-Jen (Ian); Dewan, Sanjeev; Ho, Yi-Chun (Chad)
• 署名单位: Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of California System; University of California Irvine; George Washington University
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2020.0953
• 发表日期: 2020
• 页码: 1421-1442
• 关键词: task complexity; search costs; MODEL; INFORMATION; location; demand

摘要

This research studies the performance of geofencing, a practice where mobile users are targeted within a predefined virtual geographic boundary around an advertiser's establishment. We argue the significance of distance (i.e., the mileage from a consumer to a focal establishment) and local competition (i.e., the number of alternatives in consumer vicinity) in ad responses. Drawing on the notion of the purchase funnel, we develop a two-stage hierarchical Bayesian model to examine consumer click and conversion choices. A unique data set of geofencing ad impressions is collected from one of the largest location-based marketing agencies in the United States. The results suggest that local competition matters in the click stage, whereas distance influences the propensity of conversion. Quantitatively, one additional competitor in the consumer vicinity zone lowers the click-through rate by 1.03%, whereas a 1-mile increase in distance results in a 17.64% decrease in the conversion rate. We also find a significant interactive effect, whereby a higher degree of local competition amplifies the negative impact of distance on the likelihood of conversions. Additionally, product differentiation ameliorates the effects of distance and local competition, whereas these effects are found to be more prominent during office working hours. This study discovers the stage-varying roles of distance and local competition along the customer journey and offers new directions for more effective location-based targeting.