Effects of Promotions on Location-Based Social Media: Evidence from Foursquare

Abstract

Location has been identified as a critical factor for the success of a business. For example, businesses in dense urban areas are exposed to more customers than businesses in sparsely populated neighborhoods, while proximity to a popular landmark can increase a business’s reach. This creates significant challenges for new businesses to expand their reach and customer base. However, the advancement of mobile, social, and spatial computing has led to the transformation of traditional web-based yellow pages to a mobile format (e.g., location-based social networks; LBSNs). This has allowed businesses that are not in prime locations to become visible to nearby customers. Furthermore, these platforms offer mechanisms that can serve as an affordable advertisement channel to local businesses. Specifically, a business can use LBSNs to promote special offers to customers who connect through the platform. Despite the promising anecdotal evidence, a systematic study of the effectiveness of this LBSN advertising paradigm has not yet been conducted. Using a large time-series data set of approximately 14 million venues in Foursquare, the largest LBSN to date, our work is the first to formally examine the effects of promotions through the platform. Our contribution is twofold. First, we identify no significant and robust evidence that can support the hypothesis of effective promotions for the specific platform. In particular, our main finding is that the probability of observing an increase in daily check-ins or new daily customers to a venue is rarely altered by the presence of a Foursquare promotion. Second, this finding motivates us to design a model that can predict the success of a promotion according to various relevant features. Our model can be used to inform the process of designing and launching successful promotions by evaluating the potential of candidate promotions before their actual release. The practical value of such prelaunch evaluations is elevated by the apparent scarcity of successful promotions, as revealed by our analysis.

Key words and phrases:

• and phrases: Bootstrap
• difference-in-differences
• Foursquare
• location-based advertisement
• location-based social networks
• modeling
• social media

Acknowledgments

The authors thank the anonymous reviewers for their valuable feedback on our original study, which helped shape the current manuscript.

Notes

^1. To further validate our findings we employ an alternative testing method that we present in the Appendix for interested readers.

2. We also used $r=0.3$ and $r=0.8$ and obtained similar results.

Additional information

Notes on contributors

Ke Zhang

KE ZHANG ([email protected]) received his Ph.D. in information science from the University of Pittsburgh. He has published in peer-reviewed journals and conference proceedings, including venues such as World Wide Wed Conference (ACM WWW), AAAI International Conference on Web and Social Media (AAAI ICWSM), and European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD). His research interests include mining location-based social media and urban computing, especially in understanding and modeling human urban mobility in social, economic, and external environmental contexts.

Konstantinos Pelechrinis

KONSTANTINOS PELECHRINIS ([email protected]; corresponding author) is an associate professor at the School of Computing and Information at the University of Pittsburgh. He received his Ph.D. in computer science from University of California, Riverside. His research centers on data and network science. He is interested in all aspects of the information cycle and his goal is to deliver information-centric solutions in various fields. He is a recipient of the Army Research Office Young Investigator award for his work on composite networks.

Theodoros Lappas

THEODOROS LAPPAS ([email protected]) is an assistant professor in the School of Business at Stevens Institute of Technology. He received his Ph.D. from the Department of Computer and Science Engineering at the University of California, Riverside. His research focuses on large-scale reputation systems, as well as on scalable data mining and machine learning algorithms for business analytics.