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Competitive Mobile Geo Targeting

Author

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  • Yuxin Chen

    (New York University Shanghai, 200122 Shanghai, China)

  • Xinxin Li

    (University of Connecticut, Storrs, Connecticut 06269)

  • Monic Sun

    (Boston University, Boston, Massachusetts 02215)

Abstract

We investigate in a competitive setting the consequences of mobile geo targeting, the practice of firms targeting consumers based on their real-time locations. A distinct market feature of mobile geo targeting is that a consumer could travel across different locations for an offer that maximizes his total utility. This mobile-deal seeking opportunity motivates firms to carefully balance prices across locations to avoid intrafirm cannibalization, which in turn mitigates interfirm price competition and prevents firms from going into a prisoner’s dilemma. As a result, a firm’s profit can be higher under mobile geo targeting than under uniform or traditional targeted pricing. We extend our model in three different directions: (a) a fraction of consumers are not aware of mobile offers outside of their permanent locations, (b) mobile offers can be collected when consumers travel for other reasons, and (c) firms use both permanent and real-time locations when setting prices. Our findings have important managerial implications for marketers who are interested in optimizing their mobile geo-targeting strategies.

Suggested Citation

  • Yuxin Chen & Xinxin Li & Monic Sun, 2017. "Competitive Mobile Geo Targeting," Marketing Science, INFORMS, vol. 36(5), pages 666-682, September.
  • Handle: RePEc:inm:ormksc:v:36:y:2017:i:5:p:666-682
    DOI: 10.1287/mksc.2017.1030
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    4. Baye, Irina & Reiz, Tim & Sapi, Geza, 2018. "Customer recognition and mobile geo-targeting," DICE Discussion Papers 285, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    5. Luo, Meiling & Li, Gang & Chen, Xudong, 2021. "Competitive location-based mobile coupon targeting strategy," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).
    6. Robert W. Palmatier & Andrew T. Crecelius, 2019. "The “first principles” of marketing strategy," AMS Review, Springer;Academy of Marketing Science, vol. 9(1), pages 5-26, June.
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    8. Wang, Wei & Li, Gang & Fung, Richard Y.K. & Cheng, T.C.E., 2019. "Mobile Advertising and Traffic Conversion: The Effects of Front Traffic and Spatial Competition," Journal of Interactive Marketing, Elsevier, vol. 47(C), pages 84-101.
    9. Xi Li & Zibin Xu, 2022. "Superior Knowledge, Price Discrimination, and Customer Inspection," Marketing Science, INFORMS, vol. 41(6), pages 1097-1117, November.
    10. Wan, Qin & Yang, Shilei & Shi, Victor & Qiu, Martin, 2021. "Optimal strategies of mobile targeting promotion under competition," International Journal of Production Economics, Elsevier, vol. 237(C).
    11. Ding, Long & Liu, Peng & Hu, Sen, 2023. "Geo-Fencing or Geo-Conquesting? a strategic analysis of Location-Based coupon under different market structures," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 174(C).
    12. Kurtz, Oliver T. & Wirtz, Bernd W. & Langer, Paul F., 2021. "An Empirical Analysis of Location-Based Mobile Advertising—Determinants, Success Factors, and Moderating Effects," Journal of Interactive Marketing, Elsevier, vol. 54(C), pages 69-85.
    13. Nico Neumann & Catherine E. Tucker & Kumar Subramanyam & John Marshall, 2023. "Is first- or third-party audience data more effective for reaching the ‘right’ customers? The case of IT decision-makers," Quantitative Marketing and Economics (QME), Springer, vol. 21(4), pages 519-571, December.
    14. Park, Chang Hee & Park, Young-Hoon & Schweidel, David A., 2018. "The effects of mobile promotions on customer purchase dynamics," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 453-470.
    15. Jean-Pierre Dubé & Zheng Fang & Nathan Fong & Xueming Luo, 2017. "Competitive Price Targeting with Smartphone Coupons," Marketing Science, INFORMS, vol. 36(6), pages 944-975, November.
    16. Oussama Benbrahim Ansari, 2021. "Geo-Marketing Segmentation with Deep Learning," Businesses, MDPI, vol. 1(1), pages 1-21, June.
    17. Siyuan Liu & Shaojie Tang & Jiangchuan Zheng & Lionel M. Ni, 2022. "Unsupervised Learning for Human Mobility Behaviors," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1565-1586, May.
    18. J. Miguel Villas-Boas & Yunfei (Jesse) Yao, 2021. "A Dynamic Model of Optimal Retargeting," Marketing Science, INFORMS, vol. 40(3), pages 428-458, May.

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