IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v35y2016i2p218-233.html
   My bibliography  Save this article

Mobile Ad Effectiveness: Hyper-Contextual Targeting with Crowdedness

Author

Listed:
  • Michelle Andrews

    (Goizueta Business School, Emory University, Atlanta, Georgia 30322; and Fox School of Business, Temple University, Philadelphia, Pennsylvania 19122)

  • Xueming Luo

    (Fox School of Business, Temple University, Philadelphia, Pennsylvania 19122)

  • Zheng Fang

    (Sichuan University, Sichuan 610064, China)

  • Anindya Ghose

    (Stern School of Business, New York University, New York, New York 10012)

Abstract

This research examines the effects of hyper-contextual targeting with physical crowdedness on consumer responses to mobile ads. It relies on rich field data from one of the world’s largest telecom providers who can gauge physical crowdedness in real-time in terms of the number of active mobile users in subway trains. The telecom provider randomly sent targeted mobile ads to individual users, measured purchase rates, and surveyed purchasers and nonpurchasers. Based on a sample of 14,972 mobile phone users, the results suggest that, counterintuitively, commuters in crowded subway trains are about twice as likely to respond to a mobile offer by making a purchase vis-à-vis those in noncrowded trains. On average, the purchase rates measured 2.1% with fewer than two people per square meter, and increased to 4.3% with five people per square meter, after controlling for peak and off-peak times, weekdays and weekends, mobile use behaviors, and randomly sending mobile ads to users. The effects are robust to exploiting sudden variations in crowdedness induced by unanticipated train delays underground and street closures aboveground. Follow-up surveys provide insights into the causal mechanism driving this result. A plausible explanation is mobile immersion: As increased crowding invades one’s physical space, people adaptively turn inwards and become more susceptible to mobile ads. Because crowding is often associated with negative emotions such as anxiety and risk-avoidance, the findings reveal an intriguing, positive aspect of crowding: Mobile ads can be a welcome relief in a crowded subway environment. The findings have economic significance because people living in cities commute 48 minutes each way on average, and global mobile ad spending is projected to exceed $100 billion. Marketers may consider the crowdedness of a consumer’s environment as a new way to boost the effectiveness of hyper-contextual mobile advertising.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0905 .

Suggested Citation

  • Michelle Andrews & Xueming Luo & Zheng Fang & Anindya Ghose, 2016. "Mobile Ad Effectiveness: Hyper-Contextual Targeting with Crowdedness," Marketing Science, INFORMS, vol. 35(2), pages 218-233, March.
    • Handle: RePEc:inm:ormksc:v:35:y:2016:i:2:p:218-233
    DOI: 10.1287/mksc.2015.0905
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.2015.0905
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mksc.2015.0905?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Scott K. Shriver & Harikesh S. Nair & Reto Hofstetter, 2013. "Social Ties and User-Generated Content: Evidence from an Online Social Network," Management Science, INFORMS, vol. 59(6), pages 1425-1443, June.
    2. Peter M. Guadagni & John D. C. Little, 1983. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, vol. 2(3), pages 203-238.
    3. Anindya Ghose & Sang Pil Han, 2011. "An Empirical Analysis of User Content Generation and Usage Behavior on the Mobile Internet," Management Science, INFORMS, vol. 57(9), pages 1671-1691, September.
    4. Lichtenstein, Donald R & Netemeyer, Richard G & Burton, Scot, 1995. "Assessing the Domain Specificity of Deal Proneness: A Field Study," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 22(3), pages 314-326, December.
    5. Robin J. Tanner & Rosellina Ferraro & Tanya L. Chartrand & James R. Bettman & Rick Van Baaren, 2008. "Of Chameleons and Consumption: The Impact of Mimicry on Choice and Preferences," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 34(6), pages 754-766, August.
    6. Anindya Ghose & Sang Pil Han, 2014. "Estimating Demand for Mobile Applications in the New Economy," Management Science, INFORMS, vol. 60(6), pages 1470-1488, June.
    7. Hui, Michael K & Bateson, John E G, 1991. "Perceived Control and the Effects of Crowding and Consumer Choice on the Service Experience," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 18(2), pages 174-184, September.
    8. Shankar, Venkatesh & Balasubramanian, Sridhar, 2009. "Mobile Marketing: A Synthesis and Prognosis," Journal of Interactive Marketing, Elsevier, vol. 23(2), pages 118-129.
    9. Richins, Marsha L & Dawson, Scott, 1992. "A Consumer Values Orientation for Materialism and Its Measurement: Scale Development and Validation," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 19(3), pages 303-316, December.
    10. Dickinger, Astrid & Kleijnen, Mirella, 2008. "Coupons going wireless: Determinants of consumer intentions to redeem mobile coupons," Journal of Interactive Marketing, Elsevier, vol. 22(3), pages 23-39.
    11. Dickerson, Mary Dee & Gentry, James W, 1983. "Characteristics of Adopters and Non-Adopters of Home Computers," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 10(2), pages 225-235, September.
    12. Petty, Richard E & Cacioppo, John T & Schumann, David, 1983. "Central and Peripheral Routes to Advertising Effectiveness: The Moderating Role of Involvement," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 10(2), pages 135-146, September.
    13. Jonathan Levav & Rui (Juliet) Zhu, 2009. "Seeking Freedom through Variety," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 36(4), pages 600-610, December.
    14. Garrett P. Sonnier & Leigh McAlister & Oliver J. Rutz, 2011. "A Dynamic Model of the Effect of Online Communications on Firm Sales," Marketing Science, INFORMS, vol. 30(4), pages 702-716, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Siliang Tong & Xueming Luo & Bo Xu, 2020. "Personalized mobile marketing strategies," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 64-78, January.
    2. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    3. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2019. "Technology in the 21st century: New challenges and opportunities," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 321-335.
    4. Martijn G. de Jong & Donald R. Lehmann & Oded Netzer, 2012. "State-Dependence Effects in Surveys," Marketing Science, INFORMS, vol. 31(5), pages 838-854, September.
    5. Sam Ransbotham & Nicholas H. Lurie & Hongju Liu, 2019. "Creation and Consumption of Mobile Word of Mouth: How Are Mobile Reviews Different?," Marketing Science, INFORMS, vol. 38(5), pages 773-792, September.
    6. Chenhui (Julian) Guo & Tae Hun Kim & Anjana Susarla & Vallabh Sambamurthy, 2020. "Understanding Content Contribution Behavior in a Geosegmented Mobile Virtual Community: The Context of Waze," Information Systems Research, INFORMS, vol. 31(4), pages 1398-1420, December.
    7. Anindya Ghose & Avi Goldfarb & Sang Pil Han, 2013. "How Is the Mobile Internet Different? Search Costs and Local Activities," Information Systems Research, INFORMS, vol. 24(3), pages 613-631, September.
    8. Mohan, Geetha & Sivakumaran, Bharadhwaj & Sharma, Piyush, 2012. "Store environment's impact on variety seeking behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 19(4), pages 419-428.
    9. Anindya Ghose & Hyeokkoo Eric Kwon & Dongwon Lee & Wonseok Oh, 2019. "Seizing the Commuting Moment: Contextual Targeting Based on Mobile Transportation Apps," Service Science, INFORMS, vol. 30(1), pages 154-174, March.
    10. Gázquez-Abad, Juan Carlos & Martínez-López, Francisco J., 2016. "Understanding the impact of store flyers on purchase behaviour: An empirical analysis in the context of Spanish households," Journal of Retailing and Consumer Services, Elsevier, vol. 28(C), pages 263-273.
    11. Kim, Jikyung (Jeanne) & Kim, Sanghwa & Choi, Jeonghye, 2020. "Purchase now and consume later: Do online and offline environments drive online social interactions and sales?," Journal of Business Research, Elsevier, vol. 120(C), pages 274-285.
    12. Bradlow, Eric T. & Gangwar, Manish & Kopalle, Praveen & Voleti, Sudhir, 2017. "The Role of Big Data and Predictive Analytics in Retailing," Journal of Retailing, Elsevier, vol. 93(1), pages 79-95.
    13. Bacile, Todd J. & Ye, Christine & Swilley, Esther, 2014. "From Firm-Controlled to Consumer-Contributed: Consumer Co-Production of Personal Media Marketing Communication," Journal of Interactive Marketing, Elsevier, vol. 28(2), pages 117-133.
    14. Goh, Khim-Yong & Chu, Junhong & Wu, Jing, 2015. "Mobile Advertising: An Empirical Study of Temporal and Spatial Differences in Search Behavior and Advertising Response," Journal of Interactive Marketing, Elsevier, vol. 30(C), pages 34-45.
    15. Xueming Luo & Michelle Andrews & Zheng Fang & Chee Wei Phang, 2014. "Mobile Targeting," Management Science, INFORMS, vol. 60(7), pages 1738-1756, July.
    16. Hynes, Niki & Wilson, Juliette, 2016. "I do it, but don't tell anyone! Personal values, personal and social norms: Can social media play a role in changing pro-environmental behaviours?," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 349-359.
    17. Marios Kokkodis & Theodoros Lappas & Sam Ransbotham, 2020. "From Lurkers to Workers: Predicting Voluntary Contribution and Community Welfare," Information Systems Research, INFORMS, vol. 31(2), pages 607-626, June.
    18. Carmen Balan & Mirela-Patricia Zegreanu, 2012. "Insights into the Development of the Market of Mobile Marketing Services in Romania," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 14(Special N), pages 786-801, November.
    19. Javornik, Ana, 2016. "Augmented reality: Research agenda for studying the impact of its media characteristics on consumer behaviour," Journal of Retailing and Consumer Services, Elsevier, vol. 30(C), pages 252-261.
    20. Michael Thomas, 2019. "Was Television Responsible for a New Generation of Smokers?," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 46(4), pages 689-707.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormksc:v:35:y:2016:i:2:p:218-233. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.