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Broadcast Scheduling for Mobile Advertising

Author

Listed:
  • Bert de Reyck

    (London Business School, Regent's Park, London NW1 4SA, United Kingdom)

  • Zeger Degraeve

    (London Business School, Regent's Park, London NW1 4SA, United Kingdom)

Abstract

We describe a broadcast scheduling system developed for a precision marketing firm specialized in location-sensitive permission-based mobile advertising using SMS (Short Message Service) text messaging. Text messages containing advertisements were sent to registered customers when they were shopping in one of two shopping centers in the vicinity of London. The ads typically contained a limited-time promotional offer. The company's problem was deciding which ads to send out to which customers at what particular time, given a limited capacity of broadcast time slots, while maximizing customer response and revenues from retailers paying for each ad broadcast. We solved the problem using integer programming with an interface in Microsoft Excel. The system significantly reduced the time required to schedule the broadcasts, and resulted both in increased customer response and revenues.

Suggested Citation

  • Bert de Reyck & Zeger Degraeve, 2003. "Broadcast Scheduling for Mobile Advertising," Operations Research, INFORMS, vol. 51(4), pages 509-517, August.
    • Handle: RePEc:inm:oropre:v:51:y:2003:i:4:p:509-517
    DOI: 10.1287/opre.51.4.509.16104
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    References listed on IDEAS

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    1. Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
    2. Greg Shaffer & Z. John Zhang, 1995. "Competitive Coupon Targeting," Marketing Science, INFORMS, vol. 14(4), pages 395-416.
    3. Shaffer, G. & Zhang, Z.J., 1994. "Competitive Coupon Targeting," Papers 94-02, Michigan - Center for Research on Economic & Social Theory.
    4. Baesens, Bart & Viaene, Stijn & Van den Poel, Dirk & Vanthienen, Jan & Dedene, Guido, 2002. "Bayesian neural network learning for repeat purchase modelling in direct marketing," European Journal of Operational Research, Elsevier, vol. 138(1), pages 191-211, April.
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    Cited by:

    1. Talla Nobibon, Fabrice & Leus, Roel & Spieksma, Frits C.R., 2011. "Optimization models for targeted offers in direct marketing: Exact and heuristic algorithms," European Journal of Operational Research, Elsevier, vol. 210(3), pages 670-683, May.
    2. Gerrard, Russell & Hiabu, Munir & Kyriakou, Ioannis & Nielsen, Jens Perch, 2019. "Communication and personal selection of pension saver’s financial risk," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1102-1111.
    3. Chen, Peng-Ting & Cheng, Joe Z. & Yu, Ya-Wen & Ju, Pei-Hung, 2014. "Mobile advertising setting analysis and its strategic implications," Technology in Society, Elsevier, vol. 39(C), pages 129-141.
    4. John Turner, 2012. "The Planning of Guaranteed Targeted Display Advertising," Operations Research, INFORMS, vol. 60(1), pages 18-33, February.
    5. De Reyck, Bert & Degraeve, Zeger, 2006. "MABS: Spreadsheet-based decision support for precision marketing," European Journal of Operational Research, Elsevier, vol. 171(3), pages 935-950, June.
    6. Tripathi, Arvind K. & Nair, Suresh K., 2007. "Narrowcasting of wireless advertising in malls," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1023-1038, November.
    7. Bert De Reyck & Ioannis Fragkos & Yael Grushka-Cockayne & Casey Lichtendahl & Hammond Guerin & Andrew Kritzer, 2017. "Vungle Inc. Improves Monetization Using Big Data Analytics," Interfaces, INFORMS, vol. 47(5), pages 454-466, October.
    8. Shinjini Pandey & Goutam Dutta & Harit Joshi, 2017. "Survey on Revenue Management in Media and Broadcasting," Interfaces, INFORMS, vol. 47(3), pages 195-213, June.
    9. Zhang, Jianqiang & He, Xiuli, 2019. "Targeted advertising by asymmetric firms," Omega, Elsevier, vol. 89(C), pages 136-150.
    10. Kumar, Ashish, 2021. "An empirical examination of the effects of design elements of email newsletters on consumers’ email responses and their purchase," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).

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