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Optimal Budget Allocation over Time for Keyword Ads in Web Portals

Author

Listed:
  • G. E. Fruchter

    (Bar-Ilan University)

  • W. Dou

    (University of Nevada-Las Vegas)

Abstract

This study investigates how to dynamically allocate resources with a given budget for advertising through Web portals using keyword-activated banner ads on the Internet. Identifying the factors that affect the potential number of banner ad clickthroughs in each portal, we show that the process of budget allocation between the two types of portals (generic vs specialized) that leads to the largest banner clicksthrough in the long run is an optimal control problem. Using techniques of dynamic programming, we find analytical solutions for the optimal budgeting decisions. Our analysis shows that an advertiser’s optimal portal budgeting depends nonlinearly on the number of visitors who type the same trigger keyword and the average clicksthrough rates, as well as on the advertiser and ad effectiveness. Further, we find that the maximal number of banner clickthroughs from both portals, at time t, depends on the remaining budget until the end of the planning period. The analytical results have useful managerial insight. One of the interesting features of our solution shows that, while a large visitor base may favor the generic portal, other parameters may affect it unfavorably: e.g., lower clickthrough rates of keyword banners from a more heterogeneous audience. Using a specificaction that is consistent with empirical observations, we show that, in the long run, an advertiser must always spend more ad money at the specialized portal.

Suggested Citation

  • G. E. Fruchter & W. Dou, 2005. "Optimal Budget Allocation over Time for Keyword Ads in Web Portals," Journal of Optimization Theory and Applications, Springer, vol. 124(1), pages 157-174, January.
    • Handle: RePEc:spr:joptap:v:124:y:2005:i:1:d:10.1007_s10957-004-6470-0
    DOI: 10.1007/s10957-004-6470-0
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    Citations

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    Cited by:

    1. Yanwu Yang & Daniel Zeng & Yinghui Yang & Jie Zhang, 2015. "Optimal Budget Allocation Across Search Advertising Markets," INFORMS Journal on Computing, INFORMS, vol. 27(2), pages 285-300, May.
    2. Yang, Chaolin & Xiong, Yi, 2020. "Nonparametric advertising budget allocation with inventory constraint," European Journal of Operational Research, Elsevier, vol. 285(2), pages 631-641.
    3. D Laffey & C Hunka & J A Sharp & Z Zeng, 2009. "Estimating advertisers' values for paid search clickthroughs," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(3), pages 411-418, March.
    4. Yanwu Yang & Baozhu Feng & Joni Salminen & Bernard J. Jansen, 2022. "Optimal advertising for a generalized Vidale–Wolfe response model," Electronic Commerce Research, Springer, vol. 22(4), pages 1275-1305, December.
    5. Susan Cholette & Özgür Özlük & Mahmut Parlar, 2012. "Optimal Keyword Bids in Search-Based Advertising with Stochastic Advertisement Positions," Journal of Optimization Theory and Applications, Springer, vol. 152(1), pages 225-244, January.
    6. Francisco-Javier Arroyo-Cañada & Jaime Gil-Lafuente, 2019. "A fuzzy asymmetric TOPSIS model for optimizing investment in online advertising campaigns," Operational Research, Springer, vol. 19(3), pages 701-716, September.
    7. Savas Dayanik & Mahmut Parlar, 2013. "Dynamic bidding strategies in search-based advertising," Annals of Operations Research, Springer, vol. 211(1), pages 103-136, December.

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