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Effective Multiplatform Advertising Policy
Huang, Kaifan1; Yang, Lu Xing2; Yang, Xiaofan1; Tang, Yuan Yan3
2022-07
Source PublicationIEEE Transactions on Systems, Man, and Cybernetics: Systems
ABS Journal Level3
ISSN2168-2216
Volume52Issue:7Pages:4483-4493
Abstract

Multiplatform advertising (MPA) is recognized as an effective means of enhancing marketing revenue. In the context, we refer to the scheme of dynamically allocating the advertising expenditure among the selected media platforms as an MPA policy, and we refer to the problem of developing an MPA policy with maximum benefit as the MPA problem. This article is devoted to the solution of the MPA problem. An evolutionary model for the expected market state, in which the influence of both advertising and word-of-mouth (WOM) propagation is accounted for, is established. On this basis, the expected benefit of an MPA policy is calculated. Thereby, the MPA problem is reduced to an optimal control problem we refer to as the MPA model, where the objective functional stands for the expected benefit of an MPA strategy. The optimality system for the MPA model is derived. We refer to the MPA policy obtained by solving the optimality system as the promising MPA policy. The structure of the promising MPA policy is inspected. Through extensive comparative experiments, it is concluded that the promising MPA policy is superior to the majority of MPA policies in terms of expected benefit. Finally, how the expected benefit of the promising MPA policy is influenced by some factors is investigated.

KeywordAdaptation Models Advertising Expected Benefit Marketing Mathematical Model Media Multiplatform Advertising (Mpa) Policy Numerical Models Optimal Control Optimal Control Optimality System Upper Bound Word-of-mouth (Wom) Propagation.
DOI10.1109/TSMC.2021.3096008
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Cybernetics
WOS IDWOS:000732307200001
Scopus ID2-s2.0-85112669405
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Citation statistics
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorYang, Xiaofan
Affiliation1.School of Big Data and Software Engineering, Chongqing University, Chongqing 400044, China.
2.School of Information Technology, Deakin University, Melbourne, VIC 3125, Australia.
3.Department of Computer and Information Science, University of Macau, Macau, China.
Recommended Citation
GB/T 7714
Huang, Kaifan,Yang, Lu Xing,Yang, Xiaofan,et al. Effective Multiplatform Advertising Policy[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52(7), 4483-4493.
APA Huang, Kaifan., Yang, Lu Xing., Yang, Xiaofan., & Tang, Yuan Yan (2022). Effective Multiplatform Advertising Policy. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(7), 4483-4493.
MLA Huang, Kaifan,et al."Effective Multiplatform Advertising Policy".IEEE Transactions on Systems, Man, and Cybernetics: Systems 52.7(2022):4483-4493.
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