A Nonlinear Optimization Model of Advertising Budget Allocation across Multiple Digital Media Channels

Date
2022-01-04
Authors
Park, Sung-Hyuk
Lee, Minhyung
Kim, Kitae
Shin, Dongwook
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The goal of advertisers in the digital marketing industry is to optimize their advertising budgets. Such a budget allocation problem plays a key role in maximizing advertising performance from different marketing channels under planned advertising investment. This study aimed to design a budget-performance-based nonlinear programming model to find an optimized solution for the advertising budget allocation problem. The empirical analysis results of a leading e-business company’s advertising performance data show that the proposed non-LP model generates an optimized solution. The proposed model allows marketers to simulate expected advertising returns, such as conversions or revenues from different channels within their budget constraints.
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Technology and Analytics in Emerging Markets (TAEM), advertising, conversion, optimization, performance, revenue maximization
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8 pages
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Proceedings of the 55th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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