Optimal Advertising Budget Allocation across Markets with Different Goals and Various Constraints

Wang, Xingyuan; Li, Fuan; Jia, Fan

doi:10.1155/2020/6162056

Cited by 5 publications

(7 citation statements)

References 41 publications

(45 reference statements)

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“…This policy balances the advertising budget and the inventory budget simultaneously. With some improvement, allocating advertising resources under constraints for multiple markets with diverse goals and objectives was the subject of Wang et al [42]. They have presented a model based on the Vidale-Wolfe [41] advertising response model, which considers the relationship between market conditions and specific objectives (strategic concerns), saturation level, market responses, and advertising spending.…”

Section: Media Planningmentioning

confidence: 99%

An application of approximate dynamic programming in multi-period multi-product advertising budgeting

Khalilzadeh

Neghabi

Ahadi

2023

JIMO

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<p style='text-indent:20px;'>Advertising has always been considered a key part of marketing strategy and played a prominent role in the success or failure of products. This paper investigates a multi-product and multi-period advertising budget allocation, determining the amount of advertising budget for each product through the time horizon. Imperative factors including life cycle stage, <inline-formula><tex-math id="M1">\begin{document}$ BCG $\end{document}</tex-math></inline-formula> matrix class, competitors' reactions, and budget constraints affect the joint chain of decisions for all products to maximize the total profits. To do so, we define a stochastic sequential resource allocation problem and use an approximate dynamic programming (<inline-formula><tex-math id="M2">\begin{document}$ ADP $\end{document}</tex-math></inline-formula>) algorithm to alleviate the huge size of the problem and multi-dimensional uncertainties of the environment. These uncertainties are the reactions of competitors based on the current status of the market and our decisions, as well as the stochastic effectiveness (rewards) of the taken action. We apply an approximate value iteration (<inline-formula><tex-math id="M3">\begin{document}$ AVI $\end{document}</tex-math></inline-formula>) algorithm on a numerical example and compare the results with four different policies to highlight our managerial contributions. In the end, the validity of our proposed approach is assessed against a genetic algorithm. To do so, we simplify the environment by fixing the competitor's reaction and considering a deterministic environment.</p>

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Section: Media Planningmentioning

confidence: 99%

An application of approximate dynamic programming in multi-period multi-product advertising budgeting

Khalilzadeh

Neghabi

Ahadi

2023

JIMO

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“…To overcome this limitation, the Sethi model was developed, which is a stochastic extension of the Vidale-Wolfe model [6]. A recent study extended the Vidale-Wolfe model for advertising portfolio optimization across multimarkets with different goals and various constraints [7]. On the other hand, marketing research takes economic theory and suggests that the relationship between advertising costs and performance follows the law of diminishing returns [8].…”

Section: Related Literaturementioning

confidence: 99%

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

Park¹,

Lee²,

Kim³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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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 budgetperformance-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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“…(2) based on the corresponding V i ∈ V Hover obtained from Eqs. (10) or (12). Boundary checking is also performed to ensure these new positions have satisfied the boundary constraints.…”

Section: Modified Learining Strategy Of Hover Swarmmentioning

confidence: 99%

“…The gear train design problem is formulated in a compound gear system to determine the best combinations of ratios for its four gears. Assume that x i represents the numbers of teeth for each i-th gear to be optimized and this decision variable has the boundary constraints of x i ∈ [12,60], where i = 1, . .…”

Section: ) Gear Train Design Problemmentioning

confidence: 99%

“…searching of the best hyperparameters or network architectures of machine learning and deep learning frameworks in order to maximize their classification or regression accuracies [8]- [10]. Optimization is not only limited in the scientific and engineering domains, but it also prevalent in human daily lives such as determining the best investment portfolio that can lead to maximum profit [11], budget allocation in media planning to achieve the target level of reaching with minimum cost [12], and etc.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hovering Swarm Particle Swarm Optimization

2021

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PSO is a simple and yet powerful metaheuristic search algorithm widely used to solve various optimization problems. Nevertheless, conventional PSO tends to lose its population diversity drastically and suffer with compromised performance when encountering the optimization problems with complex fitness landscapes. Extensive studies suggest the needs of preserving high population diversity for PSO to escape from the local optima in order to solve complex optimization problems effectively. Inspired by these ideas, a hovering swarm PSO (HSPSO) is proposed in this paper, where a computationally efficient diversity preservation scheme is first introduced to divide the population of HSPSO into a main swarm and a hovering swarm. An exemplar construction scheme is subsequently proposed in the main swarm of HSPSO to generate a universal exemplar by considering the promising directional information contributed by the other non-fittest particles. The proposed universal exemplar is envisioned to suppress the negative impacts of global best particle, while remain effective to guide all particles of main swarm converging towards the promising solution regions. While hovering around the main swarm, an intelligent scheme is introduced to dynamically adjust inertia weights of all hovering swarm members to achieve proper balancing of exploration and exploitation searches at swarm levels. Extensive performance analyses are conducted by using the proposed HSPSO to solve 30 benchmark functions of CEC 2014 and five real-world engineering applications. Simulation results reveal that the HSPSO is able outperform the state-of-art optimizers when solving most tested functions due to its excellent diversity preservation capability.

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Optimal Advertising Budget Allocation across Markets with Different Goals and Various Constraints

Cited by 5 publications

References 41 publications

An application of approximate dynamic programming in multi-period multi-product advertising budgeting

An application of approximate dynamic programming in multi-period multi-product advertising budgeting

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

Hovering Swarm Particle Swarm Optimization

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