Optimal Advertising Budget Allocation across Markets with Different Goals and Various Constraints
Joint Authors
Wang, Xingyuan
Li, Fuan
Jia, Fan
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-21
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Advertising budget allocation across multiple markets has drawn considerable attention in recent years.
To expand previous research and fill a gap in the current literature, this study proposes two decision models for optimal budget allocation decisions across multimarkets with different goals and various constraints.
In addition to the market parameters proposed by the Vidale–Wolfe model, the present study incorporates market goals and advertising objectives into budget allocation decisions.
Different types of markets are defined in terms of the goal set for market share or profit.
Given the characteristics of different markets, two separate decision models are developed.
Model I aims to maximize sales volume given a fixed advertising budget, while model II seeks to minimize the advertising budget given a total of targeted sales volume for all the markets.
Solutions to the two models are discussed, and a numerical example is provided to demonstrate how to apply the models in making budget allocation decision.
American Psychological Association (APA)
Wang, Xingyuan& Li, Fuan& Jia, Fan. 2020. Optimal Advertising Budget Allocation across Markets with Different Goals and Various Constraints. Complexity،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1142755
Modern Language Association (MLA)
Wang, Xingyuan…[et al.]. Optimal Advertising Budget Allocation across Markets with Different Goals and Various Constraints. Complexity No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1142755
American Medical Association (AMA)
Wang, Xingyuan& Li, Fuan& Jia, Fan. Optimal Advertising Budget Allocation across Markets with Different Goals and Various Constraints. Complexity. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1142755
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142755