Planning a TV advertising campaign: A crisp multiobjective programming model from fuzzy basic data

被引:16
作者
Perez-Gladish, B. [1 ]
Gonzalez, I. [2 ]
Bilbao-Terol, A. [1 ]
Arenas-Parra, M. [1 ]
机构
[1] Univ Oviedo, Dept Econ Cuantitat, Fac Ciencias Econ, E-33006 Oviedo, Asturias, Spain
[2] Univ Politecn Valencia, Alcoy Sch, Valencia, Spain
来源
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE | 2010年 / 38卷 / 1-2期
关键词
Ad impact; Weber and Fechner's logarithmic law; Audience expectation; Budgeting; Multiobjective programming; Fuzzy techniques; VISUAL-ATTENTION; UTILITY FUNCTION; DECISION-MAKING; OPTIMIZATION; PORTFOLIO; SYSTEM; INFORMATION; NUMBERS; TIME;
D O I
10.1016/j.omega.2009.04.004
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
This paper proposes a crisp two-objective logarithmic programming model to help companies decide their advertising campaigns on TV networks for mature products. Both objectives are: (a) to achieve the highest audience impact and (b) to reduce advertising costs as much as possible. Information input is fuzzily elaborated from statistical data, the fuzzy variables being defuzzified to introduce them into the crisp model. This fuzzy information is elicited by TV experts (often independent consultants). Although these experts know statistical information on audience in the past, they do not fully trust its predictive ability. The approach leads to the strategic advertisement (ad) placement among different broadcasts. Users (often managers of big companies) should inform the analyst about their advertising campaign budget. From Weber and Fechner-based psychological research, the ad impact during the advertising campaign is measured depending on the logarithm of ad repetitions. The crisp two-objective problem is solved by a tradeoff method subject to TV technical constraints. A case study with real world data is developed. (C) 2009 Elsevier Ltd. All rights reserved
引用
收藏
页码:84 / 94
页数:11
相关论文
共 52 条
[1]  
ACKOFF RL, 1975, SLOAN MANAGE REV, V16, P1
[2]  
[Anonymous], 1993, LECT NOTES EC MATH S
[3]  
[Anonymous], 1994, Fuzzy preference modelling and multicriteria decision support
[4]   Approximating the optimum portfolio for an investor with particular preferences [J].
Ballestero, E .
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 1998, 49 (09) :998-1000
[5]   A THEOREM CONNECTING UTILITY FUNCTION OPTIMIZATION AND COMPROMISE PROGRAMMING [J].
BALLESTERO, E ;
ROMERO, C .
OPERATIONS RESEARCH LETTERS, 1991, 10 (07) :421-427
[6]   UTILITY OPTIMIZATION WHEN THE UTILITY FUNCTION IS VIRTUALLY UNKNOWN [J].
BALLESTERO, E ;
ROMERO, C .
THEORY AND DECISION, 1994, 37 (02) :233-243
[7]   Optimization of planning an advertising campaign of goods and services [J].
Belenky, AS ;
Belenkii, I .
MATHEMATICAL AND COMPUTER MODELLING, 2002, 35 (13) :1391-1403
[8]  
BELLMAN RE, 1970, MANAGE SCI B-APPL, V17, pB141
[9]  
Branke J., 2008, SERIES LECT NOTES CO, V5252
[10]  
Caballero R., 1997, LECT NOTES EC MATH S, V455