Optimizing Response Functions of Media Exposure Distributions

The object of this paper is to show how to maximize the expected response of an advertising campaign, subject to a budget constraint. Four response functions are considered with closed-form solutions given for the resulting expected responses. These expected responses use only univariate marginal di...

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Veröffentlicht in:	The Journal of the Operational Research Society Jg. 42; H. 7; S. 537 - 542
1. Verfasser:	Danaher, Peter J.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Taylor & Francis 01.07.1991 Macmillan Press Palgrave Macmillan UK Palgrave Pergamon Press Taylor & Francis Ltd
Schlagworte:	Advertising Advertising campaigns Advertising media Applied sciences Business and Management Case-Oriented Paper Case-Oriented Papers Emergency vehicles Estimate reliability Estimation methods Exact sciences and technology Firm modelling Heuristic Heuristics Integer programming Management Mathematical functions Media Response Functions Modeling Operational research and scientific management Operational research. Management science Operations research Operations Research/Decision Theory Optimization Vehicles Media Response Functions Advertising Heuristics Optimization Integer programming Response function Constraint Heuristic method Media Budget Non linear programming
ISSN:	0160-5682, 1476-9360
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Abstract	The object of this paper is to show how to maximize the expected response of an advertising campaign, subject to a budget constraint. Four response functions are considered with closed-form solutions given for the resulting expected responses. These expected responses use only univariate marginal distributions and pairwise duplications, so they can be rapidly calculated compared with the usual cumbersome calculation based on the full frequency distribution. A simple heuristic solution to the formulated non-linear integer programming problem is given, resulting in big savings in computation time over the branch-and-bound technique, for example.
AbstractList	The object of this paper is to show how to maximize the expected response of an advertising campaign, subject to a budget constraint. Four response functions are considered with closed-form solutions given for the resulting expected responses. These expected responses use only univariate marginal distributions and pairwise duplications, so they can be rapidly calculated compared with the usual cumbersome calculation based on the full frequency distribution. A simple heuristic solution to the formulated non-linear integer programming problem is given, resulting in big savings in computation time over the branch-and-bound technique, for example. It is shown how to maximize the expected response of an advertising campaign that is subject to a budget constraint. Four response functions are considered with closed-form solutions given for the resulting expected responses. These expected responses use only univariate marginal distributions and pairwise duplications, so they can be rapidly calculated compared with the usual cumbersome calculations based on the full frequency distribution. A simple heuristic solution to the formulated nonlinear integer programming problem is given, resulting in big savings in computation time over the branch-and-bound technique, for example.
Author	Danaher, Peter J.
Author_xml	– sequence: 1 givenname: Peter J. surname: Danaher fullname: Danaher, Peter J. organization: Department of Mathematics and StatisticsUniversity of Auckland
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Copyright	Copyright © 1991, Operational Research Society 1991 Copyright 1991 The Operational Research Society Limited Operational Research Society 1991 1992 INIST-CNRS Copyright Palgrave Macmillan Limited Jul 1991
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Keywords	Media Response Functions Advertising Heuristics Optimization Integer programming Response function Constraint Heuristic method Media Budget Non linear programming
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Snippet	The object of this paper is to show how to maximize the expected response of an advertising campaign, subject to a budget constraint. Four response functions... It is shown how to maximize the expected response of an advertising campaign that is subject to a budget constraint. Four response functions are considered...
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SubjectTerms	Advertising Advertising campaigns Advertising media Applied sciences Business and Management Case-Oriented Paper Case-Oriented Papers Emergency vehicles Estimate reliability Estimation methods Exact sciences and technology Firm modelling Heuristic Heuristics Integer programming Management Mathematical functions Media Response Functions Modeling Operational research and scientific management Operational research. Management science Operations research Operations Research/Decision Theory Optimization Vehicles
Title	Optimizing Response Functions of Media Exposure Distributions
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