Multi-criteria media mix decision model for advertising multiple product with segment specific and mass media
Keywords:
Multiple products, Mass advertising, Segment Spe- cific advertising, Spectrum effect, Media Planning, Multi-objective de- cision making, Interactive Approach.Abstract
Judicious media planning decisions are crucial for successful advertising of products. Media planners extensively use mathematical models supplemented with market research and expert opinion to devise the media plans. Media planning models discussed in the literature largely focus on single products with limited studies related to the multi-product media planning. In this paper we propose a media planning model to allocate limited advertising budget among multiple products advertised in a segmented market and determine the number of advertisements to be given in different media. The proposed model is formulated considering both segment specific and mass media vehicles to maximize the total advertising reach for each product. The model also incorporates the cross product effect of advertising of one product on the other. The proposed formulation is a multi-objective linear integer programming model and interactive linear integer goal programming is discussed to solve the model. A real life case study is presented to illustrate the application of the proposed model.References
F M. Bass and R.T. Lonsdale, An exploration of linear programming in media selection, Journal of Marketing Research (1966), 179-188.
U.K. Bhattacharya, A chance constraints goal programming model for the advertising planning problem, European Journal of Operational Research 192 (2009), 382-395.
S. Broadbent, Media planning and computers by 1970: A review of the use of mathematical models in media planning, Applied Statistics (1966), 234-256.
A. Buratto, L. Grosset and B. Viscolani, Advertising a new product in a segmented market, European Journal of Operational Research 175 (2006), 1262-1267.
P.J. Danaher and R.T. Rust, Determining the optimal return on investment for an advertising campaign, European Journal of Operational Research 95 (1996), 511-521.
P. Doyle and J. Saunders, Multiproduct advertising budgeting, Marketing Science 9 (1990), 97-113.
F.R. Dwyer and J.R. Evans, A branch and bound algorithm for the list selection problem in direct mail advertising, Management Science 27 (1981), 658-667.
L. Grosset and B. Viscolani, Advertising in a segmented market: comparison of media choices, IMA Journal of Management Mathematics 19(2008), 219-226.
T.H. Hsu, T.N. Tsai and P.L. Chiang, Selection of the optimum promotion mix by integrating a fuzzy linguistic decision model with genetic algorithms, Information Sciences 179 (2009), 41-52.
P.C. Jha, R. Aggarwal and A. Gupta, Optimal media planning for multiproducts in segmented market, Applied Mathematics and Computation 217 (2011), 6802-6818.
P.C. Jha, S. Aggarwal, A. Gupta and R. Sarker, Multi-criteria media mix decision model for advertising a single product with segment specific and mass media, to appear in Journal of Industrial and Management Optimisation (2015).
P. Korhonen, S.C. Narula and J. Wallenius, An evolutionary approach to decision-making, with an application to media selection, Mathematical and Computer Modelling 12 (1989), 1239-1244.
N.K. Kwak, C.W. Lee and J.H. Kim, An MCDM model for media selection in the dual consumer/industrial market, European Journal of Operational Research 166 (2005), 255-265.
J.D. Little and L.M. Lodish, A media planning calculus, Operations Research 17 (1969), 1-35.
R.T. Marler and J.S. Arora, The weighted sum method for multiobjective optimization: new insights, Structural and multidisciplinary optimization 41 (2010), 853-862.
C.B. Royo, H. Zhang, L.A. Blanco and J. Almagro, Multistage multiproduct advertising budgeting, European Journal of Operational Research 225 (2013), 179-188.
R.F. Saen, Media selection in the presence of flexible factors and imprecise data, Journal of the Operational Research Society 62 (2011), 1695-1703.
B. Viscolani, Advertising decisions for a segmented market, Optimization 58 (2009), 469-477.
W. Wells, J. Burnett and S.E. Moriarty, Advertising: Principles and Practice, Seventh Edition Pearson Prentice Hall, 2005.
F.S. Zufryden, Media scheduling: a stochastic dynamic model approach, Management Science 19 (1973), 1395-1406.
F.S. Zufryden, Media scheduling and solution approaches, Operational Research Quarterly (1975), 283-295.
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