Media Mix Optimization - Applying a Quadratic Knapsack Model

Ulrich Pferschy, Joachim Schauer, Gerhild Maier

Abstract

In this contribution we present an optimization model for deciding on the best selection of advertising media to be used in a promotional campaign. The effect of each single medium and each pair of media is estimated from the evaluation data of past campaigns taking into account a similarity measure between the attributes and goals of campaigns. The resulting discrete optimization model is a Quadratic Knapsack Problem which we solve by a genetic algorithm. Then campaign budget is assigned to each selected advertising medium based on a statistical estimation from previous campaigns. Our optimization tool is integrated in the marketing management software solution MARMIND.

References

  1. Balachandran, V. and Gensch, D. (1974). Solving the “marketing mix” problem using geometric programming. Management Science, 21:160-171.
  2. Billionnet, A. and Soutif, Ó. (2004). An exact method based on lagrangian decomposition for the 0-1 quadratic knapsack problem. European Journal of operational research, 157(3):565-575.
  3. Caprara, A., Pisinger, D., and Toth, P. (1999). Exact solution of the quadratic knapsack problem. INFORMS Journal on Computing, 11:125-137.
  4. Everitt, B., Landau, S., Leese, M., and Stahl, D. (2011). Cluster Analysis. Wiley, 5th edition.
  5. Färe, R., Grosskopf, S., Seldon, B., and Tremblay, V. (2004). Advertising efficiency and the choice of media mix: a case of beer. International Journal of Industrial Organization, 22:503-522.
  6. Guldemir, H. and Sengur, A. (2006). Comparison of clustering algorithms for analog modulation classification. Expert Systems with Applications, 30:642-649.
  7. Julstrom, B. (2005). Greedy, genetic, and greedy genetic algorithms for the quadratic knapsack problem. In GECCO 7805: Proceedings of the 2005 conference on Genetic and evolutionary computation, pages 607- 614. ACM.
  8. Kellerer, H., Pferschy, U., and Pisinger, D. (2004). Knapsack Problems. Springer.
  9. Kumar, R. and Vassilvitskii, S. (2010). Generalized distances between rankings. In Proceedings of the 19th International World Wide Web Conference, pages 571-580. ACM.
  10. Nobibon, F., Leus, R., and Spieksma, F. (2011). Optimization models for targeted offers in direct marketing: Exact and heuristic algorithms. European Journal of Operational Research, 210:670-683.
  11. Pergelova, A., Prior, D., and Rialp, J. (2010). Assessing advertising efficiency: Does the internet play a role? Journal of Advertising, 39:39-54.
  12. Pisinger, D. (2007). The quadratic knapsack problem - a survey. Discrete Applied Mathematics, 155:623-648.
  13. Pisinger, D., Rasmussen, A., and Sandvik, R. (2007). Solution of large quadratic knapsack problems through aggressive reduction. INFORMS Journal on Computing, 19:280-290.
  14. Reynar, A., Phillips, J., and Heumann, S. (2010). New technologies drive CPG media mix optimization. Journal of Advertising Research, 50:416-427.
  15. Sculley, D. (2007). Rank aggregation for similar items. In Proceedings of the 7th SIAM International Conference on Data Mining, pages 587-592. SIAM.
  16. S önke, A. (2012). Optimizable and implementable aggregate response modeling for marketing decision support. International Journal of Research in Marketing, 29:111-122.
  17. Sorato, A. and Viscolani, B. (2011). Using several advertising media in a homogeneous market. Optimization Letters, 5:557-573.
  18. Tan, P.-N., Steinbach, M., and Kumar, V. (2006). Introduction to Data Mining. Addison-Wesley.
  19. Vakratsas, D. and Ambler, T. (1999). How advertising works: what do we really know? Journal of Marketing, 63:26-43.
  20. Yang, Z., Wang, G., and Chu, F. (2013). An effective GRASP and tabu search for the 0-1 quadratic knapsack problem. Computers and Operations Research, 40:1176-1185.
Download


Paper Citation


in Harvard Style

Pferschy U., Schauer J. and Maier G. (2014). Media Mix Optimization - Applying a Quadratic Knapsack Model . In Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-017-8, pages 363-370. DOI: 10.5220/0004825803630370


in Bibtex Style

@conference{icores14,
author={Ulrich Pferschy and Joachim Schauer and Gerhild Maier},
title={Media Mix Optimization - Applying a Quadratic Knapsack Model},
booktitle={Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2014},
pages={363-370},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004825803630370},
isbn={978-989-758-017-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Media Mix Optimization - Applying a Quadratic Knapsack Model
SN - 978-989-758-017-8
AU - Pferschy U.
AU - Schauer J.
AU - Maier G.
PY - 2014
SP - 363
EP - 370
DO - 10.5220/0004825803630370