Abstract
Worldwide the retail market is under a severe competitive pressure. The retail trade in Germany in particular is internationally recognized as the most competitive market. To survive in this market most retailers use undirected mass marketing extensively. All prospective customers receive the same huge catalogues, countless advertising pamphlets, intrusive speaker announcements and flashy banner ads. In the end the customers are not only annoyed but the response rates of advertising campaigns are dropping for years. To avoid this, an individualization of mass marketing is recommended where customers receive individual offers specific to their needs. The objective is to offer the right customer at the right time for the right price the right product or content. This turns out to be primarily a mathematical problem concerning the areas of statistics, optimization, analysis and numerics. The arising problems of regression, clustering, and optimal control are typically of high dimensions and have huge amounts of data and therefore need new mathematical concepts and algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bellman, R.: Dynamic Programming. Princeton University Press, Princeton (1957)
Berry, M.J.A., Linoff, G.S.: Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Wiley, New York (2004)
Beylkin, G., Mohlenkamp, M.J.: Algorithms for numerical analysis in high dimensions. SIAM J. Sci. Comput. 26, 2133–2159 (2005)
Börm, S., Grasedyck, L., Hackbusch, W.: Hierarchical Matrices. Lecture Note, vol. 21. Max Planck Institute for Mathematics in the Sciences, Leipzig (2003)
Bungartz, H.J., Griebel, M.: Sparse grids. Acta Numer. 13, 147–269 (2004)
Garcke, J., Griebel, M., Thess, M.: Data mining with sparse grids. Computing 67, 225–253 (2001)
Girosi, F., Jones, M., Poggio, T.: Regularization theory and neural network architectures. Neural Comput. 7, 219–265 (1995)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, Berlin (2001)
Munos, R.: A study of reinforcement learning in the continuous case by the means of viscosity solutions. Mach. Learn. 40(3), 265–299 (2000)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Zenger, C.: Sparse grids. In: Parallel Algorithms for Partial Differential Equations, Proceedings of the Sixth GAMM-Seminar, Kiel, 1990. Notes on Num. Fluid Mech., vol. 31, pp. 241–251. Vieweg, Weisbaden (1991)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Garcke, J., Griebel, M., Thess, M. (2010). Data Mining for the Category Management in the Retail Market. In: Grötschel, M., Lucas, K., Mehrmann, V. (eds) Production Factor Mathematics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11248-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-11248-5_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11247-8
Online ISBN: 978-3-642-11248-5
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)