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Data Mining for the Category Management in the Retail Market

In: Production Factor Mathematics

Author

Listed:
  • Jochen Garcke

    (TU Berlin, MA 3-3)

  • Michael Griebel

    (Universität Bonn, Institut für Numerische Simulation)

  • Michael Thess

    (Prudsys AG, The Realtime Analytics Company)

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.

Suggested Citation

  • Jochen Garcke & Michael Griebel & Michael Thess, 2010. "Data Mining for the Category Management in the Retail Market," Springer Books, in: Martin Grötschel & Klaus Lucas & Volker Mehrmann (ed.), Production Factor Mathematics, pages 81-92, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-11248-5_5
    DOI: 10.1007/978-3-642-11248-5_5
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