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Temporal Customer Segmentation Using the Self-Organizing Time Map

Zhiyuan Yao, Peter Sarlin, Tomas Eklund, Barbro Back, Temporal Customer Segmentation Using the Self-Organizing Time Map. In: Ebad Banissi, Stefan Bertschi, Camilla Forsell, Jimmy Johansson, Sarah Kenderdine, Francis T. Marchese, Muhammad Sarfraz, Liz Stuart, Anna Ursyn, Theodor G. Wyeld, Hanane Azzag, Mustapha Lebba, G. Venturini (Eds.), Proceedings of the 2012 16th International Conference on Information Visualisation (IV), 234–240, IEEE Press, 2012.

http://dx.doi.org/10.1109/IV.2012.47

Abstract:

Visual clustering provides effective tools for understanding relationships among clusters in a data space. This paper applies an adaptation of the standard Self-Organizing Map for visual temporal clustering in exploring the customer base and tracking customer behavior of a department store over a 22-week period. In contrast to traditional clustering techniques, which often provide a static snapshot of the customer base and overlook the possible dynamics, the Self-Organizing Time Map enables exploring complex patterns over time by visualizing the results in a user-friendly way. We demonstrate the effectiveness of the application using department store data with more than half a million rows of weekly aggregated customer information.

BibTeX entry:

@INPROCEEDINGS{inpYaSaEkBa12b,
  title = {Temporal Customer Segmentation Using the Self-Organizing Time Map},
  booktitle = {Proceedings of the 2012 16th International Conference on Information Visualisation (IV)},
  author = {Yao, Zhiyuan and Sarlin, Peter and Eklund, Tomas and Back, Barbro},
  editor = {Banissi, Ebad and Bertschi, Stefan and Forsell, Camilla and Johansson, Jimmy and Kenderdine, Sarah and Marchese, Francis T. and Sarfraz, Muhammad and Stuart, Liz and Ursyn, Anna and Wyeld, Theodor G. and Azzag, Hanane and Lebba, Mustapha and Venturini, G.},
  publisher = {IEEE Press},
  pages = {234–240},
  year = {2012},
}

Belongs to TUCS Research Unit(s): Data Mining and Knowledge Management Laboratory

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