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Visualizing Dynamics in Customer Behavior with the Self-Organizing Time Map

Zhiyuan Yao, Peter Sarlin, Tomas Eklund, Barbro Back, Visualizing Dynamics in Customer Behavior with the Self-Organizing Time Map. TUCS Technical Reports 1085, TUCS, 2013.

Abstract:

Visual clustering provides effective tools for understanding relationships among clusters in a data space. This paper applies the Self Organizing Time Map (SOTM) for visual dynamic clustering of the customer base and tracking customer behavior of a department store over a 22-week period. In addition, in order to objectively represent dynamics in cluster structures, we also apply a second-level clustering to the SOTM model to visualize the temporal changes of segment structures and customers’ purchasing behavior. We demonstrate the effectiveness of the application using department store data with more than half a million rows of weekly aggregated customer information.

Files:

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BibTeX entry:

@TECHREPORT{tYaSaEkBa13a,
  title = {Visualizing Dynamics in Customer Behavior with the Self-Organizing Time Map},
  author = {Yao, Zhiyuan and Sarlin, Peter and Eklund, Tomas and Back, Barbro},
  number = {1085},
  series = {TUCS Technical Reports},
  publisher = {TUCS},
  year = {2013},
}

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

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