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Combining Visual Customer Segmentation and Response Modeling

Zhiyuan Yao, Peter Sarlin, Tomas Eklund, Barbro Back, Combining Visual Customer Segmentation and Response Modeling. Neural Computing and Applications 25(1), 123–134, 2014.

http://dx.doi.org/10.1007/s00521-013-1454-3

Abstract:

Customer relationship management is a central part of Business Intelligence, and sales campaigns are often used for improving customer relationships. This paper uses advanced analytics to explore customer behavior during sales campaigns. We provide a visual, data-driven and efficient framework for customer-segmentation and campaign-response modeling. First, the customers are grouped by purchasing behavior characteristics using a self-organizing map. To this behavioral segmentation model, we link segment-migration patterns using feature plane representations. This enables visual monitoring of the customer base and tracking customer behavior before and during sales campaigns. In addition to the general segment-migration patterns, this method provides the capability to drill down into each segment to visually explore the dynamics. The framework is applied to a department store chain with more than 1 million customers.

BibTeX entry:

@ARTICLE{jYaSaEkBa14a,
  title = {Combining Visual Customer Segmentation and Response Modeling},
  author = {Yao, Zhiyuan and Sarlin, Peter and Eklund, Tomas and Back, Barbro},
  journal = {Neural Computing and Applications},
  volume = {25},
  number = {1},
  publisher = {Springer},
  pages = {123–134},
  year = {2014},
  keywords = {Business Intelligence, Customer relationship management (CRM), Visual analytics, Customer segmentation, Campaign-response modeling},
  ISSN = {1433-3058},
}

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

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