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Using SOM-Ward Clustering and Predictive Analytics for Conducting Customer Segmentation

Zhiyuan Yao, Tomas Eklund, Barbro Back, Using SOM-Ward Clustering and Predictive Analytics for Conducting Customer Segmentation. In: 2010 IEEE International Conference on Data Mining Workshops , 639–646, IEEE, 2010.

Abstract:

Continuously increasing amounts of data in data warehouses are providing companies with
ample opportunity to conduct analytical customer relationship management (CRM).
However, how to utilize the information retrieved from the analysis of these data to retain the
most valuable customers, identify customers with additional revenue potential, and achieve
cost-effective customer relationship management, continue to pose challenges for
companies. This study proposes a two-level approach combining SOM-Ward clustering and
predictive analytics to segment the customer base of a case company with 1.5 million
customers. First, according to the spending amount, demographic and behavioral
characteristics of the customers, we adopt SOM-Ward clustering to segment the customer
base into seven segments: exclusive customers, high-spending customers, and five
segments of mass customers. Then, three classification models - the support vector
machine (SVM), the neural network, and the decision tree, are employed to classify high-
spending and low-spending customers. The performance of the three classification models
is evaluated and compared. The three models are then combined to predict potential high-
spending customers from the mass customers. It is found that this hybrid approach could
provide more thorough and detailed information about the customer base, especially the
untapped mass market with potential high revenue contribution, for tailoring actionable
marketing strategies.

BibTeX entry:

@INPROCEEDINGS{inpYaEkBa10a,
  title = {Using SOM-Ward Clustering and Predictive Analytics for Conducting Customer Segmentation},
  booktitle = {2010 IEEE International Conference on Data Mining Workshops },
  author = {Yao, Zhiyuan and Eklund, Tomas and Back, Barbro},
  publisher = {IEEE},
  pages = {639–646},
  year = {2010},
  keywords = {customer segmentation, predictive analytics, self-organizing map (SOM), Ward's clustering, support vector machine (SVM), neural network (NN), decision tree},
}

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

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