• TUCS Publication Series
• How to Publish in TUCS Series
• Publication Search
• Publication Graph
• Publication Input
• Publication Input Guide
• JuFo Browser

Combining Unsupervised and Supervised Data Mining Techniques for Conducting Customer Portfolio Analysis

Zhiyuan Yao, Annika Holmbom, Tomas Eklund, Barbro Back, Combining Unsupervised and Supervised Data Mining Techniques for Conducting Customer Portfolio Analysis. In: Petra Perner (Ed.), Advances in data mining, applications and theoretical aspects, Lecture Notes in Computer Science 6171, 292–307, Springer Berlin Heidelberg, 2010.

http://dx.doi.org/10.1007/978-3-642-14400-4_23

Abstract:

Leveraging the power of increasing amounts of data to analyze customer base for attracting
and retaining the most valuable customers is a major problem facing companies in this
information age. Data mining technologies extract hidden information and knowledge from
large data stored in databases or data warehouses, thereby supporting the corporate
decision making process. In this study, we apply a two-level approach that combines SOM-
Ward clustering and decision trees to conduct customer portfolio analysis for a case
company. The created two-level model was then used to identify potential high-value
customers from the customer base. It was found that this hybrid approach could provide
more detailed and accurate information about the customer base for tailoring actionable
marketing strategies.

Files:

Full publication in PDF-format

BibTeX entry:

@INPROCEEDINGS{inpYaHoEkBa10a,
  title = {Combining Unsupervised and Supervised Data Mining Techniques for Conducting Customer Portfolio Analysis},
  booktitle = {Advances in data mining, applications and theoretical aspects},
  author = {Yao, Zhiyuan and Holmbom, Annika and Eklund, Tomas and Back, Barbro},
  volume = {6171},
  series = {Lecture Notes in Computer Science},
  editor = {Perner, Petra},
  publisher = {Springer Berlin Heidelberg},
  pages = {292–307},
  year = {2010},
  keywords = {Customer relationship management (CRM), customer portfolio analysis (CPA), Self-organizing maps (SOM), Ward’s clustering, decision trees},
}

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

Publication Forum rating of this publication: level 1

Edit publication