Sciweavers

Share
INCDM
2010
Springer

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

11 years 1 months ago
Combining Unsupervised and Supervised Data Mining Techniques for Conducting Customer Portfolio Analysis
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 highvalue 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.
Zhiyuan Yao, Annika H. Holmbom, Tomas Eklund, Barb
Added 18 Oct 2010
Updated 18 Oct 2010
Type Conference
Year 2010
Where INCDM
Authors Zhiyuan Yao, Annika H. Holmbom, Tomas Eklund, Barbro Back
Comments (0)
books