Chapter 3. Data Mining Techniques for Segmentation

  1. Konstantinos Tsiptsis1 and
  2. Antonios Chorianopoulos2

Published Online: 20 JAN 2010

DOI: 10.1002/9780470685815.ch3

Data Mining Techniques in CRM: Inside Customer Segmentation

Data Mining Techniques in CRM: Inside Customer Segmentation

How to Cite

Tsiptsis, K. and Chorianopoulos, A. (2010) Data Mining Techniques for Segmentation, in Data Mining Techniques in CRM: Inside Customer Segmentation, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470685815.ch3

Author Information

  1. 1

    CRM & Customer Intelligence Expert, Athens, Greece

  2. 2

    Data Mining Expert, Athens, Greece

Publication History

  1. Published Online: 20 JAN 2010
  2. Published Print: 15 JAN 2010

ISBN Information

Print ISBN: 9780470685822

Online ISBN: 9780470685815

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Keywords:

  • data mining modeling techniques for segmentation;
  • segmenting customers with data mining techniques;
  • clustering algorithms for input data with data reduction technique application;
  • Principal Components Analysis (PCA) - statistical technique for reducing data of original input fields;
  • rotation, recommended technique facilitating interpretation process of components;
  • clustering techniques - K-means, TwoStep and Kohonen network algorithms;
  • IBM SPSS Modeler - offering data exploration tool (Data Audit);
  • Kohonen networks - special types of neural networks for clustering;
  • understanding clusters through profiling;
  • IBM SPSS Modeler, cluster profiling and evaluation tool in graphic presentation of revealed clusters

Summary

This chapter contains sections titled:

  • Segmenting Customers with Data Mining Techniques

  • Principal Components Analysis

  • Clustering Techniques

  • Examining and Evaluating the Cluster Solution

  • Understanding the Clusters through Profiling

  • Selecting the Optimal Cluster Solution

  • Cluster Profiling and Scoring with Supervised Models

  • An Introduction to Decision Tree Models

  • Summary