Number of Clusters and the Quality of Hybrid Predictive Models in Analytical CRM

• Authors: Mariusz Łapczyński , Bartłomiej Jefmański
• Languages of publication: EN

Abstracts

EN

Making more accurate marketing decisions by managers requires building effective predictive models. Typically, these models specify the probability of customer belonging to a particular category, group or segment. The analytical CRM categories refer to customers interested in starting cooperation with the company (acquisition models), customers who purchase additional products (cross- and up-sell models) or customers intending to resign from the cooperation (churn models). During building predictive models researchers use analytical tools from various disciplines with an emphasis on their best performance. This article attempts to build a hybrid predictive model combining decision trees (C&RT algorithm) and cluster analysis (k-means). During experiments five different cluster validity indices and eight datasets were used. The performance of models was evaluated by using popular measures such as: accuracy, precision, recall, G-mean, F-measure and lift in the first and in the second decile. The authors tried to find a connection between the number of clusters and models' quality.

Keywords

EN

hybrid predictive models; analytical CRM; decision trees; k-means

• Publisher: Sciendo
• Journal: Studies in Logic, Grammar and Rhetoric
• Year: 2014
• Volume: 37
• Issue: 1
• Pages: 141-157

Dates

online

2014-08-08

Contributors

author

Mariusz Łapczyński

• Department of Market Analysis and Marketing Research, Cracow University of Economics, Poland

author

Bartłomiej Jefmański

• Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland

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Identifiers

DOI

10.2478/slgr-2014-0022