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2014 | 37 | 1 | 141-157
Article title

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

Title variants
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.
Publisher
Year
Volume
37
Issue
1
Pages
141-157
Physical description
Dates
online
2014-08-08
Contributors
  • Department of Market Analysis and Marketing Research, Cracow University of Economics, Poland, lapczynm@uek.krakow.pl
References
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Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.doi-10_2478_slgr-2014-0022
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