<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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&amp;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.</p></abstract>

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.

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

Department of Market Analysis and Marketing Research

Department of Econometrics and Computer Science

Studies in Logic, Grammar and Rhetoric

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Number of Clusters and the Quality of Hybrid Predictive Models in Analytical CRM"}

Making more accurate marketing decisions by managers requires building effective predictive models. Typically, these models specify the probability of customer...

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

Data publikacji: 08 sie 2014

Zakres stron: 141 - 157

DOI: https://doi.org/10.2478/slgr-2014-0022

Słowa kluczowe
hybrid predictive models, analytical CRM, decision trees, k-means

© 2014 Studies in Logic, Grammar and Rhetoric

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Data publikacji: 08 sie 2014

Zakres stron: 141 - 157

DOI: https://doi.org/10.2478/slgr-2014-0022

Słowa kluczowehybrid predictive models, analytical CRM, decision trees, k-means

© 2014 Studies in Logic, Grammar and Rhetoric

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
hybrid predictive models, analytical CRM, decision trees, k-means