Comparison of logistic regression and decision tree for customer churn prediction in Telecommunications

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F17%3A10238350" target="_blank" >RIV/61989100:27510/17:10238350 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Comparison of logistic regression and decision tree for customer churn prediction in Telecommunications
Original language description
Customer churn, loss of customers due to switch to another service pro-vider or non-renewal of commitment, is very common in highly com-petitive and saturated markets such as telecommunications. In order to solve this problem, predictive models need to be implemented to identi-fy customers who are at risk of churning and also key drivers of churn need to be identified. In this study, two models for prediction of customer churn in next 45 days are compared - logistic regression and decision tree. The dataset used contain 16 variables and 50,000 customers in both training and testing data set. Decision tree outperformed in predictive performance logistic regression with hit rate 81.1% and specificity 94%. The most important variables in both classification models were customer dura-tion and contract duration and in logistic regression model also value added services played a big role.
Czech name
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Czech description
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Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 12th International Conference on Strategic Management and its Support by Information Systems: May 25th-26th, 2017, Ostrava, Czech Republic
ISBN
978-80-248-4046-8
ISSN
2570-5776
e-ISSN
neuvedeno
Number of pages
9
Pages from-to
282-292
Publisher name
VŠB - Technical University of Ostrava
Place of publication
Ostrava
Event location
Ostrava
Event date
May 25, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000417344100032

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