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

Kód výsledku v 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>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

Počet stran výsledku

9

Strana od-do

282-292

Název nakladatele

VŠB - Technical University of Ostrava

Místo vydání

Ostrava

Místo konání akce

Ostrava

Datum konání akce

25. 5. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000417344100032