Use of Logistic Regression for Understanding and Prediction of Customer Churn in Telecommunications
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F19%3A10242770" target="_blank" >RIV/61989100:27510/19:10242770 - isvavai.cz</a>
Result on the web
<a href="https://www.czso.cz/csu/czso/statistika-statistics-and-economy-journal" target="_blank" >https://www.czso.cz/csu/czso/statistika-statistics-and-economy-journal</a>
DOI - Digital Object Identifier
—
Alternative languages
Result language
angličtina
Original language name
Use of Logistic Regression for Understanding and Prediction of Customer Churn in Telecommunications
Original language description
Customer churn, loss of customers due to switch to another service provider or non-renewal of commitment, is very common in highly competitive and saturated markets such as telecommunications. Predictive models need to be implemented to identify customers who are at risk of churning and also to discover the key drivers of churn. The aim of this paper is to use demographic and service usage variables to estimate logistic regression model to predict customer churn in European Telecommunications provider and to find the factors influencing customer churn. An interesting findings came out of the estimated model - younger customers who are shorter time with company, who use mobile data and sms more than traditional calls, having occasional problem with paying bills, with students account and ending contract in the near future are typical representatives of customers who tend to leave the company. An interaction terms added as explanatory variables showed that effect of usage of data and voice vary depending on the year of birth. The quality of the logistic regression model was assessed by Hosmer-Lemeshow test and pseudo R squared measures. An independent testing data set was further used to evaluate the predictive ability of the model by computation of performance metrics such as the area under the ROC curve (AUC), sensitivity and precision. The resulting model was able to catch 94.8% of customers who in fact left the company. Quality of the model was confirmed also by high value of AUC metric equal to 0.9759. Logistic regression represents a very useful tool in prediction of customer churn not only thanks to its interpretability, but also for its predictive power.
Czech name
—
Czech description
—
Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
—
OECD FORD branch
50204 - Business and management
Result continuities
Project
<a href="/en/project/EE2.3.20.0296" target="_blank" >EE2.3.20.0296: Research team for modelling of economic and financial processes at VSB-TU Ostrava</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Statistika
ISSN
0322-788X
e-ISSN
—
Volume of the periodical
99
Issue of the periodical within the volume
2
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
13
Pages from-to
129-141
UT code for WoS article
000472577100002
EID of the result in the Scopus database
2-s2.0-85072669885