<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The telecommunications market is well developed but is characterized by oversaturation and high levels of competition. Based on this, the urgent problem is to retain customers and predict the outflow of customer base by switching subscribers to the services of competitors. Data Science technologies and data mining methodology create significant opportunities for companies that implement data analysis and modeling for development of customer churn prediction models. The research goals are to compare different approaches and methods for customer churn prediction and construct different Data Science models to classify customers according to the probability of their churn from the company’s client base and predict potential customers who could stop to use the company’s services. On the example of one of the leading Ukrainian telecommunication companies, the article presents the results of different classification models, such as C5.0, KNN, Neural Net, Ensemble, Random Tree, Neural Net Ensemble, etc. All models are prepared in IBM SPSS Modeler and have a high level of quality (the overall accuracy and AUC ROC are more than 90%). So, the research proves the possibility and feasibility of using models in the further classification of customers to predict customer loyalty to the company and minimize consumer’s churn. The key factors influencing the customer churn are identified and form a basis for future prediction of customer outflow and optimization of company’s services. Implementation of customer churn prediction models will help to maintain customer loyalty, reduce customer outflow and increase business results</p>
</abstract>

The telecommunications market is well developed but is characterized by oversaturation and high levels of competition. Based on this, the urgent problem is to retain customers and predict the outflow of customer base by switching subscribers to the services of competitors. Data Science technologies and data mining methodology create significant opportunities for companies that implement data analysis and modeling for development of customer churn prediction models. The research goals are to compare different approaches and methods for customer churn prediction and construct different Data Science models to classify customers according to the probability of their churn from the company’s client base and predict potential customers who could stop to use the company’s services. On the example of one of the leading Ukrainian telecommunication companies, the article presents the results of different classification models, such as C5.0, KNN, Neural Net, Ensemble, Random Tree, Neural Net Ensemble, etc. All models are prepared in IBM SPSS Modeler and have a high level of quality (the overall accuracy and AUC ROC are more than 90%). So, the research proves the possibility and feasibility of using models in the further classification of customers to predict customer loyalty to the company and minimize consumer’s churn. The key factors influencing the customer churn are identified and form a basis for future prediction of customer outflow and optimization of company’s services. Implementation of customer churn prediction models will help to maintain customer loyalty, reduce customer outflow and increase business results

Customer churn prediction model: a case of the telecommunication market

Taras Shevchenko National University of Kyiv

ECONOMICS

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Customer churn prediction model: a case of the telecommunication market"}

The telecommunications market is well developed but is characterized by oversaturation and high levels of competition. Based on this, the urgent problem is to...

Customer churn prediction model: a case of the telecommunication market

Online veröffentlicht: 12. Dez. 2022

Seitenbereich: 109 - 130

Eingereicht: 22. Aug. 2022

Akzeptiert: 04. Nov. 2022

DOI: https://doi.org/10.2478/eoik-2022-0021

Schlüsselwörter
marketing, classify customers, telecommunications market, machine learning, prediction, Data Science models

© 2022 Yana Fareniuk et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Customer churn prediction model: a case of the telecommunication market

Online veröffentlicht: 12. Dez. 2022

Seitenbereich: 109 - 130

Eingereicht: 22. Aug. 2022

Akzeptiert: 04. Nov. 2022

DOI: https://doi.org/10.2478/eoik-2022-0021

Schlüsselwörtermarketing, classify customers, telecommunications market, machine learning, prediction, Data Science models

© 2022 Yana Fareniuk et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
marketing, classify customers, telecommunications market, machine learning, prediction, Data Science models