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 retain customers and predict the outflow of customer base by switching subscribers to the services of competitors. Data Science technologies...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2022-12-01
|
| Series: | ECONOMICS |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/eoik-2022-0021 |
| _version_ | 1797947700931461120 |
|---|---|
| author | Fareniuk Yana Zatonatska Tetiana Dluhopolskyi Oleksandr Kovalenko Oksana |
| author_facet | Fareniuk Yana Zatonatska Tetiana Dluhopolskyi Oleksandr Kovalenko Oksana |
| author_sort | Fareniuk Yana |
| collection | DOAJ |
| description | 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 |
| first_indexed | 2024-04-10T21:30:49Z |
| format | Article |
| id | doaj.art-a80f51000b8d4804a713168ab9f696cd |
| institution | Directory Open Access Journal |
| issn | 2303-5013 |
| language | English |
| last_indexed | 2024-04-10T21:30:49Z |
| publishDate | 2022-12-01 |
| publisher | Sciendo |
| record_format | Article |
| series | ECONOMICS |
| spelling | doaj.art-a80f51000b8d4804a713168ab9f696cd2023-01-19T13:20:32ZengSciendoECONOMICS2303-50132022-12-0110210913010.2478/eoik-2022-0021Customer churn prediction model: a case of the telecommunication marketFareniuk Yana0Zatonatska Tetiana1Dluhopolskyi Oleksandr2Kovalenko Oksana3Taras Shevchenko National University of Kyiv, Kyiv, UkraineTaras Shevchenko National University of Kyiv, Kyiv, UkraineWSEI University, Lublin, PolandTaras Shevchenko National University of Kyiv, Kyiv, UkraineThe 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 resultshttps://doi.org/10.2478/eoik-2022-0021marketingclassify customerstelecommunications marketmachine learningpredictiondata science modelsc59d11m31 |
| spellingShingle | Fareniuk Yana Zatonatska Tetiana Dluhopolskyi Oleksandr Kovalenko Oksana Customer churn prediction model: a case of the telecommunication market ECONOMICS marketing classify customers telecommunications market machine learning prediction data science models c59 d11 m31 |
| title | Customer churn prediction model: a case of the telecommunication market |
| title_full | Customer churn prediction model: a case of the telecommunication market |
| title_fullStr | Customer churn prediction model: a case of the telecommunication market |
| title_full_unstemmed | Customer churn prediction model: a case of the telecommunication market |
| title_short | Customer churn prediction model: a case of the telecommunication market |
| title_sort | customer churn prediction model a case of the telecommunication market |
| topic | marketing classify customers telecommunications market machine learning prediction data science models c59 d11 m31 |
| url | https://doi.org/10.2478/eoik-2022-0021 |
| work_keys_str_mv | AT fareniukyana customerchurnpredictionmodelacaseofthetelecommunicationmarket AT zatonatskatetiana customerchurnpredictionmodelacaseofthetelecommunicationmarket AT dluhopolskyioleksandr customerchurnpredictionmodelacaseofthetelecommunicationmarket AT kovalenkooksana customerchurnpredictionmodelacaseofthetelecommunicationmarket |