A Swish RNN based customer churn prediction for the telecom industry with a novel feature selection strategy
Owing to saturated markets, fierce competition, dynamic criteria, along with introduction of new attractive offers, the considerable issue of customer churn was faced by the telecommunication industry. Thus, an efficient Churn Prediction (CP) model is required for monitoring customer churn. Therefor...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2022.2083584 |
| _version_ | 1797684053836562432 |
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| author | R. Sudharsan E. N. Ganesh |
| author_facet | R. Sudharsan E. N. Ganesh |
| author_sort | R. Sudharsan |
| collection | DOAJ |
| description | Owing to saturated markets, fierce competition, dynamic criteria, along with introduction of new attractive offers, the considerable issue of customer churn was faced by the telecommunication industry. Thus, an efficient Churn Prediction (CP) model is required for monitoring customer churn. Therefore, this work proposes a novel framework to predict customer churn through a deep learning model namely Swish Recurrent Neural Network (S-RNN). Finally, SRNN is adapted to classify the Churn Customer (CC) and a normal customer. If the result is a churn customer, network utilisation history is analysed for retention process. Whereas, the number of churn customers based on the area network usage is not recognised in this frameworkOwing to saturated markets, fierce competition, dynamic criteria, along with introduction of new attractive offers, the considerable issue of customer churn was faced by the telecommunication industry. Thus, an efficient Churn Prediction (CP) model is required for monitoring customer churn. Therefore, this work proposes a novel framework to predict customer churn through a deep learning model namely Swish Recurrent Neural Network (S-RNN). Finally, S-RNN is adapted to classify the Churn Customer (CC) and a normal customer. If the result is a churn customer, network utilisation history is analysed for retention process. |
| first_indexed | 2024-03-12T00:23:50Z |
| format | Article |
| id | doaj.art-29080fdb9f184d33b0dbfddc3780cdc8 |
| institution | Directory Open Access Journal |
| issn | 0954-0091 1360-0494 |
| language | English |
| last_indexed | 2024-03-12T00:23:50Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj.art-29080fdb9f184d33b0dbfddc3780cdc82023-09-15T10:48:00ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-013411855187610.1080/09540091.2022.20835842083584A Swish RNN based customer churn prediction for the telecom industry with a novel feature selection strategyR. Sudharsan0E. N. Ganesh1Vels Institute of Science, Technology & Advanced StudiesVels Institute of Science, Technology & Advanced StudiesOwing to saturated markets, fierce competition, dynamic criteria, along with introduction of new attractive offers, the considerable issue of customer churn was faced by the telecommunication industry. Thus, an efficient Churn Prediction (CP) model is required for monitoring customer churn. Therefore, this work proposes a novel framework to predict customer churn through a deep learning model namely Swish Recurrent Neural Network (S-RNN). Finally, SRNN is adapted to classify the Churn Customer (CC) and a normal customer. If the result is a churn customer, network utilisation history is analysed for retention process. Whereas, the number of churn customers based on the area network usage is not recognised in this frameworkOwing to saturated markets, fierce competition, dynamic criteria, along with introduction of new attractive offers, the considerable issue of customer churn was faced by the telecommunication industry. Thus, an efficient Churn Prediction (CP) model is required for monitoring customer churn. Therefore, this work proposes a novel framework to predict customer churn through a deep learning model namely Swish Recurrent Neural Network (S-RNN). Finally, S-RNN is adapted to classify the Churn Customer (CC) and a normal customer. If the result is a churn customer, network utilisation history is analysed for retention process.http://dx.doi.org/10.1080/09540091.2022.2083584customer churn predictionbutterfly optimisation algorithmrecurrent neural network (rnn)brownian motion clustering large applicationswish activation function |
| spellingShingle | R. Sudharsan E. N. Ganesh A Swish RNN based customer churn prediction for the telecom industry with a novel feature selection strategy Connection Science customer churn prediction butterfly optimisation algorithm recurrent neural network (rnn) brownian motion clustering large application swish activation function |
| title | A Swish RNN based customer churn prediction for the telecom industry with a novel feature selection strategy |
| title_full | A Swish RNN based customer churn prediction for the telecom industry with a novel feature selection strategy |
| title_fullStr | A Swish RNN based customer churn prediction for the telecom industry with a novel feature selection strategy |
| title_full_unstemmed | A Swish RNN based customer churn prediction for the telecom industry with a novel feature selection strategy |
| title_short | A Swish RNN based customer churn prediction for the telecom industry with a novel feature selection strategy |
| title_sort | swish rnn based customer churn prediction for the telecom industry with a novel feature selection strategy |
| topic | customer churn prediction butterfly optimisation algorithm recurrent neural network (rnn) brownian motion clustering large application swish activation function |
| url | http://dx.doi.org/10.1080/09540091.2022.2083584 |
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