Hybrid Artificial Neural Networks Using Customer Churn Prediction
Abstract The current wave of technologies with increased awareness among customers and retaining customers has a vital role in the growth of the company. A good indicator of service satisfaction of customers and service quality is customer churn. In order to enable the organizations to understand cu...
Ausführliche Beschreibung
Autor*in: |
Ramesh, P. [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2021 |
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Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Übergeordnetes Werk: |
Enthalten in: Wireless personal communications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 124(2021), 2 vom: 01. Dez., Seite 1695-1709 |
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Übergeordnetes Werk: |
volume:124 ; year:2021 ; number:2 ; day:01 ; month:12 ; pages:1695-1709 |
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DOI / URN: |
10.1007/s11277-021-09427-7 |
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Katalog-ID: |
SPR046928375 |
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520 | |a Abstract The current wave of technologies with increased awareness among customers and retaining customers has a vital role in the growth of the company. A good indicator of service satisfaction of customers and service quality is customer churn. In order to enable the organizations to understand customers for churning, intelligible and accurate models are needed. There have been several techniques of data mining that were applied for the prediction of churn. The extensive research in Artificial Intelligence has made it feasible to study and learn the aspects accounting for such customer churn. The work presents effective solutions to all these challenging problems in Customer Churn Prediction (CCP). The study uses datasets in the telecommunication industry, the Artificial Neural Networks (ANN), and the Random Forests (RF) to determine the factors that influence consumer churn. A hybrid ANN-based work is proposed for predicting CCP. The results of the experiment proved that the proposed method achieves better levels of performance. The classification accuracy of ANN-4 hidden layers improves its result compared to RF and ANN-2 hidden layers. The maximum accuracy attained by ANN-2 hidden layers is 88.14% and by ANN-4 hidden layers is 90.34%. | ||
650 | 4 | |a Customer churn prediction (CCP) |7 (dpeaa)DE-He213 | |
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700 | 1 | |a Vijayakumar, V. |4 aut | |
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10.1007/s11277-021-09427-7 doi (DE-627)SPR046928375 (SPR)s11277-021-09427-7-e DE-627 ger DE-627 rakwb eng Ramesh, P. verfasserin aut Hybrid Artificial Neural Networks Using Customer Churn Prediction 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The current wave of technologies with increased awareness among customers and retaining customers has a vital role in the growth of the company. A good indicator of service satisfaction of customers and service quality is customer churn. In order to enable the organizations to understand customers for churning, intelligible and accurate models are needed. There have been several techniques of data mining that were applied for the prediction of churn. The extensive research in Artificial Intelligence has made it feasible to study and learn the aspects accounting for such customer churn. The work presents effective solutions to all these challenging problems in Customer Churn Prediction (CCP). The study uses datasets in the telecommunication industry, the Artificial Neural Networks (ANN), and the Random Forests (RF) to determine the factors that influence consumer churn. A hybrid ANN-based work is proposed for predicting CCP. The results of the experiment proved that the proposed method achieves better levels of performance. The classification accuracy of ANN-4 hidden layers improves its result compared to RF and ANN-2 hidden layers. The maximum accuracy attained by ANN-2 hidden layers is 88.14% and by ANN-4 hidden layers is 90.34%. Customer churn prediction (CCP) (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Random Forests (RF) (dpeaa)DE-He213 Artificial Neural Networks (ANN) (dpeaa)DE-He213 Jeba Emilyn, J. aut Vijayakumar, V. aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 124(2021), 2 vom: 01. Dez., Seite 1695-1709 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:124 year:2021 number:2 day:01 month:12 pages:1695-1709 https://dx.doi.org/10.1007/s11277-021-09427-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 124 2021 2 01 12 1695-1709 |
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10.1007/s11277-021-09427-7 doi (DE-627)SPR046928375 (SPR)s11277-021-09427-7-e DE-627 ger DE-627 rakwb eng Ramesh, P. verfasserin aut Hybrid Artificial Neural Networks Using Customer Churn Prediction 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The current wave of technologies with increased awareness among customers and retaining customers has a vital role in the growth of the company. A good indicator of service satisfaction of customers and service quality is customer churn. In order to enable the organizations to understand customers for churning, intelligible and accurate models are needed. There have been several techniques of data mining that were applied for the prediction of churn. The extensive research in Artificial Intelligence has made it feasible to study and learn the aspects accounting for such customer churn. The work presents effective solutions to all these challenging problems in Customer Churn Prediction (CCP). The study uses datasets in the telecommunication industry, the Artificial Neural Networks (ANN), and the Random Forests (RF) to determine the factors that influence consumer churn. A hybrid ANN-based work is proposed for predicting CCP. The results of the experiment proved that the proposed method achieves better levels of performance. The classification accuracy of ANN-4 hidden layers improves its result compared to RF and ANN-2 hidden layers. The maximum accuracy attained by ANN-2 hidden layers is 88.14% and by ANN-4 hidden layers is 90.34%. Customer churn prediction (CCP) (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Random Forests (RF) (dpeaa)DE-He213 Artificial Neural Networks (ANN) (dpeaa)DE-He213 Jeba Emilyn, J. aut Vijayakumar, V. aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 124(2021), 2 vom: 01. Dez., Seite 1695-1709 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:124 year:2021 number:2 day:01 month:12 pages:1695-1709 https://dx.doi.org/10.1007/s11277-021-09427-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 124 2021 2 01 12 1695-1709 |
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10.1007/s11277-021-09427-7 doi (DE-627)SPR046928375 (SPR)s11277-021-09427-7-e DE-627 ger DE-627 rakwb eng Ramesh, P. verfasserin aut Hybrid Artificial Neural Networks Using Customer Churn Prediction 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The current wave of technologies with increased awareness among customers and retaining customers has a vital role in the growth of the company. A good indicator of service satisfaction of customers and service quality is customer churn. In order to enable the organizations to understand customers for churning, intelligible and accurate models are needed. There have been several techniques of data mining that were applied for the prediction of churn. The extensive research in Artificial Intelligence has made it feasible to study and learn the aspects accounting for such customer churn. The work presents effective solutions to all these challenging problems in Customer Churn Prediction (CCP). The study uses datasets in the telecommunication industry, the Artificial Neural Networks (ANN), and the Random Forests (RF) to determine the factors that influence consumer churn. A hybrid ANN-based work is proposed for predicting CCP. The results of the experiment proved that the proposed method achieves better levels of performance. The classification accuracy of ANN-4 hidden layers improves its result compared to RF and ANN-2 hidden layers. The maximum accuracy attained by ANN-2 hidden layers is 88.14% and by ANN-4 hidden layers is 90.34%. Customer churn prediction (CCP) (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Random Forests (RF) (dpeaa)DE-He213 Artificial Neural Networks (ANN) (dpeaa)DE-He213 Jeba Emilyn, J. aut Vijayakumar, V. aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 124(2021), 2 vom: 01. Dez., Seite 1695-1709 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:124 year:2021 number:2 day:01 month:12 pages:1695-1709 https://dx.doi.org/10.1007/s11277-021-09427-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 124 2021 2 01 12 1695-1709 |
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10.1007/s11277-021-09427-7 doi (DE-627)SPR046928375 (SPR)s11277-021-09427-7-e DE-627 ger DE-627 rakwb eng Ramesh, P. verfasserin aut Hybrid Artificial Neural Networks Using Customer Churn Prediction 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The current wave of technologies with increased awareness among customers and retaining customers has a vital role in the growth of the company. A good indicator of service satisfaction of customers and service quality is customer churn. In order to enable the organizations to understand customers for churning, intelligible and accurate models are needed. There have been several techniques of data mining that were applied for the prediction of churn. The extensive research in Artificial Intelligence has made it feasible to study and learn the aspects accounting for such customer churn. The work presents effective solutions to all these challenging problems in Customer Churn Prediction (CCP). The study uses datasets in the telecommunication industry, the Artificial Neural Networks (ANN), and the Random Forests (RF) to determine the factors that influence consumer churn. A hybrid ANN-based work is proposed for predicting CCP. The results of the experiment proved that the proposed method achieves better levels of performance. The classification accuracy of ANN-4 hidden layers improves its result compared to RF and ANN-2 hidden layers. The maximum accuracy attained by ANN-2 hidden layers is 88.14% and by ANN-4 hidden layers is 90.34%. Customer churn prediction (CCP) (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Random Forests (RF) (dpeaa)DE-He213 Artificial Neural Networks (ANN) (dpeaa)DE-He213 Jeba Emilyn, J. aut Vijayakumar, V. aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 124(2021), 2 vom: 01. Dez., Seite 1695-1709 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:124 year:2021 number:2 day:01 month:12 pages:1695-1709 https://dx.doi.org/10.1007/s11277-021-09427-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 124 2021 2 01 12 1695-1709 |
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hybrid artificial neural networks using customer churn prediction |
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Hybrid Artificial Neural Networks Using Customer Churn Prediction |
abstract |
Abstract The current wave of technologies with increased awareness among customers and retaining customers has a vital role in the growth of the company. A good indicator of service satisfaction of customers and service quality is customer churn. In order to enable the organizations to understand customers for churning, intelligible and accurate models are needed. There have been several techniques of data mining that were applied for the prediction of churn. The extensive research in Artificial Intelligence has made it feasible to study and learn the aspects accounting for such customer churn. The work presents effective solutions to all these challenging problems in Customer Churn Prediction (CCP). The study uses datasets in the telecommunication industry, the Artificial Neural Networks (ANN), and the Random Forests (RF) to determine the factors that influence consumer churn. A hybrid ANN-based work is proposed for predicting CCP. The results of the experiment proved that the proposed method achieves better levels of performance. The classification accuracy of ANN-4 hidden layers improves its result compared to RF and ANN-2 hidden layers. The maximum accuracy attained by ANN-2 hidden layers is 88.14% and by ANN-4 hidden layers is 90.34%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
abstractGer |
Abstract The current wave of technologies with increased awareness among customers and retaining customers has a vital role in the growth of the company. A good indicator of service satisfaction of customers and service quality is customer churn. In order to enable the organizations to understand customers for churning, intelligible and accurate models are needed. There have been several techniques of data mining that were applied for the prediction of churn. The extensive research in Artificial Intelligence has made it feasible to study and learn the aspects accounting for such customer churn. The work presents effective solutions to all these challenging problems in Customer Churn Prediction (CCP). The study uses datasets in the telecommunication industry, the Artificial Neural Networks (ANN), and the Random Forests (RF) to determine the factors that influence consumer churn. A hybrid ANN-based work is proposed for predicting CCP. The results of the experiment proved that the proposed method achieves better levels of performance. The classification accuracy of ANN-4 hidden layers improves its result compared to RF and ANN-2 hidden layers. The maximum accuracy attained by ANN-2 hidden layers is 88.14% and by ANN-4 hidden layers is 90.34%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
abstract_unstemmed |
Abstract The current wave of technologies with increased awareness among customers and retaining customers has a vital role in the growth of the company. A good indicator of service satisfaction of customers and service quality is customer churn. In order to enable the organizations to understand customers for churning, intelligible and accurate models are needed. There have been several techniques of data mining that were applied for the prediction of churn. The extensive research in Artificial Intelligence has made it feasible to study and learn the aspects accounting for such customer churn. The work presents effective solutions to all these challenging problems in Customer Churn Prediction (CCP). The study uses datasets in the telecommunication industry, the Artificial Neural Networks (ANN), and the Random Forests (RF) to determine the factors that influence consumer churn. A hybrid ANN-based work is proposed for predicting CCP. The results of the experiment proved that the proposed method achieves better levels of performance. The classification accuracy of ANN-4 hidden layers improves its result compared to RF and ANN-2 hidden layers. The maximum accuracy attained by ANN-2 hidden layers is 88.14% and by ANN-4 hidden layers is 90.34%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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title_short |
Hybrid Artificial Neural Networks Using Customer Churn Prediction |
url |
https://dx.doi.org/10.1007/s11277-021-09427-7 |
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Jeba Emilyn, J. Vijayakumar, V. |
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Jeba Emilyn, J. Vijayakumar, V. |
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doi_str |
10.1007/s11277-021-09427-7 |
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2024-07-04T01:04:58.549Z |
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