Araştırma Makalesi
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Yıl 2023, Cilt: 36 Sayı: 2, 720 - 733, 01.06.2023
https://doi.org/10.35378/gujs.992738

Öz

Kaynakça

  • [1] Germann, F., Lilien, G., Moorman, and C., Fiedler, L., “Driving customer analytics from the top. Customer Needs and Solutions”, Customer Needs and Solutions, 7(3):43–61, (2021).
  • [2] Germann, F, Lilien, G.L, Fiedler, L, and Kraus, M., “Do retailers benefit from deploying customer analytics?”, Journal of Retailing, 90(4):587-593, (2014).
  • [3] Mishra, A. and Reddy, U. S., “A novel approach for churn prediction using deep learning”, Proceedings of the IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Coimbatore India, 1–4, (2017).
  • [4] Aspect Software. Aspect Consumer Index 2020. https://www.aspect.com/the-aspect-consumer-experience-index-2020, 2020. Online, accessed 10 April 2021.
  • [5] Uzair, A., Khan, A., Saddam, H. K., Basit, A., Haq, I., and Lee, Y. S., “Transfer learning and meta classification based deep churn prediction system for telecom industry”, arXiv preprint arXiv:1901.06091, (2019).
  • [6] Ladyzynski, P., Zbikowski, K. and Gawrysiak, P., “Direct marketing campaigns in retail banking with the use of deep learning and random forests”, Expert Systems with Applications, 134: 28–35, (2019).
  • [7] Umayaparvathi, V. and Iyakutti, K., “Automated feature selection and churn prediction using deep learning models”, International Research Journal of Engineering and Technology (IRJET), 4(3): 1846–1854, (2017).
  • [8] Wang, C., Han, D., Fan, W. and Liu, Q., “Customer churn prediction with feature embedded convolutional neural network: An empirical study in the internet funds industry”, International Journal of Computational Intelligence and Applications, 18(1): 1950003, (2019).
  • [9] Buckinx, W. and Poel, D. V., “Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting”, European Journal of Operational Research, 164(1): 252–268, (2005).
  • [10] Keiningham, T. L., Cooil, B., Aksoy, L., Tor, W. A., and Weiner, J., “The value of different customer satisfaction and loyalty metrics in predicting customer retention, recommendation, and share-of-wallet”, Managing Service Quality: An International Journal, 17(4): 361–384, (2007).
  • [11] Migueis, V. L., Poel, D. V., Camanho, A., and Cunha, J. F., “Modeling partial customer churn: On the value of first product-category purchase sequences”, Expert Systems with Applications, 39(12): 11250–11256, (2012).
  • [12] Zhong, J. and Li, W., “Predicting customer churn in the telecommunication industry by analyzing phone call transcripts with convolutional neural networks”, In Proceedings of the 2019 3rd International Conference on Innovation in Artificial Intelligence, Suzhou China, 55–59, (2019).
  • [13] Alboukaey, N., Joukhadar, A., and Ghneim, N., “Dynamic behavior-based churn prediction in mobile telecom”, Expert Systems with Applications, 162: 113779, 2020.
  • [14] Chouiekh, A. and Haj, E. H., “Deep convolutional neural networks for customer churn prediction analysis”, International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 14(1): 1–16, (2020).
  • [15] Tariq, M. U., Babar, M., Poulin, M. and Khattak, A. S., “Distributed model for customer churn prediction using convolutional neural network”, Journal of Modelling in Management, ahead-of-print, (2021).
  • [16] Lalwani, P., Mishra, M. K., Chadha, J. S. and Sethi, P., “Customer churn prediction system: A machine learning approach”, Computing, 104(2), 271–294, (2022).
  • [17] Garimella, B., Prasad, G. V. S. N. R. V. and Prasad, M. H. M. K., “Churn prediction using optimized deep learning classifier on huge telecom data”, Journal of Ambient Intelligence and Humanized Computing, (2021).
  • [18] Cenggoro, T. W., Wirastari, R. A., Rudianto, E., Mohadi, M. I., Ratj, D., and Pardamean, B., “Deep Learning as a Vector Embedding Model for Customer Churn”, Procedia Computer Science, 179, 624–631, (2021).
  • [19] Caigny, A. D., Coussement, K., Bock, K. W., and Lessmann, S., “Incorporating textual information in customer churn prediction models based on a convolutional neural network”, International Journal of Forecasting, 36(4): 1563–1578, (2020).
  • [20] Evermann, J., Rehse, J. R., and Fettke, P., “Predicting process behaviour using deep learning”, Decision Support Systems, 100: 129–140, (2017).
  • [21] Domingos, E., Ojeme, B. and Daramola, O., “Experimental Analysis of Hyperparameters for Deep Learning-Based Churn Prediction in the Banking Sector”, Computation, 9(3), 34, (2021).
  • [22] Unlu, K. D., “Predicting credit card customer churn using support vector machine based on Bayesian optimization”, Communications Faculty of Science University of Ankara Series A1Mathematics and Statistics, 70(2), 827–836, (2021).
  • [23] Ozmen, E. P. and Ozcan, T., “A novel deep learning model based on convolutional neural networks for employee churn prediction”, Journal of Forecasting, 41(3), 539–550, (2022).
  • [24] Kim, S. D. Choi, Lee, E. and Rhee, W., “Churn prediction of mobile and online casual games using play log data”, PloS One, 12(7): e0180735, 2017.
  • [25] Kristensen, J. T. and Burelli, P., “Combining sequential and aggregated data for churn prediction in casual freemium games”, 2019 IEEE Conference on Games (CoG), London England, 1–8, (2019).
  • [26] Guitart, A., Chen, P. P. and Perianez, A., “The winning solution to the IEEE CIG 2017 game data mining competition”, Machine Learning and Knowledge Extraction, 1(1): 252–264, (2019).
  • [27] Zhang, R., Li, W., Tan, W. and Mo, T., “Deep and shallow model for insurance churn prediction service”, In 2017 IEEE International Conference on Services Computing (SCC), 346–353, IEEE, (2017).
  • [28] Zhou, J., Yan, J., Yang, L., Wang, M., and Xia, P., “Customer churn prediction model based on LSTM and CNN in music streaming”, DES tech Transactions on Engineering and Technology Research, (AEMCE), (2019).
  • [29] Kim, J., Ji, H. G., Oh, S., Hwang, S., Park, E. and Pobil, A. P., “A deep hybrid learning model for customer repurchase behavior”, Journal of Retailing and Consumer Services, 59:102381, (2021).
  • [30] Dingli, A., Marmara, V. and Fournier, N. S., “Comparison of deep learning algorithms to predict customer churn within a local retail industry”. International Journal of Machine Learning and Computing, 7(5): 128–132, (2017).
  • [31] Aghdam, H. H. and Heravi, E. J., Guide to convolutional neural networks: A Practical Application to Traffic-Sign Detection and Classification. Springer, New York, NY, 10: 978–973, (2017).
  • [32] Lu, L., Zheng, Y., Carneiro, G. and Yang, L., “Deep learning and convolutional neural networks for medical image computing”. Advances in Computer Vision and Pattern Recognition, 10: 978–3, (2017).
  • [33] Geron, A., “Hands-on machine learning with Scikit-Learn”, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. 2nd ed., O’Reilly Media, (2019).

Customer Churn Prediction Using Ordinary Artificial Neural Network and Convolutional Neural Network Algorithms: A Comparative Performance Assessment

Yıl 2023, Cilt: 36 Sayı: 2, 720 - 733, 01.06.2023
https://doi.org/10.35378/gujs.992738

Öz

Churn studies have been used for many years to increase profitability as well as to make customer-company relations sustainable. Ordinary artificial neural network (ANN) and convolution neural network (CNN) are widely used in churn analysis due to their ability to process large amounts of customer data. In this study, an ANN and a CNN model are proposed to predict whether customers in the retail industry will churn in the future. The models we proposed were compared with many machine learning methods that are frequently used in churn prediction studies. The results of the models were compared via accuracy classification tools, which are precision, recall, and AUC. The study results showed that the proposed deep learning-based churn prediction model has a better classification performance. The CNN model produced a 97.62% of accuracy rate which resulted in a better classification and prediction success than other compared models.

Kaynakça

  • [1] Germann, F., Lilien, G., Moorman, and C., Fiedler, L., “Driving customer analytics from the top. Customer Needs and Solutions”, Customer Needs and Solutions, 7(3):43–61, (2021).
  • [2] Germann, F, Lilien, G.L, Fiedler, L, and Kraus, M., “Do retailers benefit from deploying customer analytics?”, Journal of Retailing, 90(4):587-593, (2014).
  • [3] Mishra, A. and Reddy, U. S., “A novel approach for churn prediction using deep learning”, Proceedings of the IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Coimbatore India, 1–4, (2017).
  • [4] Aspect Software. Aspect Consumer Index 2020. https://www.aspect.com/the-aspect-consumer-experience-index-2020, 2020. Online, accessed 10 April 2021.
  • [5] Uzair, A., Khan, A., Saddam, H. K., Basit, A., Haq, I., and Lee, Y. S., “Transfer learning and meta classification based deep churn prediction system for telecom industry”, arXiv preprint arXiv:1901.06091, (2019).
  • [6] Ladyzynski, P., Zbikowski, K. and Gawrysiak, P., “Direct marketing campaigns in retail banking with the use of deep learning and random forests”, Expert Systems with Applications, 134: 28–35, (2019).
  • [7] Umayaparvathi, V. and Iyakutti, K., “Automated feature selection and churn prediction using deep learning models”, International Research Journal of Engineering and Technology (IRJET), 4(3): 1846–1854, (2017).
  • [8] Wang, C., Han, D., Fan, W. and Liu, Q., “Customer churn prediction with feature embedded convolutional neural network: An empirical study in the internet funds industry”, International Journal of Computational Intelligence and Applications, 18(1): 1950003, (2019).
  • [9] Buckinx, W. and Poel, D. V., “Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting”, European Journal of Operational Research, 164(1): 252–268, (2005).
  • [10] Keiningham, T. L., Cooil, B., Aksoy, L., Tor, W. A., and Weiner, J., “The value of different customer satisfaction and loyalty metrics in predicting customer retention, recommendation, and share-of-wallet”, Managing Service Quality: An International Journal, 17(4): 361–384, (2007).
  • [11] Migueis, V. L., Poel, D. V., Camanho, A., and Cunha, J. F., “Modeling partial customer churn: On the value of first product-category purchase sequences”, Expert Systems with Applications, 39(12): 11250–11256, (2012).
  • [12] Zhong, J. and Li, W., “Predicting customer churn in the telecommunication industry by analyzing phone call transcripts with convolutional neural networks”, In Proceedings of the 2019 3rd International Conference on Innovation in Artificial Intelligence, Suzhou China, 55–59, (2019).
  • [13] Alboukaey, N., Joukhadar, A., and Ghneim, N., “Dynamic behavior-based churn prediction in mobile telecom”, Expert Systems with Applications, 162: 113779, 2020.
  • [14] Chouiekh, A. and Haj, E. H., “Deep convolutional neural networks for customer churn prediction analysis”, International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 14(1): 1–16, (2020).
  • [15] Tariq, M. U., Babar, M., Poulin, M. and Khattak, A. S., “Distributed model for customer churn prediction using convolutional neural network”, Journal of Modelling in Management, ahead-of-print, (2021).
  • [16] Lalwani, P., Mishra, M. K., Chadha, J. S. and Sethi, P., “Customer churn prediction system: A machine learning approach”, Computing, 104(2), 271–294, (2022).
  • [17] Garimella, B., Prasad, G. V. S. N. R. V. and Prasad, M. H. M. K., “Churn prediction using optimized deep learning classifier on huge telecom data”, Journal of Ambient Intelligence and Humanized Computing, (2021).
  • [18] Cenggoro, T. W., Wirastari, R. A., Rudianto, E., Mohadi, M. I., Ratj, D., and Pardamean, B., “Deep Learning as a Vector Embedding Model for Customer Churn”, Procedia Computer Science, 179, 624–631, (2021).
  • [19] Caigny, A. D., Coussement, K., Bock, K. W., and Lessmann, S., “Incorporating textual information in customer churn prediction models based on a convolutional neural network”, International Journal of Forecasting, 36(4): 1563–1578, (2020).
  • [20] Evermann, J., Rehse, J. R., and Fettke, P., “Predicting process behaviour using deep learning”, Decision Support Systems, 100: 129–140, (2017).
  • [21] Domingos, E., Ojeme, B. and Daramola, O., “Experimental Analysis of Hyperparameters for Deep Learning-Based Churn Prediction in the Banking Sector”, Computation, 9(3), 34, (2021).
  • [22] Unlu, K. D., “Predicting credit card customer churn using support vector machine based on Bayesian optimization”, Communications Faculty of Science University of Ankara Series A1Mathematics and Statistics, 70(2), 827–836, (2021).
  • [23] Ozmen, E. P. and Ozcan, T., “A novel deep learning model based on convolutional neural networks for employee churn prediction”, Journal of Forecasting, 41(3), 539–550, (2022).
  • [24] Kim, S. D. Choi, Lee, E. and Rhee, W., “Churn prediction of mobile and online casual games using play log data”, PloS One, 12(7): e0180735, 2017.
  • [25] Kristensen, J. T. and Burelli, P., “Combining sequential and aggregated data for churn prediction in casual freemium games”, 2019 IEEE Conference on Games (CoG), London England, 1–8, (2019).
  • [26] Guitart, A., Chen, P. P. and Perianez, A., “The winning solution to the IEEE CIG 2017 game data mining competition”, Machine Learning and Knowledge Extraction, 1(1): 252–264, (2019).
  • [27] Zhang, R., Li, W., Tan, W. and Mo, T., “Deep and shallow model for insurance churn prediction service”, In 2017 IEEE International Conference on Services Computing (SCC), 346–353, IEEE, (2017).
  • [28] Zhou, J., Yan, J., Yang, L., Wang, M., and Xia, P., “Customer churn prediction model based on LSTM and CNN in music streaming”, DES tech Transactions on Engineering and Technology Research, (AEMCE), (2019).
  • [29] Kim, J., Ji, H. G., Oh, S., Hwang, S., Park, E. and Pobil, A. P., “A deep hybrid learning model for customer repurchase behavior”, Journal of Retailing and Consumer Services, 59:102381, (2021).
  • [30] Dingli, A., Marmara, V. and Fournier, N. S., “Comparison of deep learning algorithms to predict customer churn within a local retail industry”. International Journal of Machine Learning and Computing, 7(5): 128–132, (2017).
  • [31] Aghdam, H. H. and Heravi, E. J., Guide to convolutional neural networks: A Practical Application to Traffic-Sign Detection and Classification. Springer, New York, NY, 10: 978–973, (2017).
  • [32] Lu, L., Zheng, Y., Carneiro, G. and Yang, L., “Deep learning and convolutional neural networks for medical image computing”. Advances in Computer Vision and Pattern Recognition, 10: 978–3, (2017).
  • [33] Geron, A., “Hands-on machine learning with Scikit-Learn”, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. 2nd ed., O’Reilly Media, (2019).
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Industrial Engineering
Yazarlar

Omer Faruk Seymen 0000-0003-2224-5546

Emre Ölmez 0000-0003-1686-0251

Onur Doğan 0000-0003-3543-4012

Orhan Er 0000-0002-4732-9490

Kadir Hızıroğlu 0000-0003-4582-3732

Yayımlanma Tarihi 1 Haziran 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 36 Sayı: 2

Kaynak Göster

APA Seymen, O. F., Ölmez, E., Doğan, O., Er, O., vd. (2023). Customer Churn Prediction Using Ordinary Artificial Neural Network and Convolutional Neural Network Algorithms: A Comparative Performance Assessment. Gazi University Journal of Science, 36(2), 720-733. https://doi.org/10.35378/gujs.992738
AMA Seymen OF, Ölmez E, Doğan O, Er O, Hızıroğlu K. Customer Churn Prediction Using Ordinary Artificial Neural Network and Convolutional Neural Network Algorithms: A Comparative Performance Assessment. Gazi University Journal of Science. Haziran 2023;36(2):720-733. doi:10.35378/gujs.992738
Chicago Seymen, Omer Faruk, Emre Ölmez, Onur Doğan, Orhan Er, ve Kadir Hızıroğlu. “Customer Churn Prediction Using Ordinary Artificial Neural Network and Convolutional Neural Network Algorithms: A Comparative Performance Assessment”. Gazi University Journal of Science 36, sy. 2 (Haziran 2023): 720-33. https://doi.org/10.35378/gujs.992738.
EndNote Seymen OF, Ölmez E, Doğan O, Er O, Hızıroğlu K (01 Haziran 2023) Customer Churn Prediction Using Ordinary Artificial Neural Network and Convolutional Neural Network Algorithms: A Comparative Performance Assessment. Gazi University Journal of Science 36 2 720–733.
IEEE O. F. Seymen, E. Ölmez, O. Doğan, O. Er, ve K. Hızıroğlu, “Customer Churn Prediction Using Ordinary Artificial Neural Network and Convolutional Neural Network Algorithms: A Comparative Performance Assessment”, Gazi University Journal of Science, c. 36, sy. 2, ss. 720–733, 2023, doi: 10.35378/gujs.992738.
ISNAD Seymen, Omer Faruk vd. “Customer Churn Prediction Using Ordinary Artificial Neural Network and Convolutional Neural Network Algorithms: A Comparative Performance Assessment”. Gazi University Journal of Science 36/2 (Haziran 2023), 720-733. https://doi.org/10.35378/gujs.992738.
JAMA Seymen OF, Ölmez E, Doğan O, Er O, Hızıroğlu K. Customer Churn Prediction Using Ordinary Artificial Neural Network and Convolutional Neural Network Algorithms: A Comparative Performance Assessment. Gazi University Journal of Science. 2023;36:720–733.
MLA Seymen, Omer Faruk vd. “Customer Churn Prediction Using Ordinary Artificial Neural Network and Convolutional Neural Network Algorithms: A Comparative Performance Assessment”. Gazi University Journal of Science, c. 36, sy. 2, 2023, ss. 720-33, doi:10.35378/gujs.992738.
Vancouver Seymen OF, Ölmez E, Doğan O, Er O, Hızıroğlu K. Customer Churn Prediction Using Ordinary Artificial Neural Network and Convolutional Neural Network Algorithms: A Comparative Performance Assessment. Gazi University Journal of Science. 2023;36(2):720-33.