Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

Olfat M. Mirza · G. Jose Moses · R. Rajender · E. Laxmi Lydia · Seifedine Kadry · Cheadchai Me-Ead +1 more

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

Presently, customer retention is essential for reducing customer churn in telecommunication industry. Customer churn prediction (CCP) is important to predict the possibility of customer retention in the quality of services. Since risks of customer churn also get essential, the rise of machine learning (ML) models can be employed to investigate the characteristics of customer behavior. Besides, deep learning (DL) models help in prediction of the customer behavior based characteristic data. Since the DL models necessitate hyperparameter modelling and effort, the process is difficult for research communities and business people. In this view, this study designs an optimal deep canonically correlated autoencoder based prediction (O-DCCAEP) model for competitive customer dependent application sector. In addition, the O-DCCAEP method purposes for determining the churning nature of the customers. The O-DCCAEP technique encompasses pre-processing, classification, and hyperparameter optimization. Additionally, the DCCAE model is employed to classify the churners or non-churner. Furthermore, the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm (DHOA). The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

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References

2021Customer churn prediction system: a machine learning approach 2020Leveraging unstructured call log data for customer churn prediction 2021Dynamic customer churn prediction strategy for business intelligence using text analytics with evolutionary optimization algorithms 2022A PCA-AdaBoost model for E-commerce customer churn prediction 2021Multi-objective rain optimization algorithm with WELM model for customer churn prediction in telecommunication sector 2021Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling 2022Customer Churn Prediction in Influencer Commerce: An Application of Decision Trees 2021Uplift modeling and its implications for B2B customer churn prediction: A segmentation-based modeling approach

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