Narrow Results

Competitive markets and customers’ changing needs in the bank industry necessitate accurately predicting customers who may leave the firm in the near future. Consequently, creating an approach to predict precisely and identify churn-leading causes is a part of retention strategies in customer relationship management. The approach that has been utilized in this research to predict customer churn combines decision tree (DT) and multinomial regression (MR) to classify customers with no limitation of binary classification in the churn prediction context. A customer club dataset of a commercial bank case as a real churn problem is used in this study to benchmark the hybrid forecasting approach against its building blocks. The results showed that the hybrid forecasting approach outperformed DT and MR with an average accuracy of 87.66%, 90.74% micro-average, and 90.44% macro-average of AUC. Further analysis of the model performance per class indicated that the hybrid approach’s misclassification error for the churn class decreased significantly, which is the most costly error in churn problems. Moreover, due to the structure of hybrid forecasting approach, more interoperability is obtained by assessing the impact of features in different segments, resulting in transforming them into actionable insights. The proposed approach is applied to the banking industry to prevent financial loss by detecting leading churn causes. Accordingly, after predicting the risk of customer churn, marketers and managers can determine appropriate actions that will have the most significant retention impact on each customer by applying proactive retention marketing.

The churn prediction based on telecom data has been paid great attention because of the increasing the number telecom providers, but due to inconsistent data, sparsity, and hugeness, the churn prediction becomes complicated and challenging. Hence, an effective and optimal prediction of churns mechanism, named adaptive firefly-spider optimization (adaptive FSO) algorithm, is proposed in this research to predict the churns using the telecom data. The proposed churn prediction method uses telecom data, which is the trending domain of research in predicting the churns; hence, the classification accuracy is increased. However, the proposed adaptive FSO algorithm is designed by integrating the spider monkey optimization (SMO), firefly optimization algorithm (FA), and the adaptive concept. The input data is initially given to the master node of the spark framework. The feature selection is carried out using Kendall’s correlation to select the appropriate features for further processing. Then, the selected unique features are given to the master node to perform churn prediction. Here, the churn prediction is made using a deep convolutional neural network (DCNN), which is trained by the proposed adaptive FSO algorithm. Moreover, the developed model obtained better performance using the metrics, like dice coefficient, accuracy, and Jaccard coefficient by varying the training data percentage and selected features. Thus, the proposed adaptive FSO-based DCNN showed improved results with a dice coefficient of 99.76%, accuracy of 98.65%, Jaccard coefficient of 99.52%.

The information-based prediction models using machine learning techniques have gained massive popularity during the last few decades. Such models have been applied in a number of domains such as medical diagnosis, crime prediction, movies rating, etc. Similar is the trend in telecom industry where prediction models have been applied to predict the dissatisfied customers who are likely to change the service provider. Due to immense financial cost of customer churn in telecom, the companies from all over the world have analyzed various factors (such as call cost, call quality, customer service response time, etc.) using several learners such as decision trees, support vector machines, neural networks, probabilistic models such as Bayes, etc. This paper presents a detailed survey of models from 2000 to 2015 describing the datasets used in churn prediction, impacting features in those datasets and classifiers that are used to implement prediction model. A total of 48 studies related to churn prediction in telecom industry are discussed using 23 datasets (3 public and 20 private). Our survey aims to highlight the evolution of techniques from simple features/learners to more complex learners and feature engineering or sampling techniques. We also give an overview of the current challenges in churn prediction and suggest solutions to resolve them. This paper will allow researchers such as data analysts in general and telecom operators in particular to choose best suited techniques and features to prepare their churn prediction models.

Customer churn prediction is important for bankers to preserve profitable customers. However, the relatively low churn rate makes it become an imbalanced learning problem. We implement the oversampling strategy for four well-known classification algorithms on various criteria, and identify the most suitable techniques for customer churn analysis. The study is based on real world dataset from a commercial bank in China. The experimental results indicate that the synthetic oversampling approach makes notable lifts on the criteria precision, G-mean, and the weighted accuracy. It is suitable for the application areas where the minorities are more concerned.