Incorporating textual information in customer churn prediction models based on a convolutional neural network

With the ongoing economic growth and the rise of new companies, customer churn has become a renewed focus for many businesses, particularly in the telecommunications and SaaS sectors. Accurately identifying lost customers using artificial intelligence technology is crucial for helping companies develop effective strategies to retain these valuable clients. This paper provides a comprehensive overview of recent research on the application of various machine learning models, including logistic regression, random forests, decision trees, XGBoost, and advanced deep learning models like Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN), to different types of datasets in the telecommunications and SaaS sectors. The study examines the workflow of each model, offering an in-depth analysis of their effectiveness and performance in predicting customer churn. In addition to evaluating the strengths and weaknesses of these models, the paper also addresses key challenges in machine learning, such as interpretability, applicability, and privacy concerns. It highlights current solutions employed by researchers, including SHapley Additive exPlanations (SHAP) for model interpretability, transfer learning, domain adaptation, and federated learning, to overcome these challenges. Furthermore, this paper emphasizes the ongoing need for further research to enhance the robustness and practical deployment of churn prediction models in these industries. In conclusion, this paper offers a comprehensive overview of current research on customer churn prediction models, highlighting the application of various machine learning techniques and addressing key challenges and solutions within the telecommunications and SaaS sectors.

Telecom companies are facing a serious problem of customer churn due to exponential growth in the use of telecommunication based services and the fierce competition in the market. Customer churns are the customers who decide to quit or switch use of the service or even company and join another competitor. This problem can affect the revenues and reputation of the telecom company in the business market. Therefore, many Customer Churn Prediction (CCP) models have been developed; however these models, mostly study in the context of within company CCP. Therefore, these models are not suitable for a situation where the company is newly established or have recently adopted the use of advanced technology or have lost the historical data relating to the customers. In such scenarios, Just-In-Time (JIT) approach can be a more practical alternative for CCP approach to address this issue in cross-company instead of within company churn prediction. This paper has proposed a JIT approach for CCP. However, JIT approach also needs some historical data to train the classifier. To cover this gap in this study, we built JIT-CCP model using Cross-company concept (i.e., when one company (source) data is used as training set and another company (target) data is considered for testing purpose). To support JIT-CCP, the cross-company data must be carefully transformed before being applied for classification. The objective of this paper is to provide an empirical comparison and effect of with and without state-of-the-art data transformation methods on the proposed JIT-CCP model. We perform experiments on publicly available benchmark datasets and utilize Naive Bayes as an underlying classifier. The results demonstrated that the data transformation methods improve the performance of the JIT-CCP significantly. Moreover, when using well-known data transformation methods, the proposed model outperforms the model learned by using without data transformation methods.

Customer churn is a prominent issue facing companies. Preventing customer churn, trying to retain and retain customers has become an important issue for business operations and development. Most of the current customer churn predictions use a single prediction model, which makes it difficult to accurately predict customer churn. Based on the prediction results and confidence of decision tree and neural network model, this paper designs a combined prediction model of customer churn and conducts empirical research on the effectiveness of the model. The prediction results show that compared with the single customer churn prediction model, the combined prediction model has higher accuracy and better prediction effect, and can more intuitively display the basic characteristics of the churn customers.

The telecommunication sector becomes popular in recent years and service companies suffer from a loss of valued customers to competitors. In the operational area, customer churn is a challenging issue, which makes companies, loses customers, and therefore is of bigger issue for the industry. Recent technologies and competitors were developing quickly and churn prediction becomes essential for telecom companies. Customer Churn Prediction (CCP) models offer precise detection of significant churners and therefore a retaining solution can be offered to them. Data analytics and machine learning (ML) models can be designed and used for churn investigation to reduce customer churns and improve profit. In this aspect, this article investigates a novel Tomtit Flock Optimization with Deep Variational Autoencoder for Customer Churn Prediction (TFODVAE-CCP) in the telecommunication sector. The presented TFODVAE-CCP technique mainly concentrates on the identification and classification of customer churns. To accomplish this, the presented TFODVAE-CCP technique performs data pre-processing at the primary stage. Next, the TFODVAE-CCP technique employs TFO algorithm to elect an optimal subset of features and thereby improve classification accuracy. Finally, the DVAE method can be leveraged for classifying churner/non-churner. A detailed set of experimental analyses is made on telecommunication churn dataset and the results illustrate the betterment of the TFODVAE-CCP method over other existing models.

This thesis proposes customer churn prediction model for a Software-as-a-Service inventory management company in Thailand. Software-as-a-Service is the fast growing and high market values industry as a new emerging online business. Customer churn is a critical measure for this business. Thus, this thesis focuses on seeking a customer churn prediction model specified for a Software-as-a-Service inventory management company in Thailand which is facing a high churn rate issue. This thesis executes the prediction models with four machine learning algorithms: logistic regression, support vector machine, decision tree and random forest. The results show that the optimized decision tree model is capable to outperform other classification models toward recall scorer with validated testing scores of 94.4% of recall and 88.2% of F1-score. Moreover, feature importance scores can highlight useful insights of case-study that business metrics are significantly related to churn behavior. As a result, this paper is beneficial to the case-study company to help indicate real churn customer correctly and enhance the effectiveness in making executive decision and marketing campaign.

Customer churn prediction in telecommunication industry through machine learning based Fine-tuned XGBoost algorithm

With increasing number of mobile operators and competitive market, churn prediction has become a major challenge for telecom companies. The telecom sector is highly effected due to rival competition and companies face loss of revenue when customer switch to competitor. For retaining the customer, an exact or accurate customer churn prediction model is required to estimate the number of customer who will churn out of current service provider which will be referred as churners. With the significant boom in machine learning research in recent years and having both power and high dimensional data, machine learning techniques can be used to predict those churners. This paper provides information on challenges and way of building the churn prediction model. This also provides a roadmap for the researchers to tackle supervised learning techniques and applying those to solve the real world problems.

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.

In the telecommunications sector, customer churn prediction is of critical importance as it directly affects customer loyalty and revenue. Developments in the field of artificial intelligence play an important role in the development of customer churn prediction models. This study presents a comparative evaluation using machine learning (ML) methods for customer churn prediction in the telecommunications sector. The study was conducted on open-source IBM Telco, Orange Telecom and Maven Analytic Telco datasets. 16 different machine learning classification models were evaluated separately on the datasets and the 3 models that gave the best results were selected and their customer churn prediction performances were evaluated with classification metrics. The Linear Discriminant model showed the most successful performance with 80% test accuracy on the IBM Telco dataset, the Gradient Boosting model with 94% test accuracy on Orange Telecom and the Linear Regression model with 99% test accuracy on Maven Analytic Telco.

With various structured data, such as the company size, loan balance, and savings accounts, the voice of customer (VOC), which is text data containing contact history and counseling details was analyzed in this study. To analyze unstructured data, the term frequency-inverse document frequency (TF-IDF) analysis, semantic network analysis, sentiment analysis, and a convolutional neural network (CNN) were implemented. A performance comparison of the models revealed that the predictive model using the CNN provided the best performance with regard to predictive power, followed by the model using the TF-IDF, and then the model using semantic network analysis. In particular, a character-level CNN and a word-level CNN were developed separately, and the character-level CNN exhibited better performance, according to an analysis for the Korean language. Moreover, a systematic selection model for optimal text mining techniques was proposed, suggesting which analytical technique is appropriate for analyzing text data depending on the context. This study also provides evidence that the results of previous studies, indicating that individual customers leave when their loyalty and switching cost are low, are also applicable to corporate customers and suggests that VOC data indicating customers’ needs are very effective for predicting their behavior.

In today's competitive world, companies need to analyze, identify and predict the behavior of their customers and respond to their demands earlier than their competitors.Moreover, in many industries such as mobile telecommunications, the cost of maintaining existing customers (customer retention) is much lower than the cost of attracting a new customer.Therefore, the problem of identifying customers who are going to leave the company, so-called Customer Churn Prediction (CCP), and preventing them by offering Incentives is essential in these industries.In this direction, researchers have presented competent methods using data mining and artificial intelligence tools to identify potential churners.Machine learning (ML) methods are one of the most powerful and widely used techniques to deal with the CCP problem, since they can properly extract and learn complex relationships between the customers' attributes and their churn intention.Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Decision Trees (DTs), Logistic Regression (LR), and Nave Bayes (NB) are among the well-known ML models utilized in numerous studies to tackle the CCP problem.Also, ensemble learning techniques such as Adaboost, Gradient Boost, and Extreme Gradient Boost (XG_boost) have been widely used to solve the CCP problem since they can aggregate the capabilities of multiple ML models.Hence, in order to improve the process of predicting customer churn, in this paper we propose a novel ensemble learning based approach, which is designed based on the two-level stacking technique.We employ six prominent ML models in each level of our proposed ensemble model including MLP, SVM- 4 58 46 RBF, DT, NB, KNN, and LR.We also benefit from the Gray Wolf Optimization (GWO) algorithm as an efficient swarm intelligence based search algorithm to select the most effective features and also adjust the hyper-parameters in the proposed model.We have implemented our proposed model using Python and simulated it on two well-known customer churn datasets in the telecom market (IBM_Telco and Duke_Cell2Cell) to evaluate its performance.In this direction, we first demonstrated the optimal features' subset and the parameter values obtained from applying the GWO algorithm on each dataset.Next, we compared the performance of the proposed ensemble model with each of the base learners using common evaluation criteria including accuracy, precision, recall, F1 score and AUC.The results show that the proposed ensemble model can collect the capabilities of all the base learners and it works better than each of the basic ML models.Afterward, we compared the obtained results from the suggested model with the common ensemble models (Adaboost, Gradient_boost, XG_boost, and Cat_boost) The experimental results show the superiority of the proposed method over other evaluated ensemble models in all the evaluation criteria.Eventually, our method is compared with two recent CCP approaches introduced in the literature.This analysis reveals that, except for the recall criterion in the Duke_Cell2Cell dataset, our introduced method achieves superior results compared to the considered approaches in both datasets.

Customer churn is a key issue in the telecom industry and has a significant impact on the revenue of related companies. For telecom companies, establishing an accurate customer churn prediction model is crucial for retaining existing customers. Therefore, it is necessary to develop reliable prediction models to effectively manage and reduce churn rates. This study explores how to use machine learning technology to predict customer churn based on various customer characteristics and usage patterns, and its application in accurately predicting customer churn based on demographic data, service usage behavior, and billing information. Through systematic analysis of model performance, the advantages and disadvantages of each method are highlighted, with particular emphasis on the superior predictive power of the integrated method when dealing with imbalanced data sets. This proves the potential of these methods in accurately predicting customer churn behavior, thereby providing relevant companies with methods to retain customers. In addition, the study explored the economic implications of the accuracy of attrition predictions and advocated the use of lower-cost methods to ensure the rational allocation of resources.

Social network analysis for customer churn prediction

During the last two decades, we have seen mobile communication becoming the dominant medium of communication. In numerous countries, especially the developed ones, the market is saturated to the extent that each new customer must be won over from the competitors. At the same time, public policies and standardization of mobile communication now allow customers to easily switch over from one carrier to another, resulting in a fluid market. Since the cost of winning a new customer is far greater than the cost of retaining an existing one, mobile carriers have now shifted their focus from customer acquisition to customer retention. As a result, churn prediction has emerged as the most crucial Business Intelligence (BI) application that aims at identifying customers who are about to transfer their business to a competitor i.e. to churn. This paper aims to present commonly used data mining techniques for the identification of customers who are about to churn. Based on historical data, these methods try to find patterns which can identify possible churners. Some of the well-known algorithms used during this research are Regression analysis, Decision Trees and Artificial Neural Networks (ANNs). The data set used in this study was obtained from Customer DNA website. It contains traffic data of 106,000 customers and their usage behavior for 3 months. We also discuss the use of re-sampling method in order to solve the problem of class imbalance. Our results show that in case of the data set used, decision trees is the most accurate classifier algorithm while identifying potential churners.

Customer churn prediction (CCP) is a crucial problem in telecom industry which helps to improve the revenue of the company and prevent the loss of customers. Customer churn is an important issue in service sector with highly competitive services. At the same time, the prediction of users who are probably leaving the company can be identified at an earlier stage to prevent loss of revenue. Several works have used machine learning (ML) techniques for predicting the existence of customer churn in different industries. With this motivation, this paper presents an optimal long, short-term memory with stacked autoencoder (OLSTM-SAE) technique for CCP in telecom industry. The OLSTM-SAE technique encompasses three subprocesses namely preprocessing, classification, and parameter optimization. The OLSTM-SAE technique classifies the preprocessed data into churn and non-churn customers. In addition, the grey wolf optimization (GWO) technique is used to adjust the variables involved in the LSTM-SAE model. For examining the enhanced performance of the OLSTM-SAE technique, an extensive simulation analysis takes place, and the outcomes are inspected with respect to various measures. The experimental results highlighted the betterment of the OLSTM-SAE technique in terms of different evaluation parameters.