Customer Churn Prediction Research Articles

Customer Churn Prediction Framework of Inclusive Finance Based on Blockchain Smart Contract

Customer Churn Prediction Framework of Inclusive Finance Based on Blockchain Smart Contract

Telecom Churn Prediction Using Voting Classifier Ensemble Method and Supervised Machine Learning Techniques

In the current fast-paced world, there are a lot of changes and developments in the telecom sector, due to which the telecom companies find themselves in difficulties in retaining the customers who have availed of their services. In order to solve this problem, churn prediction system is needed to predict customer churn. So far, there are many supervised machine learning churn prediction models that compare various machine learning and deep learning models, select one model, and create a whole churn prediction model. The solution proposed has various supervised machine learning models like Support Vector Machine (SVM), Random Forest Classifier, Decision Tree Classifier, and Logistic Regression Classifier and combine all the models together using an ensemble method called Voting Classifier to produce a single model that considers all the mentioned algorithms and produces an optimum result. The above-mentioned model will be trained by the telecom dataset containing the records of 7043 customers, and the target field is classified into churned and stayed. The machine learning algorithm is evaluated using various performance metrics such as the F1 score, precision, confusion matrix, classification report, and accuracy.As the result the churn prediction model has shown 84% accuracy.

Implementation of Data Mining for Churn Prediction in Music Streaming Company Using 2020 Dataset

Customer is an important asset in a company as it is the lifeline of a company. For a company to get a new customer, it will cost a lot of money for campaigns. On the other hand, maintaining old customer tend to be cheaper than acquiring a new one. Because of that, it is important to be able to prevent the loss of customers from the products we have. Therefore, customer churn prediction is important in retaining customers. This paper discusses data mining techniques using XGBoost, Deep Neural Network, and Logistic Regression to compare the performance generated using data from a company that develops a song streaming application. The company suffers from the churn rate of the customer. Uninstall rate of the customers reaching 90% compared to the customer’s installs. The data will come from Google Analytics, a service from Google that will track the customer’s activity in the music streaming application. After finding out the method that will give the highest accuracy on the churn prediction, the attribute of data that most influence on the churn prediction will be determined.

Predicting Customer Churn on OTT Platforms

No industry can thrive without customers and with customers comes the chances ofcustomer churn. Since customer churn have direct-impact on the revenue, all theindustries are focusing in understanding the factors influencing churn and aredeveloping methods to predict the customer churn effectively. Today, never as before,customers have wide variety of options to choose between any service or product. Inaddition, nowadays customers enjoy multiple subscriptions of service providers acrosssectors. In this study we aim to identify: i) Factors influencing customer churn on OTTplatform, and ii) Predict customer churn on OTT platform. The data for this study iscollected from 317 respondents, using questionnaire method, who have multiple OTTplatform subscription. The questionnaire data contains 19 items which includesdemographic features, usage of OTG platform, and user contentment factors about OTTservice. We have identified factors influencing customer churn in Over-The-Top (OTT)platform by combining Recursive Feature Elimination (RFE), Linear Regression, andRidge Regression feature ranking methods. We have used Hierarchical LogisticRegression, to understand impact of two newly introduced factors namely 'MultipleSubscription' and 'Switching Frequency' on the overall performance of the customerchurn prediction. Finally, customer churn prediction is done using Decision Tree,Random Forest, AdaBoost, and Gradient boosting techniques. We found that randomforest method gives better prediction results.

Intelligent Prediction of Customer Churn with a Fused Attentional Deep Learning Model

In recent years, churn rates in industries such as finance have increased, and the cost of acquiring new users is more than five times the cost of retaining existing users. To improve the intelligent prediction accuracy of customer churn rate, artificial intelligence is gradually used. In this paper, the bidirectional long short-term memory convolutional neural network (BiLSTM-CNN) model is integrated with recurrent neural networks (RNNs) and convolutional neural networks (CNNs) in parallel, which well solves the defective problem that RNNs and CNNs run separately, and it also solves the problem that the output results of a long short-term memory network (LSTM) layer in a densely-connected LSTM-CNN (DLCNN) model will ignore some local information when input to the convolutional layer. In order to explore whether the attention bidirectional long short-term memory convolutional neural network (AttnBLSTM-CNN) model can perform better than BiLSTM-CNN, this paper uses bank data to compare the two models. The experimental results show that the accuracy of the AttnBiLSTM-CNN model is improved by 0.2%, the churn rate is improved by 1.3%, the F1 value is improved by 0.0102, and the AUC value is improved by 0.0103 compared with the BLSTM model. Therefore, introducing the attention mechanism into the BiLSTM-CNN model can further improve the performance of the model. The improvement of the accuracy of the user churn prediction model can warn of the possibility of user churn in advance and take effective measures in advance to prevent user churn and improve the core competitiveness of financial institutions.

A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection.

Customer churn is one of the most critical issues faced by the telecommunication industry (TCI). Researchers and analysts leverage customer relationship management (CRM) data through the use of various machine learning models and data transformation methods to identify the customers who are likely to churn. While several studies have been conducted in the customer churn prediction (CCP) context in TCI, a review of performance of the various models stemming from these studies show a clear room for improvement. Therefore, to improve the accuracy of customer churn prediction in the telecommunication industry, we have investigated several machine learning models, as well as, data transformation methods. To optimize the prediction models, feature selection has been performed using univariate technique and the best hyperparameters have been selected using the grid search method. Subsequently, experiments have been conducted on several publicly available TCI datasets to assess the performance of our models in terms of the widely used evaluation metrics, such as AUC, precision, recall, and F-measure. Through a rigorous experimental study, we have demonstrated the benefit of applying data transformation methods as well as feature selection while training an optimized CCP model. Our proposed technique improved the prediction performance by up to 26.2% and 17% in terms of AUC and F-measure, respectively.

Customer churn prediction model: a case of the telecommunication market

Abstract The telecommunications market is well developed but is characterized by oversaturation and high levels of competition. Based on this, the urgent problem is to retain customers and predict the outflow of customer base by switching subscribers to the services of competitors. Data Science technologies and data mining methodology create significant opportunities for companies that implement data analysis and modeling for development of customer churn prediction models. The research goals are to compare different approaches and methods for customer churn prediction and construct different Data Science models to classify customers according to the probability of their churn from the company’s client base and predict potential customers who could stop to use the company’s services. On the example of one of the leading Ukrainian telecommunication companies, the article presents the results of different classification models, such as C5.0, KNN, Neural Net, Ensemble, Random Tree, Neural Net Ensemble, etc. All models are prepared in IBM SPSS Modeler and have a high level of quality (the overall accuracy and AUC ROC are more than 90%). So, the research proves the possibility and feasibility of using models in the further classification of customers to predict customer loyalty to the company and minimize consumer’s churn. The key factors influencing the customer churn are identified and form a basis for future prediction of customer outflow and optimization of company’s services. Implementation of customer churn prediction models will help to maintain customer loyalty, reduce customer outflow and increase business results

The Effectiveness of Homogeneous Classifier Ensembles on Customer Churn Prediction in Banking, Insurance, and Telecommunication Sectors

The prediction of customer churn is a big challenging problem for companies in different sectors such as banking, telecommunication, and insurance. It is a crucial estimation for many businesses since obtaining new customers frequently costs more than holding present ones. For this reason, analysts and researchers are focus on to investigate reasons behind of customer churn analyzing behaviors of them. In this paper, an ensemble-based framework is proposed to predict the customer churn in various sectors, namely banking, insurance, and telecommunication. To demonstrate the effectiveness of proposed ensemble framework, k-NN, logistic regression, naïve Bayes, support vector machine, decision tree, random forest, multilayer perceptron algorithms are employed. Moreover, the effects of the inclusion of feature extraction process are investigated. Experiment results indicate that that random forest algorithm is capable to predict churn customers with 89.93% of accuracy in banking, 95.90% of accuracy in telecommunication, and 77.53% of accuracy in insurance sectors when feature extraction procedure is carried out.

Customer Churn Prediction in Banking Sector - A Hybrid Approach

Abstract: No institution wants to lose customers and would do anything to prevent it. But banks can’t do anything about customer churn unless they predict it first. Predicting customer churn will help them to reach out to such customers and offer the assistance they need. With the support of precise and early churn prediction, customer resource management, and customer experience teams can be more assertive and customer-focused. It has been reported that simply reaching out to customers early enough can prevent 11 percent of attrition. The problem remains about how to predict such behaviour. Surveys are difficult to undertake and not many are interested in answering them. Past data holds the required solution. We can predict the behaviour of present and future customers by using machine learning and data science techniques to learn from this past customer data. In this paper, we aim to conduct a comparative analysis of different churn prediction models for banking institutions. Then we would implement a hybrid approach with the considered advantages from each of the best performing models for a given cluster of the dataset, to get better results.

Supervised Learning Algorithms for Predicting Customer Churn with Hyperparameter Optimization

Abstract Churn risk is one of the most worrying issues in the telecommunications industry. The methods for predicting churn have been improved to a great extent by the remarkable developments in the word of artificial intelligence and machine learning. In this context, a comparative study of four machine learning models was conducted. The first phase consists of data preprocessing, followed by feature analysis. In the third phase, feature selection. Then, the data is split into the training set and the test set. During the prediction phase, some of the commonly used predictive models were adopted, namely k-nearest neighbor, logistic regression, random forest, and support vector machine. Furthermore, we used cross-validation on the training set for hyperparameter adjustment and for avoiding model overfitting. Next, the hyperparameters were adjusted to increase the models' performance. The results obtained on the test set were evaluated using the feature weights, confusion matrix, accuracy score, precision, recall, error rate, and f1 score. Finally, it was found that the support vector machine model outperformed the other prediction models with an accuracy equal to 96.92%. Keywords: Churn Prediction, Classification Algorithms, Hyperparameter Optimization, Machine Learning, Telecommunications.

A Novel Hybrid Forecasting Approach for Customers Churn in Banking Industry

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.

Improve customer churn prediction through the proposed PCA-PSO-K means algorithm in the communication industry

Improve customer churn prediction through the proposed PCA-PSO-K means algorithm in the communication industry

Machine Learning to Develop Credit Card Customer Churn Prediction

The credit card customer churn rate is the percentage of a bank’s customers that stop using that bank’s services. Hence, developing a prediction model to predict the expected status for the customers will generate an early alert for banks to change the service for that customer or to offer them new services. This paper aims to develop credit card customer churn prediction by using a feature-selection method and five machine learning models. To select the independent variables, three models were used, including selection of all independent variables, two-step clustering and k-nearest neighbor, and feature selection. In addition, five machine learning prediction models were selected, including the Bayesian network, the C5 tree, the chi-square automatic interaction detection (CHAID) tree, the classification and regression (CR) tree, and a neural network. The analysis showed that all the machine learning models could predict the credit card customer churn model. In addition, the results showed that the C5 tree machine learning model performed the best in comparison with the three developed models. The results indicated that the top three variables needed in the development of the C5 tree customer churn prediction model were the total transaction count, the total revolving balance on the credit card, and the change in the transaction count. Finally, the results revealed that merging the multi-categorical variables into one variable improved the performance of the prediction models.

Analysis and Prediction of Telecom Customer Churn based on Machine Learning

As the telecommunications market becomes increasingly saturated, major operators are facing an increasingly severe problem of soaring customer churn rates. How to identify high-risk churn customers is the most concerned issue for operators. Thanks to the rapid development of pattern recognition technology, existing machine learning algorithms provide key technical support for telecom customer churn prediction. However, how to choose an appropriate forecasting method combined with the characteristics of the application data is still an open question. To this end, based on the analysis and comparison of the feature correlation between telecom customer data and churn, this paper compares the differences in the prediction results of different machine algorithms, so as to choose the method that best fits the characteristics of the application data to build the final customer churn prediction model. Specifically, the Spearman correlation coefficient is used to calculate the correlation between variables in the dataset, the random forest algorithm is used to score the importance of all variables, and the prediction generated by the gradient boosting tree algorithm is introduced. Finally, the gradient boosting tree algorithm is evaluated by five performance indicators: precision rate, recall rate, precision rate, F1 score and AUC (Area under the ROC curve).

Predicting Churn of Credit Card Customers Using Machine Learning and AutoML

Nowadays, a major concern for most retail banks is the risk that originates from customer fluctuation and that increases the cost of almost every financial product. In this work, the authors compared different approaches and algorithms to predict the relevant features that affect the customer churn, which means we can find ways to reduce the customer churn and create financial inclusion. This research was conducted by applying different machine learning techniques like decision tree classifier, random forest classifier, AdaBoost classifier, extreme gradient boosting, and balancing data with random under-sampling and random oversampling. The authors have also implemented AutoML to further compare different models and improve the accuracy of the model to predict customer churn. It was observed that applying AutoML highest accuracy model gave the accuracy of 97.53% in comparison to that of the decision tree classifier, which was 93.48% with the use of low processing power. Important features were ‘total transaction amount' and ‘total transaction count' to predict customer churn for a given dataset.

XAI for Churn Prediction in B2B Models: A Use Case in an Enterprise Software Company

The literature related to Artificial Intelligence (AI) models and customer churn prediction is extensive and rich in Business to Customer (B2C) environments; however, research in Business to Business (B2B) environments is not sufficiently addressed. Customer churn in the business environment and more so in a B2B context is critical, as the impact on turnover is generally greater than in B2C environments. On the other hand, the data used in the context of this paper point to the importance of the relationship between customer and brand through the Contact Center. Therefore, the recency, frequency, importance and duration (RFID) model used to obtain the customer’s assessment from the point of view of their interactions with the Contact Center is a novelty and an additional source of information to traditional models based on purchase transactions, recency, frequency, and monetary (RFM). The objective of this work consists of the design of a methodological process that contributes to analyzing the explainability of AI algorithm predictions, Explainable Artificial Intelligence (XAI), for which we analyze the binary target variable abandonment in a B2B environment, considering the relationships that the partner (customer) has with the Contact Center, and focusing on a business software distribution company. The model can be generalized to any environment in which classification or regression algorithms are required.

An effective strategy for churn prediction and customer profiling

Customer churn prediction and profiling are two major economic concerns for many companies. Different learning approaches have been proposed, however the a priori choice of the most suitable model to perform both tasks remains non-trivial as it is highly dependent on the intrinsic characteristics of the churn data. Our study compares eight supervised machine learning methods combined with seven sampling approaches on thirteen public churn data sets. Our evaluations, reported in terms of area under the curve (AUC), explore the influence of rebalancing strategies and data properties on the performance of learning methods. We rely on the Nemenyi test and Correspondence Analysis as means of visualizing the association between models, rebalancing and data. This work identifies the most appropriate methods in an attrition context and proposes an effective pipeline based on an ensemble approach and deep autoencoders segmentation. Our strategy can enlighten marketing or human resources services on the behavioral patterns of customers and their attrition probability. The described experiments are fully reproducible and our proposal can be successfully applied to a wide range of churn-like datasets.

Proactive customer retention management in a non-contractual B2B setting based on churn prediction with random forests

Customer churn prediction enables companies to target customers at risk with proactive retention measures. We develop a churn prediction model for a non-contractual business-to-business (B2B) wholesale setting and apply it in a field study. Our experiment shows that compared to random targeting, contacting the customers with the highest predicted churn probabilities reduces churn in the population significantly. We demonstrate that this also entails a positive financial impact in terms of revenue development.In addition to validating B2B churn prediction and retention in the field, we contribute to the literature by identifying the most important features. On top of the common recency, frequency and monetary value features, we show that features specific to customer relationship management such as the recency of the last contact with a field representative are important. We provide a concept on how to integrate proactive churn management into operations by leveraging existing customer care processes.

Predicting customer churn: A systematic literature review

Churn prediction is an active topic for research and machine learning approaches have made significant contributions in this domain. Models built to address customer churn, aim to identify customers who are at a high risk of terminating services offered by a company. Hence, an effective machine learning model indirectly contributes to the revenue growth of an organization, by identifying “at risk” customers, well in advance. This improves the success rate of retention campaigns and reduces costs associated with churn. The aim of this study is to explore the state-of-the-art machine learning techniques used in churn prediction. A systematic literature review, that is driven by 5 research questions and rigorous quality assessment criteria, is presented. There are 38 primary studies that are selected out of 420 studies published between 2018 and 2021. The review identifies popular machine learning techniques used in churn prediction and provides directions for future research. Firstly, the study finds that churn models lack generalization capability across industry domains. Hence, it identifies a need for researchers to explore techniques that extend beyond model experimentation, to improve efficiency of classifiers across domains. Secondly, it is observed that the traditional approaches to churn prediction depend significantly on demographic, product-usage, and revenue features alone. However, recent papers have integrated social network analysis-related features in churn models and achieved satisfactory results. Furthermore, there is a lack of scientific work that utilizes information-rich content of customer-company-interaction instances via email, chat conversations and other means. This area is the least explored. Thirdly, there is scope to investigate the effect of hybrid sampling strategies on model performance. This has not been extensively evaluated in literature. Lastly, there is no formal guideline on correct evaluation parameters to be used for models applied on imbalanced churn datasets. This is a grey area that requires greater attention.

Artificial Intelligence Based Customer Churn Prediction Model for Business Markets.

The introduction of artificial intelligence (AI) and machine learning (ML) technologies in recent years has resulted in improved company performance. Customer churn forecast is a difficult problem in many corporate sectors, particularly the telecommunications industry. Because customer churns have a direct impact on a company's total revenue, telecommunications firms have begun to develop 76 models to reduce churns at an earlier stage. Previous research has revealed that AI and ML models are effective CCP solutions. According to this viewpoint, this study proposes a unique AI-based CCP model for Telecommunication Business Markets (AICCP-TBM). The AICCP-TBM model's purpose is to control the existence of churners and non-churners in the telecom sector. The proposed AICCP-TBM model employs a Chaotic Salp Swarm Optimization-based Feature Selection (CSSO-FS) method for the best feature assortment. In addition, a Fuzzy Rule-based Classifier(FRC) is used to distinguish between client churners and non-churners. A technique known as Quantum Behaved Particle Swarm Optimization (QPSO) is used to pick the membership functions for the FRC model in order to improve the classification performance of the FRC model. The performance of the AICCP-TBM model is validated using a benchmark CCP dataset and the experimental results are reviewed from several angles. In relations of presentation, the imitation consequences demonstrated that the AICCP-TBM model surpassed the most recent state-of-the-art CPP models. The suggested AICCP-TBM method's comparative accuracy was thoroughly tested on the three datasets used. Using datasets 1-3, this technique obtained better levels of accuracy, with the maximum attainable values being 97.25 %, 97.5 % and 94.33 %. The simulation results for the AICCP-TBM model demonstrated improved prediction performance.