An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach

Introduction. Customer churn is one of the key challenges in the telecommunications sector, significantly impacting companies' financial performance. Losing customers leads to substantial economic losses, as acquiring new users is typically more expensive than retaining existing ones. This creates a need for the development of effective analytical approaches to predict customer churn and identify the factors influencing it. Objective. This study aims to assess the effectiveness of survival analysis methods in predicting customer churn in the telecommunications sector. The primary objective is to identify key factors affecting churn rates and evaluate the impact of targeted retention strategies on customer behaviour. Methodology. The study employs survival analysis methods to evaluate customer churn dynamics. The Kaplan-Meier estimator is used to analyse the temporal patterns of customer retention, identifying high-risk periods. The Cox proportional hazards model is applied to determine the influence of various factors on churn probability. The dataset consists of real-world telecommunications customer data, including demographic characteristics, contract details, and financial indicators. Preprocessing steps include data cleaning, feature selection, and transformation of categorical variables into numerical format. Model performance is evaluated using metrics such as the Concordance Index (C-index) and Brier Score. Finally, A/B testing is conducted to assess the effectiveness of retention strategies in reducing customer churn. Results. The findings indicate that the highest risk of churn occurs within the first 20 months after subscription activation. It was determined that long-term contracts and high-quality service reduce the likelihood of churn, whereas high monthly costs and certain payment methods increase it. The developed predictive models achieved an accuracy level of 85–87%. Implemented retention strategies within A/B testing resulted in an increase in the retention rate from 74% to 91%. The proposed methods can be applied by telecommunications companies, financial institutions, and other businesses operating in the subscription-based service industry to enhance loyalty programs and reduce customer churn.

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.

In recent years, with the continuous improvement of the financial system and the rapid development of the banking industry, the competition of the banking industry itself has intensified. At the same time, with the rapid development of information technology and Internet technology, customers’ choice of financial products is becoming more and more diversified, and customers’ dependence and loyalty to banking institutions is becoming less and less, and the problem of customer churn in commercial banks is becoming more and more prominent. How to predict customer behavior and retain existing customers has become a major challenge for banks to solve. Therefore, this study takes a bank’s business data on Kaggle platform as the research object, uses multiple sampling methods to compare the data for balancing, constructs a bank customer churn prediction model for churn identification by GA-XGBoost, and conducts interpretability analysis on the GA-XGBoost model to provide decision support and suggestions for the banking industry to prevent customer churn. The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. The GA-XGBoost classifier was identified as the best solution for the customer churn problem. (3) Using Shapley values, we explain how each feature affects the model results, and analyze the features that have a high impact on the model prediction, such as the total number of transactions in the past year, the amount of transactions in the past year, the number of products owned by customers, and the total sales balance. The contribution of this paper is mainly in two aspects: (1) this study can provide useful information from the black box model based on the accurate identification of churned customers, which can provide reference for commercial banks to improve their service quality and retain customers; (2) it can provide reference for customer churn early warning models of other related industries, which can help the banking industry to maintain customer stability, maintain market position and reduce corporate losses.

With the further reform and market division in the telecommunication industry, there are more and more choices for customers to select telecom products and operators, which lead to the fiercer competition for customers between telecom operators. As the technical method to identify customers churn, the data mining can help the telecom competitors to analyze some seemingly unrelated data into meaningful information. On the basis of the research on the vital problems in the telecom companies, this paper explains how to apply data mining techniques to customer churn analysis, proposes the specific procedures and technology solutions to prevent the customer churn and builds the models of the data mining by analyzing the related algorithm. Finally, based on the systematical analysis on theory and method to data mining, the paper draws the conclusion that the customers churn listing and tree algorithm can solve the practical problems of the customer churn in telecommunication industry.

Churn is the phenomenon of a customer terminating their relationship or subscription with a company or service provider[1]. It represents the rate at which customers stop using a company's product or service during a specific period of time. Attrition rate is an important metric for businesses as it directly impacts revenue, growth and customer retention. In the context of the churn data set, the churn label indicates whether a customer has been churn. Lost customers are those who decide to stop buying the company's products. On the other hand, non-churn customers are those who continue to buy the company's products. Understanding customer churn is critical for businesses to identify the patterns, factors, and metrics that lead to customer churn. By analyzing churn behavior and its associated characteristics, companies can develop strategies to retain existing customers, improve customer satisfaction, and reduce customer churn. Predictive modeling techniques can also be applied to anticipate and proactively address potential customer churn, enabling companies to take proactive steps to retain at-risk customers[2].

Background: China telecom is the largest integrated information service provider in China, its business volume all over the world. It is interesting to note that China Unicom and other telecom companies have carried out similar businesses one after another. How to prevent the loss of existing customers in the fierce competition is an important issue for telecom companies to think about.Methods: This work aims to build a variety of algorithm models for target optimization and use them to predict whether telecom companies will lose customers, respond to the early warning of customer churn, and then implement active retention measures. Data characteristics affect the final loss prediction effect. In this study, the weight contribution rate of each characteristic variable is obtained by calculating the evidence weight and then the characteristic variable information value so as to optimize the prediction accuracy of the algorithm model. Through calculation, we noted the weight contribution rate of five characteristic variables to be the highest. Including total day charge, total day minutes customer service calls, international plan, and number of voicemail messages, linear regression, decision tree, Bayesian, artificial neural network, and support vector machine are used to predict customer churn on the customer dataset published by telecom companies. The experimental results are used to test the performance of the algorithm model.Results: It is found that the characteristic variables calculated after optimization are put into multialgorithm models to predict the churn of telecom customers. Finally, it is found that it is better for the optimized characteristic variables to use the decision tree algorithm model to predict the loss of telecom customers.

Recently, telecommunication companies have been paying more attention toward the problem of identification of customer churn behavior. In business, it is well known for service providers that attracting new customers is much more expensive than retaining existing ones. Therefore, adopting accurate models that are able to predict customer churn can effectively help in customer retention campaigns and maximizing the profit. In this paper we will utilize an ensemble of Multilayer perceptrons(MLP) whose training is obtained using negative correlation learning(NCL) for predicting customer churn in a telecommunication company. Experiments results confirm that NCL based MLP ensemble can achievebetter generalization performance (high churn rate) compared with ensembleof MLP without NCL (flat ensemble) and other common datamining techniques used for churn analysis.

Abstract: The problem of customer churn is a continuous challenge for profitability in the telecommunications sector, particularly given significant competition within the industry. Therefore, the ability to use advanced analytics to identify compressed subscribers in advance, and potentially retain them, is critical. This study describes an end-to-end explainable AI framework for predicting and intervening with telco churn. We used the IBM Telco Customer Churn dataset and simulated relevant behavioural features, so we could train our system via informative preprocessing (handling missing values and inappropriate encodings), feature engineering (where we devised aspects such as charge ratio, tenure, and service stability), and XGBoost modelling (where our modelling is optimized via stratified cross-validation and business-based target metrics) to monitor our predictions. Importantly, we provide SHAP-based explainability for every prediction, where our predictions contain both global and local explainability features to provide an actionable bridge between the power of algorithmic manipulation and business consideration for the customer. Our prediction offers the functionality of a business logic layer allowing churn probabilities to be mapped to risk formulations, loyalty tier definitions, and ways to intervene with the customer prior to their leaving as well as knowing later via a secure Flask-based web app with live analysis and audit of actions performed. Our experiment produced strong prediction values (AUC-ROC value of 0.95; retention of churn class recall up to 0.93), consistent with feature ranking providing interpretably relevant information, and identifiable space for practical business consideration. In conclusion, we have provided what we believe to be a scalable and transparent model for interpretable churn management with an aim of providing timely, informed decisions and accountability to operate the retention strategy in the telecommunications environment. Index terms: Customer churn, Churn prediction, Telecom retention strategy, Explainable AI, XGBoost, SHAP (SHapley Additive exPlanations).

Nowadays, telecommunication markets are becoming more and more competitive, and customer churn is becoming more and more serious. In the tough competitive mobile market, Customer Churn Management is becoming more and more critical. In developing countries, most customers switch service providers because of good promotional incentives and lower monthly costs offered by competitive service providers. How to predict customer churn quickly and accurately becomes very important. In this paper, the researchers successfully analyzed the customer churn using big data feature analysis and multi-feature analysis. User data were modeled by XGBoost algorithm. The model is optimized repeatedly with GridSearchCV as a parameter tool. The accuracy of the model on the test set is 85.1%. The researchers predicted about 11000 customer lists per month that may be about to churn. Using K-means clustering method, 11000 churn target customers per month were classified into three categories and telecom companies are suggested to take some solutions which are found by feature analysis to retain customers. This big data analysis can be used to retain customers for the telecom industry.

The telecommunication industry need a customer churn prediction due to many competitors. The companies also lack of churn prediction to retain the customer. This problem not only affect the growth of the business but also affect the revenues. To retain the existing customer is very crucial task for the company. A rapid increasing in technology, the various machine learning and deep learning are tools are developed which can be used by telecoms companies to monitor the churn behaviour of customers. In this study, a brief idea on the customer churn problem on various machine learning techniques such as XGBoost, Gradient Boost, AdaBoost, ANN, Logistic Regression and Random Forest are analysed. Also the various deep learning techniques such as Convolutional Neural Network, stacked auto encoders to predict the customer churn problem are analysed by comparing the models in terms of accuracy.

In all customer based industries, customer churn is considered as one of the most important and challenging concerns since it can lead to a serious profit loss. Therefore, developing accurate churn prediction models can significantly help Customer Relationship Management in planning effective retention campaigns and consequently helps in maximizing the profit of the service provider. In this paper, we propose the use of an Echo State Network (ESN) with a Support Vector Machine (SVM) training algorithm for predicting customer churn in telecommunication companies. The proposed approach is trained and tested based on two datasets: the first is a popular online available dataset while the second is obtained from a local service provider. Experiment results show that ESN with SVM readout outperform other popular machine learning models used in the literature for the same customer churn prediction problems.

The integration of Artificial Intelligence (AI) into financial forecasting has transformed traditional stock market prediction methods. This research paper explores the effectiveness of AI techniques in forecasting stock trends within the Indian stock market over the last few years. We examine AI methodologies, including machine learning (ML), deep learning (DL), and hybrid models applied to the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE). By comparing historical data with AI-predicted trends, this study evaluates prediction accuracy and market relevance (Patel et al., 2015). Furthermore, the research outlines existing study gaps and proposes a future scope of integrating AI with behavioral finance and real-time analytics (Chen et al., 2022). The prediction of stock market trends remains a significant challenge due to the stochastic and non-linear nature of financial time series data (Zhang & Zhou, 2020). With the proliferation of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL) techniques, there has been a paradigm shift in the modeling and forecasting of stock price movements (Fischer & Krauss, 2018). This paper presents a comprehensive study on the effectiveness of AI in predicting stock market trends within the Indian financial ecosystem, focusing on a comparative analysis of AI models implemented over the last five years (2018–2023) on major indices and stocks listed on the NSE and BSE. This research evaluates the performance of multiple AI algorithms—including Support Vector Machines (SVM), Random Forests (RF), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and hybrid ensemble models—in forecasting short- and medium-term price trends using historical stock data (Krauss et al., 2017; Chen & He, 2021). Standard evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and classification accuracy are employed to assess model efficacy. The results indicate that while traditional machine learning algorithms offer moderate predictive power, advanced deep learning and hybrid models significantly outperform them, particularly during periods of high market volatility, such as the COVID-19 pandemic and post-lockdown recovery phases (Weng et al., 2018; Jain & Jain, 2021). The study identifies key research gaps, including limited sectoral diversification in datasets, inadequate integration of sentiment and behavioral data, overfitting issues in complex models, and the lack of real-time prediction systems in the Indian context (Gupta & Pathak, 2022). Furthermore, current models often ignore nonquantitative factors such as investor sentiment, macroeconomic indicators, and global events, which can critically impact prediction accuracy (Nassirtoussi et al., 2014). These limitations suggest the need for a more holistic and interdisciplinary approach to AI-driven financial forecasting. The scope for future research includes the development of real-time, adaptive AI systems using high-frequency trading data, the incorporation of behavioral finance through social media and news analytics, and the exploration of quantum computing-based AI models (Liu et al., 2023). From a practical standpoint, the findings of this study offer valuable insights for institutional investors, financial analysts, regulatory bodies, and developers of AIpowered trading platforms. The study concludes that while AI is not a definitive solution for market prediction, it provides a powerful augmentative tool that, when designed with robustness, transparency, and adaptability, can significantly enhance decision-making in India’s fastevolving financial markets.

The focus of this study is on developing a decision support system (DSS) in order to forecast spare parts demand for a company producing high technology products in Turkey. The company is one of the world’s leading original design manufacturers in the field of consumer electronics and white goods. Accurate forecasts of customer demand for preliminary products and spare parts play an important role in order to reduce costs and increase customer satisfaction. Currently, the company’s forecasting system is based on personnel experience and a statistical approach, which lacks the ability of capturing demand data behaviour. The approach followed results in an increased forecasting error, thus increases production costs, results in lack of spare parts, and decreases customer satisfaction. The aim of this project is to develop a DSS to minimize the forecasting error; therefore help the company develop a policy for optimizing the stock levels kept, reducing costs and increasing customer satisfaction. In order to understand the behaviour of customer demand of spare parts, the company’s television products are chosen for the pilot study, since these products are highly influenced by rapid technological changes and changes in the product models. The spare parts are classified into different groups, using ABC analysis, in order to develop a forecasting model for each group. In the solution methodology part, three different statistical methodologies for the forecasting process were respectively studied; Winter’s, Double Exponential Smoothing and Moving Average Methods. Winter’s Method is used for the data which exhibit trend and seasonality, Double Exponential Smoothing is used for the data which exhibit trend and Moving Average Method is used for the data which exhibit stationary behaviour. In the DSS developed, the above-mentioned methodologies are coded, using Excel VBA programming language, historical data’s behaviour is analysed and forecasts for future spare parts demand are made. The forecasting results are compared based on the minimum error (PAE), to decide upon which is the most appropriate forecasting methodology to use according to the specific spare parts past data behaviour.

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.

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.