Data analysis and machine learning in the context of customer churn prediction

Machine Tools’ Running State Monitoring in the Learning Factory—Based on Machine Learning Method and Vibration Data

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.

The classification model in machine learning has been employed to address different problems. Machine Learning classification is an effective method to realize customer churn prediction. This article provides a comparative study of machine learning from the perspective of predicting customer churn. Customer churn is one of the obstacles hindering the development of companies. Through classification approaches based on machine learning, customer churn can be predicted precisely, thus providing decision-making capabilities to these companies. Customer churn prediction is a typical classification problem and can be addressed by the use of a decision tree or random forest model. In this paper, decision tree and random forest models are employed to predict customer churn, using sales data provided by a chemical company from 2012 to 2020. We analyze the underlying risk of customer churn and the customer churn factors, including the low-priced count (LC), total amount of money (TM), and creation time (CT). The prediction results of the two models are evaluated by calculating various metrics. The experimental results indicate that the low-priced count (LC) is the most essential factor for customer churn. The results of the comparison, considering the training error and generalization error including: confusion matrix, ROC curve, AUC, precision, recall, and F1 score, reveal that the random forest model has better prediction accuracy than the decision tree model.

The technologies of artificial intelligence and machine learning have made a fundamental leap in their capabilities in the last five years. The growth of processing power and the emergence of more and more effective methods of machine learning allows AI to not just solve the most typical tasks associated with the field, such as statistical analysis and optimization of mathematical processes, but also to find new applications in related fields of research, as well as practical applications, including those on the free market, available to the mass consumer. Image generation, audio, animation, self-learning models of control of robotic platforms and virtual mechanical models – these and many more novel applications of the recent years have led to a media-boom around AI and a growing interest from developers and authors from various fields and industries. That being said, the methods for developing, research, testing, and integration of AI have largely remained unchanged and still require the knowledge of programming languages, machine learning libraries, as well as a deep understanding and experience specifically in the narrow field of AI. This barrier of specialization not only demands inclusion of machine learning specialists in the development process of otherwise trivial computer applications, typical for the field of AI, but also prevents small teams and independent developers from using the latest advances in these technologies without significant monetary and time investments into studying the subject. I offer a novel solution to this issue in the form of a prototype graphical interface that allows the user without technical education and without the need for knowledge of programming languages to develop and tune various architectures of neural nets and other machine learning methods, methods of unsupervised machine learning, and to test these methods on a wide range of experimental tasks – from mathematical equations to controlling virtual mechanical models in a simulated physical environment. In this article, I give a brief description of its structure and organisation, its fundamental principles of operation, and the capabilities of this GUI.

This research is dedicated to forecasting bank customer churn by using machine learning methods. In the highly competitive landscape of the banking industry, retaining customers has emerged as a critical factor for a bank's success. Eleven distinct machine learning models were utilized in the analysis of a bank customer dataset. Prior to model implementation, comprehensive data preprocessing was conducted, encompassing the management of missing values, encoding of categorical variables, and detection of outliers. The models were then evaluated under various data split ratios and sampling methods, with recall serving as the primary evaluation metric.The study's findings indicate that LightGBM, CatBoost, and XGBoost demonstrate superior performance in predicting customer churn. Additionally, Gradient Boost, Ada Boost, and Bagging prove to be effective in handling imbalanced data. Several key predictors of customer churn, such as Total_Trans_Amt and Total Trans ct, were identified. These results offer valuable practical implications for banks in formulating customer retention strategies. However, it is necessary to recognize the research's limitations. There may be deficiencies in data representation. What's more, external factors that could influence customer churn have been excluded.

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.

Introduction: The Churn means Attrition. In the world of competition and customer needs, the superior quality service, to predict customer churn, has become a pressing issue for the HRMS Vendors or service providers. Machine Learning is capable to solve this challenging problem. The customer churn can be predicted, and proactively, HRMS vendors can apply a strategy to retain customers and manage the customer relationship. Objectives: The objective of this paper is to meticulously analyse and summarize the previous studies conducted for customer churn prediction. Furthermore, this paper will serve as a valuable resource for future research in the context of HRMS vendors or service providers. Methods: This paper focuses on machine learning algorithms like Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting, Neural Network, etc. The paper highlights the accuracy of the outperformed models. This paper includes the previous studies between 2021 and 2025. In total, 30 papers were reviewed meticulously. To provide more clarity, all the previous studies are summarized in a table format. The paper was searched on Google with the keywords ‘Customer Churn’ AND ‘Machine Learning’. The Duplicates were removed, and the previous studies were analyzed. Results: The findings showcase that various machine learning models like RF, DT, Logistic Regression, Gradient Boosting are applied to predict churn. Furthermore, by this analysis it is clearly stated that hybrid or ensemble models work best for customer churn model prediction. This study guides on selecting the best model as per the relevant industry. Conclusions: This comprehensive survey has meticulously analysed the implementation of machine learning algorithms to predict customer churn and highlighted its importance to fully transform the business to build client retention strategies. In this paper, different machine models are examined, and for each previous study, the machine learning models used & their results are discussed.

In today's conditions, customer loyalty has gained importance with the increase in the competitive environment between companies, the development of marketing strategies and the improvement of companies. Therefore, it is essential to acquire customers for a company to survive. Retaining an existing customer in the telecommunication sector is less costly than gaining a new customer. Customer churn analysis is the process of predicting customers with high abandonment requests by examining the offers and utilizable behaviors. Customer churn analysis provides services to develop various campaigns aiming to increase the company’s loyalty by predicting the customers who are planning to move to another company. In this way, it gives the company a competitive advantage. This study aims to make predictions by developing models for customer churns through data mining and machine learning methods in the telecommunication sector. In addition, we believe that the application in this article will contribute to data analysts and academicians who will want to analyze customer churn with different data sets in telecommunication and other sectors in the future. The analysis in this study is carried out on a data set obtained from an open-access database, including 20 transaction records for the customer from 7043 customers and whether the customer left the company. Among the data mining methods, Random Forest (RF), Support Vector Machines (SVM) and Multilayer Artificial Neural Networks (ANN) are modeled in open-source Phyton environment. The results have shown that ANN has fared better at classifying customers than other machine learning methods.

Customer churn prediction plays a vital role in modern business, accurately influencing strategic and operational decisions that influence customer loyalty to a service. Customer churn focuses on customer retention being more profitable than attracting new customers because long-term customers provide lower profits and costs while losing customers increases the costs and need to attract new customers. However, customer churn still occurs frequently and cannot be predicted. If customer churn is left unchecked, it will endanger the company or banking industry because it can cause loss of income, damage reputation, and decrease market share. Random Forest, a data mining technique, was used in this research because of its ability to predict and handle many variables. This research aims to predict customer churn using the Random Forest method with datasets from Europe, especially France, Spain, and Germany, hoping to benefit the banking industry by identifying customers at high risk of abandoning services. This research is expected to benefit business people from customer churn predictions. Especially in the banking industry, it can help identify customers at high risk of abandoning service. Thus, companies can take appropriate steps to retain these customers, increase customer retention, strengthen customer loyalty and optimize their business performance. The results of this research are an accurate system for predicting customer churn in the future. The research obtained accuracy results of 87% in predicting customer churn using accuracy testing in the form of a confusion matrix.

Although the overall incidence of pediatric oncological diseases tends to increase over the years, it is among the rare diseases of the pediatric population. The diagnosis, treatment, and healthcare management of this group of diseases are important. Prevention of treatment-related complications is vital for patients, particularly in the pediatric population. Nowadays, the use of artificial intelligence and machine learning technologies in the management of oncological diseases is becoming increasingly important. With the advancement of software technologies, improvements have been made in the early diagnosis of risk groups in oncological diseases, in radiology, pathology, and imaging technologies, in cancer staging and management. In addition, these technologies can be used to predict the outcome in chemotherapy treatment of oncological diseases. In this context, this study identifies artificial intelligence and machine learning methods used in the prediction of complications due to chemotherapeutic agents used in childhood cancer treatment. For this purpose, the concepts of artificial intelligence and machine learning are explained in this review. A general framework for the use of machine learning in healthcare and pediatric oncology has been drawn and examples of studies conducted on this topic in pediatric oncology have been given. IMPACT: Artificial intelligence and machine learning are advanced tools that can be used to predict chemotherapy-related complications. Algorithms can assist clinicians' decision-making processes in the management of complications. Although studies are using these methods, there is a need to increase the number of studies on artificial intelligence applications in pediatric clinics.

The article is devoted to big data investments for economic decisions. The work reflects some aspects of the use of big data in various fields of economic activity, especially forecasting market trends of the company's activities, economic feasibility of investments, investment management and making investment decisions through optimization and risk management. It is about the methodology of data processing and analysis, as well as machine learning and artificial intelligence technologies, provides a valuable presentation of information. Also considered are the difficulties of implementing big data, such as privacy issues, ethical issues and data reliability issues. The author's conclusion is that the use of big data to a large extent allows to highlight the accuracy and efficiency of solving economic issues, but requires appropriate skills and technologies for the implementation of data. Keywords: big data, economic decision making, forecasting, business process optimization, machine learning, artificial intelligence, risk management, data analysis.

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 increasing number of network freight transport platforms, improving service quality is the key to retain customers in the enterprise. In order to improve the service quality of network freight platform, this paper evaluates and analyzes the service quality problems such as redundant platform interface design, lack of personalized service, lack of professionalism of customer service personnel, and threat to information security, which exist in the operation of the current network freight platform, and puts forward countermeasure suggestions such as simplifying the user process and interaction design, introducing machine learning and artificial intelligence technology, setting clear service standards and performance indicators, and adopting a multi-factor authentication system, in order to improve the service quality of network freight platform. It also puts forward countermeasures such as simplifying the user process and interaction design, introducing machine learning and artificial intelligence technology, setting clear service standards and performance indicators, and adopting a multi-factor authentication system, with a view to providing some reference for the improvement of the service quality of network freight transportation platforms.

In honor of this issue’s theme of “Artificial Intelligence (AI) Engineering—Realizing the Potential of AI,” this edition of the “Practitioners’ Digest” brings you recent papers on AI and machine learning (ML) engineering, which we believe will be of interest to practitioners. These papers were published in the First International Conference on AI Engineering (CAIN 2022), the 26th International Conference on Evaluation and Assessment in Software Engineering 2022 (EASE 2022), and the 2022 IEEE/Association for Computing Machinery First International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI).

The purpose of this research is to investigate the nature of the relationship between artificial intelligence (AI) and machine learning (ML) technologies in service-based organizations on the one hand and organizational resilience on the other, as well as the potential impact of external market shocks on this relationship. A quantitative survey of 171 service-based groups was used to perform the research. The study's findings indicate that using AI and ML technologies in service delivery has a beneficial and substantial effect on organizational resilience. Furthermore, the research discovered that the degree to which external market shocks influence the efficacy of these technologies in improving an organization's resilience moderates the connection between the use of AI and ML technologies and organizational resilience. According to the results, service-based companies can improve their resilience by incorporating AI and ML technologies into their service delivery processes. They must, however, evaluate the possible influence of external market shocks on the efficacy of these technologies in increasing resilience. The research has ramifications for service-based organizations and adds to the body of knowledge on organizational resilience and the application of AI and ML technologies in service delivery.