Thematische Bibliographien / Customer churning

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Auswahl der wissenschaftlichen Literatur zum Thema „Customer churning“

Autor: Grafiati

Veröffentlicht am 30. Juni 2021

Zuletzt aktualisiert am 21. Juni 2025

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Zeitschriftenartikel zum Thema "Customer churning"

Shafiei, Gol Elham, Abbas Ahmadi, and Azadeh Mohebi. "Intelligent approach for attracting churning customers in banking industry based on collaborative filtering." Journal of Industrial and Systems Engineering 9, no. 4 (2016): 9–25. https://doi.org/10.5281/zenodo.13999871.

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During recent years, increased competition among banks has caused many developments in banking experiences and technology, while leading to even more churning customers due to their desire of having the best services.&lrm; Therefore, it is an extremely significant issue for the banks to identify churning customers and attract them to the banking system again.&lrm; In order to tackle this issue, this paper proposes a novel personalized collaborating filtering recommendation approach joint with the user clustering technology.&lrm; In the proposed approach, first a hybrid algorithm based on Particle Swarm Optimization (PSO) and K-mean cluster the loyal customers.&lrm; The clusters of loyal customers are used to identify the features of the churning customers.&lrm; Finally, the list of appropriate banking services are recommended for the churning customers based on a collaborative filtering recommendation system.&lrm; The recommendation system uses the information of loyal customers to offer appropriate services for the churning customers.&lrm; We applied successfully the proposed intelligent approach to return the churning customers of an Iranian bank.

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S.P. Valli, Sharmila Sankar, C. Hema, and Mohammad Munzir. "Enhancement of XG-Boost Using Custom Hyper Parameter Tuning for Bank Churning." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 07 (2024): 1909–14. http://dx.doi.org/10.47392/irjaeh.2024.0261.

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Bank is an important component of our society which deals with money transaction i.e., lending and deposit of money. Customer churn is termination of business of a customer with the company. Bank customer churn creates an impact on revenue and operational efficiencies of banks, where a customer switches or leaves availing the services of bank. Bank is an important part of our society since it makes money by lending money to others. To understand customer churning behavior it is necessary to retain customers and increase the number of customers. In order to predict the bank customer churning behavior a few algorithms such as XGBoost, CatBoost, AdaBoost, Random Forest, K Near Neighbor, Decision Tree, and Logistic Regression are analyzed. Finally, the best model has been recommended by analyzing the above-mentioned algorithms

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Ascarza, Eva. "Retention Futility: Targeting High-Risk Customers Might be Ineffective." Journal of Marketing Research 55, no. 1 (2018): 80–98. http://dx.doi.org/10.1509/jmr.16.0163.

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Companies in a variety of sectors are increasingly managing customer churn proactively, generally by detecting customers at the highest risk of churning and targeting retention efforts towards them. While there is a vast literature on developing churn prediction models that identify customers at the highest risk of churning, no research has investigated whether it is indeed optimal to target those individuals. Combining two field experiments with machine learning techniques, the author demonstrates that customers identified as having the highest risk of churning are not necessarily the best targets for proactive churn programs. This finding is not only contrary to common wisdom but also suggests that retention programs are sometimes futile not because firms offer the wrong incentives but because they do not apply the right targeting rules. Accordingly, firms should focus their modeling efforts on identifying the observed heterogeneity in response to the intervention and to target customers on the basis of their sensitivity to the intervention, regardless of their risk of churning. This approach is empirically demonstrated to be significantly more effective than the standard practice of targeting customers with the highest risk of churning. More broadly, the author encourages firms and researchers using randomized trials (or A/B tests) to look beyond the average effect of interventions and leverage the observed heterogeneity in customers' response to select customer targets.

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Kim, Myung-Joong, Juil Kim, and Sun-Young Park. "Understanding IPTV churning behaviors: focus on users in South Korea." Asia Pacific Journal of Innovation and Entrepreneurship 11, no. 2 (2017): 190–213. http://dx.doi.org/10.1108/apjie-08-2017-026.

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PurposeThis study aims to investigate customers’ churning out of Internet Protocol Television (IPTV) service, one of the most prevalent forms of IT convergence. Design/methodology/approachBased on the review of current literature, a research model is introduced to depict the effects of select independent variables on customer churning behavior. First of all, the two groups are compared in terms of predictor variables, including switching barriers, voice of customer (VOC), membership period and degree of contents usage. Then, a curvilinear regression was applied to understand the association relationship between the level of IPTV contents usage and variables of switching barriers, VOC and membership period. Third, a logit regression was performed to predict customer churning through the variables of switching barriers, VOC, membership period and level of IPTV contents usage. FindingsThrough the empirical analysis, this study analyzed the factors affecting customer churning behavior of IPTV service providers based on switching barriers, VOC and contents usage. Originality/valueAlthough several studies on IPTV have been undertaken globally, they have largely depended on self-reporting surveys to examine dynamics between antecedent variables and IPTV performance in terms of customer satisfaction, usage intension and customer retention. This empirical study is performed to understand influential factors of IPTV service defection through the weblog analysis of 3,906 service users, who represented both service defectors and non-defectors during a specific month.

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Abdullah Kafabih. "The Influence of Network Coverage Image and Digital Marketing Promotion on Churning Intention Mediated by Digital Satisfaction and Moderated by Private Identity: Approach on Telecommunication Customers in Indonesia." Journal of Information Systems Engineering and Management 10, no. 6s (2025): 127–37. https://doi.org/10.52783/jisem.v10i6s.706.

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The telecommunications industry is a key sector contributing significantly to the national economy. Amidst intense competition among telecommunications operators, many telecommunications customers tend to switch to other operators (Churning). The objective of this research is to investigate how Network Coverage Image and Digital Marketing Promotion impact Churning Intention, with Digital Satisfaction acting as a mediator and Private Identity as a moderating factor. The research targeted telecommunication customers from all operators in Jakarta who had experienced switching SIM cards to another operator. A total of 386 samples were gathered, and the data were analyzed using SmartPLS 3.0. The study's findings reveal that Network Coverage Image and Digital Satisfaction significantly reduce Churning Intention, whereas Digital Marketing Promotion significantly increases it. Private Identity, however, does not significantly influence Churning Intention as a moderating variable for Network Coverage Image. This research offers a novel contribution by expanding the understanding of the push-pull-mooring (PPM) theory as it relates to customer churn intentions in the telecommunications industry, specifically by examining the effects of network coverage image, digital marketing promotion, digital satisfaction, and customer private identity.

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Aldhafferi, Nahier, Abdullah Alqahtani, Fatema Sabeen Shaikh, et al. "Learning trends in customer churn with rule-based and kernel methods." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 5364. http://dx.doi.org/10.11591/ijece.v12i5.pp5364-5374.

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<span>In the present article an attempt has been made to predict the occurrences of customers leaving or ‘churning’ a business enterprise and explain the possible causes for the customer churning. Three different algorithms are used to predict churn, viz. decision tree, support vector machine and rough set theory. While two are rule-based learning methods which lead to more interpretable results that might help the marketing division to retain or hasten cross-sell of customers, one of them is a kernel-based classification that separates the customers on a feature hyperplane. The nature of predictions and rules obtained from them are able to provide a choice between a more focused or more extensive program the company may wish to implement as part of its customer retention program.</span>

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Nahier, Aldhafferi, Alqahtani Abdullah, Sabeen Shaikh Fatema, et al. "Learning trends in customer churn with rule-based and kernel methods." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 5364–74. https://doi.org/10.11591/ijece.v12i5.pp5364-5374.

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In the present article an attempt has been made to predict the occurrences of customers leaving or ‘churning’ a business enterprise and explain the possible causes for the customer churning. Three different algorithms are used to predict churn, viz. decision tree, support vector machine and rough set theory. While two are rule-based learning methods which lead to more interpretable results that might help the marketing division to retain or hasten cross-sell of customers, one of them is a kernel-based classification that separates the customers on a feature hyperplane. The nature of predictions and rules obtained from them are able to provide a choice between a more focused or more extensive program the company may wish to implement as part of its customer retention program.

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P A, Jufin, and Amrutha N. "Bank Customer Churn Prediction." Indian Journal of Data Mining 2, no. 2 (2023): 1–5. http://dx.doi.org/10.54105/ijdm.b1628.112222.

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In the current challenging era, there is a stiff competition happening between the banking industries. To strengthen the grade and level of services they provide, banks focus on customer retention as well as the customer churning. Customer churning becomes one of the duties of corporate intelligences to speculate the number of customers leaving from the bank or presumed to be churned. It also helps in predicting the number of customers retained. The primary objective of this paper is "Bank customer churn prediction" is to build a model that can distinguish and visualize which factors or attributes contribute to customer churn. In addition to that, this paper also discusses a comparison between various classification algorithms. Machine learning is a modern technology that has the potential to solve classification problems. Using supervised machine learning techniques, a best model is chosen that will assign a probability to the churn to simplify customer service to prevent customer churn. Few methodologies are compared in order to accomplish different accuracy levels. XGBoost is considered in order to check if a better model can be obtained that provides best result in terms of accuracy. The other three machine learning algorithms compared are Logistic regression, Support vector machine [SVM], and Random Forest.

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Jufin, P. A. "Bank Customer Churn Prediction." Indian Journal of Data Mining (IJDM) 2, no. 2 (2023): 1–5. https://doi.org/10.54105/ijdm.B1628.112222.

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<strong>Abstract: </strong>In the current challenging era, there is a stiff competition happening between the banking industries. To strengthen the grade and level of services they provide, banks focus on customer retention as well as the customer churning. Customer churning becomes one of the duties of corporate intelligences to speculate the number of customers leaving from the bank or presumed to be churned. It also helps in predicting the number of customers retained. The primary objective of this paper is "Bank customer churn prediction" is to build a model that can distinguish and visualize which factors or attributes contribute to customer churn. In addition to that, this paper also discusses a comparison between various classification algorithms. Machine learning is a modern technology that has the potential to solve classification problems. Using supervised machine learning techniques, a best model is chosen that will assign a probability to the churn to simplify customer service to prevent customer churn. Few methodologies are compared in order to accomplish different accuracy levels. XGBoost is considered in order to check if a better model can be obtained that provides best result in terms of accuracy. The other three machine learning algorithms compared are Logistic regression, Support vector machine [SVM], and Random Forest.

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Muneer, Amgad, Rao Faizan Ali, Amal Alghamdi, Shakirah Mohd Taib, Ahmed Almaghthawi, and Ebrahim Abdulwasea Abdullah Ghaleb. "Predicting customers churning in banking industry: A machine learning approach." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (2022): 539–49. https://doi.org/10.11591/ijeecs.v26.i1.pp539-549.

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In this era, machines can understand human activities and their meanings. We can utilize this ability of machines in various fields or applications. One specific field of interest is a prediction of churning customers in any industry. Prediction of churning customers is the state of art approach which predicts which customer is near to leave the services of the specific bank. We can use this approach in any big organization that is very conscious about their customers. However, this study aims to develop a model that offers a meaningful churn prediction for the banking industry. For this purpose, we develop a customer churn prediction approach with the three intelligent models random forest (RF), AdaBoost, and support vector machine (SVM). This approach achieves the best result when the synthetic minority oversampling technique (SMOTE) is applied to overcome the unbalanced dataset and the combination of undersampling and oversampling. The method on SMOTED data has produced excellent results with a 91.90 F1 score and overall accuracy of 88.7% using RF. Furthermore, the experimental results show that RF yielded good results for the full feature-selected datasets.

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Dissertationen zum Thema "Customer churning"

Osman, Yasin, and Benjamin Ghaffari. "Customer churn prediction using machine learning : A study in the B2B subscription based service context." Thesis, Blekinge Tekniska Högskola, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21872.

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The rapid growth of technological infrastructure has changed the way companies do business. Subscription based services are one of the outcomes of the ongoing digitalization, and with more and more products and services to choose from, customer churning has become a major problem and a threat to all firms. We propose a machine learning based churn prediction model for a subscription based service provider, within the domain of financial administration in the business-to-business (B2B) context. The aim of our study is to contribute knowledge within the field of churn prediction. For the proposed model, we compare two ensemble learners, XGBoost and Random Forest, with a single base learner, Naïve Bayes. The study follows the guidelines of the design science methodology, where we used the machine learning process to iteratively build and evaluate the generated model, using the metrics, accuracy, precision, recall, and F1- score. The data has been collected from a subscription-based service provider, within the financial administration sector. Since the used dataset is imbalanced with a majority of non- churners, we evaluated three different sampling methods, that is, SMOTE, SMOTEENN and RandomUnderSampler, in order to balance the dataset. From the results of our study, we conclude that machine learning is a useful approach for prediction of customer churning. In addition, our results show that ensemble learners perform better than single base learners and that a balanced training dataset is expected to improve the performance of the classifiers.

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Wang, Shu-Ching, and 王淑靜. "Customer Churning Factors in Broadband ISP Industry." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/42354065901116516315.

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碩士<br>國立臺北科技大學<br>商業自動化與管理研究所<br>92<br>In Taiwan, the broadband Internet access market has reached the saturation stage, with a penetration rate of 46.6% in April 2004. In addition, the lack of product differentiation was unable to create customer loyalty. As a result, high acquisition costs often cannot be recovered, as customers are frequently lured away by competitors’ attractive promotional packages or gifts. Of all the issues facing Broadband Internet Service Providers (BISP), they need to focus on sales and marketing, customer churn, content, and servicing policies. Therefore, how to offere differentiated and multiple services that meet customer needs and foster long-term customer relationship to reduce churn rate and enhance long-term profitability, has become a critical issue facing BISP’s. This thesis will focus the discussion on the causes of customer churn. Utilizing Discriminant Analysis, Factor Analysis, and 1-Way ANOVA, and description statistical analysis to provide hypotheses and answers to the following two questions: the cause of customer churn, and customer behavior before and after churn, in order to provide guidance for developing retention and loyalty programs for the BISP. The analyses revealed that there isn’t significant co-relation between churn pattern and population changes. The main causes of customer churn are service oriented or price oriented. For customers contemplating switching service providers, the main considerations are service oriented, price/package oriented, and product quality oriented. Varying emphasis of these three factors will affect the final selection of service provider.

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Ko, Hung-Chieh, and 柯宏杰. "Determinants of The Customer Churning Behavior: the US wireless telecommunication industry." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/08833281652812401620.

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碩士<br>國立交通大學<br>經營管理研究所<br>93<br>The US wireless communication industry has been one of the fastest-growing businesses in recent years. As the market gradually saturates and the competition is intensified, the enterprise faces the problem of customer churn seriously. Under such fierce competition, the customer retention has become the major concern.The goal of this study is to understand the customer churning behavior. For the enterprise, if it can effectively reduce the customer churning behavior, it not only enhances the enterprise profit, but also helps to save the cost of expenditure. It is the key in managing the company for the long-term. Based on the binominal logit model in discrete choice theory, the paper develop a churn behavior model which estimates the probability of customer churn associated with service variable、economic variables、use variables and customer demographics. The study results in some generalized outcomes which can be applied to wireless service providers in forming strategies of customer churn management and customer retention

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蕭大立. "The study of customer churning factors - An example of a construction products supplier." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/32963529488635922024.

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碩士<br>國立政治大學<br>商管專業學院碩士學位學程(AMBA)<br>98<br>A great deal of effort has been made on the causes of customer churn in the consumer products industry. What seems to be lacking, however, is this subject in the industrial products industry. This study will focus the discussion on the causes of customer churn and customer switching behavior in the construction products supplier, in order to provide guidance for developing retention and loyalty programs. This study can be divided into five parts; the first part reviews the literature on this subject. The second part introduces the methodology to be utilized throughout the study, first with structural diagram of study followed by study methods, and object in study. The third part utilizes using SPSS for Windows as the tool to conduct statistical analysis, including description statistical analysis, reliability test, Discriminant Analysis, Factor Analysis, and One-way ANOVA. The fourth part discusses the experimental result of this study, and compares it with customer switching behavior in the consumer products industry. The last part is a conclusion of the thesis. The results of this study show as follows. 1. The main causes of customer churn are product and service oriented or price oriented. The main causes of customer switch are service and brand strategy, product strategy or price strategy. 2. Customer switching behavior includes decreasing purchased frequency and transferring to a new service provider. 3. Customer switching behavioral model in the service industry is different from the model in the construction products supplier. 4. The customers who have longer purchasing duration have higher recognition of importance for deliver time. Purchasing frequency and product price are not the best variables to predict if the customers would churn or not.

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Liao, Tzu-Yi, and 廖子逸. "Determinants of Customer Churning Behavior in Mobile Communications- A Case Study of Operator in Taiwan." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/xj5kmj.

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碩士<br>國立交通大學<br>管理學院經營管理學程<br>103<br>In retrospect the telecommunication operation in Taiwan, government has implemented liberal policy in the telecommunications service industry in 1997. The decision has opened the door for private telecommunication firms and the telecommunications market changed to the condition of fierce competition. As of today, there are 9 operators running 2G, 3G, 4G, PHS and WiMAX system in Taiwan. With the growth of the mobile penetration and the Number Portability policy’s opening, the NP policy intensifies the market competition develops well. According to National Communications Commission’s data, the total effective cellphone numbers in Taiwan reached 28 millions in Dec. 2014. Because churn management has became a fundamental concern since the Taiwan mobile communication market is on the verge of saturation, companies’ strategies to handle churn issue have been directed to survive or maintain an advantage in such a competitive marketplace. This study constructs questionnaire by phone interviewing with mobile users and using data mining methodology. This study examines the key factors for customer churn rate and builds up the churn rate prediction model to help mobile operators to identify the possible NP threat.

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Bücher zum Thema "Customer churning"

DeHaan, Peter Lyle. Sticky Customer Service: Stop Churning Customers and Start Growing Your Business. DeHaan Publishing Inc, Peter, 2021.

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DeHaan, Peter Lyle. Sticky Customer Service: Stop Churning Customers and Start Growing Your Business. DeHaan Publishing Inc, Peter, 2021.

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Buchteile zum Thema "Customer churning"

Hur, Yeon, and Sehun Lim. "Customer Churning Prediction Using Support Vector Machines in Online Auto Insurance Service." In Advances in Neural Networks – ISNN 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11427445_149.

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Tonati, Samuele, Marzio Di Vece, Roberto Pellungrini, and Fosca Giannotti. "Ensemble Counterfactual Explanations for Churn Analysis." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-78980-9_21.

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Abstract Counterfactual explanations play a crucial role in interpreting and understanding the decision-making process of complex machine learning models, offering insights into why a particular prediction was made and how it could be altered. However, individual counterfactual explanations generated by different methods may vary significantly in terms of their quality, diversity, and coherence to the black-box prediction. This is especially important in financial applications such as churn analysis, where customer retention officers could explore different approaches and solutions with the clients to prevent churning. The officer’s capability to modify and explore different explanations is pivotal to his ability to provide feasible solutions. To address this challenge, we propose an evaluation framework through the implementation of an ensemble approach that combines state-of-the-art counterfactual generation methods and a linear combination score of desired properties to select the most appropriate explanation. We conduct our experiments on three publicly available churn datasets in different domains. Our experimental results demonstrate that the ensemble of counterfactual explanations provides more diverse and comprehensive insights into model behavior compared to individual methods alone that suffer from specific weaknesses. By aggregating, evaluating, and selecting multiple explanations, our approach enhances the diversity of the explanation, highlights common patterns, and mitigates the limitations of any single method, offering to the user the ability to tweak the explanation properties to their needs.

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Dalmia, Hemlata, Ch V. S. S. Nikil, and Sandeep Kumar. "Churning of Bank Customers Using Supervised Learning." In Lecture Notes in Networks and Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3172-9_64.

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"Predicting Churn for Mobile Phone Providers." In Decision and Prediction Analysis Powered With Operations Research. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-4179-7.ch008.

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This chapter presents a prediction of the churn for mobile phone providers. The term “churn” is a marketing term, meaning that a customer transfers loyalty from one provider to another. This prediction is particularly relevant in this industry, where companies try to keep their customers from churning. The prediction will give the company a better understanding of what behavior leads to churning, and to implement actions for preventing such behavior. The snapshots of 1005 customer data are available, about 14% of whom have churned. Thus, this data is used to predict the churn “Yes-No” variable. The churn values for the first 15 customers are predicted by using neural network.

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Hiziroglu Abdulkadir and Seymen Omer Faruk. "Modelling Customer Churn Using Segmentation and Data Mining." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2014. https://doi.org/10.3233/978-1-61499-458-9-259.

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Customer churn management has drawn much attention from many researchers and practitioners to improve customer retention. The term churn is related to predictions on when a customer abandons his relationship with a company; therefore it has become mandatory for most organizations seeking sustainable and profitable growth. Also increasing in churn rates make companies confront the inevitable heavy marketing campaigns to retain or acquiring new customers. Current churn literature reveals the fact that acquiring new customers costs more than keeping existing ones. However, studies related to churn management mainly focused on methodological improvements regarding the predictive ability, which failed to illustrate a dynamic process in the change of customers' churning behaviour. This paper proposes a model with multi-dimensions of customer churning level via combining segmentation concept within data mining framework to expand the prediction of customer churn. Additionally, comparison to other prediction models, proposed model provides more accurate predictions on customer behaviour and better understanding of relationship between customer and company, mostly applicable in service providing sectors. The potential implications of the model for managers and practitioners are also provided within the paper.

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Mutanen Teemu, Nousiainen Sami, and Ahola Jussi. "Customer churn prediction – a case study in retail banking." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2010. https://doi.org/10.3233/978-1-60750-633-1-77.

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This work focuses on one of the central topics in customer relationship management (CRM): transfer of valuable customers to a competitor. Customer retention rate has a strong impact on customer lifetime value, and understanding the true value of a possible customer churn will help the company in its customer relationship management. Customer value analysis along with customer churn predictions will help marketing programs target more specific groups of customers. We predict customer churn with logistic regression techniques and analyze the churning and nonchurning customers by using data from a consumer retail banking company. The result of the case study show that using conventional statistical methods to identify possible churners can be successful.

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Kazienko, Przemyslaw, and Dymitr Ruta. "The Impact of Customer Churn on Social Value Dynamics." In Networking and Telecommunications. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-986-1.ch074.

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Modern telecommunication service providers implicitly create interactive social networks of individuals that both depend on and influence each other through complex social relationships grown on friendship, shared interests, locality, and so forth. While delivering services on the individual basis, the network effects exerted from customer-to-customer interactions remain virtually unexplored and unexploited. The focus of this article is on customer churn, where social network effects are widely ignored yet may play a vital role in revenue protection. The key assumption made is that a value loss of a churning customer extends beyond his revenue stream and directly affects interaction within local neighborhoods. The direction and strength of this impact are evaluated experimentally by direct measurements of the total neighborhood value of the churning customer along with other standard social network measures taken before and after the churn event.

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Chiurunge, Panashe, Agripah Kandiero, and Sabelo Chizwina. "Customer Churn Prediction for Financial Institutions Using Deep Learning Artificial Neural Networks in Zimbabwe." In Theoretical and Conceptual Frameworks in ICT Research. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-7998-9687-6.ch010.

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The research was conducted to develop a customer churn predictive modelling using deep neural networks for financial institutions in Zimbabwe using a local leading financial institution. This was based on a need to perform a customer churn analysis and develop a very high accurate and reliable customer churn predictive model. In this era, every customer counts, hence once acquired a business should do everything in its power to keep that customer because the cost of acquiring a new customer is far greater than the cost of keeping an existing one. Therefore the need to ascertain customers who have churned and also be at a position to anticipate those who are churning or are about to churn then take corrective measures to keep such customers on board. The study followed one of the data science research methodologies called CRoss industry standard process for data mining (CRISP-DM) which involves understanding the business, understanding the data, data preparation, modelling, validating the model then deployment of the model.

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Tri Wibowo, Agus, Andi Chaerunisa Utami Putri, Muhammad Reza Tribosnia, Revalda Putawara, and M. Mujiya Ulkhaq. "A Machine Learning Application to Predict Customer Churn: A Case in Indonesian Telecommunication Company." In Advanced Mathematical Applications in Data Science. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815124842123010013.

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This study aims to develop a churn prediction model which can assist telecommunication companies in predicting customers who are most likely subject to churn. The model is developed by employing machine learning techniques on big data platforms. Customer churn is one of the most critical issues, especially in high investment telecommunication companies. Accordingly, the companies are looking for ways to predict potential customers to churn and take necessary actions to reduce the churn. To accomplish the objective of the study, it first compares eight machine learning techniques, i.e., ridge classifier, gradient booster, adaptive boosting, bagging classifier, k-nearest neighbour (kNN), decision tree, logistic regression, and random forest. By using five evaluation performance metrics (i.e., accuracy, AUC score, precision score, recall score, and the F score), kNN is selected since it outperforms other techniques. Second, the selected technique is used to predict the likelihood of customers churning.

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Krishna, Addepalli V. N., Shriansh Pandey, and Raghav Sarda. "A Secured Predictive Analytics Using Genetic Algorithm and Evolution Strategies." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8048-6.ch049.

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In the banking sector, the major challenge will be retaining customers. Different banks will be offering various schemes to attract new customers and retain existing customers. The details about the customers will be provided by various features like account number, credit score, balance, credit card usage, salary deposited, and so on. Thus, in this work an attempt is made to identify the churning rate of the possible customers leaving the organization by using genetic algorithm. The outcome of the work may be used by the banks to take measures to reduce churning rates of the possible customers in leaving the respective bank. Modern cyber security attacks have surely played with the effects of the users. Cryptography is one such technique to create certainty, authentication, integrity, availability, confidentiality, and identification of user data can be maintained and security and privacy of data can be provided to the user. The detailed study on identity-based encryption removes the need for certificates.

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Konferenzberichte zum Thema "Customer churning"

Nakano, Satoshi, Akiya Inoue, Kiyotaka Otsuka, Takeshi Kurosawa, Motoi Iwashita, and Ken Nishimatsu. "Mobile-Carrier Churning Behavior Modeling Based on Customer Satisfaction." In 2012 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel & Distributed Computing (SNPD). IEEE, 2012. http://dx.doi.org/10.1109/snpd.2012.70.

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Panda, Amiya Ranjan, Manoj Kumar Mishra, Dvij Kalsi, Kaushik Jyoti Bhuyan, Soumyadeep Saha, and Kaustabh Jyoti Bhuyan. "Classification of Customer Churning based on OTT platform data." In 2024 International Conference on Emerging Systems and Intelligent Computing (ESIC). IEEE, 2024. http://dx.doi.org/10.1109/esic60604.2024.10481640.

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N V, Manoj Kumara, Bharath Kumar K K, and Arun Chandra Mudhol. "Machine Learning based Prediction of Customer Churning in Banking Sector." In 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS). IEEE, 2022. http://dx.doi.org/10.1109/icaiss55157.2022.10011126.

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Maskale, Vikram, Vivekanand Vaidya, Yash Patil, Yogesh Bagal, and Vidya Dhamdhre. "To Design and Implement Application for Bank Customer Churning Rate Prediction and Analysis using Machine Learning Algorithm." In 2024 MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon). IEEE, 2024. http://dx.doi.org/10.1109/mitadtsocicon60330.2024.10575438.

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Ferreira, Tomas, Pedro Pita, and Isabel Sofia Brito. "Predict Churning Customers – An Explorative Study." In 2022 17th Iberian Conference on Information Systems and Technologies (CISTI). IEEE, 2022. http://dx.doi.org/10.23919/cisti54924.2022.9820260.

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Ali, Muhammad, Aziz Ur Rehman, and Shamaz Hafeez. "Prediction of Churning Behavior of Customers in Telecom Sector Using Supervised Learning Techniques." In 2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS). IEEE, 2018. http://dx.doi.org/10.1109/cccs.2018.8586836.

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Ali, Muhammad, Aziz Ur Rehman, Shamaz Hafeez, and Muhammad Usman Ashraf. "Prediction of Churning Behavior of Customers in Telecom Sector Using Supervised Learning Techniques." In 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). IEEE, 2018. http://dx.doi.org/10.1109/iccceee.2018.8515857.

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Dambir, Gaurav. "Multidisciplinary and Integrated Approach to Predict Automotive Axle System Efficiency." In Energy & Propulsion Conference & Exhibition. SAE International, 2024. http://dx.doi.org/10.4271/2024-01-4314.

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<div class="section abstract"><div class="htmlview paragraph">In today’s competitive automotive market, customers are now looking for system efficiency as one of the important design parameters of system performance along with durability and reliability. It is essential to ensure products are designed to utilize maximum input power and have better system efficiency. In automotives, transmission and axle systems are power transmitting elements from prime mover to wheels and are one of the main contributors to overall vehicle efficiency. Hence, predicting and assessing overall system efficiency of these aggregates is of paramount importance. System efficiency is driven by component power losses for various speeds and torques, which are arising out of component design parameters, complex interaction within system, operating conditions, lubrication, temperatures etc.</div><div class="htmlview paragraph">To capture multi-physics of speed and torque dependent losses of automotive axle, multidisciplinary and integrated approach is proposed in this paper, Efficiency predictive model is developed in simulation tool by modelling detailed system consists of power transmitting components and flexible housings. Churning losses of hypoid gears, differential system, and auxiliary components etc. which are speed dependent, are evaluated by Computational Fluid Dynamics (CFD) principles. Frictional losses of hypoid gears and bearings are evaluated by different ISO methods. With these, total system losses are evaluated including oil seal losses, and overall system efficiency is calculated thereby. Developed methods are applied to commercial vehicle axle and validated with physical test. This helps to evaluate system loss components and their respective contributions.</div><div class="htmlview paragraph">Developed predictive methods can be easily extended to e-axle system efficiency prediction. Using these methods, system efficiency and power losses can be predicted and analyzed at design stage before prototypes are built and help to carry out required design changes to improve system efficiency. Actionable insights from predictive model helps to ensure axle system and components are designed for better efficiency right at initial stage of design.</div></div>

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