Journal articles on the topic 'Risk of Churn'
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Bonelli, Federico, Silvia Figini, and Alessandra Grossi. "ENSEMBLE CHURN RISK ANALYTICS." Advances and Applications in Statistics 66, no. 1 (January 5, 2021): 61–76. http://dx.doi.org/10.17654/as066010061.
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Edwine, Nabahirwa, Wenjuan Wang, Wei Song, and Denis Ssebuggwawo. "Detecting the Risk of Customer Churn in Telecom Sector: A Comparative Study." Mathematical Problems in Engineering 2022 (July 18, 2022): 1–16. http://dx.doi.org/10.1155/2022/8534739.
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Churn rate describes the rate at which customers abandon a product or service. Identifying churn-risk customers is essential for telecom sectors to retain old customers and maintain a higher competitive advantage. The purpose of this paper is to explore an effective method for detecting the risk of customer churn in telecom sectors through comparing the advanced machine learning methods and their optimization algorithms. Based on two different telecom datasets, Mutual Information classifier was firstly utilized to select the most critical features relevant to customer churn. Next, the controlled-ratio undersampling strategy was employed to balance both minority and majority classes. Key hyperparameter optimization algorithms of Grid Search, Random Search, and Genetic Algorithms were then combined to fit the three promising machine learning models-Random Forest, Support Vector Machines, and K-nearest neighbors into the customer churn prediction problem. Six evaluation metrics-Accuracy, Recall, Precision, AUC, F1-score and Mean Absolute Error, were last used to evaluate the performance of the proposed models. The experimental results have revealed that the RF algorithm optimized by Grid Search based on a low-ratio undersampling strategy (RF-GS-LR) outperformed other models in extracting hidden information and understanding future churning behaviors of customers on both datasets, with the maximum accuracy of 99% and 95% on the applied dataset 1-2 respectively.
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Park, Woong, and Hyunchul Ahn. "Not All Churn Customers Are the Same: Investigating the Effect of Customer Churn Heterogeneity on Customer Value in the Financial Sector." Sustainability 14, no. 19 (September 28, 2022): 12328. http://dx.doi.org/10.3390/su141912328.
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This research suggests a way to sustain a firm’s business by focusing on the economic aspects of relationship marketing by managing the heterogeneity of churn customers. In general, firms have regarded churn customers as a homogeneous segment, for they have not been conscious that churn ego can be various. However, customer churn can be divided into voluntary and involuntary, implying that firms should reform the retention strategy by focusing on egos that seem homogenous but are heterogeneous in terms of churn behavior. Using a multiple regression model, this study analyzed customer data from an insurance company to investigate the heterogeneous impacts of churn customers. It measured the impact based on the period and revenue in the second lifetime, comprehensively representing customer satisfaction. Empirical results show that customer churn heterogeneity significantly affects customers’ second-lifetime behavior. The analysis reveals how the firm effectively performed customer regaining initiatives and successfully maintained persistency. This research also concludes that voluntary and involuntary churn occurred by intrinsic and extrinsic motivation. Finally, this research implicates the retention strategy that differs from the heterogeneity to achieve a firm’s high performance and suggests an empirical method of spurious loyalty avoidance by hedging loyal customer selection risk.
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Abbasimehr, Hossein, Mohammad Jafar Tarokh, and Mostafa Setak. "Determination of Algorithms Making Balance Between Accuracy and Comprehensibility in Churn Prediction Setting." International Journal of Information Retrieval Research 1, no. 2 (April 2011): 39–54. http://dx.doi.org/10.4018/ijirr.2011040103.
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Predictive modeling is a useful tool for identifying customers who are at risk of churn. An appropriate churn prediction model should be both accurate and comprehensible. However, reviewing the past researches in this context shows that much attention is paid to accuracy of churn prediction models than comprehensibility of them. This paper compares three different rule induction techniques from three categories of rule based classifiers in churn prediction context. Furthermore logistic regression (LR) and additive logistic regression (ALR) are used. After parameter setting, eight distinctive algorithms, namely C4.5, C4.5 CP, RIPPER, RIPPER CP, PART, PART CP, LR, and ALR, are obtained. These algorithms are applied on an original training set with the churn rate of 30% and another training set with the churn rate of 50%. Only the models built by applying these algorithms on a training set with the churn rate of 30% make balance between accuracy and comprehensibility. In addition, the results of this paper show that ALR can be an excellent alternative for LR, when models only from accuracy perspective are evaluated.
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Zhao, Ming, Qingjun Zeng, Ming Chang, Qian Tong, and Jiafu Su. "A Prediction Model of Customer Churn considering Customer Value: An Empirical Research of Telecom Industry in China." Discrete Dynamics in Nature and Society 2021 (August 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/7160527.
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Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. When the growth of new customers cannot meet the needs of enterprise development, the enterprise will fall into a survival dilemma. Focusing on the customer churn prediction model, this paper takes the telecom industry in China as the research object, establishes a customer churn prediction model by using a logistic regression algorithm based on the big data of high-value customer operation in the telecom industry, effectively identifies the potential churned customers, and then puts forward targeted win-back strategies according to the empirical research results. This paper analyzes the trends and causes of customer churn through data mining algorithms and gives the answers to such questions as how the customer churn occurs, the influencing factors of customer churn, and how enterprises win back churned customers. The results of this paper can better serve the practice of customer relationship management in the telecom industry and provide a reference for the telecom industry to identify high-risk churned customers in advance, enhance customer loyalty and viscosity, maintain “high-value” customers, and continue to provide customers with “value” and reduce the cost of maintaining customers.
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Ascarza, Eva. "Retention Futility: Targeting High-Risk Customers Might be Ineffective." Journal of Marketing Research 55, no. 1 (February 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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Mishachandar, B., and Kakelli Anil Kumar. "Predicting customer churn using targeted proactive retention." International Journal of Engineering & Technology 7, no. 2.27 (August 2, 2018): 69. http://dx.doi.org/10.14419/ijet.v7i2.27.10180.
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With the advent of innovative technologies and fierce competition, the choices for customers to choose from have increased tremendously in number. Especially in the case of a telecommunication industry, where deregulation is at its peak. Every year a new company springs up offering fitter options for its customers. This has turned the concentration of the business doers on churn prediction and business management models to sustain their places. Businesses approach churn in two ways, one is through targeted customer retention and through cause identification strategy. The literature of this paper provides a comprehensible understanding of the so far employed techniques in predicting customer churn. From that, it is quite evident that less attention has been given to the accuracy and the intuitiveness of churn models developed. Therefore, a novel approach of combining the models of Machine Learning and Big Data Analytics tools was proposed to deal churn prediction effectively. The purpose of this proposed work is to apply a novel retention technique called the targeted proactive retention to predict customer churning behavior in advance and help in their retention. This proposed work will help telecom companies to comprehend the risk associated with customer churn by predicting the possibility and the time of occurrence.
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Nagaraj, Kalyan, Sharvani GS, and Amulyashree Sridhar. "Encrypting and Preserving Sensitive Attributes in Customer Churn Data Using Novel Dragonfly Based Pseudonymizer Approach." Information 10, no. 9 (August 31, 2019): 274. http://dx.doi.org/10.3390/info10090274.
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With miscellaneous information accessible in public depositories, consumer data is the knowledgebase for anticipating client preferences. For instance, subscriber details are inspected in telecommunication sector to ascertain growth, customer engagement and imminent opportunity for advancement of services. Amongst such parameters, churn rate is substantial to scrutinize migrating consumers. However, predicting churn is often accustomed with prevalent risk of invading sensitive information from subscribers. Henceforth, it is worth safeguarding subtle details prior to customer-churn assessment. A dual approach is adopted based on dragonfly and pseudonymizer algorithms to secure lucidity of customer data. This twofold approach ensures sensitive attributes are protected prior to churn analysis. Exactitude of this method is investigated by comparing performances of conventional privacy preserving models against the current model. Furthermore, churn detection is substantiated prior and post data preservation for detecting information loss. It was found that the privacy based feature selection method secured sensitive attributes effectively as compared to traditional approaches. Moreover, information loss estimated prior and post security concealment identified random forest classifier as superlative churn detection model with enhanced accuracy of 94.3% and minimal data forfeiture of 0.32%. Likewise, this approach can be adopted in several domains to shield vulnerable information prior to data modeling.
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Adesua, O., P. A. Danquah, and O. B. Longe. "A Comparative Study of Predicting Customer Churn and Lifetime." advances in multidisciplinary & scientific research journal publication 26, no. 1 (December 10, 2020): 1–6. http://dx.doi.org/10.22624/isteams/v26p1-ieee-ng-ts.
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The problem to be investigated in this research is that of predicting customers who are at risk of leaving the company, a term called churn prediction in telecommunication. The aim of this research is to predict customer churn and further focus on creating customer lifetime profiles. These profiles will allow the company to fit their customer base into n categories and make a long estimation on when a customer is potentially going to terminate their service with the company. The research then proceeds to provide a comparative analysis of neural networks and survival analysis in their capabilities of predicting customer churn and lifetime. . Key words: GSM networks, Base station, Mobile station, Signal strength, GSM service provider
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Lomax, Susan, and Sunil Vadera. "Case Studies in Applying Data Mining for Churn Analysis." International Journal of Conceptual Structures and Smart Applications 5, no. 2 (July 2017): 22–33. http://dx.doi.org/10.4018/ijcssa.2017070102.
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The advent of price and product comparison sites now makes it even more important to retain customers and identify those that might be at risk of leaving. The use of data mining methods has been widely advocated for predicting customer churn. This paper presents two case studies that utilize decision tree learning methods to develop models for predicting churn for a software company. The first case study aims to predict churn for organizations which currently have an ongoing project, to determine if organizations are likely to continue with other projects. While the second case study presents a more traditional example, where the aim is to predict organizations likely to cease being a subscriber to a service. The case studies include presentation of the accuracy of the models using a standard methodology as well as comparing the results with what happened in practice. Both case studies show the significant savings that can be made, plus potential increase in revenue by using decision tree learning for churn analysis.
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Gupta, Rajeev Kumar, Santosh Bharti, Nikhlesh Pathik, and Ashutosh Sharma. "Predicting Churn of Credit Card Customers Using Machine Learning and AutoML." International Journal of Information Technology Project Management 13, no. 3 (July 1, 2022): 1–19. http://dx.doi.org/10.4018/ijitpm.313422.
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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.
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Xia, Ye, Bohan Cui, and Yunhuai Duan. "Analysis and Prediction of Telecom Customer Churn based on Machine Learning." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 131–45. http://dx.doi.org/10.54097/hset.v16i.2495.
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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).
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Mahajan, Vishal, and Renuka Mahajan. "Variable Selection of Customers for Churn Analysis in Telecommunication Industry." International Journal of Virtual Communities and Social Networking 10, no. 1 (January 2018): 17–32. http://dx.doi.org/10.4018/ijvcsn.2018010102.
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The telecommunication industry considers customer relationship management as a significant issue for organizational adaptation. Mobile service providers have enforced CRM with the objective to reduce the number of customers that churn. The objective of this article is to detect high impact factors leading to customer churn in the mobile industry over the present-day market situation in Delhi-NCR by using a questionnaire survey and examine their importance. The study is done to understand usage patterns of customers using mobile data services. The data collected was analyzed using descriptive statistics to identify the most common issues to identify attributes of selecting a service provider, cellular usage, and service quality. Thus, the authors have selected possible variables for modeling the decision tree to build a churn prediction model. A renewed customer service, after analyzing this experience, could predict those customers who are at risk of switching to a different provider.
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Pettersson, Magnus. "SPC with Applications to Churn Management." Quality and Reliability Engineering International 20, no. 5 (July 29, 2004): 397–406. http://dx.doi.org/10.1002/qre.654.
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Saias, José, Luís Rato, and Teresa Gonçalves. "An Approach to Churn Prediction for Cloud Services Recommendation and User Retention." Information 13, no. 5 (April 28, 2022): 227. http://dx.doi.org/10.3390/info13050227.
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The digital world is very dynamic. The ability to timely identify possible vendor migration trends or customer loss risks is very important in cloud-based services. This work describes a churn risk prediction system and how it can be applied to guide cloud service providers for recommending adjustments in the service subscription level, both to promote rational resource consumption and to avoid CSP customer loss. A training dataset was built from real data about the customer, the subscribed service and its usage history, and it was used in a supervised machine-learning approach for prediction. Classification models were built and evaluated based on multilayer neural networks, AdaBoost and random forest algorithms. From the experiments with our dataset, the best results for a churn prediction were obtained with a random forest-based model, with 64 estimators, having 0.988 accuracy and 0.997 AUC value.
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Makurumidze, Linda, Wellington Simbarashe Manjoro, and Wellington Makondo. "Implementing Random Forest to Predict Churn." International Journal of Computer Science and Mobile Computing 11, no. 2 (February 28, 2022): 75–84. http://dx.doi.org/10.47760/ijcsmc.2022.v11i02.009.
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For one to remain afloat in business, the best marketing technique is to maintain the current customers rather getting new ones [1]. [2] Shows that it costs more to get a fresh client than maintaining the available ones. An organization that intends to keep its customers must speculate which of them is at risk of abandoning their service and put all their concentration on those customers in an effort to retain them. This paper’s contribution is to create a prototype, which aid banks to foretell clients that are prone to abandon their service. This paper makes use of four algorithms namely Gradient boost, Random forest, Adaboost and Decision tree to classify and segment bank clients based on a number of features. The paper then selects the best performing algorithm, that is Random forest , to build a prediction model that can used by banks to identify the most likely clients to churn away.
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Dumitrache, Andreea, Denisa Melian, Delia Bălăcian, Alexandra Nastu, and Stelian Stancu. "Churn prepaid customers classified by HyperOpt techniques." Proceedings of the International Conference on Applied Statistics 2, no. 1 (December 1, 2020): 139–51. http://dx.doi.org/10.2478/icas-2021-0013.
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Abstract The telecommunications industry is representative when it comes to a country’s economy. In this industry, the customer plays a very important role in maintaining a stable income. The churn customer is one of the most important concerns for large companies. This increased attention is due to its direct effect on the revenues of large companies in the telecommunications industry, companies being in a constant search to develop ways to predict this type of customer. The aim of our paper is to identify potential customers at risk of churn using modern data mining techniques, often used in the business world. From the nine techniques tested, we choose as the churn prediction model, the technique with the highest performance. The effectiveness of the model is tested and evaluated by the f1-score. The model developed in the paper uses machine learning techniques on the Python platform, exploring a wide range of algorithms from logistic regression and the method of balancing the analyzed data set (Balanced Random Forest) to supervised learning methods (K-Nearest Neighbors, Naive Bayes) and optimization packages (Ligh GBM, CATBoost, ADABoost, RUSBoost, Stochastic Gradient Descent). The techniques analyzed in this paper cover a diverse range of methods that are compared in terms of performance. RUSBoost proves to be the best churn prediction model for telecom customers in this study. RUSBoost has the lowest loss function of all the tested techniques.
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Loukili, Manal. "Supervised Learning Algorithms for Predicting Customer Churn with Hyperparameter Optimization." International Journal of Advances in Soft Computing and its Applications 14, no. 3 (November 28, 2022): 50–63. http://dx.doi.org/10.15849/ijasca.221128.04.
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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.
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Bi, Wenjie, Meili Cai, Mengqi Liu, and Guo Li. "A Big Data Clustering Algorithm for Mitigating the Risk of Customer Churn." IEEE Transactions on Industrial Informatics 12, no. 3 (June 2016): 1270–81. http://dx.doi.org/10.1109/tii.2016.2547584.
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Mbarek, R., and Y. Baeshen. "Telecommunications Customer Churn and Loyalty Intention." Marketing and Management of Innovations, no. 4 (2019): 110–17. http://dx.doi.org/10.21272/mmi.2019.4-09.
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Nowadays the telecommunications sector is becoming very complex Because of the panoply of high-speed technological services. Customers are abandoning the services offered by telecommunications operators because of their dissatisfaction with the services they offer. «Churn» or the migration of customers from one telecommunications operator to another is the main problem facing the telecommunications industries worldwide. Business managers consider the quality of service to be paramount. As a consequence, they have devised reliable criteria to assess the flow of customers within the market and check and evaluate whether customers are satisfied with the services they are offered. This, in turn, helps to establish customer loyalty and provide a healthy and sustainable trading agreement. Service quality control assessment is pivotal to identify the leverage and evaluate the internal and external competition in the industry. Although this concept is not foreign, rather it is an essential business management tool. The goal of this study is to determine the significant criteria for the cause migration of a Tunisie Telecom customer to another operator. Telecommunication is an essential lifelong component that contributes to the comfortability of our daily lives. The various means of telephone communication play a significant role in improving the effectiveness of communication industry. Every telecommunication operator is aware today that it is cheaper to retain an existing customer than to seek to recruit a new one. Indeed, we noticed that the telecommunications market is characterized by intense competition, where a change in the quality of service or a negative interaction perceived by the customer could risk losing them. As a result, the majority of operators introduce studies and action plans to retain customers and keep them as long as possible. The notion of keeping customers and building loyalty is probably one of the biggest challenges that operators around the world face in global competition. In order to achieve the goals set by telecom operators and to achieve maximum profitability, operators must effectively analyze market data and adopt a most effective targeted communications strategy for their customers. Keywords: churn analysis, customer loyalty, mobile marketing, telecommunications, telecommunications customer.
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Rutter, Wilbur, William Cavers, Shivaani Prakash, Elisea Avalos-Reyes, and Kjel Andrew Johnson. "Review of health care access duration among cancer patients at a large health plan." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e18758-e18758. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e18758.
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e18758 Background: Cancer patients have unique, long-term health care needs and access to timely and affordable care is essential. Little is known about cancer patients’ propensity to leave their health plan during and after treatment. This study aimed to examine health care access duration among cancer survivors. Methods: Adult patients diagnosed with cancer between 11/1/2015 and 10/31/2016 were identified and followed until 11/1/2021. Control patients were identified as adults with no cancer diagnosis between 11/1/2015 and 11/1/2021. Patients with skin cancer were excluded due to limited care requirements. The primary outcomes were annual patient churn and median length of health plan enrollment duration. Propensity score (PS) matching was conducted between the groups utilizing patient demographics to balance comorbidities between groups. Comorbidity was described with the National Cancer Institute comorbidity index (NCI CI). Appropriate descriptive statistics were calculated and Cox proportional hazards (CPH) models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results: Overall, 3.2 million patients met the eligibility criteria, with 18% having cancer. Significant differences in the unmatched population were found, with the cancer group being older and having more comorbidities (p < 0.001). Following PS matching, the differences in underlying demographic variables were minimal, despite being statistically significant. Unadjusted annual churn rate, defined as the percent of cancer patients leaving the health plan per year, averaged 25.44% overall, with the non-cancer group experiencing more churn most years (Table). Median length of health plan enrollment duration was 716 days (Interquartile range (IQR) = 819) overall, with the cancer group having less median duration than the non-cancer group in the matched analysis (713 [IQR 808] vs. 717 [IQR 822], p < 0.001). After adjusting for the variables in the model, having a cancer diagnosis was associated with decreased risk of health plan disenrollment in multivariate CPH regression (HR 0.987 [95% CI 0.983 – 0.991]). Additional factors that increased the risk of disenrollment included: male gender (HR 1.011 [1.008-1.015]), Medicare eligibility (HR 1.290 [1.279-1.301]) and increasing NCI CI (1.124 [1.118-1.129]). Conclusions: Cancer patients experienced less churn from their health plan over a five-year follow-up. They were less at risk for disenrollment at any time than non-cancer patients when confounders are accounted for in multivariate modeling.[Table: see text]
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Turkmen, Ahmet, Cenk Anil Bahcevan, Youssef Alkhanafseh, and Esra Karabiyik. "User behaviour analysis and churn prediction in ISP." New Trends and Issues Proceedings on Advances in Pure and Applied Sciences, no. 12 (April 30, 2020): 57–67. http://dx.doi.org/10.18844/gjpaas.v0i12.4987.
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There is no doubt that customer retention is vital for the service sector as companies’ revenue is significantly based on their customers’ financial returns. The prediction of customers who are at the risk of leaving a company’s services is not possible without using their connection details, support tickets and network traffic usage data. This paper demonstrates the importance of data mining and its outcome in the telecommunication area. The data in this paper are collected from different sources like Net Flow logs, call records and DNS query logs. These different types of data are aggregated together to decrease the missing information. Finally, machine learning algorithms are evaluated based on the customer dataset. The results of this study indicate that the gradient boosting algorithm performs better than other machine learning algorithms for this dataset.
Keywords: Data analysis, customer satisfaction, subscriber churn, machine learning, telecommunication.
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Deligiannis, Alexandros, and Charalampos Argyriou. "Designing a Real-Time Data-Driven Customer Churn Risk Indicator for Subscription Commerce." International Journal of Information Engineering and Electronic Business 12, no. 4 (August 8, 2020): 1–14. http://dx.doi.org/10.5815/ijieeb.2020.04.01.
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Gramegna, Alex, and Paolo Giudici. "Why to Buy Insurance? An Explainable Artificial Intelligence Approach." Risks 8, no. 4 (December 14, 2020): 137. http://dx.doi.org/10.3390/risks8040137.
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We propose an Explainable AI model that can be employed in order to explain why a customer buys or abandons a non-life insurance coverage. The method consists in applying similarity clustering to the Shapley values that were obtained from a highly accurate XGBoost predictive classification algorithm. Our proposed method can be embedded into a technologically-based insurance service (Insurtech), allowing to understand, in real time, the factors that most contribute to customers’ decisions, thereby gaining proactive insights on their needs. We prove the validity of our model with an empirical analysis that was conducted on data regarding purchases of insurance micro-policies. Two aspects are investigated: the propensity to buy an insurance policy and the risk of churn of an existing customer. The results from the analysis reveal that customers can be effectively and quickly grouped according to a similar set of characteristics, which can predict their buying or churn behaviour well.
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Park, So-Hyun, Mi-Yeon Kim, Yeon-Ji Kim, and Young-Ho Park. "A Deep Learning Approach to Analyze Airline Customer Propensities: The Case of South Korea." Applied Sciences 12, no. 4 (February 12, 2022): 1916. http://dx.doi.org/10.3390/app12041916.
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In the airline industry, customer satisfaction occurs when passengers’ expectations are met through the airline experience. Considering that airline service quality is the main factor in obtaining new and retaining existing customers, airline companies are applying various approaches to improve the quality of the physical and social servicescapes. It is common to use data analysis techniques for analyzing customer propensity in marketing. However, their application to the airline industry has traditionally focused solely on surveys; hence, there is a lack of attention paid to deep learning techniques based on survey results. This study has two purposes. The first purpose is to find the relationship between various factors influencing customer churn risk and satisfaction by analyzing the airline customer data. For this, we applied deep learning techniques to the survey data collected from the users who have used mostly Korean airplanes. To the best of our knowledge, this is the one of the few attempts at applying deep learning to analyze airline customer propensities. The second purpose is to analyze the influence of the social servicescape, including the viewpoints of the cabin crew and passengers using aircraft, on airline customer propensities. The experimental results demonstrated that the proposed method of considering human services increased the accuracy of predictive models by up to 10% and 9% in predicting customer churn risk and satisfaction, respectively.
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YAN, Jinzhe, Zhao Zhao, and Yeonggil Kim. "What Factors Influence the Churn Intention in the Context of Online Learning Platform." Korea International Trade Research Institute 18, no. 5 (October 31, 2022): 37–50. http://dx.doi.org/10.16980/jiyc.22.5.202210.37.
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Purpose - With the rapid development of Internet technology, the large-scale popularization of intelligent terminals, and the influx of users into online learning platforms caused by COVID-19, online education has become a new outlet for the internet. However, with the control of COVID-19, online learning platforms face problems of low user stickiness and severe loss. Therefore, it is necessary to discuss the factors that affect users’ willingness to pay for online learning platforms.
Design/Methodology/Approach - Using Unified Theory of Acceptance and Use of Technology and Regulatory Focus theory, this study constructed the influencing factor model of paying user turnover intention of online learning platform. This study collected 332 valid sample data through a questionnaire survey, and tested the main effect and moderating effect by adopting regression analysis. This study explored the factors affecting the churn intention of paying users of online learning platforms and the influence degree of each factor, and explored the influencing factors of the churn intention of paying users on online learning platforms from multiple angles.
Findings - The results identified that the influencing factor model of user turnover intention constructed in this paper is practical, seven hypotheses are tenable, and three hypotheses are partially supported. Finally, based on the above research conclusions, this study puts forward strategies to reduce the churn intention of paying users on online learning platforms on three aspects: user segmentation, continuous use process, and influencing factors of online learning platform users, to provide some reference value for online learning platform operators and designers.
Research Implications - Based on the previous analysis results, this study puts forward corresponding improvement strategies from three aspects: user segmentation, continuous use process, and influencing factors in order to provide a specific reference value for reducing the churn of paying users’ online learning platforms.
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Loisel, Stéphane, Pierrick Piette, and Cheng-Hsien Jason Tsai. "APPLYING ECONOMIC MEASURES TO LAPSE RISK MANAGEMENT WITH MACHINE LEARNING APPROACHES." ASTIN Bulletin 51, no. 3 (June 4, 2021): 839–71. http://dx.doi.org/10.1017/asb.2021.10.
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AbstractModeling policyholders’ lapse behaviors is important to a life insurer, since lapses affect pricing, reserving, profitability, liquidity, risk management, and the solvency of the insurer. In this paper, we apply two machine learning methods to lapse modeling. Then, we evaluate the performance of these two methods along with two popular statistical methods by means of statistical accuracy and profitability measure. Moreover, we adopt an innovative point of view on the lapse prediction problem that comes from churn management. We transform the classification problem into a regression question and then perform optimization, which is new to lapse risk management. We apply the aforementioned four methods to a large real-world insurance dataset. The results show that Extreme Gradient Boosting (XGBoost) and support vector machine outperform logistic regression (LR) and classification and regression tree with respect to statistic accuracy, while LR performs as well as XGBoost in terms of retention gains. This highlights the importance of a proper validation metric when comparing different methods. The optimization after the transformation brings out significant and consistent increases in economic gains. Therefore, the insurer should conduct optimization on its economic objective to achieve optimal lapse management.
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Schlegel, J. P., C. J. Macke, T. Hibiki, and M. Ishii. "Modified distribution parameter for churn-turbulent flows in large diameter channels." Nuclear Engineering and Design 263 (October 2013): 138–50. http://dx.doi.org/10.1016/j.nucengdes.2013.04.008.
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Prończuk, Anna. "Churn Risk Identification as an Important Aspect of Marketing Controlling – the Case of a German Start-Up Company." Journal of Economics and Management 34 (2018): 170–83. http://dx.doi.org/10.22367/jem.2018.34.08.
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p A, manoj Kumar, and Arun Aggarwal. "Determinants of Technology Adaption Within the Framework of TOE: An Insurance Sector Perspective." ECS Transactions 107, no. 1 (April 24, 2022): 3417–28. http://dx.doi.org/10.1149/10701.3417ecst.
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Over the last four years, several interesting technology drivers have hit the market. Each of the technology has affected the insurance industry significantly. The cost of acquiring the customer through the digital medium is the least compared to the traditional methods. These changes in the industry could be analyzed based on the social, technological, economic, environmental, and political aspects. The social changes include non-interaction with customary channels including agents and using social media and online channels where they can make the decisions. Robo advisors, Internet of Things, digital platforms, telematics, telemetry, data analytics, and big data are the main disruptors in the insurance industry. Big data has enabled new pricing models with greater risk segmentation. Artificial Intelligence (AI) and Machine Learning (ML) algorithms are used for predicting customer churn in the insurance industry.
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Singh, Shweta, and Sumit Singh. "Accounting for risk in the traditional RFM approach." Management Research Review 39, no. 2 (February 15, 2016): 215–34. http://dx.doi.org/10.1108/mrr-11-2015-0272.
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Purpose – The Purpose of this study is to provide an alternative way to create customer valuation metric while accounting for customer riskiness. Customer relationship management (CRM) emphasizes the importance of measuring customer value. Analytics has paved the way for innovation by providing companies valuable insights into the behavior of customers. Earlier models used to measure customer value do not take into account the types and level of risk posed by customers, such as probability of churn, regularity of purchases, etc. The authors put forth a new and innovative approach to measuring customer value while, at the same time, adjusting for customer riskiness. Design/methodology/approach – Using a non-parametric approach used in the operations research area, the authors create a risk-adjusted regency, frequency, monetary value (RARFM) score for each customer. These scores are used to segment the customers into two groups – customers with high and low RARFM scores. The authors then identify the underlying demographics and behavioral characteristics that separate the two groups. Findings – Findings of this paper indicate that customers who perform the best on the RARFM metric tend to be more experienced, and are more likely to exhibit behavioral tendencies that help them perform well in their jobs, such as purchasing promotional goods that act as sales aid and enhance their performance. Originality/value – The paper is innovative in its approach in terms of creating a new metric for calculating customer value. Few papers have proposed ways to handle and adjust for customer riskiness. Here, the authors propose three kinds of customer risk. Current paper provides a twist to traditional RFM analysis by creating a RARFM score for each customer, and provides a scientific way of assigning weights to RFM.
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Meyer, R. M., and S. P. Clarke. "Shifts with nurse understaffing and high patient churn linked to heightened inpatient mortality risk in a single site study." Evidence-Based Nursing 14, no. 4 (September 6, 2011): 122–23. http://dx.doi.org/10.1136/ebn.2011.100052.
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Shen, Siu-Tsen. "People and their smartphones – mapping mobile interaction in the modern connected world." Engineering Computations 33, no. 6 (August 1, 2016): 1642–58. http://dx.doi.org/10.1108/ec-06-2015-0153.
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Purpose – The purpose of this paper is to investigate how a balanced study group consisting of 152 participants interact with, operate, customize, and control their smartphone applications. Design/methodology/approach – This work uses a qualitative research methodology involving an online user study questionnaire, supported by e-mailed user screenshots and online conversations. Findings – In terms of smartphone age, 72 per cent of the participants’ smartphones were less than two years old. This high level of churn rate was anticipated and will please retailers and marketers alike. This study found that the majority of smartphone users regularly arrange their app icons and that their categorization principle was based primarily on application associated functionality and frequency of use. This group of users seemed less concerned about the risks of privacy and security, and even when they had lost or had their smartphone stolen, few (5.2 per cent) had suffered from fraud, in contrast to the general perception of risk. Originality/value – This is one of the few studies to have investigated the area of smartphone use from the users’ perspective, leading to important insights into application user behaviour and icon arrangement, and as well as alternative possible implications for launcher design.
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Taylor, C. E., M. J. Pettigrew, and I. G. Currie. "Random Excitation Forces in Tube Bundles Subjected to Two-Phase Cross-Flow." Journal of Pressure Vessel Technology 118, no. 3 (August 1, 1996): 265–77. http://dx.doi.org/10.1115/1.2842189.
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Data from two experimental programs have been analyzed to determine the characteristics of the random excitation forces associated with two-phase cross-flow in tube bundles. Large-scale air-water flow loops in France and Canada were used to generate the data. Tests were carried out on cantilevered, clamped-pinned, and clamped-clamped tubes in normal-square, parallel-triangular, and normal-triangular configurations. Either strain gages or force transducers were used to measure the vibration response of a centrally located tube as the tube array was subjected to a wide range of void fractions and flow rates. Power spectra were analyzed to determine the effect of parameters such as tube diameter, frequency, flow rate, void fraction, and flow regime on the random excitation forces. Normalized expressions for the excitation force power spectra were found to be flow-regime dependent. In the churn flow regime, flow rate and void fraction had very little effect on the magnitude of the excitation forces. In the bubble-plug flow regime, the excitation forces increased rapidly with flow rate and void fraction.
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Kramarenko, I. V., and L. A. Konstantinova. "Features of using Cox regression in various instrumental environments." Vestnik Universiteta, no. 10 (November 27, 2022): 80–88. http://dx.doi.org/10.26425/1816-4277-2022-10-80-88.
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The presence of large amounts of data in information and analytical systems makes it necessary to study them using machine learning and artificial intelligence methods. These models require the definition of tuning parameters related to the specifics of the subject area. The article presents a Cox regression model to solve the problem of customer churn. Cox regression is recognized as a model with high accuracy of predictions in healthcare. Therefore, it is interesting to use the model in other industries. The paper presents the results and comparative analysis of calculations on the Cox model using three tools: Statistical Package for the Social Sciences, programming language R and Russian software – analytical platform Loginom. A distinctive feature of the developed probabilistic model is the determination of the risk of event occurrence in conditions of incomplete data, as well as the identification of indicators that have a significant impact on the degree of its manifestation.
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Dong-Keun Yoo and 서승원. "The effect of medical service quality and perceived risk on customer satisfaction, repurchase intention, and churn intention as to hospital sizes." Journal of Korea Service Management Society 10, no. 3 (September 2009): 97–130. http://dx.doi.org/10.15706/jksms.2009.10.3.004.
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Autor, David H., David Dorn, Gordon H. Hanson, and Jae Song. "Trade Adjustment: Worker-Level Evidence *." Quarterly Journal of Economics 129, no. 4 (September 24, 2014): 1799–860. http://dx.doi.org/10.1093/qje/qju026.
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Abstract We analyze the effect of exposure to international trade on earnings and employment of U.S. workers from 1992 through 2007 by exploiting industry shocks to import competition stemming from China’s spectacular rise as a manufacturing exporter paired with longitudinal data on individual earnings by employer spanning close to two decades. Individuals who in 1991 worked in manufacturing industries that experienced high subsequent import growth garner lower cumulative earnings, face elevated risk of obtaining public disability benefits, and spend less time working for their initial employers, less time in their initial two-digit manufacturing industries, and more time working elsewhere in manufacturing and outside of manufacturing. Earnings losses are larger for individuals with low initial wages, low initial tenure, and low attachment to the labor force. Low-wage workers churn primarily among manufacturing sectors, where they are repeatedly exposed to subsequent trade shocks. High-wage workers are better able to move across employers with minimal earnings losses and are more likely to move out of manufacturing conditional on separation. These findings reveal that import shocks impose substantial labor adjustment costs that are highly unevenly distributed across workers according to their skill levels and conditions of employment in the pre-shock period.
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KHOSHGOFTAAR, TAGHI M., and ROBERT M. SZABO. "DYNAMIC MODELS FOR TESTING BASED ON TIME SERIES ANALYSIS." International Journal of Reliability, Quality and Safety Engineering 13, no. 06 (December 2006): 581–97. http://dx.doi.org/10.1142/s0218539306002434.
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In this paper, we investigate a dynamic software quality model that incorporates software process and software product measures as covariates. Furthermore, the model is not based on execution time between failures. Instead, the method relies on data commonly available from simple problem tracking and source code control systems. Fault counts, testing effort, and code churn measures are collected from each build during the system test phase of a large telecommunications software system. We use this data to predict the number of faults to expect from one build to the next. The technique we use is called time series analysis and forecasting. The methodology assumes that future predictions are based on the history of past failures and related covariates. We show that the quality model incorporating testing effort as a covariate is better than the quality model derived from fault counts alone.
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Mrvelj, Štefica, and Marko Matulin. "Modeling the Level of User Frustration for the Impaired Telemeeting Service Using User Frustration Susceptibility Index (UFSI)." Electronics 10, no. 18 (September 9, 2021): 2202. http://dx.doi.org/10.3390/electronics10182202.
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Modern users are accustomed to always-accessible networks ready to serve all of their communication, entertainment, information, and other needs, at the touch of their devices. Spoiled with choices provided on the competitive markets, the risk of customer churn makes network and service providers sensitive to user Quality of Experience (QoE). Services that enable people to work and industries to function in these pandemic times, such as the telemeeting service, are becoming ever more critical, not just for the end-users but also for the providers. Nevertheless, the heterogeneity of end-users network environments and the uniqueness of the service (bidirectional video and audio transmissions and interactivity between the meeting peers) imposes specific QoE requirements. Hence, this paper focuses on understanding how different service quality degradations affect user perception and frustration with such impaired service. The impact of eight quality degradations was analyzed. Based on the conducted user study, we used the multiple regression analysis and developed three models capable of predicting user Level of Frustration (LoF) for the specific degradations that we have analyzed. The models work with the User Frustration Susceptibility Index (UFSI), which categorizes users into groups based on their tendency to become frustrated with the impaired service.
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Li, Dongdong, Chunfa Li, and Runde Gu. "Evolutionary Game Analysis of Promoting Industrial Internet Platforms to Empower Manufacturing SMEs through Value Cocreation Cooperation." Discrete Dynamics in Nature and Society 2021 (September 10, 2021): 1–14. http://dx.doi.org/10.1155/2021/4706719.
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A good partnership is conducive to promoting the empowerment of manufacturing small- and medium-sized enterprises (SMEs) via industrial Internet platforms. By analyzing the factors influencing the cooperation motives of both parties and individual behavior, this paper puts forward the design of a cost-sharing and scale revenue-sharing mechanism and establishes an evolutionary game model. Then, the evolutionary stability strategies (ESSs) of individuals and the evolutionary equilibrium state of the system are analyzed. The results show that the key factors affecting the strategic choices of industrial Internet platforms and manufacturing SMEs are different and will change with the number of platform customers and the level of digitalization of enterprises. By sharing the access cost of SMEs and the scale revenue of the platform, mutual trust between the two parties can be enhanced, and SMEs will be more motivated to access the platform. Moreover, the platform network externality, customer churn risk, and cost-sharing ratio have different influences on the process of reaching evolutionary equilibrium in the system. Collaborative revenue expectations are critical to the behavioral strategies of both parties. In comprehensive consideration of the results of this study, it is recommended that industrial Internet platforms be subsidized in the initial stage of cooperation.
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Ferguson, Tracy, CAPT Anthony Lloyd, and Jon Turban. "Enhancing Preparedness and Response ≈ Transition Management Architecture Improvements." International Oil Spill Conference Proceedings 2017, no. 1 (May 1, 2017): 2017100. http://dx.doi.org/10.7901/2169-3358-2017.1.000100.
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Experts continue to debate about the range of threats that could realistically occur in America today. Disagreements range through the prevention, preemption, and response strategies with advocates continuing to argue for robust “whole-of-government” capabilities to muster and effective response. The debate is complicated by the increased societal churn driven by the changing popular culture, intense effects of technology change and impacts from social media and the 24 hour news cycle. Whether you can hear it, or see it, or not, the truth remains regarding an underlying latency of increased risk in our society. Further compounding this is the change in the oil economy. Latent risk has risen there as well, challenging current preparedness efforts. Increased flexibility, transitional success, better data sharing methods, and deeper situational awareness is needed for planning, preparedness, and response success. Coast Guard legal authorities are foundational in this regard especially as it relates to the proper apportionment of National Contingency Plan resources. The Coast Guard Vessel Response and Facility Response Plan regulations reflect an appropriate effort to assure the retention and allocation of those resources to meet preparedness and response requirements. How can we be sure, however, that this “force lay down” is effective? Can those resources be better accessed to support NCP requirements? This poster will depict a way to envision better transition of VRP/FRP resources. It will also explain a capability and architecture developed to ease the rapid shifts from day-to-day operations to a rapidly expanding crisis.
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Kawahara, A., M. Sadatomi, T. Tomino, and Y. Sato. "Prediction of turbulent mixing rates of both gas and liquid phases between adjacent subchannels in a two-phase slug-churn flow." Nuclear Engineering and Design 202, no. 1 (November 2000): 27–38. http://dx.doi.org/10.1016/s0029-5493(00)00300-9.
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Pavlovich, Y. G., and I. F. Kirinovich. "A/B testing as an effective instrument for adaptation of the user interface at the iterative model of developing applications for mobile devices." Doklady BGUIR 19, no. 1 (February 23, 2021): 30–36. http://dx.doi.org/10.35596/1729-7648-2021-19-1-30-36.
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Changing the user interface always entails risks of decreasing application ergonomics and user churn. The purpose of this work is to find methods for changing the user interface without the need to use and increase the number of active users. Using Alpha/Beta testing and analysis of application metrics, it is possible to include design in each iteration and remotely manage the application configuration, which improves the user interface of the application. The way to integrate the UI modification and adaptation phase into an iterative software development model is proposed in this article. The successful user interface improvement experiments that have improved application metrics are described. In this work, the problem was highlighted, lack of information and control over user purchases, which negatively affected the number of purchases. A hypothesis is put forward that the user must have an effective visualization and control tool in the application. As a result of the experiment, the hypothesis was confirmed by an increase in the number of purchases and user activity. There was also a data security issue in the application, which put users at risk of data loss. An experiment was conducted to change the default value of user settings, which led to an increase of the positive metrics for using the data reservation functionality. The problem of polling users is also considered, which is an important component in the process of improving the ergonomics of an application. The method of remote user polling was used, which allowed to receive quick feedback from users and quickly respond to requests from users. The result of the work is the confirmation of the hypothesis of changes in the user interface in the cycles of the iterative development model, the positive dynamics of application metrics, as well as the satisfaction of users with the changes.
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Wu, Siqi, Yichuan Zhu, and Yanan Li. "Research on credit strategy of small, medium, and micro enterprises based on commercial banks." BCP Business & Management 22 (July 15, 2022): 280–85. http://dx.doi.org/10.54691/bcpbm.v22i.1240.
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In order to help banks formulate Credit Strategies for small, medium-sized and micro enterprises, this paper analyzes enterprises from two aspects. On the one hand, starting from the enterprise's data and from the enterprise's relevant data, including qualitative indicators such as reputation level, invoice information, credit record and operating income, a model is established to quantitatively analyze the operation status and reputation status of the enterprise. On the other hand, through the analysis of the "maximum lending rate" and "maximum lending rate" of the bank, we can analyze the "maximum lending rate" and "how much lending rate" of the bank. Combining the above variables, we can get the best credit strategy of the bank under the factors of credit risk and income. Firstly, without considering the influence of other data, this paper abstracts five indicators to measure the reputation status and enterprise-scale from the data of small and medium-sized enterprises with credit rating, quantifies the indicators. At the same time, the Euclidean distance is calculated by MATLAB modeling, the correlation between lending enterprises and non-lending enterprises is analyzed, and the high-risk enterprises in lending enterprises are selected. Then, among the remaining enterprises that can issue loans, five different credit strategies are formulated according to the weighting of their credit rating and enterprise scale as the primary basis for the number of bank loans and interest rate. Secondly, the focus is on how to comprehensively quantify the credit status and business problems of small, medium-sized and micro enterprises without credit records. Without considering the influence of other data, this paper mainly analyzes the correlation between the enterprises without credit records and the enterprises mentioned above that do not grant loans. It preliminarily obtains the enterprises that can grant loans. Then, through decision tree modeling, the loan demand of enterprises with different credit levels is estimated as a variable. By fitting the curve between interest rate and customer churn rate, different interest rate ranges under different levels are obtained as the second variable. Finally, through the above two variables, we establish the profit function of bank credit, find the corresponding variables at the maximum value, and formulate relevant credit strategies.
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Raubenheimer, H. "Serial correlation and TEV bias in index funds." South African Journal of Business Management 34, no. 2 (June 30, 2003): 45–53. http://dx.doi.org/10.4102/sajbm.v34i2.681.
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Index or passive fund managers and investors analyse the interim volatility of the difference between their fund’s returns and the index’s returns, i.e. the fund’s tracking error variance** (TEV) in order to monitor the success with which tracker funds mimic their benchmark. The objective of a passive or index fund manager should be to keep TEV as close to zero as possible. Pope and Yadav (1994) show that an index fund that is overweight relative to it’s index in either relatively less or relatively more liquid stocks, is expected to exhibit negative serial correlation in its TE’s. Consequently, estimates of TEV will be upwardly biased, particularly when using high frequency (such as daily or weekly) data.This article finds evidence of negative serial correlation in the weekly, monthly and quarterly TE’s of domestic index funds. Consequently it is shown that TEV will likely be overestimated. There are two important implications of this upward bias in TEV estimation. Firstly, index funds, which are expected to offer close to zero benchmark-relative or active risk, may appear far more ‘risky’ than they actually are thus damaging their value-proposition to investors. Secondly, when funds appear to have greater TEV than they actually do, the manager may ‘churn’ the fund’s assets more than necessary in order to bring the fund back into alignment with its index thus incurring greater and unnecessary transaction costs.The analyses in this article therefore suggest that TE measurements should be examined for negative serial correlation before estimates of TEV are made. If serial correlation is detected, estimates of TEV should either be made from lower frequency, uncorrelated TE measurements, if they are available, or an adjustment technique such as the Lo-MacKinlay adjustment should be applied to correct for the bias in TEV estimation.
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Kumar, V., Agata Leszkiewicz, and Angeliki Herbst. "Are you Back for Good or Still Shopping Around? Investigating Customers' Repeat Churn Behavior." Journal of Marketing Research 55, no. 2 (April 2018): 208–25. http://dx.doi.org/10.1509/jmr.16.0623.
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Service firms develop win-back strategies to rectify issues that cause customer churn and rebuild relationships with lost customers. To better support retention, it is important to understand how the revived relationship evolves and possibly ends again. To examine customers' repeat churn behavior, we develop a “mixture cure-competing risks” model, jointly estimating the duration of second lifetimes, multiple reasons for churn, and heterogeneity of customers in exhibiting a related churn reason. The proposed model is tested using a data set from a large telecommunications provider including information on customer behavior and marketing activities during customers' first and second lifetimes. We find support for the existence of a “cured” group of returning customers, defined as those who are not susceptible to churn for the same reason they churned previously. Our findings suggest that mitigating repeat churn behavior can extend customers' second lifetime tenure and increase profitability by $150,000 over the lifetime of the customers in the sample (leading to gains of over $15 million for deferring second-lifetime churn in a million returning customers), depending on the type of churn.
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Mustafa, Nurulhuda, Lew Sook Ling, and Siti Fatimah Abdul Razak. "Customer churn prediction for telecommunication industry: A Malaysian Case Study." F1000Research 10 (December 13, 2021): 1274. http://dx.doi.org/10.12688/f1000research.73597.1.
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Background: Customer churn is a term that refers to the rate at which customers leave the business. Churn could be due to various factors, including switching to a competitor, cancelling their subscription because of poor customer service, or discontinuing all contact with a brand due to insufficient touchpoints. Long-term relationships with customers are more effective than trying to attract new customers. A rise of 5% in customer satisfaction is followed by a 95% increase in sales. By analysing past behaviour, companies can anticipate future revenue. This article will look at which variables in the Net Promoter Score (NPS) dataset influence customer churn in Malaysia's telecommunications industry. The aim of This study was to identify the factors behind customer churn and propose a churn prediction framework currently lacking in the telecommunications industry. Methods: This study applied data mining techniques to the NPS dataset from a Malaysian telecommunications company in September 2019 and September 2020, analysing 7776 records with 30 fields to determine which variables were significant for the churn prediction model. We developed a propensity for customer churn using the Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbours Classifier, Classification and Regression Trees (CART), Gaussian Naïve Bayes, and Support Vector Machine using 33 variables. Results: Customer churn is elevated for customers with a low NPS. However, an immediate helpdesk can act as a neutral party to ensure that the customer needs are met and to determine an employee's ability to obtain customer satisfaction. Conclusions: It can be concluded that CART has the most accurate churn prediction (98%). However, the research is prohibited from accessing personal customer information under Malaysia's data protection policy. Results are expected for other businesses to measure potential customer churn using NPS scores to gather customer feedback.
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Leggat, Sandra, and Cathy Balding. "The impact of leadership churn on quality management in Australian hospitals." Journal of Health Organization and Management 33, no. 7/8 (November 7, 2019): 809–20. http://dx.doi.org/10.1108/jhom-08-2018-0216.
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Purpose The purpose of this paper is to explore the relationship between frequent turnover (churn) of the chief executive officer (CEO), quality manager and members of the governing board with the management of quality in eight Australian hospitals. Design/methodology/approach A mixed method three-year longitudinal study was conducted using validated quality system scales, quality indicators and focus groups involving over 800 board members, managers and clinical staff. Findings There were unexpected high levels of both governance and management churn over the three years. Churn among CEOs and quality managers was negatively associated with compliance in aspects of the quality system used to plan, monitor and improve quality of care. There was no relationship with the quality of care indicators. Staff identified lack of vision and changing priorities with high levels of churn, which they described as confusing and demotivating. There was no relationship with quality processes or quality indicators detected for churn among governing board members. Practical implications Governing boards must recognise the risks associated with management change and minimise these risks with robust clinical governance processes. Originality/value This research is the first that we are aware of that identifies the impact of frequent leadership turnover in the health sector on quality management.
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Couzin-Frankel, Jennifer. "Questions churn about vaping's long-term risks." Science 366, no. 6469 (November 28, 2019): 1059–60. http://dx.doi.org/10.1126/science.366.6469.1059.
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HIEU, NGUYEN VAN, and NGUYEN HUNG SON. "COMPOSITE CHERN-SIMONS GAUGE BOSON IN ANYON GAS." International Journal of Modern Physics B 05, no. 01n02 (January 1991): 391–401. http://dx.doi.org/10.1142/s0217979291000249.
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It was shown that in a free anyon gas there exists a composite vector gauge field with the effective action containing a Chern-Simons term. The momentum dependence of the energy of the composite boson was found. The mixing between Chern-Simons boson and photon gives rise to the appearance of new quasiparticles -Chern-Simons polaritons. The dispersion equations of Chern-Simons polaritons were derived.