Relevant bibliographies by topics / Risk of Churn

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Risk of Churn'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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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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Dissertations / Theses on the topic "Risk of Churn"

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VEGLIO, VALERIO. "Web data mining to monitoring marketing performance. Focus on potential customers risk of churn." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2013. http://hdl.handle.net/10281/40161.

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This PhD thesis shows the strategic magnitude of the predictive data mining models in today competitive landscape for discovering hidden knowledge collected in huge databases in order to maximize the probability of customer conversion and minimize their risk of churn. The main challenge for decision makers is to discover those customer are likely to churn. In particular, the attention has been paid on the main data mining techniques helpful to forecast the potential customers risk of churn within global organizations with an outside-in perspective in the web marketing field. The database analyzed was provided by a global company which develop web analytics services all over the world.
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Meng, Chun [Verfasser], and Afra [Akademischer Betreuer] Wohlschläger. "Brain connectome in major depression and preterm born individuals at risk for depression / Chun Meng. Betreuer: Afra Wohlschläger." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2014. http://d-nb.info/1062877330/34.

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Vogel, Susanne [Verfasser], and K. H. Felix [Akademischer Betreuer] Chun. "Kritische Bewertung der Vorhersagefähigkeit eines pathologisch unilateralen, organbegrenzten Prostatakarzinoms in einer "low-risk" Patientenkohorte / Susanne Vogel. Betreuer: K. H. Felix Chun." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2012. http://d-nb.info/1020931469/34.

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Zhou, Xiaoyan [Verfasser], Ulrich C. E. [Akademischer Betreuer] Zanke, Chia Chuen [Akademischer Betreuer] Kao, and Jinhai [Akademischer Betreuer] Zheng. "Morphodynamic Response of Yangtze River Estuary to Sea Level Rise and Human Interferences / Xiaoyan Zhou. Betreuer: Ulrich C. E. Zanke ; Chia Chuen Kao ; Jinhai Zheng." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2011. http://d-nb.info/1111908338/34.

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Uden, Lars. "Understanding churn and its reasons: The GuestU case." Master's thesis, 2016. http://hdl.handle.net/10362/16522.

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In the age of computerisation, entrepreneurs around the world aim to revolutionise their field with ground-breaking ICTs. GuestU is one of those offering mobile application development to small and medium-sized tourism enterprises. To assure the young start-ups steady growth this paper aims to identify reasons for the high customer churn risk. Primary data is used to analyse end-user behaviour and particularly the success of acquisition and activation. Results show troubling download quantities as well as retention rates leading to the recommendations for GuestU to incentivise and guide their customers in increasing promotion for the applications.
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Abreu, Jorge Eduardo Carvalho. "Customer lifetime value in insurance." Master's thesis, 2019. http://hdl.handle.net/10362/62423.

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Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
Throughout the years, companies from several business sectors have strived to strengthen their client portfolio by acquiring and retaining the most profitable. For this to happen, current and potential clients must be clearly classified based on their past and future interactions with a company throughout the lifetime of their relationship. This report presents how the previous scenario was implemented using Customer Lifetime Value (CLV) in one of the biggest bancassurance companies in Portugal, during a 9-month internship. Before delving into the detailed set of this project phases, the concept of CLV was reviewed, as well as the characteristics which define its several approaches, followed by the alignment of the chosen approach to the company reality. This CLV model was limited to a 12-month future horizon, covered 7 company dimensions (one global, plus 1 per lines of business) and took into consideration as main future client interactions churn, cross-sell, upsell and risk of claiming. These previous components were modeled with the help of SAS Enterprise Miner or estimated using SAS Enterprise Guide and analyzing historical events. Besides a purely monetary CLV, it was also generated an ordinal output using a set of business rules and a ranking data discretization method. Finally, a back-test validation procedure was executed to evaluate the reliability of both types of outputs in each of the considered dimensions and its results were analyzed.
Ao longo dos anos, empresas de diversos setores têm-se esforçado para fortalecer o seu portfolio de clientes, adquirindo e retendo os mais lucrativos. Para que isto acontecer, os clientes atuais e potenciais têm de ser devidamente categorizados com base nas suas interações passadas e futuras com uma determinada empresa, ao longo do ciclo de vida da sua relação com a mesma. Este relatório vez por sua vez apresentar como o cenário anterior foi implementado durante um estágio de 9 meses numa das maiores empresas de bancassurance em Portugal, recorrendo ao Customer Lifetime Value (CLV). Antes de aprofundar o conjunto de fases deste projeto, foi feita uma revisão do conceito de CLV, assim como das principais características que definem as diversas abordagens, seguido do alinhamento da abordagem escolhida com a realidade da companhia. Este modelo foi limitado a um horizonte futuro de 12 meses, compreendeu 7 dimensões (uma global e uma por cada linha de negócio) e integrou como principais interações futuras do cliente o churn, cross-sell, upsell e risco de sinistralidade. Estes componentes foram modelados com a ajuda da ferramenta SAS Enterprise Miner, ou estimados utilizando o SAS Enterprise Guide para analisar eventos passados. Além de um CLV puramente monetário, também foi criado um output ordinal recorrendo a um conjunto de regras de negócio e um método de ranking data discretization. No fim, foi executado um procedimento de validação back-test com o intuito de avaliar a credibilidade dos dois tipos de outputs ao longo das várias dimensões e foi feita uma análise dos resultados finais.
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JU, CHEN YI, and 陳邑如. "International situation and the rise of Wu Yue in the late Chun Qiu period." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/18419295210502731754.

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碩士
玄奘大學
中國語文學系碩士在職專班
98
The occasion of the Chun Qiu and the Warring States period is a great ancient society have changed their age, royal atelectasis, vassal state hegemony , in this period, there were some usurpations, killing emperors, and endless of attacks& wars. therefore, to protect the Zhou Dynasty, made strong " Revere the Emperor "," Expel the Barbarians "slogan, using the strength of great powers to maintain peace, the weak are participating alliance for mutual self-protection. Armaments of the Central Plains region will maintain peace for about 40 years, from The state of Jin, The state of Chu gradually weakened, Yangtze River downstream of Wu and Yue strive for hegemony between the two countries are rising ,has become the overlord of the late Chun Qiu period. Chun Qiu period overlord repeatedly, such as: Qi Huan Gong , the Duke Wen of Jin, and King Zhuang of Chu and other monarchs, Each to their own country to the world stage under the spotlight, while the decline of such hegemony is often an opportunity for the rise of another dominant; But why only the late Chun Qiu period King Helu of Wu, King Fuchai of Wu, with the king Goujian of Yue, They not only mounted the Central Plains, and strive for hegemony in the era of the hegemony in the Chun Qiu and Warring States Period between the tragic and flame. Scope of this thesis to the late Chun Qiu period mainly, before and after the period of Armaments Negotiation Qin, Qi, Jin, Lu, Chu, Zheng, Song and diplomatic situation in other countries. And in the late Chun Qiu period of Wu and Yue's emergence is the rise of the contents of the study to explore this period of Wu and Yue's development trend and the process of transformation to the survival of their rise and fall with little comprehensive understanding of the causes.
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Books on the topic "Risk of Churn"

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Shen ni bang de gong guan ce hua (Shanghai) you xian gong si, ed. Wei ji men: Chuan mei ju feng yu 40 pin pai cheng bai. Shanghai Shi: Wen hui chu ban she, 2006.

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Jin rong feng xian chuan dao ji li yan jiu: Study on conduction mechanism of finacial risk. Beijing Shi: Zhongguo shi chang chu ban she, 2009.

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Ji yu jin rong cui ruo xing de yin hang wei ji chuan ran yan jiu: Jiyu jinrong cuiruoxing de yinhang weiji chuanran yanjiu. Chengdu Shi: Sichuan da xue chu ban she, 2011.

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Gong gong wei ji chuan bo zhong de bo yi: Public crisis communication gaming. Shanghai: Shanghai she hui ke xue yuan chu ban she, 2010.

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Zhongguo lun chuan hang yun ye de xing qi: Rise of China's shipping industry. 2nd ed. Beijing: Zhongguo she hui ke xue chu ban she, 2007.

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Zang chuan Fo hua du liang jing: Bod brgyud naṅ bstan lha ris kyi thig rtsa. Xining: Qinghai ren min chu ban she, 1992.

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Shang ye yin hang jin rong chan pin chuang xin de feng xian chuan ran yu mian yi yan jiu: Commercial bank financial products innovation risk infection and emmunisation [i.e. immunisation]. Beijing Shi: Zhongguo jin rong chu ban she, 2011.

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1949-, Dkon-mchog-bstan-ʼdzin, and Sichuan min zu chu ban she., eds. Sde-dge par khaṅ chos mdzod chen moʼi śiṅ par lha ris daṅ deʼi gsal bśad: Dege yin jing yuan Zang chuan mu ke ban hua ji / [Genqiudenzi deng bian]. Chengdu: Si-khron mi rigs dpe skrun khaṅ, 2002.

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Zhongzhen, Qiusong, and Shan yue bian ji bu., eds. Xizang chuan qi da shi: Mileriba tang ka hua zhuan = The wonderful mirror of faith : the biography of the great yogi Milarepa = Rnal ʼbyor gyi dbaṅ phyug chen po rje btsun Mi-la-ras-paʼi rnam par thar brñan ris ṅo mtshar dad paʼi me loṅ. Taibei Shi: Shan yue wen hua you xian gong si, 2006.

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Qiusong and Shan yue bian ji bu., eds. Xizang chuan qi da shi: Mileriba tang ka hua zhuan = The wonderful mirror of faith : the biography of the great yogi Milarepa = Rnal ʼbyor gyi dbaṅ phyug chen po rje btsun Mi-la-ras-paʼi rnam par thar brñan ris ṅo mtshar dad paʼi me loṅ. Taibei Shi: Shan yue wen hua you xian gong si, 2006.

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Book chapters on the topic "Risk of Churn"

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Giudici, Paolo, and Emanuele Dequarti. "Statistical Models to Predict Academic Churn Risk." In Classification and Multivariate Analysis for Complex Data Structures, 41–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13312-1_4.

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Wolstenholme, Eric, and Douglas McKelvie. "Making Predictive Risk Assessment ‘Dynamic’: The Underlying Churn Effect." In The Dynamics of Care, 245–60. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21878-2_14.

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Melgarejo Galvan, Alonso Raul, and Katerine Rocio Clavo Navarro. "Big Data Architecture for Predicting Churn Risk in Mobile Phone Companies." In Information Management and Big Data, 120–32. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55209-5_10.

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Cui, Shiyu, Penghan Lai, Yuwei Deng, and Xiaojiang Zheng. "Risk Decision and Predicting of Customer Churn Based on Principal Component Analysis." In Proceedings of the 2022 3rd International Conference on E-commerce and Internet Technology (ECIT 2022), 693–701. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-005-3_71.

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Mukhopadhyay, Debajyoti, Aarati Malusare, Anagha Nandanwar, and Shriya Sakshi. "An Approach to Mitigate the Risk of Customer Churn Using Machine Learning Algorithms." In Machine Learning for Predictive Analysis, 133–42. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7106-0_13.

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Zhang, Ling, Fan-sen Kong, and Yan-hua Ma. "Risk Control Research on Chang Chun Street Networks with Intelligent Materials Based on Complex Network." In Advances in Computer Science, Intelligent System and Environment, 755–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23777-5_121.

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Brown, Mike. "Healing Comes First." In Advances in Human Resources Management and Organizational Development, 121–39. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7016-6.ch007.

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“Job pressure” is the number one cause of stress according to The American Psychological Association. However, there has been no systematic transformation in business practice to intentionally establish stress-reducing psychosocial work environments and to stop the “churn and burn” of employment. Such stress is compounded and becomes a sort of combat stress for employees in high-risk, high-emotion professions. Healing Comes First is an analysis of the critical impact of work stress on the individual employee and organizational productivity. Using the Jobs Demand-Resource Model as a foundational framework, this chapter provides leaders a pathway forward from identifying symptoms of a “stressed out” work environment to enacting mitigating strategies to reduce work stress consequences. Furthermore, the chapter recommends the incorporation of trauma-sensitive practices and the creation of a positive psychosocial work environment to help mitigate the effects of work stress on productivity.
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Thorniley, Tessa. "Navigating British Publishing and Finding an Anglophone Readership." In Chiang Yee and His Circle, 107–27. Hong Kong University Press, 2022. http://dx.doi.org/10.5790/hongkong/9789888754137.003.0008.

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Tessa Thorniley’s chapter considers the period during and shortly after the war, when Chinese writers in Britain were enjoying something of a heyday. At this time, a number of publishers who were sympathetic towards China helped them to find new readers and achieve a measure of literary acclaim. The chapter traces the rise to prominence of Chiang Yee, Hsiao Ch’ien, Chun-chan Yeh, Lo Hsiao Chien (Kenneth Lo, 1913–1995) and Tsui Chi, and their individual relationships with influential publishers.
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Tomaiuolo, Michele, Monica Mordonini, and Agostino Poggi. "A P2P Architecture for Social Networking." In Applying Integration Techniques and Methods in Distributed Systems and Technologies, 220–45. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8295-3.ch009.

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Social networking systems are usually huge centralized systems owned by a single company. However, this solution has many drawbacks (e.g., lack of privacy, lack of anonymity, risks of censorship, and operating costs). This chapter proposes a novel P2P system that leverages existing, widespread, and stable technologies such as DHTs and BitTorrent. In particular, it introduces a key-based identity system and a model of social relations for distributing content efficiently among interested readers. The proposed system, called Blogracy, is a micro-blogging social networking system focused on (1) anonymity and resilience to censorship, (2) authenticatable content, and (3) semantic interoperability using activity streams. This chapter presents the model and the implementation of the Blogracy system, discusses the experimentations to study its behavior, and presents their results regarding (1) communication delays for some simulations of node churn, (2) delays measured in test operations over PlanetLab in direct communication, and (3) through the I2P anonymizing network.
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Franchi, Enrico, Agostino Poggi, and Michele Tomaiuolo. "Blogracy." In Censorship, Surveillance, and Privacy, 675–96. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7113-1.ch036.

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The current approach to build social networking systems is to create huge centralized systems owned by a single company. However, such strategy has many drawbacks, e.g., lack of privacy, lack of anonymity, risks of censorship and operating costs. In this paper the authors propose a novel P2P system that leverages existing, widespread and stable technologies such as DHTs and BitTorrent. In particular, they introduce a key-based identity system and a model of social relations for distributing content efficiently among interested readers. The system they propose, Blogracy, is a micro-blogging social networking system focused on: (i) anonymity and resilience to censorship; (ii) authenticatable content; (iii) semantic interoperability using activity streams. The authors have implemented the system and conducted various experiments to study its behaviour. The results are presented here, regarding (i) communication delays for some simulations of node churn, (ii) delays measured in test operations over PlanetLab, in direct communication, and (iii) through the I2P anonymizing network.
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Conference papers on the topic "Risk of Churn"

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Akan, Oguzhan, and Abhishek Verma. "Machine Learning Models for Customer Churn Risk Prediction." In 2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). IEEE, 2022. http://dx.doi.org/10.1109/uemcon54665.2022.9965633.

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Borbora, Zoheb H., and Jaideep Srivastava. "User Behavior Modelling Approach for Churn Prediction in Online Games." In 2012 International Conference on Privacy, Security, Risk and Trust (PASSAT). IEEE, 2012. http://dx.doi.org/10.1109/socialcom-passat.2012.84.

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Kumar, V. Sunil, Sowmyashree S, Piyush Kumar Pareek, Ashish Khanna, Victor Hugo Costa de Albuquerque, and Deepak Gupta. "Churn Prediction in Financial Risk Management using Deep Learning Techniques." In 2022 Second International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE). IEEE, 2022. http://dx.doi.org/10.1109/icatiece56365.2022.10047688.

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Dulhare, Uma N., and Ifrah Ghori. "An efficient hybrid clustering to predict the risk of customer churn." In 2018 2nd International Conference on Inventive Systems and Control (ICISC). IEEE, 2018. http://dx.doi.org/10.1109/icisc.2018.8398883.

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Dimlo, U. M. Fernandes, Rosario Huerta-Soto, Laura Nivin-Vargas, John Tarazona-Jimenez, Carla Reyes-Reyes, and Nitin Girdharwal. "Kernelized Extreme Learning Machine Enabled Churn Predictive Financial Risk Assessment Model." In 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS). IEEE, 2022. http://dx.doi.org/10.1109/icaiss55157.2022.10010834.

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Bracher, Shane, Mark Holmes, Liam Mischewski, Asadul Islam, Michael McClenaghan, Daniel Ricketts, Glenn Neuber, Hoyoung Jeung, and Priya Vijayarajendran. "Advanced analytics on SAP HANA: Churn risk scoring using call network analysis." In 2015 IEEE 31st International Conference on Data Engineering (ICDE). IEEE, 2015. http://dx.doi.org/10.1109/icde.2015.7113386.

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Borbora, Zoheb, Jaideep Srivastava, Kuo-Wei Hsu, and Dmitri Williams. "Churn Prediction in MMORPGs Using Player Motivation Theories and an Ensemble Approach." In 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust (PASSAT) / 2011 IEEE Third Int'l Conference on Social Computing (SocialCom). IEEE, 2011. http://dx.doi.org/10.1109/passat/socialcom.2011.122.

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Christianti, Devina, Sarini Abdullah, and Siti Nurrohmah. "Bayes Risk Post-Pruning in Decision Tree to Overcome Overfitting Problem on Customer Churn Classification." In Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia. EAI, 2020. http://dx.doi.org/10.4108/eai.2-8-2019.2290487.

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Adejola, Adenike, Omowumi Iledare, and Paraclete Nnadili. "Data-Driven Insights from Nigeria's Natural Gas Data Using PowerBI." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/208238-ms.

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Abstract Each year, the Nigerian gas industry churns out big data on all channels of its value chain. The data is collated, analyzed, and reported by government agencies, corporate companies, institutions, and even academia. Some of these reports are the NNPC and DPR annual oil and gas reports. The annual oil and gas reports contain data tables, charts, and data driven insights. Considering the growing uncertainty in business intelligence triggered by the COVID-19 pandemic and the fast-paced 4th industrial revolution, the future of data reporting, analyzing, and presentation is also experiencing a new normal. Oil and gas stakeholders desire quick data-driven and actionable insights to reduce business risks caused by the impacts of these key drivers. This article explores and presents the use of Power BI on Nigerian gas data from 2000 to 2018. It extracts data on demand, production, utilization, gas flare volumes, export, current infrastructure capacity, domestic gas supply, and other relevant data categories. The collated data is developed into a dataset by appending and merging tables from the different reports. This data is prepared, and model relationships are created to answers questions on demand, production, infrastructure, and sustainability of the Nigerian Gas market. Empirical results show that new insights can be obtained from the dataset using new tools and a thoughtful data design process. These insights are presented on a dashboard where key takeaways for quick business decisions and policy implementations are easily assessed. The method is proposed as the future of annual energy reporting. It is also a continuous improvement process that can be applied by all oil and gas stakeholders in their data architecture.
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