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Journal articles on the topic 'Customer churning'
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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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Kim, Myung-Joong, Juil Kim, and Sun-Young Park. "Understanding IPTV churning behaviors: focus on users in South Korea." Asia Pacific Journal of Innovation and Entrepreneurship 11, no. 2 (August 7, 2017): 190–213. http://dx.doi.org/10.1108/apjie-08-2017-026.
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PurposeThis study aims to investigate customers’ churning out of Internet Protocol Television (IPTV) service, one of the most prevalent forms of IT convergence. Design/methodology/approachBased on the review of current literature, a research model is introduced to depict the effects of select independent variables on customer churning behavior. First of all, the two groups are compared in terms of predictor variables, including switching barriers, voice of customer (VOC), membership period and degree of contents usage. Then, a curvilinear regression was applied to understand the association relationship between the level of IPTV contents usage and variables of switching barriers, VOC and membership period. Third, a logit regression was performed to predict customer churning through the variables of switching barriers, VOC, membership period and level of IPTV contents usage. FindingsThrough the empirical analysis, this study analyzed the factors affecting customer churning behavior of IPTV service providers based on switching barriers, VOC and contents usage. Originality/valueAlthough several studies on IPTV have been undertaken globally, they have largely depended on self-reporting surveys to examine dynamics between antecedent variables and IPTV performance in terms of customer satisfaction, usage intension and customer retention. This empirical study is performed to understand influential factors of IPTV service defection through the weblog analysis of 3,906 service users, who represented both service defectors and non-defectors during a specific month.
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Kabue, Hellen W. "Creating Customer Value for Enhanced Customer Satisfaction and Retention." Research in Economics and Management 5, no. 3 (June 11, 2020): p7. http://dx.doi.org/10.22158/rem.v5n3p7.
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Customers are increasingly becoming sophisticated due to forces such as advancement in technology, changing social roles and globalization. As a result, customer churning is today a common reality that most companies have to deal with in order to satisfy and retain their customers. Creating customer value has emerged as one of the winning strategic tools that firms could use to gain competitive advantage in the contemporary marketing environment. This paper is an empirical study that presents a comprehensive analysis of the relationship between customer value, customer satisfaction and customer retention. Data was obtained through a survey involving clients of Commercial Banks in Kenya; the survey yielded a total of 385 responses. A self administered questionnaire was used for the customers’ survey while interviews were conducted for Management. Descriptive statistics and regression data analysis methods were employed utilizing SPSS software. The findings of the study revealed that customer value has a positive statistically significant relationship with both customer satisfaction and customer retention.
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Makinde, Ayodeji Samuel, Abayomi O. Agbeyangi, and Wilson Nwankwo. "Predicting Mobile Portability Across Telecommunication Networks Using the Integrated-KLR." International Journal of Intelligent Information Technologies 17, no. 3 (July 2021): 50–62. http://dx.doi.org/10.4018/ijiit.2021070104.
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Mobile number portability (MNP) across telecommunication networks entails the movement of a customer from one mobile service provider to another. This, often, is as a result of seeking better service delivery or personal choice. Churning prediction techniques seek to predict customers tending to churn and allow for improved customer sustenance campaigns and the cost therein through an improved service efficiency to customer. In this paper, MNP predicting model using integrated kernel logistic regression (integrated-KLR) is proposed. The Integrated-KLR is a combination of kernel logistic regression and expectation-maximization clustering which helps in proactively detecting potential customers before defection. The proposed approach was evaluated with five others, mostly used algorithms: SOM, MLP, Naïve Bayes, RF, J48. The proposed iKLR outperforms the other algorithms with ROC and PRC of 0.856 and 0.650, respectively.
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Zhao, Xue. "Research on E-Commerce Customer Churning Modeling and Prediction." Open Cybernetics & Systemics Journal 8, no. 1 (December 31, 2014): 800–804. http://dx.doi.org/10.2174/1874110x01408010800.
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Rachid, Ait Daoud, Amine Abdellah, Bouikhalene Belaid, and Lbibb Rachid. "Clustering Prediction Techniques in Defining and Predicting Customers Defection: The Case of E-Commerce Context." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 4 (August 1, 2018): 2367. http://dx.doi.org/10.11591/ijece.v8i4.pp2367-2383.
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<p><span>With the growth of the e-commerce sector, customers have more choices, a fact which encourages them to divide their purchases amongst several e-commerce sites and compare their competitors’ products, yet this increases high risks of churning. A review of the literature on customer churning models reveals that no prior research had considered both partial and total defection in non-contractual online environments. Instead, they focused either on a total or partial defect. This study proposes a customer churn prediction model in an e-commerce context, wherein a clustering phase is based on the integration of the k-means method and the Length-Recency-Frequency-Monetary (LRFM) model. This phase is employed to define churn followed by a multi-class prediction phase based on three classification techniques: Simple decision tree, Artificial neural networks and Decision tree ensemble, in which the dependent variable classifies a particular customer into a customer continuing loyal buying patterns (Non-churned), a partial defector (Partially-churned), and a total defector (Totally-churned). Macro-averaging measures including average accuracy, macro-average of Precision, Recall, and F-1 are used to evaluate classifiers’ performance on 10-fold cross validation. Using real data from an online store, the results show the efficiency of decision tree ensemble model over the other models in identifying both future partial and total defection.</span></p>
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Chouiekh, Alae, and El Hassane Ibn El Haj. "Deep Convolutional Neural Networks for Customer Churn Prediction Analysis." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 1 (January 2020): 1–16. http://dx.doi.org/10.4018/ijcini.2020010101.
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Several machine learning models have been proposed to address customer churn problems. In this work, the authors used a novel method by applying deep convolutional neural networks on a labeled dataset of 18,000 prepaid subscribers to classify/identify customer churn. The learning technique was based on call detail records (CDR) describing customers activity during two-month traffic from a real telecommunication provider. The authors use this method to identify new business use case by considering each subscriber as a single input image describing the churning state. Different experiments were performed to evaluate the performance of the method. The authors found that deep convolutional neural networks (DCNN) outperformed other traditional machine learning algorithms (support vector machines, random forest, and gradient boosting classifier) with F1 score of 91%. Thus, the use of this approach can reduce the cost related to customer loss and fits better the churn prediction business use case.
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Sreeejesh, S. "Cellular Customer Churns Due to Mobile Number Portability." International Journal of Interdisciplinary Telecommunications and Networking 5, no. 1 (January 2013): 43–57. http://dx.doi.org/10.4018/jitn.2013010104.
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Retaining existing customer has been considered to be one of the most critical challenges for telecommunication service providers than for attracting new ones. In telecommunication, the service offered is different from that of a general commodity sale as in the former case the service is considered to be a continuous process, wherein the service provider can offer the differentiated services throughout the customer’s tenure. This differentiation in service offered creates a demarcation from the competitors and hence establishes competitive advantage for that service provider for attracting new customers and retaining the existing ones, which ultimately determines the profitability. In this paper, the author captures this differentiation factor by investigating customer switching behavior under Mobile Number Portability (MNP) in Indian telecommunication market. It is shown that only limited attention has been paid to the customer churn under MNP and none of the existing studies incorporated psychological constructs as the determinants of customer churn. In this context, the study used discriminant analysis to understand the factors that best discriminate between switchers and non-switchers and predict (develop a churn prediction model) the customer churn behavior through incorporating psychological constructs. The findings indicate that service quality, customer satisfaction, attachment, commitment and switching costs are the major factors differentiating the switching and non-switching decisions. Service quality of the service provider found to be as the differentiating factor in churning decision. The results of the study have implications for both academicians and relationship mangers; they are using psychological constructs to predict customer switching behavior.
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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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Karuppaiah, Sivasankar, and N. P. Gopalan. "Enhanced Churn Prediction Using Stacked Heuristic Incorporated Ensemble Model." Journal of Information Technology Research 14, no. 2 (April 2021): 174–86. http://dx.doi.org/10.4018/jitr.2021040109.
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In a rapidly growing industry like telecommunications, customer churn prediction is a crucial challenge affecting the sustainability of the business as a whole. The fact that retaining a customer is more profitable than acquiring new customers is important to predict potential churners and present them with offers to prevent them from churning. This work presents a stacked CLV-based heuristic incorporated ensemble (SCHIE) to enable identification of potential churners so as to provide them with offers that can eventually aid in retaining them. The proposed model is composed of two levels of prediction followed by a recommendation to reduce customer churn. The first level involves identifying effective models to predict potential churners. This is followed by result segregation, CLV-based prediction, and user shortlisting for offers. Experimental results indicate high efficiencies in predicting potential churners and non-churners. The proposed model is found to reduce the overall loss by up to 50% in comparison to state-of-the-art models.
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Thapa, Prasanna. "An Analysis of the Implementation of the Factors of Customer Retention by Nepal Telecom." International Journal of Social Sciences and Management 5, no. 3 (July 27, 2018): 89–97. http://dx.doi.org/10.3126/ijssm.v5i3.20408.
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The world is travelling in information era where information is the essential element for every individual, institution, society and country. Communication of information is done by every individual to perform their daily activities, so the mobile telecommunication service has become a basic need where everybody needs telecommunication services. From the latest MIS Report of Nepal Telecommunication Authority (NTA), Market Penetration Rate (MPR) for mobile service 134.41%, which clearly indicates that the number of mobile service users has surpassed the population, and this actually means, at least mathematically , that there are no more people who aren’t using a mobile service in the market. This also means that there are no new customers for mobile service as they must be a new customer to us churning out from some other mobile service provider. Additionally, the data of MPR above 100% means the mobile market has reached the saturation level. When a market becomes highly saturated it’s not only hard to find a new customer, but also costly to get a new customer. Now, the main focus of every company is about retention of customers. Customer retention will be more and more crucial for survival of the company in the mobile telecommunication sector in upcoming days. There are different factors of influence of a customer retention like price, service quality, promotion mix, innovation and customer care. This study will analyze the implementation of the factors of customer retention by Nepal Telecom.Int. J. Soc. Sc. Manage. Vol. 5, Issue-3: 89-97
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Khodabandehlou, Samira, and Mahmoud Zivari Rahman. "Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior." Journal of Systems and Information Technology 19, no. 1/2 (March 13, 2017): 65–93. http://dx.doi.org/10.1108/jsit-10-2016-0061.
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Purpose This paper aims to provide a predictive framework of customer churn through six stages for accurate prediction and preventing customer churn in the field of business. Design/methodology/approach The six stages are as follows: first, collection of customer behavioral data and preparation of the data; second, the formation of derived variables and selection of influential variables, using a method of discriminant analysis; third, selection of training and testing data and reviewing their proportion; fourth, the development of prediction models using simple, bagging and boosting versions of supervised machine learning; fifth, comparison of churn prediction models based on different versions of machine-learning methods and selected variables; and sixth, providing appropriate strategies based on the proposed model. Findings According to the results, five variables, the number of items, reception of returned items, the discount, the distribution time and the prize beside the recency, frequency and monetary (RFM) variables (RFMITSDP), were chosen as the best predictor variables. The proposed model with accuracy of 97.92 per cent, in comparison to RFM, had much better performance in churn prediction and among the supervised machine learning methods, artificial neural network (ANN) had the highest accuracy, and decision trees (DT) was the least accurate one. The results show the substantially superiority of boosting versions in prediction compared with simple and bagging models. Research limitations/implications The period of the available data was limited to two years. The research data were limited to only one grocery store whereby it may not be applicable to other industries; therefore, generalizing the results to other business centers should be used with caution. Practical implications Business owners must try to enforce a clear rule to provide a prize for a certain number of purchased items. Of course, the prize can be something other than the purchased item. Business owners must accept the items returned by the customers for any reasons, and the conditions for accepting returned items and the deadline for accepting the returned items must be clearly communicated to the customers. Store owners must consider a discount for a certain amount of purchase from the store. They have to use an exponential rule to increase the discount when the amount of purchase is increased to encourage customers for more purchase. The managers of large stores must try to quickly deliver the ordered items, and they should use equipped and new transporting vehicles and skilled and friendly workforce for delivering the items. It is recommended that the types of services, the rules for prizes, the discount, the rules for accepting the returned items and the method of distributing the items must be prepared and shown in the store for all the customers to see. The special services and reward rules of the store must be communicated to the customers using new media such as social networks. To predict the customer behaviors based on the data, the future researchers should use the boosting method because it increases efficiency and accuracy of prediction. It is recommended that for predicting the customer behaviors, particularly their churning status, the ANN method be used. To extract and select the important and effective variables influencing customer behaviors, the discriminant analysis method can be used which is a very accurate and powerful method for predicting the classes of the customers. Originality/value The current study tries to fill this gap by considering five basic and important variables besides RFM in stores, i.e. prize, discount, accepting returns, delay in distribution and the number of items, so that the business owners can understand the role services such as prizes, discount, distribution and accepting returns play in retraining the customers and preventing them from churning. Another innovation of the current study is the comparison of machine-learning methods with their boosting and bagging versions, especially considering the fact that previous studies do not consider the bagging method. The other reason for the study is the conflicting results regarding the superiority of machine-learning methods in a more accurate prediction of customer behaviors, including churning. For example, some studies introduce ANN (Huang et al., 2010; Hung and Wang, 2004; Keramati et al., 2014; Runge et al., 2014), some introduce support vector machine ( Guo-en and Wei-dong, 2008; Vafeiadis et al., 2015; Yu et al., 2011) and some introduce DT (Freund and Schapire, 1996; Qureshi et al., 2013; Umayaparvathi and Iyakutti, 2012) as the best predictor, confusing the users of the results of these studies regarding the best prediction method. The current study identifies the best prediction method specifically in the field of store businesses for researchers and the owners. Moreover, another innovation of the current study is using discriminant analysis for selecting and filtering variables which are important and effective in predicting churners and non-churners, which is not used in previous studies. Therefore, the current study is unique considering the used variables, the method of comparing their accuracy and the method of selecting effective variables.
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P., Ajitha, Sivasangari A., Gomathi R.M., and Indira K. "Prediction of Customer Plan using Churn Analysis for Telecom Industry." Recent Advances in Computer Science and Communications 13, no. 5 (November 5, 2020): 926–29. http://dx.doi.org/10.2174/2213275912666190410114104.
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Background: In creating nations like India, there are in excess of 10 administrators giving versatile administration in each circle. With the presentation of number convenience portable client are progressively changing starting with one administrator then onto the next. This conduct is called beat. The explanation behind beat might be many like valuing isn't alluring, visit call drops, message drops, more client care calls and so forth. Presently the administrator in INDIA is aware of the need of client. At that point, it is past the point of no return as the client has officially settled on choice and hard to persuade and retain. So a robotized instrument is needed at administrator end to predict which client may beat with high exactness. Objective: With fast utilization of outfit classifiers to enhance exactness, we additionally propose a gathering cross breed classifier that predicts with more precision. Methods: Hybrid model contains regression, perceptron and confrontation both regression and perceptron run parallel after completion execution both the results will be compared in a confrontation level. Conclusion: The report of customer who are predicted to churn and the reason for churning if reported. Also it will store aggregate reporting HBASE database.
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Liu, Annie H., Richa Chugh, and Albert Noel Gould. "Working smart to win back lost customers the role of coping choices and justice mechanisms." European Journal of Marketing 50, no. 3/4 (April 11, 2016): 397–420. http://dx.doi.org/10.1108/ejm-10-2014-0642.
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Purpose The purpose of this paper is to examine how the cognitive appraisals, coping choices and behavioral responses by business-to-business (B2B) sales professionals confronting the acutely stressful experience of losing a customer, and their pursuit of justice in the win-back process, influences reacquisition outcomes. The paper further examines the role of sales experience as a moderator between coping choices and successful win back. Design/methodology/approach In all, 98 critical incidents were reported by sales professionals from B2B firms across various industries. NVivo 9, content analysis and logistic regression were used to analyze the data. Findings The results show that problem-focused coping (PFC) and pro-active responses positively affect win-back outcome. By contrast, emotion-focused coping (EFC) and re-active responses have a negative association with customer reacquisition. The findings also show that sales experience moderates the relationship between levels of EFC and win-back outcomes. Specifically, for sales professionals with low levels of EFC, sales experience helps improve chances of winning back lost customers. But for sales professionals using higher levels of EFC, more sales experience decreases win-back probability. Additionally, the findings show that procedural, interactional and distributive justice all contribute to successful customer reacquisition. Research limitations/implications The few published studies of how B2B sales professionals deal with customer defections reveal a mixture of bereavement and drivenness in striving for new accounts. The authors’ focus and findings on the use of PFC and EFC strategies, justice mechanisms and the uneven role of experience in responding to this stressful context suggests that there is much to be gained from additional research. Specifically, probes into how sales professionals may be inadvertently skewed to EFC behaviors by either overly simplistic training systems, learning- versus performance-based incentives or their experience with prior customer defections. Practical implications The findings highlight the importance of PFC strategies and the delivery of procedural, interactional and distributive justice strategies to productively adapt to customer defections, activate switch back behavior and win back lost customers. Sales force training systems need to address the increased churning in B2B markets and integrate win-back procedures in sales training programs so that sales professionals do not default to EFC and/or strive for new accounts when facing the stress of customer defection. Originality/value The findings contribute to customer defection management and sales literature by integrating coping and justice theories in exploring sales professionals’ cognitive appraisals and coping responses to the acute stress of losing a current customer.
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Chatterjee, Swagato. "Modeling Loyalty Intention and Word-of-Mouth Behavior towards Fast Moving Technology Products (FMTP)." International Journal of E-Services and Mobile Applications 8, no. 3 (July 2016): 20–37. http://dx.doi.org/10.4018/ijesma.2016070102.
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Fast moving technology products (FMTP), for instance mobile applications, have been characterized as a category which has a high churning rate. This makes customer loyalty one of the major concerns for FMTP marketers. The current study develops and validates an integrated model of loyalty intentions and word-of-mouth (WOM) behavior towards FMTPs using the theories of TAM, planned behavior, social diffusion and satisfaction–loyalty links. The models have been tested in two datasets. Android-based mobile applications (apps) have been used as a sample product from the FMTP category. While SEM has been used to build the structural model of loyalty intention using a survey dataset, 3SLS analysis has been performed on a dataset of consumers' actions obtained from web-scrapping to model loyalty behavior in terms of WOM. The results suggest the comparative importance of various design and marketing aspects of an app that impacts consumer loyalty intentions and WOM behavior.
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W. Keep, William, and Peter J. Vander Nat. "Multilevel marketing and pyramid schemes in the United States." Journal of Historical Research in Marketing 6, no. 2 (May 13, 2014): 188–210. http://dx.doi.org/10.1108/jhrm-01-2014-0002.
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Purpose – This paper aims to analyze the evolution of direct selling – a retail channel that successfully sold products ranging from cosmetics to radios to automobiles – to multilevel marketing (MLM), an industry now apparently heavily reliant on selling to itself. As the courts have found some MLM companies to be pyramid schemes, the analysis includes the overlap between the legal MLM model and an illegal pyramid scheme. Design/methodology/approach – The development of direct selling in the USA was examined, followed by the factors contributing to the design and growth of the MLM model and its non-commission-based compensation structure. Then, the key legal decisions regarding illegal pyramid schemes operating under the guise of MLM, the relative stagnation of direct selling and the state of the MLM industry were examined. Findings – As the MLM model operates on the dual premise of retailing through a network of distributors and recruiting new distributors to do the same, it was found that federal regulators and the courts consistently focus on the “retail question” – the existence and extent of sales to consumers external to the distributor network. The authors argue that without a significant external customer base, internal consumption by an ever-churning base of participants resembles neither employee purchases nor a buying club. Social implications – As the MLM model facilitated the growth of pyramid scheme fraud, creating victims rather than customers, this research highlights successful efforts to regulate this type of consumer fraud. Originality/value – Few papers have been written on MLM and pyramids schemes, and none thus far has taken an historical perspective.
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Huang, Wanqiu, Xuguang Jia, Fen Tian, Yu Zhang, and Zhe Zhou. "The Method of Finding Potentially Churning Customers Based on Social Networks." International Journal of Multimedia and Ubiquitous Engineering 10, no. 11 (November 30, 2015): 95–104. http://dx.doi.org/10.14257/ijmue.2015.10.11.09.
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Flores-Méndez, Mario Rogelio, Marcos Postigo-Boix, José Luis Melús-Moreno, and Burkhard Stiller. "A model for the mobile market based on customers profile to analyze the churning process." Wireless Networks 24, no. 2 (August 4, 2016): 409–22. http://dx.doi.org/10.1007/s11276-016-1334-8.
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Manchanda, Rita, and Seema Kakran. "Gendered power transformations in India’s Northeast: Peace politics in Nagaland." Cultural Dynamics 29, no. 1-2 (February 2017): 63–82. http://dx.doi.org/10.1177/0921374017709232.
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As the middle space for ‘post ceasefire-cold peace’ politics expanded in Nagaland in India’s Northeast, the Naga women’s question has emerged as symbolic of the intense social churning in traditional hierarchies around three sites of inequality: decision-making in the public sphere, patriarchal customary laws and property rights. The article tracks the shift in Naga women’s peace politics, from motherhood politics to asserting more equal modes of citizenship, and explores the emancipatory potential of Naga women’s emergence in the public sphere as key stakeholders in the peace process within a context of growing tensions in the relationship between gender and ethnicity.
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Postigo-Boix, Marcos, and José L. Melús-Moreno. "A social model based on customers’ profiles for analyzing the churning process in the mobile market of data plans." Physica A: Statistical Mechanics and its Applications 496 (April 2018): 571–92. http://dx.doi.org/10.1016/j.physa.2017.12.121.
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Kennedy, David. "Australian Mobile Survey 2021: Mobile Buying and Churn Drivers Stable." Journal of Telecommunications and the Digital Economy 9, no. 2 (June 29, 2021): 117–27. http://dx.doi.org/10.18080/jtde.v9n2.422.
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Venture Insights has been surveying Australian mobile consumers annually since 2018. The latest survey was conducted in March 2021 and included 1,019 respondents. The results show that price and network performance remain the main purchasing drivers for mobile services, with more than 90% of respondents rating them as important or very important. On price, 86% did not expect to spend more each year for their mobile phone services, which is consistent with previous surveys. The persistent importance of price shows that mobile services are seen as a commodity by many customers. A total of 38% of respondents were considering churning their mobile phone service; of these respondents, 40% chose price as the key churn driver. There is consistent focus on Mobile Virtual Network Operators (MVNOs) driven by price. The survey results suggested that the total market share for MVNOs could increase by 6 percentage points, if all respondents indicating a move to an MVNO actually did so. Only 20% of respondents indicated they were willing to pay more for 5G mobile services or handsets. A majority (55%) of respondents change their mobile phones every 2-3 years; 39% said they would consider purchasing a recycled/refurbished mobile phone at a lower price.
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Shirole, Rahul, Laxmiputra Salokhe, and Saraswati Jadhav. "Customer Segmentation using RFM Model and K-Means Clustering." International Journal of Scientific Research in Science and Technology, June 1, 2021, 591–97. http://dx.doi.org/10.32628/ijsrst2183118.
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Today as the competition among marketing companies, retail stores, banks to attract newer customers and maintain the old ones is in its peak, every company is trying to have the customer segmentation approach in order to have upper hand in competition. So Our project is based on such customer clustering method where we have collected, analyzed, processed and visualized the customer’s data and build a data science model which will help in forming clusters or segments of customers using the k-means clustering algorithm and RFM model (Recency Frequency Monetary) for already existing customers. The input dataset we used is UK’s E-commerce dataset from UCI repository for Machine Learning which is based on customer’s purchasing behavioral. At the very simple the customer clusters would be like super customer, intermediate customers, customers on the verge of churning out based on RFM score .Along with this we also have created a web model where an e-commerce startup or e-commerce business analyst can analyze their own customers based on model we created .So using this it will be easy to target customers accordingly and achieve business strength by maintaining good relationship with the customers .
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Luther, Bernhard, Nicola Winter, Henning Nobmann, Thomas Winter, Patrick Erdelt, and Alwin Haensel. "The Modelling and Assessment of Online Customer Interaction, Customer Journeys and Churning." SSRN Electronic Journal, 2019. http://dx.doi.org/10.2139/ssrn.3404493.
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Jamjoom, Arwa A. "The use of knowledge extraction in predicting customer churn in B2B." Journal of Big Data 8, no. 1 (August 17, 2021). http://dx.doi.org/10.1186/s40537-021-00500-3.
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AbstractData mining techniques were used to investigate the use of knowledge extraction in predicting customer churn in insurance companies. Data were included from a health insurance company for providing insight into churn behaviour based on a design and application of a prediction model. Additionally, three promising data mining techniques were identified for the prediction of modeling, including logistic regression, neural network, and K-means. The decision tree method was used in the modeling phase of CRISP-DM for identifying the attributes of churned customers. The predictive analysis task is undertaken through classification and regression techniques. K-means clustering variation is selected for exploring if the clustering algorithms categorize the customers in churning and non-churning groups with homogeneous profiles. The findings of the study show that data mining procedures can be very successful in extracting hidden information and get to know customer's information. The 50:50 training set distribution resulted in effective outcomes when the logistic regression technique was used throughout this study. A 70:30 distribution worked effectively for the neural network technique. In this regard, it is concluded that each technique works effectively with a different training set distribution. The predicted findings can have direct implications for the marketing department of the selected insurance company, whereas the models are anticipated to be readily applicable in other environments via this data mining approach. This study has shown that the prediction models can be utilized throughout a health insurance company's marketing strategy and in a general academic context with a combination of a research-based emphasis with a business problem-solving approach.
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Praseeda, C. K., and B. L. Shivakumar. "Fuzzy particle swarm optimization (FPSO) based feature selection and hybrid kernel distance based possibilistic fuzzy local information C-means (HKD-PFLICM) clustering for churn prediction in telecom industry." SN Applied Sciences 3, no. 6 (May 10, 2021). http://dx.doi.org/10.1007/s42452-021-04576-7.
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Abstract Customer churn has been considered as one of the key issues in the operations of the corporate business sector, as it influences the turnover directly. In particular, the telecom industries are seeking to develop new approaches to predict potential customer to churn. So, it needs the appropriate algorithms to overcome the increasing problem of churn. This work proposed a churn prediction model that employs both strategies of classification and clustering, that helps in recognizing the churn consumers and giving the reasons after the churning of subscribers in the industry of telecom. The process of information gain and fuzzy particle swarm optimization (FPSO) has been executed by the method of feature selection, besides the divergence kernel-based support vector machine (DKSVM) classifier is employed in categorizing churn customers in the proposed approach. In this way, the compelling guidelines on retention have generated since the process plays a vital role in customer relationship management (CRM) to suppress the churners. After the classification process, the churn customers are divided into clusters through the process of fragmenting the data of churning customer. The cluster-based retention offers have provided by the clustering algorithm of hybrid kernel distance-based possibilistic fuzzy local information C-means (HKD-PFLICM), whereas the measurement of distance have accomplished through the kernel functions such as the hyperbolic tangent kernel and Gaussian kernel. The results reveal that proposed churn prediction model (FPSO- DKSVM) produced better churn classification results compared to other existing algorithms such as K-means, flexible K-Medoids, fuzzy local information C-means (FLICM), possibilistic FLICM (PFLICM) and entropy weighting FLICM (EWFLICM). Article highlights Customer churn is a major concern in most of the companies as it influences the turnover directly. The performance of churn prediction has been improved by applying artificial intelligence and machine learning techniques. Churn prediction plays a crucial role in telecom industry, as they are in the position to maintain their precious customers and organize their Customer Relationship Management.
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Lal, Bechoo, and Suraj Kumar. "Predictive Model on Churn Customers using SMOTE and XG-Boost Additive Model and Machine Learning Techniques in Telecommunication Industries." International Journal of Scientific Research in Science and Technology, August 7, 2021, 646–61. http://dx.doi.org/10.32628/ijsrst218498.
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In this research paper the researcher builds a predictive model on churn customers using SMOTE and XG-Boost additive model and machine learning techniques in Telecommunication Industries. Customer’s churning is one of the global research issues in telecommunication industries. In somehow customers are not satisfying from telecommunication customer services, call rate, international plan, data pack, and others which are having a significant impact on customer’s services. The researcher used the SMOTE and XGboost technique to handle the imbalanced dataset and gives the higher-level accuracy for predictive model to identify the category of customer whether they are in churn or not churn. The researcher used the comparative study between logistics regression and random forest algorithms to classify the category of churn customers and non-churn customers in Telecommunication Industries. The predictive model is verifying at 96% accuracy level and can be capable to handle imbalance dataset. As per the data analysis the score of the confusion matrix is such as accuracy 94%, Precision for “ did not leave “ is 0.97 whereas recall is 0.96, and F1score is 0.97 with the support features of 903. For the churn customers precision is 0.80, recall is 0.81, F1-score is 0.80 and support features is 160, the data analysis report shows that the predictive model is having 94% accuracy whereas at 6% does not predict accurately about the customers status. Finally, the researcher concluded that the predictive model is more accurate and can be capable to handle imbalance dataset. The researchers assure that the predictive model would be benefited for the telecommunication industries to categories the churn/ non-churn customers and accordingly the organization can make changes their business plan and policies which would be benefited for the customers.
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Abbasimehr, Hossein, and Mostafa Shabani. "A new methodology for customer behavior analysis using time series clustering." Kybernetes ahead-of-print, ahead-of-print (July 19, 2019). http://dx.doi.org/10.1108/k-09-2018-0506.
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Purpose The purpose of this paper is to propose a new methodology that handles the issue of the dynamic behavior of customers over time. Design/methodology/approach A new methodology is presented based on time series clustering to extract dominant behavioral patterns of customers over time. This methodology is implemented using bank customers’ transactions data which are in the form of time series data. The data include the recency (R), frequency (F) and monetary (M) attributes of businesses that are using the point-of-sale (POS) data of a bank. This data were obtained from the data analysis department of the bank. Findings After carrying out an empirical study on the acquired transaction data of 2,531 business customers that are using POS devices of the bank, the dominant trends of behavior are discovered using the proposed methodology. The obtained trends were analyzed from the marketing viewpoint. Based on the analysis of the monetary attribute, customers were divided into four main segments, including high-value growing customers, middle-value growing customers, prone to churn and churners. For each resulted group of customers with a distinctive trend, effective and practical marketing recommendations were devised to improve the bank relationship with that group. The prone-to-churn segment contains most of the customers; therefore, the bank should conduct interesting promotions to retain this segment. Practical implications The discovered trends of customer behavior and proposed marketing recommendations can be helpful for banks in devising segment-specific marketing strategies as they illustrate the dynamic behavior of customers over time. The obtained trends are visualized so that they can be easily interpreted and used by banks. This paper contributes to the literature on customer relationship management (CRM) as the proposed methodology can be effectively applied to different businesses to reveal trends in customer behavior. Originality/value In the current business condition, customer behavior is changing continually over time and customers are churning due to the reduced switching costs. Therefore, choosing an effective customer segmentation methodology which can consider the dynamic behaviors of customers is essential for every business. This paper proposes a new methodology to capture customer dynamic behavior using time series clustering on time-ordered data. This is an improvement over previous studies, in which static segmentation approaches have often been adopted. To the best of the authors’ knowledge, this is the first study that combines the recency, frequency, and monetary model and time series clustering to reveal trends in customer behavior.
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"Prediction of Churn in Telecom Service: Exploring Call Behaviors and using Machine Learning." International Journal of Innovative Technology and Exploring Engineering 9, no. 2 (December 10, 2019): 3831–34. http://dx.doi.org/10.35940/ijitee.b7189.129219.
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Churn has a significant impact on mobile network operators and telecommunications service providers. Many studies on churn have been reported, but no one can say that they can create universal human tools for predicting churn or that we can see all the reasons for it. The purpose of this study is to derive the call behavior factors of churning customers and to find ways to reduce the churn of target customers who exhibit these call behaviors. For this, this study uses decision tree and machine learning for the prediction of churn in telecom service. Based on the analysis results, first, the fact that the total number of customers who have more than 316.7 in churn shows that the higher the number of calls, the higher the chance of churn. Second, among customers with total day minutes above 316.7, those with customer service calls above 8.5 show a high likelihood of churn among complaining customers. The overall accuracy is 91.4%. Among the customers who predicted not to be churned, the accuracy that would not be churned was 92.87%, and the accuracy that was churned was 78.4% among the customers predicted to be churned
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"Customer Churn Prediction and Upselling using MRF (Modified Random Forest) technique." International Journal of Innovative Technology and Exploring Engineering 9, no. 3 (January 10, 2020): 475–82. http://dx.doi.org/10.35940/ijitee.c8392.019320.
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Customer Churn Prediction has become one of the eminent topic in the telecom industry, it has gained a lot of attention in the research industry due to fierce competition from the various, and hence companies have focused on the larger size of the data for churning and upselling prediction. The model of customer churn prediction detects and identify the customer who are willing to terminate the subscription, customer churn prediction and upselling can be done through the data mining process. Hence, In this paper we have introduce a model Named MRF(Modified Random Forest), this model helps in enhancing the accuracy and also helps in ignoring the regression issue. Our methodology has been performed on the provided orange Datasets. For the evaluation of our algorithm comparative analysis between the existing and proposed methodology is done considering the two scenario i.e. churn and upselling. Later our model is compared with the various existing churn prediction model, the result of the analysis indicates that our model outperforms the existing method including the standard random forest in terms of AUC and classification accuracy.