Significance-Based Feature Extraction for Customer Churn Prediction Data in the Telecom Sector

Eria, Kamya; Marikannan, Booma Poolan

doi:10.1166/jctn.2019.8303

Cited by 12 publications

(9 citation statements)

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“…On the other hand, there is an abundance of classification models proposed for churn prediction, including: Support Vector Machines, Naïve Bayes, Decision Trees and Neural Networks [9]; Support Vector Data Description (SVDD) with random under-sampling and SMOTE oversampling [10]; combinations of random under-sampling and boosting algorithm [11]; random forest combined with random oversampling [12]; Multilayer Perceptron (MLP) neural network [13]; Reverse Nearest Neighborhood and One Class support vector machine (OCSVM) [14]; hybrid combination of well known oversampling technique SMOTE with under-sampling technique [15]; ensemble learning [16] and transfer learning methods [17]. Both complaints and churn are relatively rare events, and building statistical patterns to predict them is extremely difficult due to the imbalance of the data sets: one class (the complaints/churn) is much smaller than the other classes.…”

Section: State Of the Artmentioning

confidence: 99%

Analyzing the Impact of Complaints on Customer Satisfaction in the Travel Industry

Drissi¹,

Ettl²,

Gentile³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Customer satisfaction is crucial for the long term success of any travel service provider. Therefore, identifying situations that can lead to customer dissatisfaction is critical. The strongest evidence of customers dissatisfaction are their complaints. While complaints do not occur very often, they almost always lead to loss of customer goodwill which can cost travel providers millions of dollars in future revenues. In this paper, we describe an approach to proactively identify customers that have the highest propensity to complain as they encounter a travel disruption event. These are invaluable insights that can empower customer service teams with information to deliver a more timely, relevant and impactful service experience. We use three key aspects in this approach: (i) specialized feature engineering for the travel industry; (ii) handling extremely imbalanced data and (iii) adaptation of binary classification, anomaly detection and learning to rank models to our specific task. This research is an important step towards more individualized understanding of customer behavior, and potential service enhancements to further increase customer satisfaction.

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Section: State Of the Artmentioning

confidence: 99%

Analyzing the Impact of Complaints on Customer Satisfaction in the Travel Industry

Drissi¹,

Ettl²,

Gentile³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…A great deal of research has been devoted to these topics, which can be categorized based on the type of business entities, namely, enterprises, social media, and education, who are primarily interested in churn prediction, site migration, and student dropout, respectively. The first of these focuses on predicting whether and when a customer is likely to stop doing business with a profitable enterprise [57]. The second aims to predict whether a social media user will move from one site, such as Flickr, to another, such as Instagram, a movement known as site migration [199].…”

Section: Businessmentioning

confidence: 99%

“…For all three types, the procedure is first to collect features of a customer's profile and activities over a period of time and then conventional or sequential classifiers or regressors are generally used to predict the occurrence or time-to-event of the future targeted activity. There are a few publicly available datasets, including Telecom Churn datasets [57] and MOOC dataset [122].…”

Section: Businessmentioning

confidence: 99%

Event Prediction in the Big Data Era

Zhao

2021

ACM Comput. Surv.

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Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the occurrence of such events in advance to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth, also thanks to advances in high performance computers and new Artificial Intelligence techniques. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex (e.g., spatial, temporal, and semantic) dependencies, and streaming data feeds. Due to the strong interdisciplinary nature of event prediction problems, most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This article aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts’ searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided to introduce wider applications to model developers to help them expand the impacts of their research. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions are discussed. Additional resources related to event prediction are included in the paper website: http://cs.emory.edu/∼lzhao41/projects/event_prediction_site.html.

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“…A great deal of research has been devoted to these topics, which can be categorized based on the type of business entities namely enterprises, social media, and education, who are primarily interested in churn prediction, site migration, and student dropout, respectively. The first of these focuses on predicting whether and when a customer is likely to stop doing business with a profitable enterprise [71]. The second aims to predict whether a social media user will move from one site, such as Flickr, to another, such as Instagram, a movement known as site migration [236].…”

Section: Businessmentioning

confidence: 99%

Event Prediction in Big Data Era: A Systematic Survey

Zhao¹

2020

Preprint

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Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the occurrence of such events in advance in order to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth, also thanks to advances in high performance computers and new Artificial Intelligence techniques. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex (e.g., spatial, temporal, and semantic) dependencies, and streaming data feeds. Due to the strong interdisciplinary nature of event prediction problems, most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts’ searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided to introduce wider applications to model developers to help them expand the impacts of their research. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions for this promising and important domain are elucidated and discussed.

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Significance-Based Feature Extraction for Customer Churn Prediction Data in the Telecom Sector

Cited by 12 publications

References 0 publications

Analyzing the Impact of Complaints on Customer Satisfaction in the Travel Industry

Analyzing the Impact of Complaints on Customer Satisfaction in the Travel Industry

Event Prediction in the Big Data Era

Event Prediction in Big Data Era: A Systematic Survey

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