Book ChapterDOI
Customer churn time prediction in mobile telecommunication industry using ordinal regression
Rupesh K. Gopal,Saroj K. Meher +1 more
- pp 884-889
TLDR
It is noticed from the results that ordinal regression could be an alternative technique for survival analysis for churn time prediction of mobile customers and state-of-the-art methods for tenure prediction - survival analysis.Abstract:
Customer churn in considered to be a core issue in telecommunication customer relationship management (CRM). Accurate prediction of churn time or customer tenure is important for developing appropriate retention strategies. In this paper, we discuss a method based on ordinal regression to predict churn time or tenure of mobile telecommunication customers. Customer tenure is treated as an ordinal outcome variable and ordinal regression is used for tenure modeling. We compare ordinal regression with the state-of-the-art methods for tenure prediction - survival analysis. We notice from our results that ordinal regression could be an alternative technique for survival analysis for churn time prediction of mobile customers. To the best knowledge of authors, the use of ordinal regression as a potential technique for modeling customer tenure has been attempted for the first time.read more
Citations
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Proceedings ArticleDOI
Evaluation Measures for Ordinal Regression
TL;DR: This work proposes a simple way to turn standard measures for OR into ones robust to imbalance, and shows that, once used on balanced datasets, the two versions of each measure coincide, and argues that these measures should become the standard choice for OR.
Proceedings Article
Predicting Customer Churn in Mobile Networks Through Analysis of Social Groups
TL;DR: This work proposes a novel framework, termed Group-First Churn Prediction, which eliminates the a priori requirement of knowing who recently churned and exploits the structure of customer interactions to predict which groups of subscribers are most prone to churn, before even a single member in the group has churned.
Proceedings ArticleDOI
Churn Prediction in MMORPGs: A Social Influence Based Approach
TL;DR: A method for churn prediction which combines social influence and player engagement factors has shown to improve prediction accuracy significantly for the dataset as compared to prediction using the conventional diffusion model or the player engagementfactor, thus validating the hypothesis that combination of both these factors could lead to a more accurate churn prediction.
Journal ArticleDOI
Improved churn prediction in telecommunication industry using data mining techniques
Abbas Keramati,Ruholla Jafari-Marandi,Mohammad Aliannejadi,I. Ahmadian,M. Mozaffari,U. Abbasi +5 more
TL;DR: Data mining classification techniques including Decision Tree, Artificial Neural Networks, K-Nearest Neighbors, and Support Vector Machine are employed to improve churn prediction and a hybrid methodology which made considerable improvements to the value of some of evaluations metrics is proposed.
Journal ArticleDOI
Prediction of subscriber churn using social network analysis
TL;DR: A new churn prediction algorithm based on a social network analysis of the call graph is developed that quantifies the strength of social ties between users based on multiple attributes and applies an influence diffusion model over the call graphs to determine the net accumulated influence from churners.
References
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Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
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A Dynamic Model of the Duration of the Customer's Relationship with a Continuous Service Provider: The Role of Satisfaction
TL;DR: In this paper, the authors developed and estimated a dynamic model of the duration of the provider-customer relationship that focuses on the role of customer satisfaction, and the model is estimated as a left-truncated, proportional hazards regression with cross-sectional and time series data describing cellular customers perceptions and behavior over a 22-month period.