Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry

By Michael C. Mozer, Richard H. Wolniewicz, D. B. Grimes, E. Johnson, Howard Kaushansky. IEEE Trans. Neural Networks Learn. Syst., 2000

367 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Marketing

View Paper: https://www.semanticscholar.org/paper/508f71d19adf9caf48ed995011eb7ecbcc94cbe5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

In Marketing

Citations in the corpus

Application of data mining techniques in customer relationship management: A literature review and classification (2009, methodology): “…Hung et al. (2006) Self-organizing map and Markov chain Kim et al. (2005) Logistic regression, neural network and decision tree Koh and Chan (2002), Mozer et al. (2000), Smith et al. (2000) Self-organizing map and decision tree Chu, Isai, and Ho (2007) Data mining by evolutionary learning Au et…”
Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting (2005, methodology): “Shopping behaviour across product categories Defection may occur when customers are not pleased (anymore) with a specific product or service (Mozer et al., 2000; Rust and Zahorik, 1993; Mittal and Lassar, 1998).”
A novel evolutionary data mining algorithm with applications to churn prediction (2003, background): “A related work on churn prediction in a database of 46 744 subscribers has been presented in [32].”
The Playtime Principle: Large-scale cross-games interest modeling (2014, background): “For example, being able to identify people who will use a particular product or play a game for a long time vs. those who will quickly stop, informs about the value of these different customers [7].”
Social ties and their relevance to churn in mobile telecom networks (2008, methodology): “Traditional solutions have used data mining techniques [1,8,24] that create a customer profile from her calling pattern (often described by hundreds of variables), and then predict the probability of churn based on certain attributes of the subscriber.”
New insights into churn prediction in the telecommunication sector: A profit driven data mining approach (2012, background): “Piatetsky-Shapiro and Masand (1999) and Mozer et al. (2000) have also formulated expressions to calculate the profit generated by a retention campaign.”
Optimizing Classifier Performance via an Approximation to the Wilcoxon-Mann-Whitney Statistic (2003, background): “In the telecommunications industry, typical monthly churn rates are in the neighborhood of 2% (Mozer et al., 2000).”
Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry (2006, methodology): “For example, detailed call data is used to predict the probability of customer churn (Mozer, Wolniewicz, Grimes, Johnson, & Kaushansky, 2000; Ng and Liu, 2000; Wei and Chiu, 2002); a subscriber’s remaining tenure with the company is estimated using internal company databases (Drew, Mani, Betz, &…”
Computer assisted customer churn management: State-of-the-art and future trends (2007, background): “[30] x x x Morgan [50] x Boone and Roehm [51] x Shin and Sohn[46] x Vellido et al.”
A comparison of machine learning techniques for customer churn prediction (2015, methodology): “In the following, we briefly present five well established and popular techniques used for churn prediction, taking into consideration reliability, efficiency and popularity in the research community [4,7,9,16,17,19,29,30].”
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers (2001, background): “The decision-making task is the one outlined above, to choose which past donors to request a new donation from, This task is completely analogous to typical one-to-one marketing tasks for many other organizations, both nonprofit and for-profit (Mozer et al., 2000; Piatetsky-Shapiro & Masand, 1999), ”
Churn prediction in new users of Yahoo! answers (2012, background): “Some features are related to the service and are independent of the user, such as call quality, billing, customer service and pricing [12, 15], and may be utilized to model a prior on churn likelihood in the service at a given time.”
Churn in Social Networks (2010, background): “In subsequent studies, factors such as these have been decomposed into feature profiles of the customer and customer experience in order to be able to predict churn [49, 33, 1].”