Computer assisted customer churn management: State-of-the-art and future trends

By John Hadden, Ashutosh Tiwari, Rajkumar Roy, Dymitr Ruta. Computers & Operations Research, 2007

348 citations recorded by the source corpus.

Subfields: Information Systems, Marketing, Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/ebd2e031cb6680048d668cf0e53ad48ac6eb20c9

How researchers see this paper

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

In Information Systems

1. Mohand Tahar Kechadi
2. Brian Buckley
3. Dolores Romero Morales
4. Emilio Carrizosa
5. Bing Quan Huang

In Marketing

1. Emilio Carrizosa
2. Mohand Tahar Kechadi
3. Brian Buckley
4. Dolores Romero Morales
5. Bing Quan Huang

In Organizational Behavior and Human Resource Management

1. Mohand Tahar Kechadi
2. Brian Buckley
3. Bing Quan Huang

Citations in the corpus

• Supervised classification and mathematical optimization (2013, background): “Typical business applications are credit scoring [11], bankruptcy [176], fraud detection [87], customer targeting [73], customer loyalty [106] [112], market basket analysis [61], recommender systems [62], revenue management [122], services booking cancellations [203], country risk ratings [114],…”
• A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector (2019, background): “Researchers are focused on differentiating customers to identify the ones who are likely to churn to another service provider [11].”
• Customer churn prediction in telecommunications (2012, background): “In addition, most of the literature (Hadden et al., 2006; Hung et al., 2006, 2010; John et al., 2007; Wei & Chiu, 2002) shows the features that are the aggregated call-details are important for the customer churn prediction.”