Review of Data Mining Techniques for Churn Prediction in Telecom

Telecommunication sector generates a huge amount of data due to increasing number of subscribers, rapidly renewable technologies; data based applications and other value added service. This data can be usefully mined for churn analysis and prediction. Significant research had been undertaken by researchers worldwide to understand the data mining practices that can be used for predicting customer churn. This paper provides a review of around 100 recent journal articles starting from year 2000 to present the various data mining techniques used in multiple customer based churn models. It then summarizes the existing telecom literature by highlighting the sample size used, churn variables employed and the findings of different DM techniques. Finally, we list the most popular techniques for churn prediction in telecom as decision trees, regression analysis and clustering, thereby providing a roadmap to new researchers to build upon novel churn management models

Review of Data Mining Techniques for Churn Prediction in Telecom

Telecommunication sector generates a huge amount of data due to increasing number of subscribers, rapidly renewable technologies; data based applications and other value added service. This data can be usefully mined for churn analysis and prediction. Significant research had been undertaken by researchers worldwide to understand the data mining practices that can be used for predicting customer churn. This paper provides a review of around 100 recent journal articles starting from year 2000 to present the various data mining techniques used in multiple customer based churn models. It then summarizes the existing telecom literature by highlighting the sample size used, churn variables employed and the findings of different DM techniques. Finally, we list the most popular techniques for churn prediction in telecom as decision trees, regression analysis and clustering, thereby providing a roadmap to new researchers to build upon novel churn management models

A Grounded Theory Approach to Information Technology Adoption

This study explores the nature of information technology adoption based on phenomena found in the real world. We selected the grounded theory method (GTM) for this study, which involved two sites located in the USA. and eight sites located in Taiwan. The results exemplify a multi-year, multi-site grounded theory approach to generating theory that helps explain information technology (IT) adoption in an organizational context. The core categories of the model are developed from, grounded on, and extracted from the data, and are casually linked into four adoption processes: motivation, solutions fit, values, and decision stage. The nature of the information technology adoption model could help researchers and practitioners understand that managers have one or more motivations, seek IT solutions to fulfill their motivations, evaluate IT solutions, and make decision after judging IT value. We also present an assessment of the theory, and discuss its relevance and directions for future research

Bank of China Hong Kong Annual Report 2010

Other Relevant Crises: Global Financial Crisis (2007-2009

Does Internet Connectivity Affect Export Performance? Evidence from Transition Economies

exports, information technology, FDI

An Evaluative Study of Functioning of Co-operative Banks, with Special Reference to Gujarat State

Not availabl

Banking reforms in the transitional economy of China

Ph.DDOCTOR OF PHILOSOPH

Advanced Information Systems and Technologies

This book comprises the proceedings of the VI International Scientific Conference “Advanced Information Systems and Technologies, AIST-2018”. The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing, computer networking and telecomunications, modern methods and information technologies of sustainable development. They will be useful for students, graduate students, researchers who interested in computer science