Automated Cellular Modeling and Prediction on a Large Scale
Artificial Intelligence Review - Issues on the application of data mining
On the discovery of significant statistical quantitative rules
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 8th ACM international workshop on Data warehousing and OLAP
Computer assisted customer churn management: State-of-the-art and future trends
Computers and Operations Research
Expert Systems with Applications: An International Journal
Handling class imbalance in customer churn prediction
Expert Systems with Applications: An International Journal
Design science in information systems research
MIS Quarterly
IEEE Transactions on Neural Networks
A Systematic Review of Business and Information Technology Alignment
ACM Transactions on Management Information Systems (TMIS)
Does Knowledge Management Matter? The Empirical Evidence from Market-Based Valuation
ACM Transactions on Management Information Systems (TMIS)
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In business, information is abundant. Yet, effective use of that information to inform and drive business operations is a challenge. Our industry-university collaborative project draws from a rich dataset of commercial demographics, transaction history, product features, and Service Quality Index (SQI) factors on shipping transactions at FedEx. We apply inductive methods to understand and predict customer churn in a noncontractual setting. Results identify several SQI variables as important determinants of churn across a variety of analytic approaches. Building on this we propose the design of a Business Intelligence (BI) dashboard as an innovative approach for increasing customer retention by identifying potential churners based on combinations of predictor variables such as demographics and SQI factors. This empirical study contributes to BI research and practice by demonstrating the application of data analytics to the fundamental business operations problem of customer churn.