A statistical process control approach to business activity monitoring

Abstract

Statistical Process Control (SPC) techniques have been successfully used in manufacturing industries to trigger and identify the root cause of variations so as to promote quality improvement. This paper develops a SPC framework to identify important changes deserved in business activity monitoring. To model and track thousands of diversified customer behaviors, the proposed SPC system consists of efficient and robust profiling methods to accommodate different behavior patterns including business changes, structural breakdowns, and unnecessary errors. Several customer profiling techniques are discussed and the activity monitoring performance based on the profiling algorithms is compared in a simulation example and a customer churn detection example in a telecommunications setting. The enhanced system will allow business managers and engineers to establish successful customer loyalty programs for churn prevention and fraud detection.

Keywords:

• Bayesian Model Averaging
• churn detection
• customer relationship management
• EWMA
• fraud detection
• LAD.

Acknowledgements

The authors would like to thank the referee whose suggestions have helped improve the quality of this paper. This work was supported by National Science Foundation under grants DMI-0200224 and IIS-0542881.