A Privacy Preserved Data Mining Framework for Customer Relationship Management
Shuting Xu, Mable Qiu · Journal of Relationship Marketing · 2008
Collecting customer information and analyzing the information using data mining techniques are the primary processes of customer relationship management. One of the important issues in such processes is how to protect the trade secrecy of corporations and privacy of customers contained in the data sets collected and used for the purpose of data mining. In this article, we propose a privacy preserved data mining framework for customer relationship management that not only enables firms to protect the private information but maintains the performance and utility of the data mining analysis as well. We use churn prediction as a case study to show how this framework works in the real world.