Personalized marketing via mobile devices, also known as Mobile Personalized Marketing (MPM), has become an increasingly important marketing tool because the ubiquity, interactivity and localization of mobile devices offers great potential for understanding customers' preferences and quickly advertising customized products or services. A tremendous challenge in MPM is to factor a mobile user's context into the prediction of the user's preferences. This paper proposes a novel framework with a three-stage procedure to discover the correlation between contexts of mobile users and their activities for better predicting customers' preferences. Our framework helps not only to discover sequential rules from contextual data, but also to overcome a common barrier in mining contextual data, i.e. elimination of redundant rules that occur when multiple dimensions of contextual information are used in the prediction. The effectiveness of our framework is evaluated through experiments conducted on a mobile user's context dataset. The results show that our framework can effectively extract patterns from a mobile customer's context information for improving the prediction of his/her activities.