Mining high value travelers using a new model designed for online CRM systems: a case study in Taiwan

The purpose of this research is to investigate the mining of high value travelers for online travel agencies. For the research the AHP (analytic hierarchy process) procedure was applied with a new model of customer value on a database (RFM model based). In this article, the customer values of online travelers can be analyzed via a proposed equation (FMLC-CV), and the travelers can be ranked via a proposed FMLC model and the AHP procedure. According to the research logic, high value travelers can be discovered in the travelers’ database. Through the value ranking the value markets can be clustered according to value scores as well. The results can be applied to online CRM systems to help the travel industry realize the high value markets of travelers, and implement various marketing plans for different markets

A new methodology to study customer electrocardiogram using RFM analysis and clustering

One of the primary issues on marketing planning is to know the customer's behavioral trends. A customer's purchasing interest may fluctuate for different reasons and it is important to find the declining or increasing trends whenever they happen. It is important to study these fluctuations to improve customer relationships. There are different methods to increase the customer's willingness such as planning good promotions, an increase on advertisement, etc. This paper proposes a new methodology to measure customer's behavioral trends called customer electrocardiogram. The proposed model of this paper uses K-means clustering method with RFM analysis to study customer's fluctuations over different time frames. We also apply the proposed electrocardiogram methodology for a real-world case study of food industry and the results are discussed in details