| dc.description.abstract | To modern companies, Customer relationship management is exactly an indispensable part of the company. In making the decision, it also becomes a vital criterion. By the core of Customer relationship management, we precisely classify different types of customers and give suitable stimulations corresponding to their characteristics. Hence, those who are not left are relatively avoidable. Potential consumers are capable of activating through some proper stimuli. Nowadays, when differentiating customers, we almost believe that the commonest method is the static partition method. It is the most convenient way that we only analyze some given data. However, everyone attaches importance to customer relationship management progressively. Time must be considered as a critical factor. As time goes on, consumer behavior is going to change constantly, since customer behavior is indeed dynamic. As a result, it is exactly a primary choice that we analyze customers based on time series data mining.
In Taiwan, many airline companies are facing great variations in the market and tremendous competition from others. Maintaining a positive relationship with customers is not ignored, cultivating customers’ loyalties becomes in fact still more important as well. In order to establish a long-term relationship, comprehensive customer management is a primary principle. In this study, the REM model used commonly in the field of customer relationship management, and the new indicators, applying to airline industries, better and more efficiently evaluate the value of customers. Furthermore, we use time series data mining regarding consumer behavior to show trends of customer behavior. In addition, we come up with an optimal strategy to keep connecting a favorable relationship in advance. | en_US |