The Improved Customer Satisfaction Prediction System on Airlines Service by Using Cloud Computing Based Intelligent Strategy System

Abstract

The main purpose of this research is applying an intelligent strategy to improve the customer satisfaction prediction system of airlines service. Historically, the measurement of customer satisfaction collects by questionnaire. We collect and computing the customer satisfaction of each flight by the cloud computing based prediction system. It compares the customer questionnaire, then continuous modifying the accuracy of prediction system by neural network methodology. It is more efficiency and precisely to improve the customer satisfaction for the long-term perspective. We are proposing a parameter of evaluation module that selected 12 influence factors from MEPH and 12 satisfaction evaluation factors of airlines service (by customer perception of service quality). In this study, we are using back-propagation neural network method to build the module. The module could be used by calculating the airlines service satisfaction from the quality factors such as material, machine, product, and staff. However, we could get the prediction of service satisfaction through the way of data fusion by the 12 satisfaction indicators. The results could be used to make the service quality strategy, in order to lead a higher customer satisfaction. The findings help airlines managers to predict their customer satisfaction more efficiently, making changes to service quality strategy easily to meet the customers’ satisfaction level. Even if the managers pre-set a customers’ satisfaction level, a real-time cloud computing could help managers deploy the resources to achieve the goal. After all, in order to prove the feasibility of the parameters and the intelligent evaluation methodology, this study will collect data and test the evaluation of quality from an experimentation of airlines service system in Kaohsiung city in Taiwan.