Extraction of attribute importance from satisfaction surveys with data mining techniques: a comparison between neural networks and decision trees

Abstract

When a public transport manager conducts a customer satisfaction survey (CSS), the goal is to determine the overall satisfaction of passengers with the service, as well as their satisfaction with specific aspects (e.g., frequency, speed, and comfort). Another fundamental objective is to assess the importance to customers of each attribute individually. Asking directly about this importance involves a number of drawbacks; therefore, most studies extract this importance from surveys that ask questions only about global satisfaction and specific satisfaction regarding each attribute. This paper investigates the capability and performance of two emerging data mining methods, namely, decision trees and neural networks, for extracting the importance of attributes from CSS. A total of 858 surveys about the metropolitan bus service in Granada (Spain) were used to model estimation and evaluation. The main advantages and disadvantages of each method are studied from the standpoint of public transport managers.