Analysis of Customer Complaints Data using Data Mining Techniques

Abstract

The Metropolitan Transportation Authority (MTA) is a public transportation service provider for the NewYork region. It is considered as the largest transportation network in North America, serving a population of 15.3 million people in the 5,000-square-mile area fanning out from New York City through Long Island, southeastern New York State, and Connecticut [1]. The public transportation services offered includes: subways, buses, and railroads which provide 2.73 billion trips each year to New Yorkers [1]. MTA receives and manages customers’ complaints through Customer Relationship Management System (CRM) for all agencies [2]. In this work, we apply data mining techniques on a real domain problem using a data set of MTA customer feedback. We analyze customer complaints data for useful information that help companies to improve the quality of services and identify factors that lead to low levels of customer satisfaction. We then classify agencies based on the different areas of received customer complaints. The results of our work can be used to aid decision makers in MTA to focus on areas which needs more improvement.