Investigating Marketplace Customer Satisfaction:data Mining Approach

Abstract

Customer satisfaction is perceived as business strategy’s key component and critical differentiation in a competitive marketplace. The recorded huge transactions per day in marketplaces can be used as useful information to evaluate customer satisfaction. A sophisticated method, such as data mining, is necessary to analyze this massive, multifaceted, and versatile empirical data generating accurate predictions. This research purported to investigate marketplace customer satisfaction as a reference to determine service and quality improvements. In conclusion this study draws two conclusive results: (1) The majority of marketplace consumers preferred the lead-time sensitive over price-sensitive; and (2) The Neural Net empirically showed as the most appropriate robust data mining technique among other techniques to predict marketplace customer satisfaction indicating by fittest accuracy, F score and ROC curve