Predicting Service Reliability - Using Survival Analysis of Customer Fuzzy Satisfaction

It had been known that the main objective of adding service was creating value to improve customer satisfaction. Therefore, if customer satisfaction was plotted in time series variable, service reliability function was reflected. The benefits obtained from understanding the service reliability function were knowing the trend of service life cycle and analyzing the time to react for service in order that the company could offer service innovation before the service became unfavorable. This research was aimed to analyze service reliability function by using the definition concept of product reliability function which was called survival analysis. To reduce bias data because of linguistic variable such as customer satisfaction, fuzzy logic was used in this research. The data was collected by doing a survey to 100 SAMSAT customers about their satisfaction. SAMSAT is a public unit giving service in tax. Then, fuzzied customer satisfaction was plotted in time series to describe the survival analysis of service. In other words, the plotting result was used to determine the right time for innovating service. So, the conclusion was drawn that survival analysis implemented in service field could help the managerial level in terms of innovation management. In addition, fuzzy logic used could bold the bias definition of customer satisfaction. Furthermore, this framework would be able to be used in mobile application development for future research in terms of supporting a company to define the right moment of service innovation based on a sample customer satisfaction survey.