A non-linear programming model with fuzzy evaluations for customer satisfaction index estimation

Abstract

Customer satisfaction index (CSI) is a cause-and-effect model of advanced customer satisfaction analysis. CSI models are used by several private and public institutions for developing key customer strategies throughout the world. Index values are based on predictions of customer evaluations. In the literature CSI is mostly modeled with linear statistical estimation methods. In a few of the studies, non-linear approach is used for estimation. Estimation of CSI with minimum error results in a more reliable and robust prediction. Therefore, in this study we propose a non-linear programming model for estimating CSI with fuzzy customer evaluations minimizing estimation errors. The estimation model brings significant contributions in this field of study. With the help of the model, we can find weights of measurement variables of a latent variable with minimized squared errors which is a key success factor in producing reliable indexes. In addition the model enables us to find coefficients of prediction equations that contribute to extend evaluation of index results. The model is also tested with data of a comprehensive survey application and application results are included.