Decision Tree and MCDA Under Fuzziness to Support E-Customer Satisfaction Survey

Abstract

The proposed work extends existing approaches by analyzing customer click stream data and online reviews to implicitly identify satisfaction level when customer’s rate is not available and find the website criteria score that positively influence e-customer satisfaction. Fuzzy mining customer navigation data is our task to set up inputs of the two proposed supervised evaluation approaches; a multi criteria analysis approach for the website assessment and a new decision tree algorithm to classify customers. A case study from the B2C Chinese website “TMALL” has been used for validating our proposal, and a comparison between the proposed approaches has shown promising results.