Analysis of a customer satisfaction survey using Rough Sets theory

Abstract

PurposeThis paper seeks to present the use of Rough Sets (RS) theory as a processing method to improve the results in customer satisfaction survey applications.Design/methodology/approachThe research methodology is to apply an innovative tool to discover knowledge on customer behavior patterns instead of using conventional statistical methods. The RS theory was applied to discover the voice of customers in market research. The collected data contained 422 records. Each record included 20 condition attributes as well as two decision attributes. The important attributes that ensured high quality of classification were generated first. Then decision rules for classifying high and low overall satisfaction and loyalty categories were derived.FindingsThree important facts were found: the important product and service attributes that lead to overall satisfaction and loyalty; the percentage of latently dissatisfied customers; and customer decision rules.Research limitations/implicationsThe study is limited by the case company and its experience. These rules were presented to the company's sales and marketing managers who believed that they provided them with valuable information for creating strategies to increase customer satisfaction and retention.Originality/valueRS theory provides a mathematical tool to discover patterns hidden in survey data. The paper describes a new attempt of applying a RS‐based method to analyze overall customer satisfaction and loyalty behavior through regular satisfaction questionnaire surveys.