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.

Critical Success Factors-Based Content Management System Development for Small and Medium Size Enterprises

“…The features were views and navigation in which sellers can present an attractive product display to prospective buyers (Sahney, 2008;Behjati and Othaman, 2012) to build consumer trust and satisfaction (Cyr, 2008;Lin, 2007), provision of contact between the seller and the buyer that may bridge communication which is good for building buyer's trust (Sahney, 2008;Behjati and Othaman, 2012) which itself is a form of improved service to consumers, an advantage in the e-commerce system (April and Pather, 2008). References from previous customers in the form of testimonials may also be one of the factors to build buyers trust to make purchases thereafter (Behjati and Othaman, 2012;Chen, 2009) which in turn may build customer loyalty as a factor of acceptance of the e-commerce system (Al-Abdallah, 2013).…”

Entrepreneurial Risk-Taking in Sustainable Energy: University Spin-Off Firms and Market Introduction in Northwest Europe

“…Rough-set analysis is increasingly recognized in literature as a useful classificatory method, including elements of causal relations. This holds for example for comparing performance of firms and marketing strategies, evaluation of development of urban revitalization projects, university incubator projects, transport systems, etc., particularly when it comes to analyzing small samples and qualitative data [109][110][111][112][113].…”

“…Next, a validation of the rules is performed by using K-fold cross-validation. This is a method to evaluate predictive models by randomly partitioning the sample into K subsamples in which one of them acts as a validation set for testing the model and the rest of the K-1 subsamples are put together to form a training set [111]. The results have a sufficient level of accuracy (almost 70% in total) for obtained decision rules (Appendix B).…”

“…In our test, the cross-validation process [111] was repeated 50 times using 'training sets'. The final results prove sufficient accuracy (almost 70% in total) for the obtained rules, and also indicate somewhat higher accuracy for rules concerning positive development compared to problematic development.…”

Rough Set Method for Determining Knowledge Attribute on Customer Satisfaction

“…Question about the cause-dependency effect between the performance and overall attributes. The current problem encountered from the complete text archive of the journals available in satisfaction is a causal and associative relationship between factors factor that affects the customer's pusher in a simple and multiple way (Chen, 2009;Wang and Chou, 2013;Ali et al, 2016;Hadiansah, 2017).…”