Kano model integration with data mining to predict customer satisfaction

Khaled Al Rabaiei, Fady Alnajjar, Amir Ahmad

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

The Kano model is one of the models that help determine which features must be included in a product or service to improve customer satisfaction. The model is focused on highlighting the most relevant attributes of a product or service along with customers’ estimation of how the presence of these attributes can be used to predict satisfaction about specific services or products. This research aims to develop a method to integrate the Kano model and data mining approaches to select relevant attributes that drive customer satisfaction, with a specific focus on higher education. The significant contribution of this research is to solve the problem of selecting features that are not methodically correlated to customer satisfaction, which could reduce the risk of investing in features that could ultimately be irrelevant to enhancing customer satisfaction. Questionnaire data were collected from 646 students from UAE University. The experiment suggests that XGBoost Regression and Decision Tree Regression produce best results for this kind of problem. Based on the integration between the Kano model and the feature selection method, the number of features used to predict customer satisfaction is minimized to four features. It was found that ANOVA features selection model’s integration with the Kano model gives higher Pearson correlation coefficients and higher R2 values.

Original languageEnglish
Article number66
JournalBig Data and Cognitive Computing
Volume5
Issue number4
DOIs
Publication statusPublished - Dec 2021

Keywords

  • Customer satisfaction
  • Data mining
  • Feature selection
  • The Kano model

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Computer Science Applications
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Kano model integration with data mining to predict customer satisfaction'. Together they form a unique fingerprint.

Cite this