Kano Model Integration with Data Mining to Predict Customer Satisfaction

Khaled Al Rabaiei,Amir Ahmad,Fady Alnajjar

doi:10.3390/bdcc5040066

Khaled Al Rabaiei, Amir Ahmad + Show 1 more

Open Access

https://doi.org/10.3390/bdcc5040066

Copy DOI

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.

Highlights

Integrating the Kano model with data mining techniques could improve the selection of relevant characteristics that drive customer satisfaction [5]
For tuning the model and improving the performance of the model, we have used some of the common feature selection methods like correlation-based feature selection, chi square-based feature selection, mutual information lasso feature selection, and ANOVA t-test based feature selection [22]
The Kano (Dimensional and delight features) are 7, 13, 25, 27, 35, 12, 23, 31, and 36, so the common features between Kano and other feature selection methods are shown in Table 4 below

Summary

Introduction

It is important for any corporation to maintain its existing customers, make a profitable share, and improve the profit margins. Corporations need to satisfy their customers’ needs and go even beyond [1]. The client’s satisfaction can be considered as one of the significant aspects that play a big part in the success or failure of any business [2]. Companies endeavor to meet and exceed customers’ expectations to gain their loyalty. An unhappy customer is a critical and challenging problem that can adversely affect the business; he/she could lead to a ‘churn’ of the customer, which could result in the failure of the business [3]

Objectives

Methods

Results

Discussion

Conclusion