A Novel Approach to Identify the Categories of Attributes for the Three-Factor Structure in Customer Satisfaction

Amir Ahmad,Omar Barukab,Alex Alexandridis

doi:10.1155/2020/9506941

Amir Ahmad, Omar Barukab + Show 1 more

Open Access

https://doi.org/10.1155/2020/9506941

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Abstract

Evaluation of customer satisfaction is an important area of marketing research in which products are defined by attributes that can be grouped into different categories depending on their contribution to customer satisfaction. It is important to identify the category of an attribute so that it can be prioritized by a manager. The Kano model is a well-known method to perform this task for an individual customer. However, it requires filling in a form, which is a difficult and time-consuming exercise. Many existing methods require less effort from the customer side to perform data collection and can be used for a group of customers; however, they are not applicable to individuals. In the present study, we develop a data-analytic method that also uses the dataset; however, it can identify the attribute category for an individual customer. The proposed method is based on the probabilistic approach to analyze changes in the customer satisfaction corresponding to variations in attribute values. We employ this information to reveal the relationship between an attribute and the level of customer satisfaction, which, in turn, allows identifying the attribute category. We considered the synthetic and real housing datasets to test the efficiency of the proposed approach. The method correctly categorizes the attributes for both datasets. We also compare the result with the existing method to show the superiority of the proposed method. The results also suggest that the proposed method can accurately capture the behavior of individual customers.

Highlights

Measuring customer satisfaction plays an important role in understanding the customer behavior [1]
It is important to identify the relationship between total customer satisfaction and that corresponding to particular attributes so that managers can focus their limited resources on critical attributes [2, 4, 5]
In some cases [6, 7], the Kano model has been used to determine the importance of individual attributes to customer satisfaction. e Kano model is used to divide the product attributes into the following six categories: must-be, one-dimensional, attractive, indifferent, reverse, and questionable attributes. e definitions of these attributes are as follows: (1) Must-be (M) attributes: these are the attributes that the customers expect to be presented by default. e high values of these attributes contribute little to total customer satisfaction; the low values lead to the high extent of dissatisfaction

Summary

Introduction

Measuring customer satisfaction plays an important role in understanding the customer behavior [1]. E Kano model is used to divide the product attributes into the following six categories: must-be, one-dimensional, attractive, indifferent, reverse, and questionable attributes. It is easy to collect these datasets as customers describe their experience about the product attributes, and these values are likely to be more accurate as they are based on real experience These methods can be used to define the categories of attributes for all customers presented in a dataset and are not applicable to identify the attribute category for individual customers. We propose a novel method that employs the customer satisfaction data ( as the example of these data presented in Table 2) that can be applied to identify the category of an attribute for individual customers or for a set of customers.

Literature Survey

Results and Discussion

Method