A rule-based method for identifying the factor structure in customer satisfaction

Amir Ahmad, Lipika Dey, Sami M. Halawani

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

The analysis of customer satisfaction datasets has shown that product-related features fall into three categories (i.e., basic, performance, and excitement), which affect overall satisfaction differently. Because the relationship between product features and customer satisfaction is characterized by non-linearity and asymmetry, feature values are studied to understand the characteristics of a feature. However, existing methods are computationally expensive and work for ordinal features only. We propose a rule-based method that can be used to analyze data features regarding various characteristics of customer satisfaction. The inputs for these rules are derived by using a probabilistic feature-selection technique. In this feature selection method, mutual associations between feature values and class decisions in a pre-classified database are computed to measure the significance of feature values. The proposed method can be used for both types of features: ordinal and categorical. The proposed method is more computationally efficient than previously recommended methods. We performed experiments on a synthetic dataset with known characteristics, and our method correctly predicted the characteristics of the dataset. We also performed experiments with a real-housing dataset. The knowledge extracted from the dataset by using this method is in agreement with the domain knowledge.

Original languageEnglish
Pages (from-to)118-129
Number of pages12
JournalInformation Sciences
Volume198
DOIs
Publication statusPublished - Sep 1 2012
Externally publishedYes

Keywords

  • Categorical features
  • Customer satisfaction
  • Feature value importance
  • Market research
  • Three-factor theory

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

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