Research Repository

Benefit-based consumer segmentation and performance evaluation of clustering approaches: An evidence of data-driven decision-making

Arunachalam, Deepak and Kumar, Niraj (2018) 'Benefit-based consumer segmentation and performance evaluation of clustering approaches: An evidence of data-driven decision-making.' Expert Systems with Applications, 111. 11 - 34. ISSN 0957-4174

[img]
Preview
Text
Final Manuscript_ESWA.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (3MB) | Preview

Abstract

This study evaluates the performance of different data clustering approaches for searching the profitable consumer segments in the UK hospitality industry. The paper focuses on three aspects of datasets including the ordinal nature of data, high dimensionality and outliers. Data collected from 513 sample points are analysed in this paper using four clustering approaches: Hierarchical clustering, K-Medoids, fuzzy clustering, and Self-Organising Maps (SOM). The findings suggest that Fuzzy and SOM based clustering techniques are comparatively more efficient than traditional approaches in revealing the hidden structure in the data set. The segments derived from SOM has more capability to provide interesting insights for data-driven decision making in practice. This study makes a significant contribution to literature by comparing different clustering approaches and addressing misconceptions of using these for market segmentation to support data-driven decision making in business practices.

Item Type: Article
Additional Information: Data-driven decisions
Uncontrolled Keywords: Big Data Analytics, Data visualisation, Consumer segmentation, Cluster analysis, Business intelligence
Divisions: Faculty of Social Sciences > Essex Business School
Depositing User: Elements
Date Deposited: 10 Aug 2020 08:00
Last Modified: 08 Mar 2021 13:16
URI: http://repository.essex.ac.uk/id/eprint/28436

Actions (login required)

View Item View Item