Arunachalam, D and Kumar, N (2018) Benefit-based consumer segmentation and performance evaluation of clustering approaches: An evidence of data-driven decision-making. Expert Systems with Applications, 111. pp. 11-34. ISSN 0957-4174
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Crown Copyright © 2018 Published by Elsevier Ltd. This is an author produced version of a paper published in Expert Systems with Applications. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Big Data Analytics; Data visualisation; Consumer segmentation; Cluster analysis; Business intelligence; Data-driven decisions |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Management Division (LUBS) (Leeds) > Logistics, Info, Ops and Networks (LION) (LUBS) |
Depositing User: | Symplectic Publications |
Date Deposited: | 28 Feb 2019 11:17 |
Last Modified: | 09 Mar 2019 01:39 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.eswa.2018.03.007 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:143070 |