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Brand switching pattern discovery by data mining techniques for the telecommunication industry in Australia

Citation

Islam, MD and D'Alessandro, S and Furner, M and Johnson, L and Gray, D and Carter, L, Brand switching pattern discovery by data mining techniques for the telecommunication industry in Australia, Australasian Journal of Information Systems, 20 pp. 1-17. ISSN 1449-8618 (2016) [Refereed Article]


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Official URL: https://journal.acs.org.au/index.php/ajis/article/...

DOI: doi:10.3127/ajis.v20i0.1420

Abstract

There is more than one mobile-phone subscription per member of the Australian population. The number of complaints against the mobile-phone-service providers is also high. Therefore, the mobile service providers are facing a huge challenge in retaining their customers. There are a number of existing models to analyse customer behaviour and switching patterns. A number of switching models may also exist within a large market. These models are often not useful due to the heterogeneous nature of the market. Therefore, in this study we use data mining techniques to let the data talk to help us discover switching patterns without requiring us to use any models and domain knowledge. We use a variety of decision tree and decision forest techniques on a real mobile-phone-usage dataset in order to demonstrate the effectiveness of data mining techniques in knowledge discovery. We report many interesting patterns, and discuss them from a brand-switching and marketing perspective, through which they are found to be very sensible and interesting.

Item Details

Item Type:Refereed Article
Keywords:decision tree, decision forest, ensemble of decision trees, data mining, brand switching, switching behaviour
Research Division:Commerce, Management, Tourism and Services
Research Group:Marketing
Research Field:Consumer-Oriented Product or Service Development
Objective Division:Economic Framework
Objective Group:Management and Productivity
Objective Field:Marketing
UTAS Author:D'Alessandro, S (Professor Steven D'Alessandro)
ID Code:138319
Year Published:2016
Deposited By:Marketing
Deposited On:2020-04-01
Last Modified:2020-04-03
Downloads:0

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