Brand Switching Pattern Discovery by Data Mining Techniques for the Telecommunication Industry in Australia

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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.
Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalAustralasian Journal of Information Systems
Volume20
DOIs
Publication statusPublished - 2016

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Telecommunication industry
Data mining
Mobile phones
Decision trees
Marketing
Brand switching
Telecommunications industry
Mobile phone
Service provider

Cite this

@article{e3850c830058485182925de272842cf6,
title = "Brand Switching Pattern Discovery by Data Mining Techniques for the Telecommunication Industry in Australia",
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.",
author = "Islam, {Md Zahidul} and Steven D'Alessandro and Michael Furner and Lester Johnson and David Gray and Leanne Carter",
note = "Imported on 16 May 2017 - DigiTool details were: publisher = Australia: University of Canberra, 2016. Volume no. (773r) = 20; Parent title (773t) = Australasian Journal of Information Systems. ISSNs: 1326-2238;",
year = "2016",
doi = "10.3127/ajis.v20i0.1420",
language = "English",
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pages = "1--17",
journal = "Australian Journal of Information Systems",
issn = "1326-2238",
publisher = "UQ Business School, The University of Queensland",

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T1 - Brand Switching Pattern Discovery by Data Mining Techniques for the Telecommunication Industry in Australia

AU - Islam, Md Zahidul

AU - D'Alessandro, Steven

AU - Furner, Michael

AU - Johnson, Lester

AU - Gray, David

AU - Carter, Leanne

N1 - Imported on 16 May 2017 - DigiTool details were: publisher = Australia: University of Canberra, 2016. Volume no. (773r) = 20; Parent title (773t) = Australasian Journal of Information Systems. ISSNs: 1326-2238;

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

U2 - 10.3127/ajis.v20i0.1420

DO - 10.3127/ajis.v20i0.1420

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SP - 1

EP - 17

JO - Australian Journal of Information Systems

JF - Australian Journal of Information Systems

SN - 1326-2238

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