Abstract
The telecommunications industry is highly competitive, which means that the mobile providers need a
business intelligence model that can be used to achieve an optimal level of churners, as well as a minimal
level of cost in marketing activities. Machine learning applications can be used to provide guidance on
marketing strategies. Furthermore, data mining techniques can be used in the process of customer
segmentation. The purpose of this paper is to provide a detailed analysis of the C.5 algorithm, within naive
Bayesian modelling for the task of segmenting telecommunication customers behavioural profiling
according to their billing and socio-demographic aspects. Results have been experimentally implemented.
business intelligence model that can be used to achieve an optimal level of churners, as well as a minimal
level of cost in marketing activities. Machine learning applications can be used to provide guidance on
marketing strategies. Furthermore, data mining techniques can be used in the process of customer
segmentation. The purpose of this paper is to provide a detailed analysis of the C.5 algorithm, within naive
Bayesian modelling for the task of segmenting telecommunication customers behavioural profiling
according to their billing and socio-demographic aspects. Results have been experimentally implemented.
| Original language | English (Ireland) |
|---|---|
| Journal | International Journal of Data Mining & Knowledge Management Process (IJDKP) |
| Volume | 7 |
| Issue number | 1 |
| Publication status | Published - 10 Jan 2017 |
Keywords
- Bayesian Modelling
- Data mining
- Customer relationship management
- Churn Management
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