A Churn prediction model based on gaussian processes
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The telecommunication’s market has changed to a saturated one during the past few years. This makes it relatively costly to acquire new customers than to retain existing ones. Recent research has further showed that identifying potential customers who intend to leave a service provider for another (churners) and offering them an incentive to keep their subscriptions can produce significant savings for the telecommunication company. However, in most cases, it turned out that most of the used techniques to solve this problem fails to address the complex relationship between customer features and churn. Therefore, they struggle to achieve acceptable prediction accuracy. This might be due to the fact that real customers’ data are large, unbalanced, with missing values, and with non-linear dependencies between features. In this thesis, I investigated a new class of techniques for predicting customer churn based on Gaussian processes - a recent approach to regression and classification based on Bayesian reasoning principles. I developed a Gaussian process based model for churn prediction and tested it on a real customers’ data belonging to a French telecommunication’s company (Orange). The method obtained an area under the curve (AUC) score of 0.77. This score outperformed most of the previously used techniques that I evaluated on the same data set. The best of them had an AUC score of 0.73. To conclude, my empirical results proved that Gaussian processes can provide state-of-the-art performance in churn prediction, and the approach and results that I report offer a solid basis for further development of Gaussian processes for churn prediction.
Masteroppgave i industriell økonomi og teknologiledelse IND590 2013 – Universitetet i Agder, Grimstad