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Predicting Churn in Mobile Telecommunications Industry
| Content Provider | Semantic Scholar |
|---|---|
| Author | Brandusoiu, Ionut Toderean, Gavril |
| Copyright Year | 2013 |
| Abstract | In today's competitive market, the customer retention represents one of the focal concern for telecommunications companies. The telecommunications sector can be considered on the top of the list with an annual churn rate of approximately 30%. Since the cost associated with subscriber acquisition is higher than the cost of subscriber retention, the need for predictive models to identify subscribers that are at risk of churning has become an important aspect for modern telecommunications operators. But, because the pre-paid mobile telephony market is not easily traceable and definable, implementing a predictive model would be a complex process. In this paper, we present an advanced methodology that helps to predict subscribers churn in pre-paid mobile telecommunications industry, by applying data mining algorithms on dataset that contains call details records. To construct the models we will use k-nearest neighbor, logistic regression, and bayesian networks algorithms. To evaluate and compare the models, we use the gain measure. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://users.utcluj.ro/~atn/papers/ATN_3_2013_2.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
