Predicting time-to-churn of prepaid mobile telephone customers using social network analysis
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Mobile phone carriers in a saturated market must focus on customer retention to maintain profitability. This study investigates the incorporation of social network information into churn prediction models to improve accuracy, timeliness, and profitability. Traditional models are built using customer attributes, however these data are often incomplete for prepaid customers. Alternatively, call record graphs that are current and complete for all customers can be analysed. A procedure was developed to build the call graph and extract relevant features from it to be used in classification models. The scalability and applicability of this technique are demonstrated on a telecommunications data set containing 1.4 million customers and over 30 million calls each month. The models are evaluated based on ROC plots, lift curves, and expected profitability. The results show how using network features can improve performance over local features while retaining high interpretability and usability.
Keywordsdecision support systems telecommunications churn prediction social network analysis survival analysis
This research was made possible with support of the Odysseus program (Grant B.0915.09) and Grant G.0816.12N of the Fund for Scientific Research Flanders (FWO).
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