Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods |
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| Institution: | 1. Department of Decision Sciences and Information Management, KU Leuven, Leuven, Belgium;2. School of Management, University of Southampton, Southampton, United Kingdom;1. Department of Decision Sciences and Information Management, KU Leuven, Naamsestraat 69, 3000 Leuven, Belgium;2. Department of Decision Analytics and Risk, University of Southampton, United Kingdom;3. Faculty of Economic and Social Sciences and Solvay Business School, Vrije Universiteit Brussel, Belgium;4. Grandata Labs, Bartolome Cruz 1818 V. Lopez, Buenos Aires, Argentina;1. KU Leuven, Department of Mathematics, Celestijnenlaan 200B, Leuven 3001, Belgium;2. KU Leuven, Faculty of Economics and Business, Naamsestraat 69, Leuven 3000, Belgium;3. University of Southampton, School of Management, Highfield Southampton, SO17 1BJ, United Kingdom |
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| Abstract: | Cross-Company Churn Prediction (CCCP) is a domain of research where one company (target) is lacking enough data and can use data from another company (source) to predict customer churn successfully. To support CCCP, the cross-company data is usually transformed to a set of similar normal distribution of target company data prior to building a CCCP model. However, it is still unclear which data transformation method is most effective in CCCP. Also, the impact of data transformation methods on CCCP model performance using different classifiers have not been comprehensively explored in the telecommunication sector. In this study, we devised a model for CCCP using data transformation methods (i.e., log, z-score, rank and box-cox) and presented not only an extensive comparison to validate the impact of these transformation methods in CCCP, but also evaluated the performance of underlying baseline classifiers (i.e., Naive Bayes (NB), K-Nearest Neighbour (KNN), Gradient Boosted Tree (GBT), Single Rule Induction (SRI) and Deep learner Neural net (DP)) for customer churn prediction in telecommunication sector using the above mentioned data transformation methods. We performed experiments on publicly available datasets related to the telecommunication sector. The results demonstrated that most of the data transformation methods (e.g., log, rank, and box-cox) improve the performance of CCCP significantly. However, the Z-Score data transformation method could not achieve better results as compared to the rest of the data transformation methods in this study. Moreover, it is also investigated that the CCCP model based on NB outperform on transformed data and DP, KNN and GBT performed on the average, while SRI classifier did not show significant results in term of the commonly used evaluation measures (i.e., probability of detection, probability of false alarm, area under the curve and g-mean). |
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| Keywords: | Churn prediction Cross-company Data transformation Box-cox Rank Log Z-Score |
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