%0 Conference Paper %1 Huang2015Telco %A Huang, Yiqing %A Zhu, Fangzhou %A Yuan, Mingxuan %A Deng, Ke %A Li, Yanhua %A Ni, Bing %A Dai, Wenyuan %A Yang, Qiang %A Zeng, Jia %B Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data %C New York, NY, USA %D 2015 %I ACM %K mobile-phones churn %P 607--618 %R 10.1145/2723372.2742794 %T Telco Churn Prediction with Big Data %U http://dx.doi.org/10.1145/2723372.2742794 %X We show that telco big data can make churn prediction much more easier from the \$3\$V's perspectives: Volume, Variety, Velocity. Experimental results confirm that the prediction performance has been significantly improved by using a large volume of training data, a large variety of features from both business support systems (BSS) and operations support systems (OSS), and a high velocity of processing new coming data. We have deployed this churn prediction system in one of the biggest mobile operators in China. From millions of active customers, this system can provide a list of prepaid customers who are most likely to churn in the next month, having \$0.96\$ precision for the top \$50000\$ predicted churners in the list. Automatic matching retention campaigns with the targeted potential churners significantly boost their recharge rates, leading to a big business value. %@ 978-1-4503-2758-9

@inproceedings{Huang2015Telco, abstract = {{We show that telco big data can make churn prediction much more easier from the \$3\$V's perspectives: Volume, Variety, Velocity. Experimental results confirm that the prediction performance has been significantly improved by using a large volume of training data, a large variety of features from both business support systems (BSS) and operations support systems (OSS), and a high velocity of processing new coming data. We have deployed this churn prediction system in one of the biggest mobile operators in China. From millions of active customers, this system can provide a list of prepaid customers who are most likely to churn in the next month, having \$0.96\$ precision for the top \$50000\$ predicted churners in the list. Automatic matching retention campaigns with the targeted potential churners significantly boost their recharge rates, leading to a big business value.}}, added-at = {2019-06-10T14:53:09.000+0200}, address = {New York, NY, USA}, author = {Huang, Yiqing and Zhu, Fangzhou and Yuan, Mingxuan and Deng, Ke and Li, Yanhua and Ni, Bing and Dai, Wenyuan and Yang, Qiang and Zeng, Jia}, biburl = {https://www.bibsonomy.org/bibtex/208177446dbbe4c918169da7a0b61a57a/nonancourt}, booktitle = {Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data}, citeulike-article-id = {13699873}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=2742794}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/2723372.2742794}, doi = {10.1145/2723372.2742794}, interhash = {b80ccf3275feffbf98b38cff95c47f16}, intrahash = {08177446dbbe4c918169da7a0b61a57a}, isbn = {978-1-4503-2758-9}, keywords = {mobile-phones churn}, location = {Melbourne, Victoria, Australia}, pages = {607--618}, posted-at = {2015-08-11 11:50:44}, priority = {2}, publisher = {ACM}, series = {SIGMOD '15}, timestamp = {2019-08-22T16:22:22.000+0200}, title = {{Telco Churn Prediction with Big Data}}, url = {http://dx.doi.org/10.1145/2723372.2742794}, year = 2015 }