Telco Churn Prediction with Big Data
Summary: Telco churn prediction with big data leverages Volume, Variety, Velocity to boost accuracy. Deployed at a leading Chinese operator, it mixes BSS/OSS features over millions of customers, achieving 0.96 precision on top 50k churn predictions and optimizing campaigns. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Authors
- 1. Yiqing Huang (Collaborative Innovation Center of Novel Software Technology and Industrialization; Soochow University)
- 2. Fangzhou Zhu (Collaborative Innovation Center of Novel Software Technology and Industrialization; Soochow University)
- 3. Mingxuan Yuan (Huawei)
- 4. Ke Deng (Royal Melbourne Institute of Technology)
- 5. Yanhua Li (Huawei)
- 6. Bing Ni (Huawei)
- 7. Wenyuan Dai (Huawei)
- 8. Qiang Yang (Hong Kong University of Science and Technology; Huawei)
- 9. Jia Zeng (Collaborative Innovation Center of Novel Software Technology and Industrialization; Huawei; Soochow University)
BibTeX Citation
@inproceedings{huang_sigmod15,
title = {{Telco Churn Prediction with Big Data}},
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},
series = {{SIGMOD} '15},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2723372.2742794},
url = {https://dl.acm.org/doi/10.1145/2723372.2742794},
year = {2015}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,029 | Differential Privacy in Telco Big Data Platform | 2015 | VLDB | 6.946445e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 835 | Scaling Factorization Machines to Relational Data | 2013 | VLDB | 0.00013721583 |
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