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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Motahari, S. Taeho Jung Hui Zang Janakiraman, K. Xiang-Yang Li Hoo, K.S. |
| Copyright Year | 2014 |
| Description | Author affiliation: Bandpage, San Francisco, CA, USA (Janakiraman, K.) || Illinois Inst. of Technol., Chicago, IL, USA (Taeho Jung; Xiang-Yang Li) || Sprint Adv. Anal. Lab., Burlingame, CA, USA (Motahari, S.; Hoo, K.S.) || Guavus Inc., San Mateo, CA, USA (Hui Zang) |
| Abstract | Wireless carriers have various churn models that are mainly based on profiling the customers and assigning churn probabilities to them. Profiling is usually limited to their individual data, such as their subscription history, demographics, usage, etc. However, our analysis of a major wireless carrier data shows that such churn prediction methods do not fully model wireless subscriber churn, and that the subscribers can be influenced by other subscribers' churn in their social network. We propose a novel method to identify `churn influencers', whose influence makes their social contacts churn subsequently. To build our model, we scored the subscribers' influence level in a way that can take current churn models into account. We further used large scale call records to identify social network and communication features that abstract the strong influencers. Using real world churn data, we trained classification tools to classify high influencers with up to ninety nine percent precision. |
| Starting Page | 3402 |
| Ending Page | 3407 |
| File Size | 165329 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479930838 |
| DOI | 10.1109/WCNC.2014.6953127 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-04-06 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Wireless communication Social network services Mathematical model Feature extraction Linear regression Correlation Business Influence prediction Wireless Subscribers Churn |
| Content Type | Text |
| Resource Type | Article |
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