Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/WCNC.2014.6953127 | Wireless Communications and Networking Conference |
Keywords | Field | DocType |
data communication,probability,radiocommunication,social networking (online),churn data,churn influencers,churn models,churn prediction methods,churn probabilities,classification tools,communication features,demographics,social contacts,social network,subscription history,wireless carriers,wireless subscriber churn,Churn,Influence prediction,Wireless Subscribers | Wireless,Social network,Profiling (computer programming),Computer science,Computer network,Demographics,Influencer marketing | Conference |
ISSN | Citations | PageRank |
1525-3511 | 1 | 0.35 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sara Motahari | 1 | 40 | 4.46 |
| Taeho Jung | 2 | 462 | 36.24 |
| Hui Zang | 3 | 1052 | 77.25 |
| Krishna Janakiraman | 4 | 1 | 0.35 |
| Xiang-Yang Li | 5 | 6855 | 435.18 |
| Kevin Soo Hoo | 6 | 1 | 0.35 |