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Social Network Analysis and Churn Prediction in Telecommunications Using Graph Theory.
Stefan M Kostić1, Mirjana I Simić1, Miroljub V Kostić2
1School of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia.
Entropy (Basel, Switzerland)
|December 8, 2020
Summary
Identifying key customer nodes in telecommunications networks using graph theory helps predict customer churn. Analyzing influential nodes and their connections allows proactive retention strategies for telco operators.
Area of Science:
- Data Science
- Network Analysis
- Telecommunications
Background:
- Telecommunications market saturation necessitates deep customer insights.
- Social network analytics and graph theory offer valuable tools for understanding customer dynamics.
Purpose of the Study:
- To identify vital nodes in a telecommunications social network graph for churn prediction.
- To demonstrate how analyzing influential nodes can proactively predict customer churn.
Main Methods:
- Analysis of a large telco network graph using graph theory.
- Node clustering based on metrics like in/out degree, influence, eigenvector, authority, and hub values.
Main Results:
- Identification of critical nodes whose departure increases churn likelihood among connected customers.
- Successful proactive churn prediction using identified influential nodes and historical data.
Conclusions:
- The proposed method effectively predicts customer churn in telecommunications networks.
- The approach is adaptable to other fields with homophilic or friendship connections driving churn.
Keywords:
call data recordchurn predictiondata mininggraph theorymachine learningsocial network analysisMore Related Videos
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