2. Problem statement
1How to segment Telecom customers and track
their dynamics
1How to optimize / reformulate tariff plans
1How to predict churn
3. The data
•3 months of CDR
–Data consumption
–Phone calls and Topups
–SMS
•User description (geo, sociodemographics)
4. The techniques
Deep Neural Networks and Autoencoders (Keras framework)
Random Forest
Extreme Gradient Boosting
Graph analysis (Igraph)
SOM and tSNE
Scikit Learn (Python)
5. Data processing (for churn prediction)
Churn (1) / no churn
(0)
Customer activity is
Converted into
heatmaps
6. Network data also considered
We also include network data (like the number of churners connected to a
node)
16. Conclusions
•Deep Convolutional Networks achieve top performance
•Network data very important (who is connected to who)
•We found 5 well defined segments
•Payments are determined by calls not data
•SOM create relatively stable segments
•Intercommunity diverse is some cases