Customer insights from telecom data using deep learning

1. Analytics on Telecom CDR Data
RedZebra Analytics
Oct 2014

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)

7. Three distinct users activity

8. Approach: Convolutional Neural Network
INPUT
User
activity
heatmap OUTPUT
Churn / no
churn

9. Results
Method AUC - train AUC - test
Random Forest 0.75 0.74
Extreme Gradient Boosting 0.80 0.76
Variational Autoencoders 0.78 0.75
Convolutional Neural Networks 0.79 0.77
Convolutional Neural Networks have the best performance

10. Some templates of user activity discovered by the neural
network

11. SMS activity per age group

12. Clustering
Techniques used cluster and visualize data:
•K-means
•Self-organized maps (SOM)
•tSNE

13. Visualization of sample of users with tSNE

14. Segmentation with Self Organized Maps

15. Distance to code-vectors:
how stable is the population

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