Customer insights from telecom data using deep learning
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Transcript of Customer insights from telecom data using deep learning
Analytics on Telecom CDR Data
RedZebra AnalyticsOct 2014
Problem statement
1How to segment Telecom customers and track their dynamics
2How to optimize / reformulate tariff plans
3How to predict churn
The data
• 3 months of CDR– Data consumption– Phone calls and Topups– SMS
• User description (geo, sociodemographics)
The techniques
Deep Neural Networks and Autoencoders (Keras framework)
Random Forest
Extreme Gradient Boosting
Graph analysis (Igraph)
SOM and tSNE
Scikit Learn (Python)
Data processing (for churn prediction)
Churn (1) / no churn (0)
Customer activity is Converted into heatmaps
Network data also considered
We also include network data (like the number of churners connected to a node)
Three distinct users activity
Approach: Convolutional Neural Network
INPUTUser activityheatmap
OUTPUTChurn / no churn
Results
Method AUC - train AUC - testRandom Forest 0.75 0.74Extreme Gradient Boosting 0.80 0.76Variational Autoencoders 0.78 0.75Convolutional Neural Networks 0.79 0.77
Convolutional Neural Networks have the best performance
Some templates of user activity discovered by the neural network
SMS activity per age group
Clustering
Techniques used cluster and visualize data:• K-means• Self-organized maps (SOM)• tSNE
Visualization of sample of users with tSNE
Segmentation with Self Organized Maps
Distance to code-vectors: how stable is the population
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