Transcript
Page 1: Customer insights from telecom data using deep learning

Analytics on Telecom CDR Data

RedZebra AnalyticsOct 2014

Page 2: Customer insights from telecom data using deep learning

Problem statement

1How to segment Telecom customers and track their dynamics

2How to optimize / reformulate tariff plans

3How to predict churn

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The data

• 3 months of CDR– Data consumption– Phone calls and Topups– SMS

• User description (geo, sociodemographics)

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The techniques

Deep Neural Networks and Autoencoders (Keras framework)

Random Forest

Extreme Gradient Boosting

Graph analysis (Igraph)

SOM and tSNE

Scikit Learn (Python)

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Data processing (for churn prediction)

Churn (1) / no churn (0)

Customer activity is Converted into heatmaps

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Network data also considered

We also include network data (like the number of churners connected to a node)

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Three distinct users activity

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Approach: Convolutional Neural Network

INPUTUser activityheatmap

OUTPUTChurn / no churn

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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

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Some templates of user activity discovered by the neural network

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SMS activity per age group

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Clustering

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

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Visualization of sample of users with tSNE

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Segmentation with Self Organized Maps

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Distance to code-vectors: how stable is the population

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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


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