On this presentation we will have an opportunity to see how can we predict churn in telco industry. Marko will tell us how he done this at Telenor Srbija.

1. Dr. Marko Mitić
Business Data Analyst at Telenor Serbia
Predicting churn in telco industry:
machine learning approach

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Contents
• Introduction to machine learning
• Churn definition & telco data
• Algorithm description
• Data exploration
• Modelling in R language
• Conclusion

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Introduction to machine learning
Supervised learning
Each training example is a pair consisting of an input object and a desired output value
• Regression (real values)
• Classification (discrete labels)
Unsupervised learning
Draw inferences from datasets data without labeled responses
• Clustering
• Dimensionality reduction
Reinforcement learning
Agents ought to take actions in an environment
so as to maximize cumulative reward

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Introduction to machine learning
Regression Classification
Clustering
Reinforcement
Learning

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Introduction to machine learning
Training set
(observed)
Universal set
(unobserved)
Testing set
(unobserved)
Data acquisition
Practical usage
Classification

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Churn definition
Churn rate (sometimes called attrition rate),
is a measure of the number of individuals or items moving out of a collective group over a specific period of time
= Customer leaving
Pay TV
E-mail/website subscribers
Legal sector
Recreation
Newspaper subscribers

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Telco data
Real telco data available in latest C50 library in R language
Feature engineering: 3/6 months average usage, average total charge,...

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Algorithms
1. Logistic Regression
• In logistic regression the outcome variable is binary, and the purpose of the
analysis is to assess the effects of multiple explanatory variables
Odds of success = P / 1-P = = e α + β1X1 + β2X2 + …+βpXp
The joint effects of all explanatory variables put together on the odds is
Logit P = α+β1X1+β2X2+..+βpXp

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Algorithms
2. Support Vector Machines
• SVMs maximize the margin around the separating
hyperplane.
• The decision function is fully specified by a subset
of training samples, the support vectors.
wTxi + b ≥ 1, if yi = 1
wTxi + b ≤ −1, if yi = −1
w
2
ρ • Margin

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Algorithms
3. Neural Network
• A neuron network (NN) is a computational model based on the structure and functions of
biological neural networks.
• A neural network usually involves a large number of processing units with the aim of
successfully mapping input to output space through iterative process

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Algorithms
4. Boosting
• Adaboost

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Evaluation metrics
Confusion matrix
• Accuracy, Precision, Recall

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Evaluation metrics
ROC curve and AUC

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Data exploration

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Modelling in R (1)
Logistic Regression

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Modelling in R (1.1)
ROC and AUC

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Modelling in R (2)
Support Vector Machines

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Modelling in R (4)
BP Neural Networks

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Conclusions
• 3 machine algorithms for churn prediction are presented
• Logistic Regression and BP Neural Net with boosting gave best results
• Good base for successfull broadcast campaign towards potential churners
Works even better
• Implementation of more complex ML algorithms (Random Forest, Gradient
Boosting Machines, Deep NNs)
• Generate hybrid ensemble models

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Thank you!