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Dr. Marko MitićBusiness 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 learningSupervised learningEach training example is a pair consisting of an input object and a desired output value
• Regression (real values)• Classification (discrete labels)
Unsupervised learningDraw inferences from datasets data without labeled responses
• Clustering• Dimensionality reduction
Reinforcement learningAgents ought to take actions in an environment so as to maximize cumulative reward
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Introduction to machine learning
Regression Classification
Clustering ReinforcementLearning
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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 definitionChurn 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 TVE-mail/website subscribersLegal sectorRecreation Newspaper subscribers
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Telco dataReal telco data available in latest C50 library in R language
Feature engineering: 3/6 months average usage, average total charge,...
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Algorithms1. 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 isLogit P = α+β1X1+β2X2+..+βpXp
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Algorithms2. 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 = 1wTxi + b ≤ −1, if yi = −1
w2ρ• Margin
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Algorithms3. 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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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