Predicting churn in telco industry: machine learning approach - Marko Mitić

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

yana-ckwivjw2n5n1lxfv@guest.airbnb.com
Contents
• Introduction to machine learning
• Churn definition & telco data
• Algorithm description
• Data exploration
• Modelling in R language
• Conclusion

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

Regression Classification
Clustering
Reinforcement
Learning

Training set
(observed)
Universal set
(unobserved)
Testing set
(unobserved)
Data acquisition
Practical usage
Classification

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

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

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

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

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

Algorithms
4. Boosting
• Adaboost

Evaluation metrics
Confusion matrix
• Accuracy, Precision, Recall

Evaluation metrics
ROC curve and AUC

Data exploration

Modelling in R (1)
Logistic Regression

Modelling in R (1.1)
ROC and AUC

Modelling in R (2)
Support Vector Machines

Modelling in R (4)
BP Neural Networks

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

Thank you!

Predicting churn in telco industry: machine learning approach - Marko Mitić

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Predicting churn in telco industry: machine learning approach - Marko Mitić