predicting churn in telco industry: machine learning approach - marko mitić

Dr. Marko MitićBusiness 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 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

Introduction to machine learning

Regression Classification

Clustering ReinforcementLearning

Introduction to machine learning

Training set (observed)

Universal set(unobserved)

Testing set(unobserved)

Data acquisition

Practical usage

Classification

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

Telco dataReal telco data available in latest C50 library in R language

Feature engineering: 3/6 months average usage, average total charge,...

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

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

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

Algorithms4. Boosting

• Adaboost

Evaluation metricsConfusion matrix

• Accuracy, Precision, Recall

Evaluation metricsROC 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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