This document discusses customer churn prediction in the telecom industry. It outlines the business problem of reducing customer churn and retention costs. It describes collecting internal customer and transaction data as well as external data, preparing the data through ETL, feature selection including filtering and engineering added features, and modeling techniques like gradient boosting, random forest and neural networks. Key metrics for evaluating imbalanced classification models on churn are discussed, as well as balancing model excellence with business priorities in deployment.

1. Customer Churn
A Data Science Use Case in Telecom
Chris Chen - Data Analyst@Shaw Communications

2. The Problem
Who?
Why?
How?

3. CRISP-DM: Cross Industry
Standard Process for Data Mining
Business Understanding
Data Understanding
Data Preparation
Modeling
Evaluation
Deployment

4. Business Understanding
Business objectives:
• Reduce customer
churns
• Minimize the costs
(efforts) of retention
• Conduct insights
Success criteria
• Metrics
• Non-metrics

5. Data Understanding
Data sources
Internal: Customer Data, Product Data, Transactions
and Customer interactions
External
Data qualities: missing values, duplicates, outliers etc.
First insights: Binary Classification, Skewed (Imbalanced)

6. Data Preparation
ETL
Feature selection
Feature engineering
Train/ validation/ test

7. Feature Selection (subtraction)
• Expert voting system
• Engage SMEs from various background: tech vs non-
tech, marketing vs customer care, management vs
frontline sales.
• 10-15 most important features that may have impacts
on customer churn/ customer retention

8. Wrapper based methods
Random Forest/ Boosting Tree - also good hints for feature
engineering
Filter methods
Missing Values Ratio
Low Variance Filter ( less informative features )
High Correlation Filter ( similar features)
Can be good candidates for interactions. e.g. Age vs Income
PCA
Feature Selection (subtraction) - science

9. Feature Engineering ( addition)
Business acumen, combined with domain knowledge and
model understandings
Ordinal vs Nominal: label encoding, one-hot-encoding
Transformation: normalization, log and so on
Imputation: missing values
Feature Interactions
Time series

10. Modeling
Classification:
Gradient Boosting Tree (GBT) - I’m big fan of Xgboost
Random Forest (RF)
Logistic Regression (LR) or Elastic Net (EN)
Neural Network (NN)
Support Vector Machine (SVM)

11. Modeling - Metric
• Precision = True Positive/ (True Positive
+ False Positive)
• Recall = True Positive / (True Positive +
False Negative)
• Accuracy = (TP + TN)/ (TP + TN +FP +
FN)
For a dataset that contains 99% non-
churn customers and 1% churn
customers if we predicted all customers as
churn then the accuracy would be 99%
Area Under Curve (AUC): Precision vs
Recall Trade off for skewed
classifications
True Positive
(Churn Customers
that were correctly
predicted as churns)
False Positive
(No-churn Customers
that were incorrectly
predicted as churn
customers)
False Negative
(Churn customers
that were incorrectly
predicted as non-
churns)
True Negative
(Non-churn
customers that were
correctly predicted as
non-churns)
Actuals
Predictions

12. Modeling - Ensemble

13. Evaluation - Model excellence vs Business
excellence
Accuracy
Interpretability
Low
Low
High
High
Radom Forest
Boosting
Deep Learning
Neural Network
Linear/ Logistic Regresson
Decision Trees
Naive Bayes
Nearest Neighbours
SVM

14. Deployment
Rule of Thumb:
Business Engagement

15. Thank you!