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Identifying Customer Churn Using Data Analytics


24 November 2017


9.00 am to 5.00 pm


Data Analytics Consulting Centre
Department of Statistics and Applied Probability
Computer Laboratory 2 (S16-05-102)
Faculty of Science
6 Science Drive 2
Singapore 117546


This course will introduce key metrics and techniques, enabling businesses to understand which customers are about to leave. These metrics include “latency”, the average time between purchases or services, “churn rate” and the probability of a customer leaving. By understanding the power of customer churn analysis and its impact on business, businesses can devise strategies to prevent customer churn.

Who Should Attend?

If you are required to undertake business analytics projects or gain more business knowledge on churn identification and management, this course is for you!

• Churn modelling analysts 
• Data analysts 
• Data engineers 
• Data managers 
• Financial analysts
• Machine learning scientists 
• Statisticians
• Technology professionals

This hands-on course uses Python programming. Participants should be familiar with computers and basic statistics.


Participants will acquire the following skills at the end of the course:

Applying data analytics techniques to identify customers who are about to leave

Determining the probability of a customer leaving

Running a machine learning algorithm using Python programming

Understanding the factors that may influence customer churn

Understanding how to evaluate and interpret customer churn model outcomes


Participants will enjoy an introductory course fee of $700 (excluding GST). 
This includes course materials, lunch and tea breaks.

Early bird

$ 560
(excluding GST) 
  • Before 24 September 2017

Participants who complete the workshop will be awarded a Certificate of Participation.

NUS Faculty of Science reserves the right to postpone or cancel the workshop by giving seven (7) days notice prior to the start of the workshop.

• Cancellation before or on 9 October 2017 : Full fee refund
• 10 October 2017 to 8 November 2017 : 50% fee refund
• After 8 November 2017 : No refund


The course will cover the following topics:

• Introduction to churn
• Importance of data analytics in identifying customer churn
• Factors influencing customer churn
• Business impact of the Customer Churn Model
• Introduction to latency
• Types of machine learning techniques
• Logistic Regression Method 
• Latency for business strategy
• Building a strategy to prevent customers leaving  

The course will include hands-on practicals on churn modelling using Python programming.


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