This course looks at what is churn and how to identify which customers are likely to leave you. It also looks at the factors that are likely related to churn and how to reduce churn.
The course concludes with the benefits of churn and goes into detail on how to predict which customers are likely to churn using a Logistic Regression Model. We end the course with methods on evaluating churn models for their business effectiveness and accuracy.
What am I going to get from this course?
At the end of this course students will understand:
- What is Churn
- The overall benefits of Churn
- Which factors are likely related to Churn
- How to reduce Churn
- How to prevent Customers from leaving them
- How to calculate Churn
- How to evaluate Churn Models for their business effectiveness and accuracy
Prerequisites and Target Audience
What will students need to know or do before starting this course?
There are no pre-requisites for this course. This course is an introductory course. It is for students of all levels.
Who should take this course? Who should not?
This course is an introductory course. It is for all students who work in the Retail, Financial or Telecommunications Industries who are interested in learning more on how to prevent customers from leaving their business.
This course is also for data analysts, data scientists and data modellers who would like to understand what is churn, what are the benefits of churn for the business, how to run Logistic Regression Models and how to evaluate the business effectiveness and accuracy of churn models.
Module 1: Introduction
Course Overview & Objectives
Highlights what the learner will be able to do by the end of the course.
What is Churn?
Defines Churn. Provides a definition for the Churn Rate and discusses what is an acceptable Churn Rate. Concludes with how changes in the Churn Rate impact business.
Why do Customers Churn?
Highlights some of the reasons why customers churn. For example, Bad On-boarding, Bad Customer Service, Lack of Ongoing Customer Success, Natural Causes, etc
Module 2: Customer Churn
Types of Customer Churn
Describes what is Negative Churn, Voluntary Churn and Involuntary Churn
How to Calculate Customer Churn
Gives the method for calculating customer churn rate.
Why Calculate Customer Churn?
Gives examples why business needs to calculate Customer Churn
Tactics for Reducing
Churn Gives examples how business can reduce Customer Churn.
Module 3: Customer Lifetime Value
Customer Lifetime Value
Defines Customer Lifetime Value and describes the impact of Customer Lifetime Value on business. Provides the relationship between the Customer Lifetime Value and the Churn Rate.
How to Calculate Customer Lifetime Value
Gives the formula for calculating Customer Lifetime Value
What is Latency?
Defines what is Latency and gives an example on how to calculate Latency.
What are the Benefits of Calculating Latency?
Describes the benefits of calculating Latency with a focus on how the Latency metric can help the business to make key decisions as to whom to market the Retention Campaign to.
Module 5: Churn Analysis/Regression Techniques
Churn Analysis in the Telecommunication Industry
Background information on Churn in the Telecommunication Industry
Churn Analysis using the Logistic Regression Technique
An introduction to the Logistic Regression Technique.
Preparing the Data for the Logistic Regression
A brief description on Data Preparation. This includes how to do data cleaning, data relevancy check, de-duplication, outlier check and handle missing values.
Data Partitioning for the Logistic Regression Technique
This lecture describes how to divide the data set into a training data set and a test data set.
Data Balancing of the Logistic Regression Technique
A brief description on how to balance your data output classes for optimal results
Module 6: How to Build & Interpret the Logistic Regression Model
Identifying the Significant Variables for Churn
Uses the Wald Test coefficient summary output table to explain how the significant values and the signs of the coefficients are important metrics to determine whether a variable contributes to the outcome of interest and whether the model is likely reliable and optimum.
Interpreting the Odds Ratio
Describes how to interpret the odds ratio and relate it to the business for decision making.
Module 7: Evaluating the Churn Model Accuracy
Evaluating the Churn Model - Accuracy
This lecture looks at how we use the Confusion Matrix, the sensitivity and specificity of the model to measure model accuracy .
Evaluating the Churn Model - Receiver Operating Curve (ROC)
The Receiver Operating Curve helps you to determine the threshold value for classification and helps you to better understand the trade-offs for the sensitivity and specificity measures.
Evaluating the Churn Model - Area Under the Curve (AUC)
This lecture describes the Area Under the Curve method for assessing the accuracy of the Churn Model. The closer the value is to 1, the more accurate is the model.
Evaluating the Churn Model - Lift Charts
This lecture helps us to see how we can use Lift Charts to determine effectiveness of the predictive model against a random model.