BUS5CA : Customer Analytics and Social Media - Customer Churn Analysis - Management Assignment Help

Assignment Task :

Learning Objective: 

The learning objective of this last assignment is to further develop your customer analytics  skills via performing customer churn analysis tasks. 

Case Study: 

Customer retention is a critical stage for customer relationship management (CRM), in  particular for established businesses after their initial exponential growth. Churn  management or attrition management is important as when customers leave, there are  negative impacts on revenues. Churn analytics has been widely applied to proactive  customer retention where descriptive and predictive analytics are utilised to identify and  predict customer propensity to churn. 

Alpha Bank is conducting an analysis on their existing customer base with their  demographics information and account information recorded. As a business analyst, you are  tasked to analyse the data to provide insights of the churn population and develop as well  as evaluate predictive models for customer retention purposes. 

Requirements: 

The project is seeking insights and solutions relating to: 

• Understanding the characteristics of its churned and not-churned customers;

• Understanding the characteristics of loyal customers (i.e. customers who do not  churn and are above a certain threshold of the tenure value*); 

• Developing and evaluating models to predict customer propensity to churn;

• Recommending potential campaigns to buy back or win back the valued customers  who churned. 

Task 1: Understanding the characteristics of churned, non-churned customers and loyal  customers  

Conduct descriptive analysis based on the customer data and construct customer profiles  for each customer group. 

Task 2: Developing and evaluating models to predict propensity to churn  

a) What is the overall churn rate and the group churn rate for the categorical variables?  (For example: gender (yes and no), country (France, Germany and Spain), etc.)

b) Identify the combination of two categorical variables that has the highest group churn  rate. 

c) Use SAS Enterprise Miner to develop and evaluate at least three predictive models for  churn prediction. 

• Apply standardization (z-score normalization) on the continuous/interval variables.  Why you need to apply this? (You may use the Transform Variable node covered in  the workshop activities in Week 8.) 

• What are the selected variables used for building the prediction models? • What are the predictive performance of various models and how they rank against  one another?

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