Reference no: EM132393452

BUS5CA Customer Analytics and Social Media

Semester 2 2019 - La Trobe University, Melbourne, Victoria, Australia

Assignment - Customer Churn Analysis

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 Telecommunication is conducting an analysis on their existing customer base with their demographics information, account information and service status 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 (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 churn rate for the categorical variables? (For example: senior citizen (yes and no), partner (yes and no), etc.)

b) 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 9.)
• 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? (Note: You should drill down to various machine learning metrics, such as Accuracy, ROC, Lift, etc.)
• How do you best interpret the model?

Task 3: Campaign recommendations based on insights obtained from Tasks 1 and 2

Provide campaign recommendations based on insights obtained from the first two tasks above.

You are required to: Prepare a report with answers for the above three key tasks.

Report Guidelines: The report should consist of a table of contents, an introduction, and logically organized sections/topics, a conclusion and a list of references where necessary. Choose a fitting sequence of sections/topics for the body of the report. You must include diagrams, tables and charts from the analytics solutions to effectively present your results.

Page limit: at least fifteen (15) pages for report writing but no more than twenty-five (25) pages including appendices.

Attachment:- Customer Churn Analysis Assignment File.rar