Reference no: EM132656617
BUS5CA Customer Analytics and Social Media - La Trobe University
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 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.
Data Descriptions:
The dataset required for this assignment is available on the remote server under the F drive: ‘F:\BUS5CA\Assignment3_Dataset\Bank_Churn.csv'. You should import the dataset file into your SAS project, without keeping a copy under your own workspace folder.
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.
Hints:
• Compare variables for churned, non-churned customers and loyal customers using descriptive analytics.
• Loyal customers are a subset of non-churned customers. They are the top non-churned customers based on the tenure variable (tenure >= 9).
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? (Note: You should drill down to various machine learning metrics, which include the overall accuracy, the misclassification rate (churn / non-churn), ROC, Lift.)
• How do you best interpret the model?
Task 3: Campaign recommendations based on insights obtained from Task 1 and Task 2
Provide campaign recommendations based on insights obtained from the first two tasks above.
Hint: You need to use your knowledge in campaign management or perform some research to answer this question.
You are required to:
a) Prepare a report with answers for the above three key tasks.
(You can use an appendix for any additional screenshots which you feel are important for the report. Please do not put screenshots in the main content of the report. The main content of the report should consist of well-formatted graphs and tables.)
b) The written report should be saved with the file name:
StudentID_Assignment3_Report.doc
c) Save the SAS project for Task 2 as the SPK file with the file name:
StudentID_Assignment3_Task2.spk
d) If you have some R code for Task 1 and 2, save it as: StudentID_Assignment3_Task1.R or StudentID_Assignment3_Task2.R; or if you have used Excel for Task 1 and 2, save it as:
StudentID_Assignment3_Task1.xlsx or StudentID_Assignment3_Task2.xlsx. The same submission rule applies for any other visualization/analytics tools.
e) Submit the written report, the SAS Model files, and the supporting R files/Excel files or other visualisation/analytics files to the LMS Assignment submission site.
Attachment:- Customer Analytics and Social Media.rar