Reference no: EM133013321

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), especially 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.

Saturn 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 non-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 LMS ("telco_churn.csv"). You should import the dataset file into your SAS project to proceed further analysis.

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 churned, non-churned and loyal customers.

Hints:
• Use descriptive analystics and variables in the dataset to show the characteristics of churned, non-churned, and loyal customers.
• Loyal customers are a subset of non-churned customers. They are the top decile of non- churned customers based on the tenure variable.

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

a) What is the overall churn rate and the group churn rate for each categorical variable? (For example: senior citizen (yes and no), partner (yes and no), etc.) Identify the categorical variable that has the highest group churn rate.
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 Transform Variable node and follow the steps that we covered in the workshop activities in Week 8, or simply go to the Edit Variables in Transform Variable node and change the method from Default to Standardize for the interval variables.)
• 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 (churned/non-churned), ROC, Lift.)
• Discuss which is the best model and how do you best interpret the model?

Hints:
• Refer to the workshop activities in Week 11.
• Use 70% training data, 30% validation data partitioned randomly.
• You can get the confusion matrix from the output window of the model comparison
node under the name ‘Event Classification Table'.
• You may use other analytics tools to support this task if needed (such as Excel or R).
• Overall churn rate = (Number of churning customers / Total number of customers in the dataset)
• Group churn rate = (Number of churning customers in the group / Total number of customers in the group)

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. Your discussion should be supported by external references.

You are required to:

a) Prepare a report with answers for the above three key tasks.
You must include diagrams, tables, charts and most important screenshots from the analytics solutions to effectively present your results. (You can use an appendix for any additional screenshots which can support your report)

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 via the LMS assignment submission links.

Attachment:- Customer Analytics and Social Media.rar