BUS5CA - Customer Analytics and Social Media - Alpha Bank Case Study - Accounting and Finance 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.

Task 1: Understanding the characteristics of churned, non-churned customers and loyal customers (10%)

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 (20%)

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?

Hints:

• Refer to the workshop activities in Week 10.

• 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 (10%)

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.

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