A telecommunications company needs to predict customer churn (i.e., customers who decide to switch to a competitor). The company has historic records of each customer, including monthly consumption patterns, calls to customer service, and whether the customer ultimately quit the service. All of this data is stored in Amazon S3. The company needs to know which customers are likely going to churn soon so that they can win back their loyalty.
What is the optimal approach to meet these requirements?
A. Use the Amazon Machine Learning service to build the binary classification model based on the dataset stored in Amazon S3. The model will be used regularly to predict churn attribute for existing customers.
B. Use AWS QuickSight to connect it to data stored in Amazon S3 to obtain the necessary business insight. Plot the churn trend graph to extrapolate churn likelihood for existing customers.
C. Use EMR to run the Hive queries to build a profile of a churning customer. Apply a profile to existing customers to determine the likelihood of churn.
D. Use a Redshift cluster to COPY the data from Amazon S3. Create a User Defined Function in Redshift that computes the likelihood of churn.
