We do a deeper dive into Amazon Machine Learning, using a specific business problem as an example – predicting if the customer is about to leave your service, also known as customer churn. We examine several practical aspects of building and using a model, including the use of the recipe language for training data manipulation and modeling the costs of false positive/negative errors.
25. Cost of Errors
• Cost of Customer Churn and Acquisition (false negative):
• foregone cashflow
• advertising costs
• POS and sign-up admin costs
• Customer Retention Cost (false + true positive)
• Discounts
• Phone upgrades
• etc
26. Financial Outcome of Applying a Model
Prior Churn Churn Cost Cost without ML
14.49% $500.00 $72.46
False Negative True + False Pos Retention Cost Cost with ML
4.80% 26.40% $100.00 $50.40
27. Financial Outcome of Applying a Model
Prior Churn Churn Cost Cost without ML
14.49% $500.00 $72.46
False Negative True + False Pos Retention Cost Cost with ML
4.80% 26.40% $100.00 $50.40
• $22.06 of savings per customer
• With 100,000 customers over $2MM in savings with ML