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© 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Denis V. Batalov, PhD
AWS Solutions Architect, EMEA
November 30, 2016
Predicting Customer Churnwith Amazon Machine Learning
@dbatalov
MAC307
Customer churn
Machine learning
Science
• Computer Science
• Statistics
• Neuroscience
• Operations Research
Artificial Intelligence
• Rule extraction from data
• Inspired by human learning
• Adaptive algorithms
Engineering
• Training: Data Models
• Prediction: Models Forecast
• Decision: Forecast Actions
ML: Robotics
ML: Robotics
ML: Image recognition
Supervised learning
Supervised learning
Input Outcome
Supervised learning
Input Outcome
Input
Input
Input
Outcome
Outcome
Outcome
Supervised Learning
Input Outcome
Input
Input
Input
Outcome
Outcome
Outcome
Supervised
Learning
known historical data
Amazon ML
Supervised learning
Input Outcome
Input
Input
Input
Outcome
Outcome
Outcome
Supervised
Learning
Unseen Input Same Outcome
known historical data
Amazon ML
Amazon Machine Learning service
Amazon Machine Learning service
Amazon Machine Learning service
Amazon Machine Learning service
Telco churn dataset
• US telco customers, their cell phone plans and usage
• 21 attributes, 3333 rows:
• Customer: State, Area_Code, Phone
• Plan: Intl_Plan, VMail_Plan
• Behavior: VMail_Messages, Day_Mins, Day_Calls,
Day_Charge, Eve_Mins, Eve_Calls, Eve_Charge,
Night_Mins, Night_Calls, Night_Charge, Intl_Mins,
Intl_Calls, Intl_Charge
• Other: Account_Length, CustServ_Calls, Churn
Telco churn dataset
• US telco customers, their cell phone plans and usage
• 21 attributes, 3333 rows:
• Customer: State, Area_Code, Phone
• Plan: Intl_Plan, VMail_Plan
• Behavior: VMail_Messages, Day_Mins, Day_Calls,
Day_Charge, Eve_Mins, Eve_Calls, Eve_Charge,
Night_Mins, Night_Calls, Night_Charge, Intl_Mins,
Intl_Calls, Intl_Charge
• Other: Account_Length, CustServ_Calls, Churn
Telco churn dataset
KS, 128, 415, 382-4657, 0, 1, 25, 265.100000, 110, 45.070000, 197.400000, 99, 16.780000, 244.700000, 91, 11.010000, 10.000000, 3, 2.700000, 1, 0
OH, 107, 415, 371-7191, 0, 1, 26, 161.600000, 123, 27.470000, 195.500000, 103, 16.620000, 254.400000, 103, 11.450000, 13.700000, 3, 3.700000, 1, 0
NJ, 137, 415, 358-1921, 0, 0, 0, 243.400000, 114, 41.380000, 121.200000, 110, 10.300000, 162.600000, 104, 7.320000, 12.200000, 5, 3.290000, 0, 0
OH, 84, 408, 375-9999, 1, 0, 0, 299.400000, 71, 50.900000, 61.900000, 88, 5.260000, 196.900000, 89, 8.860000, 6.600000, 7, 1.780000, 2, 0
OK, 75, 415, 330-6626, 1, 0, 0, 166.700000, 113, 28.340000, 148.300000, 122, 12.610000, 186.900000, 121, 8.410000, 10.100000, 3, 2.730000, 3, 0
AL, 118, 510, 391-8027, 1, 0, 0, 223.400000, 98, 37.980000, 220.600000, 101, 18.750000, 203.900000, 118, 9.180000, 6.300000, 6, 1.700000, 0, 0
Console: Creating datasource for Amazon ML
Console: Creating datasource for Amazon ML
Console: Building the Amazon ML model
Recipe
{ "groups": {
"NUMERIC_VARS_NORM": "group('Intl_Charge','Night_Calls','Day_Calls','Eve_Calls','Eve_Mins','Intl_Mins','VMail_Message','Intl_Calls','Day_Mins','Night_Mins','Day_Charge','Night_Charge','Eve_Charge','Account_Length')” },
"assignments": {},
"outputs": [
"ALL_BINARY",
"State",
"Area_Code",
"normalize(NUMERIC_VARS_NORM)",
"CustServ_Calls"
]
}
Recipe: normalize() function
Account_Length Normalized Value
128 0.808771865
107 -0.047574816
137 1.175777586
84 -0.985478323
75 -1.352484044
118 0.400987732
Building the Amazon ML model
Cost of errors
• Cost of customer churn and acquisition (false negative):
• Foregone cash flow
• Advertising costs
• POS and sign-up admin costs
• Customer retention cost (false + true positive)
• Discounts
• Phone upgrades
• Etc.
Financial outcome of applying a model
Prior Churn Churn Cost Cost without ML
14.49% $500.00 $72.46
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% 12.10% + 14.30% $100.00 $50.40
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% 12.10% + 14.30% $100.00 $50.40
• Threshold 0.3 0.17
• $22.06 of savings per customer
• With 100,000 customers over $2MM in savings with ML
What’s next?
• https://aws.amazon.com/getting-started/projects/build-
machine-learning-model/
• https://aws.amazon.com/machine-learning/developer-
resources/
• https://github.com/dbatalov/cost_based_ml
Thank you!
Denis V. Batalov, PhD
AWS Solutions Architect, EMEA
@dbatalov
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