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AmazonMachineLearningCaseStudy:Predic9ngCustomerChurnDenisV.Batalov,Solu9onsArchitect,EMEA
Customer Churn
Machine Learning
Science• ComputerScience• Sta9s9cs• Neuroscience• Opera9onsResearch
Ar9ficialIntelligence• Ruleextrac9onfromdata• Inspiredbyhumanlearning• Adap9vealgorithms
Engineering• Training:DataàModels• Predic9on:ModelsàForecast• Decision:ForecastàAc9ons
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
Supervised Learning
Input Outcome Input
Input Input
Outcome
Outcome
Outcome
Supervised Learning
Unseen Input Same Outcome
known historical data
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
Creating Datasource for Amazon ML
Creating Datasource for Amazon ML
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 cashflow • 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
False Negative True + False Pos Retention Cost Cost with ML 4.80% 26.40% $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% 26.40% $100.00 $50.40
• $22.06 of savings per customer • With 100,000 customers over $2MM in savings with ML
@dbatalov
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