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