Amazon Machine Learning Case Study: Predicting Customer Churn

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  • AmazonMachineLearningCaseStudy:Predic9ngCustomerChurnDenisV.Batalov,Solu9onsArchitect,EMEA

  • Customer Churn

  • Machine Learning

    ScienceComputerScienceSta9s9csNeuroscienceOpera9onsResearch

    Ar9ficialIntelligenceRuleextrac9onfromdata InspiredbyhumanlearningAdap9vealgorithms

    EngineeringTraining:DataModelsPredic9on:ModelsForecastDecision:ForecastAc9ons

  • 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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