Amazon Machine Learning Case Study: Predicting Customer Churn

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<ul><li><p>AmazonMachineLearningCaseStudy:Predic9ngCustomerChurnDenisV.Batalov,Solu9onsArchitect,EMEA</p></li><li><p>Customer Churn </p></li><li><p>Machine Learning </p><p>ScienceComputerScienceSta9s9csNeuroscienceOpera9onsResearch</p><p>Ar9ficialIntelligenceRuleextrac9onfromdata InspiredbyhumanlearningAdap9vealgorithms</p><p>EngineeringTraining:DataModelsPredic9on:ModelsForecastDecision:ForecastAc9ons</p></li><li><p>ML: Robotics </p></li><li><p>ML: Robotics </p></li><li><p>ML: Image Recognition </p></li><li><p>Supervised Learning </p></li><li><p>Supervised Learning </p><p>Input Outcome </p></li><li><p>Supervised Learning </p><p>Input Outcome Input </p><p>Input Input </p><p>Outcome </p><p>Outcome </p><p>Outcome </p></li><li><p>Supervised Learning </p><p>Input Outcome Input </p><p>Input Input </p><p>Outcome </p><p>Outcome </p><p>Outcome </p><p>Supervised Learning </p><p>known historical data </p></li><li><p>Supervised Learning </p><p>Input Outcome Input </p><p>Input Input </p><p>Outcome </p><p>Outcome </p><p>Outcome </p><p>Supervised Learning </p><p>Unseen Input Same Outcome </p><p>known historical data </p></li><li><p>Amazon Machine Learning Service </p></li><li><p>Amazon Machine Learning Service </p></li><li><p>Amazon Machine Learning Service </p></li><li><p>Amazon Machine Learning Service </p></li><li><p>Telco Churn Dataset </p><p> US telco customers, their cell phone plans and usage 21 attributes, 3333 rows: </p><p> Customer: State, Area_Code, Phone Plan: Intl_Plan, VMail_Plan Behavior: VMail_Messages, Day_Mins, Day_Calls, </p><p>Day_Charge, Eve_Mins, Eve_Calls, Eve_Charge, Night_Mins, Night_Calls, Night_Charge, Intl_Mins, Intl_Calls, Intl_Charge</p><p> Other: Account_Length, CustServ_Calls, Churn</p></li><li><p>Telco Churn Dataset </p><p> US telco customers, their cell phone plans and usage 21 attributes, 3333 rows: </p><p> Customer: State, Area_Code, Phone Plan: Intl_Plan, VMail_Plan Behavior: VMail_Messages, Day_Mins, Day_Calls, </p><p>Day_Charge, Eve_Mins, Eve_Calls, Eve_Charge, Night_Mins, Night_Calls, Night_Charge, Intl_Mins, Intl_Calls, Intl_Charge</p><p> Other: Account_Length, CustServ_Calls, Churn</p></li><li><p>Telco Churn Dataset </p><p>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 </p><p>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 </p><p>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 </p><p>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 </p><p>OK, 75, 415, 330-6626, 1, 0, 0, 166.700000, 113, 28.340000, 148.300000, 122, 12.610000, </p><p>186.900000, 121, 8.410000, 10.100000, 3, 2.730000, 3, 0 </p><p>AL, 118, 510, 391-8027, 1, 0, 0, 223.400000, 98, 37.980000, 220.600000, 101, 18.750000, </p><p>203.900000, 118, 9.180000, 6.300000, 6, 1.700000, 0, 0 </p></li><li><p>Creating Datasource for Amazon ML </p></li><li><p>Creating Datasource for Amazon ML </p></li><li><p>Building the Amazon ML Model </p></li><li><p>Recipe </p><p>{ "groups": { </p><p> "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') }, </p><p> "assignments": {}, </p><p> "outputs": [ </p><p> "ALL_BINARY", </p><p> "State", </p><p> "Area_Code", </p><p> "normalize(NUMERIC_VARS_NORM)", </p><p> "CustServ_Calls" </p><p> ] </p><p>} </p></li><li><p>Recipe: normalize() function </p><p>Account_Length Normalized Value 128 0.808771865 107 -0.047574816 137 1.175777586 84 -0.985478323 75 -1.352484044 118 0.400987732 </p></li><li><p>Building the Amazon ML Model </p></li><li><p>Cost of Errors </p><p> Cost of Customer Churn and Acquisition (false negative): foregone cashflow advertising costs POS and sign-up admin costs </p><p> Customer Retention Cost (false + true positive) Discounts Phone upgrades etc </p></li><li><p>Financial Outcome of Applying a Model </p><p>Prior Churn Churn Cost Cost without ML 14.49% $500.00 $72.46 </p><p>False Negative True + False Pos Retention Cost Cost with ML 4.80% 26.40% $100.00 $50.40 </p></li><li><p>Financial Outcome of Applying a Model </p><p>Prior Churn Churn Cost Cost without ML 14.49% $500.00 $72.46 </p><p>False Negative True + False Pos Retention Cost Cost with ML 4.80% 26.40% $100.00 $50.40 </p><p> $22.06 of savings per customer With 100,000 customers over $2MM in savings with ML </p></li><li><p>@dbatalov</p></li></ul>