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Amazon Machine Learning Case Study: Predicting Customer Churn

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We do a deeper dive into Amazon Machine Learning, using a specific business problem as an example – predicting if the customer is about to leave your service, also known as customer churn. We examine several practical aspects of building and using a model, including the use of the recipe language for training data manipulation and modeling the costs of false positive/negative errors.

Published in: Technology

Amazon Machine Learning Case Study: Predicting Customer Churn

  1. 1. Amazon Machine Learning Case Study: Predic9ng Customer Churn Denis V. Batalov, Solu9ons Architect, EMEA
  2. 2. Customer Churn
  3. 3. Machine Learning Science • Computer Science • Sta9s9cs • Neuroscience • Opera9ons Research Ar9ficial Intelligence • Rule extrac9on from data • Inspired by human learning • Adap9ve algorithms Engineering • Training: Data à Models • Predic9on: Models à Forecast • Decision: Forecast à Ac9ons
  4. 4. ML: Robotics
  5. 5. ML: Robotics
  6. 6. ML: Image Recognition
  7. 7. Supervised Learning
  8. 8. Supervised Learning Input Outcome
  9. 9. Supervised Learning Input Outcome Input Input Input Outcome Outcome Outcome
  10. 10. Supervised Learning Input Outcome Input Input Input Outcome Outcome Outcome Supervised Learning known historical data
  11. 11. Supervised Learning Input Outcome Input Input Input Outcome Outcome Outcome Supervised Learning Unseen Input Same Outcome known historical data
  12. 12. Amazon Machine Learning Service
  13. 13. Amazon Machine Learning Service
  14. 14. Amazon Machine Learning Service
  15. 15. Amazon Machine Learning Service
  16. 16. 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
  17. 17. 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
  18. 18. 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
  19. 19. Creating Datasource for Amazon ML
  20. 20. Creating Datasource for Amazon ML
  21. 21. Building the Amazon ML Model
  22. 22. Recipe { "groups": { "NUMERIC_VARS_NORM": "group('Intl_Charge','Night_Calls','Day_Calls','Eve_Calls','Eve_Mins','Int l_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" ] }
  23. 23. 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
  24. 24. Building the Amazon ML Model
  25. 25. 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
  26. 26. 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
  27. 27. 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
  28. 28. @dbatalov

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