Gordon Linoff, Co-Founder, Data Miners, Inc. Como aplicar CRM usando minería de datos. How to apply CRM using data mining techniques.

1. Understanding Customers Who Stop Gordon S. Linoff Founder Data Miners, Inc. [email_address]

9. Visualizing Customers, The Calendar Timeline Customer Starts Customer Stops Customer Still Active

17. As the tree gets bigger, more nodes are added 13.5% 13.8% 86.5% 86.2% 39,628 19,814 3.5% 3.0% 96.5% 97.0% 11,112 5,678 14.9% 15.6% 85.1% 84.4% 23,361 11,529 28.7% 29.3% 71.3% 70.7% 5,155 2,607 < 0.7% < 3.8% Handset Churn Rate >= 3.8% CALL0/CALL3 58.0% 56.6% 42.0% 43.4% 219 99 39.2% 40.4% 60.8% 59.6% 148 57 27.0% 27.9% 73.0% 72.1% 440 218 < 0.0056 < 0.18 >= 0.18 67.3% 66.0% 32.7% 34.0% 110 47 70.9% 52.0% 29.1% 48.0% 55 25 25.9% 44.4% 74.1% 55.6% 54 27 < 4855 < 88455 >= 88455 Total Amt Overdue

22. Customers on the Tenure Timeline, Rather Than the Calendar Timeline Known Customer Start Known tenure when customers stop Unknown tenure for active customers (censored)

26. Hazards Are Like An X-Ray Into Customers Peak here is for non-payment and end of initial promotion Bumpiness is due to within week variation

27. Hazards Can Show Interesting Features of the Customer Lifecycle 0% 1% 2% 3% 4% 5% 6% 7% 8% 0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480 510 540 570 600 630 660 690 720 750 780 810 840 870 900 930 960 990 Tenure (Days After Start) Daily Churn Hazard Peak here is for non-payment after one year Peak here is for non-payment after two years

29. Survival is Similar to Retention Retention is jagged. It is calculated on customers who started exactly w weeks ago. Survival is smooth. It is calculated using customers who started up to w weeks ago.

30. Average Tenure Is The Area Under The Curves

35. A Better Way to Look at This

37. The Forecasting Solution is a Bit Complicated

38. Using Survival for Customer Centric Forecasting 0 10 20 30 40 50 60 70 80 90 100 110 120 130 Day of Month 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 Number Actual Predicted