Practical Predictive Analytics

Comparing the models

Even though the survival curves are similar, we can see that at the end of 12 months, 56% of the customers were retained, as opposed to the original 27%. We could attribute that to the intervention that took place at month 6.

Use the summary(survfit) function to compare the modes:

> summary(survfit(CoxModel.2)) Call: survfit(formula = CoxModel.2) v time n.risk n.event survival std.err lower 95% CI upper 95% CI 1 1488 15 0.994 0.00157 0.991 0.997 2 1455 52 0.973 0.00359 0.966 0.980 3 1393 34 0.958 0.00461 0.949 0.967 4 1342 20 0.950 0.00518 0.940 0.960 5 1315 39 0.932 0.00624 0.920 0.945 6 1245 42 0.913 0.00736 0.898 0.927 7 1156 24 0.898 0.00801 0.883 0.914 8 1020 32 0.877 0.00902 0.859 0.895 9 850 40 0.846 0.01052 ...