Examining survival curves

Kaplan Meir survival curves are usually a good place to start when examining the effect of different single factors upon the survival rate, since they are easy to construct and visualize. Later on, we will example cox regression, which can examine multiple factors.

Kaplan Meir (KM) curves are actually step functions in which the survival object, or hazard rate, is estimated at each discrete time point. This survival rate is computed by calculating the number of customers who have survived (are still active), divided by the number of customers at risk. The number of customers at risk (which is the denominator) excludes all customers who have already churned, or haven't achieved the tenure specified at any particular time point.

To illustrate, if we table ChurnStudy by the number of months active (Xtenure2), we can see that for month 1, there were 44 members whose survival rate is calculated as (1984 -19) (Number left after end of month 1 / 1984):

table(ChurnStudy$Xtenure2...