DIFFERENT QUALITIES OF CUSTOMERS
 

 

 

 

 

 

 

 

 

 

 

 

A compounding reason is that marketing efforts change over time, attracting different qualities of customers. For instance, customers arriving by differ­ ent channels often have different retention characteristics, and the mix of customers from different channels is likely to change over time. Survival Hazards give the probability that a customer might stop at a particular point in time. Survival, on the other hand, gives the probability of a customer surviving up to that time. Survival values are calculated directly from the hazards. At any point in time, the chance that a customer survives to the next unit of time is simply 1 - hazard, which is called conditional survival at time t (it is conditional because it assumes that the customers survived up to time t). Calculating the full survival at a given time requires accumulating all the con­ ditional survivals up to that point in time by multiplying them together. The survival value starts at 1 (or 100 percent) at time 0, since all customers included in analysis survive to the beginning of the analysis. Since the hazard is always between 0 and 1, the conditional survival is also between 0 and 1. Hence, survival itself is always getting smaller—because each successive value is being multiplied by a number less than 1. The survival curve itself starts at 1, gently goes down, sometimes flattening, perhaps, out but never rising up.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Survival curves make more sense for customer retention purposes than the retention curves described earlier.The differences between the retention curve and the survival curve may, at first, seem nonintuitive. The retention curve is actually pasting together a whole bunch of different pictures of customers from the past, like a photo col­ lage pieced together from a bunch of different photographs to get a panoramic image. In the collage, the picture in each photo is quite clear. However, the boundaries do not necessarily fit together smoothly. Different pictures in the collage look different, because of differences in lighting or perspective— differences that contribute to the aesthetic of the collage. The same thing is happening with retention curves, where customers who start at different points in time have different perspectives. Any given point on the retention curve is close to the actual retention value; however, taken as a whole, it looks jagged. One way to remove the jaggedness is to focus on cus­ tomers who start at about the same time, as suggested earlier in this chapter. However, this greatly reduces the amount of data contributing to the curve. Instead of using retention curves, use survival curves. That is, first calculate the hazards and then work back to calculate the survival curve.