DATA CALCULATED BY THE HAZZARDS
 

 

 

 

 

 

 

 

 

 

 

 

 

One caveat: In order for the calculation to be accurate, every customer included in the population count must have the opportunity to stop at that particular time. This is a property of the data used to calculate the hazards, rather than the method of calculation. In most cases, this is not a problem, because hazards are calculated from all customers or from some subset based on initial con­ ditions (such as initial product or campaign). There is no problem when a customer is included in the population count up to that customer ’s tenure, and the customer could have stopped on any day before then and still be in the data set. An example of what not to do is to take a subset of customers who have stopped during some period of time, say in the past year. What is the problem? Consider a customer who stopped yesterday with 2 years of tenure. This cus­ tomer is included in all the population counts for the first year of hazards. However, the customer could not have stopped during the first year of tenure. The stop would have been more than a year in the past and precluded the customer from being in the data set. Because customers who could not have stopped are included in the population counts, the population counts are too big making the initial hazards too low. Later in the chapter, an alternative method is explained to address this issue.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

At this point, it is worth stopping and looking at some examples of hazards. These examples are intended to help in understanding what is happening, by looking at the hazard probabilities. The first two examples are basic, and, in fact, we have already seen examples of them in this chapter. The third is from real-world data, and it gives a good flavor of how hazards can be used to provide an x-ray of customers’ lifetimes. Constant Hazard The constant hazard hardly needs a picture to explain it. What it says is that the hazard of customers leaving is exactly the same, no matter how long the customers have been around. This looks like a horizontal line on a graph. Say the hazard is being measured by days, and it is a constant 0.1 percent. That is, one customer out of every thousand leaves every day. this means that about percent of the customers have left. It takes about 692 days for half the customers to leave. It will take another 692 days for half of them to leave. And so on, and so on. The constant hazard means the chance of a customer leaving does not vary with the length of time the customer has been around. This sounds a lot like the exponential retention curve, the one that looks like the decay of radioactive elements. In fact, a constant retention hazard would conform to an exponential form for the retention curve. We say “would” simply because, although this does happen in physics, it does not happen much in marketing.