The customer loyalty model in Example 11.11 assumes that once a customer leaves (becomes disloyal), that customer never becomes loyal again. Assume instead that there are two probabilities that drive the model, the retention rate and the rejoin rate, with values 0.75 and 0.15, respectively. The simulation should follow a customer who starts as a loyal customer in year 1. From then on, at the end of any year when the customer was loyal, this customer remains loyal for the next year with probability equal to the retention rate. But at the end of any year the customer is disloyal, this customer becomes loyal the next year with probability equal to the rejoin rate. During the customer’s nth loyal year with the company, the company’s mean profit from this customer is the nth value in the mean profit list in column B. Keep track of the same two outputs as in the example, and also keep track of the number of times the customer rejoins.
EXAMPLE 11.11 THE LONG-TERM VALUE OF A CUSTOMER AT CCAMERICA
CCAmerica is a credit card company that does its best to gain customers and keep their business in a highly competitive industry. The first year a customer signs up for service typically results in a loss to the company because of various administrative expenses. However, after the first year, the profit from a customer is typically positive, and this profit tends to increase through the years. The company has estimated the mean profit from a typical customer to be as shown in column B of Figure 11.32. For example, the company expects to lose $40 in the customer’s first year but to gain $87 in the fifth year—provided that the customer stays loyal that long. For modeling purposes, we assume that the actual profit from a customer in the customer’s nth year of service is normally distributed with mean shown in Figure 11.32 and standard deviation equal to 10% of the mean. At the end of each year, the customer leaves the company, never to return, with probability 0.15, the churn r ate. Alternatively, the customer stays with probability 0.85, the retention rate. The company wants to estimate the NPV of the net profit from any such customer who has just signed up for service at the beginning of year 1, at a discount rate of 15%, assuming that the cash flow occurs in the middle of the year.5 It also wants to see how sensitive this NPV is to the retention rate.
Figure 11.32 Mean Profit as a Function of Years as Customer
Objective To use simulation to find the NPV of a customer and to see how this varies with the retention rate.
WHERE DO THE NUMBERS COME FROM? The numbers in Figure 11.32 are undoubtedly averages, based on the historical records of many customers. To build in randomness for any particular customer, we need a probability distribution around the numbers in this figure. We arbitrarily chose a normal distribution centered on the historical average and a standard deviation of 10% of the average. These are educated guesses. Finally, the churn rate is a number very familiar to marketing people, and it can also be estimated from historical customer data