A firm that sells software services has been piloting a new product and has records of 500 customers who have either bought the services or decided not to. The target value is the estimated profit from each sale (excluding sales costs). The global mean is about $2500. However, the cost of the sales effort is not cheap—the company figures it comes to $2500 for each of the 500 customers (whether they buy or not). The firm developed a predictive model in hopes of being able to identify the top spenders in the future. The cumulative gains and decile lift charts for the validation set are shown in Figure 5,13,

a. If the company begins working with a new set of 1000 leads to sell the same services, similar to the 500 in the pilot study, without any use of predictive modeling to target sales efforts, what is the estimated profit?

b. If the firm wants the average profit on each sale to roughly double the sales effort cost, and applies an appropriate cutoff with this predictive model to a new set of 1000 leads, how far down the new list of 1000 should it proceed (how many deciles)?

c. Still considering the new list of 1000 leads, if the company applies this predictive model with a lower cutoff of $2500, how far should it proceed down the ranked leads, in terms of deciles?

d. Why use this two-stage process for predicting sales—why not simply develop a model for predicting profit for the 1000 new leads?

(a) Cumulative Gains Chart (b) 500,000 Cumulative gains 400,000 300,000 200,000 100,000 0 2.00 1.75 Decile Lift Chart 1.50- 1.25 1.00- 0.75- 0.50- 0.25- 0.00 0 50 100 No. of cases 150 200 82 8 8 8 Percentile FIGURE 5.13 CUMULATIVE GAINS (a) AND DECILE LIFT CHARTS (b) FOR SOFTWARE SERVICES PRODUCT SALES (VALIDATION SET)