When Not to Model (1,934 words)
Many companies are using models successfully to improve their response rates and increase profits in marketing to outside prospect lists. To use a model successfully, the marketer has to have a marketing situation that meets both of the following criteria:
1. The customer response to a promotion must be significantly determined by factors that the marketer can append to a prospect list; and
2. The lift in profits from using the model must more than pay for the cost of the appended data and the cost of building and running the model.
For example, if you are selling encyclopedias through the mail, you may rent a list of parents of high school students. Appended to this list you might be able to get the estimated household income, the PRIZM cluster code, the type of dwelling, the incidence of home ownership and a few other factors. It is possible that you could put these factors into a model and be able to prove that you can sell the encyclopedias profitably to people who: have a child in high school; have a household income over $X; are homeowners of a home worth over $X; and live in PRIZM clusters A, B, C and D.
You might find that if people have these four characteristics, your response rate is 2 percent or better. If they lack them, your response rate is below 1 percent. It could be that it cost you $Y per thousand to append this data to a rented list of parents of high school kids, and that your model costs you $35,000. If your mailing is big enough, the profits from using it to direct your mailing could pay for the cost of the model and the appended data.
Companies are doing this kind of analysis all the time. Banks use it to sell credit cards and home equity loans. Life insurance companies use it to sell life insurance policies, annuities, health insurance and retirement plans. Brokers use it to sell mutual funds. In many situations, such data appending and modeling is a profitable solution to the marketing problem.