Chapter 1-
Setting the Objective
In the years following World War II, the United States experienced an economic boom. Mass marketing swept the
nation. Consumers wanted every new gadget and machine. They weren't choosy about colors and features. New products
generated new markets. And companies sprang up or expanded to meet the demand.
Eventually, competition began to erode profit margins. Companies began offering multiple products, hoping to compete
by appealing to different consumer tastes. Consumers became discriminating, which created a challenge for marketers.
They wanted to get the right product to the right consumer. This created a need for target marketing- that is, directing
an offer to a "target" audience. The growth of target marketing was facilitated by two factors: the availability of
information and increased computer power.
We're all familiar with the data explosion. Beginning with credit bureaus tracking our debt behavior and warranty cards
gathering demographics, we have become a nation of information. Supermarkets track our purchases, and Web sites
capture our shopping behavior whether we purchase or not! As a result, it is essential for businesses to use data just to
stay competitive in today's markets.
Targeting models, which are the focus of this book, assist marketers in targeting their best customers and prospects.
They make use of the increase in available data as well as improved computer power. In fact, logistic regression,
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which is used for numerous models in this book, was quite impractical for general use before the advent of computers.
One logistic model calculated by hand took several months to process. When I began building logistic models in 1991, I
had a PC with 600 megabytes of disk space. Using SAS, it took 27 hours to process one model! And while the model
was processing, my computer was unavailable for other work. I therefore had to use my time very efficiently. I would
spend Monday through Friday carefully preparing and fitting the predictive variables. Finally, I would begin the model
processing on Friday afternoon and allow it to run over the weekend. I would check the status from home to make sure
there weren't any problems. I didn't want any unpleasant surprises on Monday morning.
In this chapter, I begin with an overview of the model-building process. This overview details the steps for a successful
targeting model project, from conception to implementation. I begin with the most important step in developing a
targeting model: establishing the goal or objective. Several sample applications of descriptive and predictive targeting
models help to define the business objective of the project and its alignment with the overall goals of the company. Once
the objective is established, the next step is to determine the best methodology. This chapter defines several methods for
developing targeting models along with their advantages and disadvantages. The chapter wraps up with a discussion of
the adaptive company culture needed to ensure a successful target modeling effort.
Defining the Goal
The use of targeting models has become very common in the marketing industry. (In some cases, managers know they
should be using them but aren't quite sure how!) Many applications like those for response or approval are quite
straightforward. But as companies attempt to model more complex issues, such as attrition and lifetime value, clearly
and specifically defining the goal is of critical importance. Failure to correctly define the goal can result in wasted
dollars and lost opportunity.
The first and most important step in any targeting-model project is to establish a clear goal and develop a process to
achieve that goal. (I have broken the process into seven major steps; Figure 1.1 displays the steps and their companion
chapters.)
In defining the goal, you must first decide what you are trying to measure or predict. Targeting models generally fall into
two categories, predictive and descriptive. Predictive models calculate some value that represents future activity. It can
be a continuous value, like a purchase amount or balance, or a
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Figure 1.1
Steps for successful target modeling.
probability of likelihood for an action, such as response to an offer or default on a loan. A descriptive model is just as it
sounds: It creates rules that are used to group subjects into descriptive categories.
Companies that engage in database marketing have multiple opportunities to embrace the use of predictive and