Download Imagine you are someone who loves to cook! In fact, one of your

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Imagine you are someone who loves to cook! In fact, one of your favorite activities is preparing a gourmet dinner for an
appreciative crowd! What is the first thing you do? Rush to the cupboards and start throwing any old ingredients into a
bowl? Of course not! You carefully plan your meal. During the planning phase, there are many things to consider: What
will you serve? Is there a central theme or purpose? Do you have the proper tools? Do you have the necessary
ingredients? If not, can you buy them? How long will it take to prepare everything? How will you serve the food, sitdown or buffet style? All of these steps are important in the planning process.
Even though these considerations seem quite logical in planning a gourmet dinner, they also apply to the planning of
almost any major project. Careful planning and preparation are essential to the success of any data mining project. And
similar to planning a gourmet meal, you must first ask, ''What do I want to create?" Or "What is my goal?" "Do I have
the support of management?" "How will I reach my goal?" "What tools and resources will I need?" "How will I evaluate
whether I have succeeded?" "How will I implement the result to ensure its success?"
The outcome and eventual success of any data modeling project or analysis depend heavily on how well the project
objective is defined with respect to the specific business goal and how well the successful completion of the project will
serve the overall goals of the company. For example, the specific business goal might be to learn about your customers,
improve response rates, increase sales to current customers, decrease attrition, or optimize the efficiency of the next
campaign. Each project may have different data requirements or may utilize different analytic methods, or both.
We begin our culinary data journey with a discussion of the building blocks necessary for effective data modeling. In
chapter 1, I introduce the steps for building effective data models. I also provide a review of common data mining
techniques used for marketing risk and customer relationship management. Throughout this chapter, I detail the
importance of forming a clear objective and ensuring the necessary support within the organization. In chapter 2 I
explore the many types and sources of data used for data mining. In the course of this chapter, I provide numerous case
studies that detail data sources that are available for developing a data model.
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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.
TE
AM
FL
Y
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,
Team-Fly®
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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
descriptive models. In general, their goal is to attract and retain profitable customers. They use a variety of channels to
promote their products or services, such as direct mail, telemarketing, direct sales, broadcasting, magazine and
newspaper inserts, and the Internet. Each marketing effort has many components. Some are generic to all industries;
others are unique to certain industries. Table 1.1 displays some key leverage points that provide targeting model
development opportunities along with a list of industry types that might use them.
One effective way to determine the objective of the target modeling or profiling project is to ask such questions as these:
•Do you want to attract new customers?
•Do you want those new customers to be profitable?
•Do you want to avoid high -risk customers?
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Table 1.1 Targeting Model Opportunities by Industry
INDUSTRY
RESPONSE
RISK
ATTRITION
CROSS-SELL
& UP-SELL
NET PRESENT
VALUE
LIFETIME
VALUE
Banking
X
X
X
X
X
X
Insurance
X
X
X
X
X
X
Telco
X
X
X
X
X
X
Retail
X
X
X
X
Catalog
X
X
X
X
Resort
X
X
X
X
X
Utilities
X
X
X
X
X
Publishing
X
X
X
X
X
X
•Do you want to understand the characteristics of your current customers?
•Do you want to make your unprofitable customers more profitable?
•Do you want to retain your profitable customers?
•Do you want to win back your lost customers?
•Do you want to improve customer satisfaction?
•Do you want to increase sales?
•Do you want to reduce expenses?
These are all questions that can be addressed through the use of profiling, segmentation, and/or target modeling. Let's
look at each question individually:
•Do you want to attract new customers? Targeted response modeling on new customer acquisition campaigns will bring
in more customers for the same marketing cost.
•Do you want those new customers to be profitable? Lifetime value modeling will identify prospects with a high
likelihood of being profitable customers in the long term.
•Do you want to avoid high-risk customers? Risk or approval models will identify customers or prospects that have a
high likelihood of creating a loss for the company. In financial services, a typical loss comes from nonpayment on a
loan. Insurance losses result from claims filed by the insured.
•Do you want to understand the characteristics of your current customers? This involves segmenting the customer base
through profile analysis. It is a valuable exercise for many reasons. It allows you to see the characteristics of your most
profitable customers. Once the segments are