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Identifying Good Prospects
The simplest definition of a good prospect”and the one used by many
companies”is simply someone who might at least express interest in becom­
ing a customer. More sophisticated definitions are more choosey. Truly good
prospects are not only interested in becoming customers; they can afford to
become customers, they will be profitable to have as customers, they are
unlikely to defraud the company and likely to pay their bills, and, if treated
well, they will be loyal customers and recommend others. No matter how sim­
ple or sophisticated the definition of a prospect, the first task is to target them.
Targeting is important whether the message is to be conveyed through
advertising or through more direct channels such as mailings, telephone calls,
or email. Even messages on billboards are targeted to some degree; billboards
for airlines and rental car companies tend to be found next to highways that
lead to airports where people who use these services are likely to be among
those driving by.
Data Mining Applications 89


Data mining is applied to this problem by first defining what it means to be
a good prospect and then finding rules that allow people with those charac­
teristics to be targeted. For many companies, the first step toward using data
mining to identify good prospects is building a response model. Later in this
chapter is an extended discussion of response models, the various ways they
are employed, and what they can and cannot do.


Choosing a Communication Channel
Prospecting requires communication. Broadly speaking, companies intention­
ally communicate with prospects in several ways. One way is through public
relations, which refers to encouraging media to cover stories about the com­
pany and spreading positive messages by word of mouth. Although highly
effective for some companies (such as Starbucks and Tupperware), public rela­
tions are not directed marketing messages.
Of more interest to us are advertising and direct marketing. Advertising can
mean anything from matchbook covers to the annoying pop-ups on some
commercial Web sites to television spots during major sporting events to prod­
uct placements in movies. In this context, advertising targets groups of people
based on common traits; however, advertising does not make it possible to
customize messages to individuals. A later section discusses choosing the right
place to advertise, by matching the profile of a geographic area to the profile of
prospects.
Direct marketing does allow customization of messages for individuals.
This might mean outbound telephone calls, email, postcards, or glossy color
catalogs. Later in the chapter is a section on differential response analysis,
which explains how data mining can help determine which channels have
been effective for which groups of prospects.


Picking Appropriate Messages
Even when selling the same basic product or service, different messages are
appropriate for different people. For example, the same newspaper may
appeal to some readers primarily for its sports coverage and to others primar­
ily for its coverage of politics or the arts. When the product itself comes in
many variants, or when there are multiple products on offer, picking the right
message is even more important.
Even with a single product, the message can be important. A classic exam­
ple is the trade-off between price and convenience. Some people are very price
sensitive, and willing to shop in warehouses, make their phone calls late at
night, always change planes, and arrange their trips to include a Saturday
night. Others will pay a premium for the most convenient service. A message
90 Chapter 4


based on price will not only fail to motivate the convenience seekers, it runs
the risk of steering them toward less profitable products when they would be
happy to pay more.
This chapter describes how simple, single-campaign response models can be
combined to create a best next offer model that matches campaigns to cus­
tomers. Collaborative filtering, an approach to grouping customers into like-
minded segments that may respond to similar offers, is discussed in Chapter 8.


Data Mining to Choose the Right Place to Advertise
One way of targeting prospects is to look for people who resemble current
customers. For instance, through surveys, one nationwide publication deter­
mined that its readers have the following characteristics:
59 percent of readers are college educated.
––

46 percent have professional or executive occupations.
––

21 percent have household income in excess of $75,000/year.
––

7 percent have household income in excess of $100,000/year.
––


Understanding this profile helps the publication in two ways: First, by tar­
geting prospects who match the profile, it can increase the rate of response to
its own promotional efforts. Second, this well-educated, high-income reader­
ship can be used to sell advertising space in the publication to companies
wishing to reach such an audience. Since the theme of this section is targeting
prospects, let™s look at how the publication used the profile to sharpen the
focus of its prospecting efforts. The basic idea is simple. When the publication
wishes to advertise on radio, it should look for stations whose listeners match
the profile. When it wishes to place “take one” cards on store counters, it
should do so in neighborhoods that match the profile. When it wishes to do
outbound telemarketing, it should call people who match the profile. The data
mining challenge was to come up with a good definition of what it means to
match the profile.


Who Fits the Profile?
One way of determining whether a customer fits a profile is to measure
the similarity”which we also call distance”between the customer and the
profile. Several data mining techniques use this idea of measuring similarity
as a distance. Memory-based reasoning, discussed in Chapter 8, is a technique
for classifying records based on the classifications of known records that
Data Mining Applications 91


are “in the same neighborhood.” Automatic cluster detection, the subject of
Chapter 11, is another data mining technique that depends on the ability to
calculate a distance between two records in order to find clusters of similar
records close to each other.
For this profiling example, the purpose is simply to define a distance metric
to determine how well prospects fit the profile. The data consists of survey
results that represent a snapshot of subscribers at a particular time. What sort
of measure makes sense with this data? In particular, what should be done
about the fact that the profile is expressed in terms of percentages (58 percent
are college educated; 7 percent make over $100,000), whereas an individual
either is or is not college educated and either does or does not make more than
$100,000?
Consider two survey participants. Amy is college educated, earns
$80,000/year, and is a professional. Bob is a high-school graduate earning
$50,000/year. Which one is a better match to the readership profile? The
answer depends on how the comparison is made. Table 4.1 shows one way to
develop a score using only the profile and a simple distance metric.
This table calculates a score based on the proportion of the audience that
agrees with each characteristic. For instance, because 58 percent of the reader­
ship is college educated, Amy gets a score of 0.58 for this characteristic. Bob,
who did not graduate from college, gets a score of 0.42 because the other
42 percent of the readership presumably did not graduate from college. This
is continued for each characteristic, and the scores are added together.
Amy ends with a score of 2.18 and Bob with the higher score of 2.68. His higher
score reflects the fact that he is more similar to the profile of current readers
than is Amy.

Table 4.1 Calculating Fitness Scores for Individuals by Comparing Them along Each
Demographic Measure

READER­ YES NO AMY BOB
SHIP SCORE SCORE AMY BOB SCORE SCORE


College 58% 0.58 0.42 YES NO 0.58 0.42
educated

Prof or exec 46% 0.46 0.54 YES NO 0.46 0.54
Income >$75K 21% 0.21 0.79 YES NO 0.21 0.79
Income >$100K 7% 0.07 0.93 NO NO 0.93 0.93

Total 2.18 2.68
92 Chapter 4


The problem with this approach is that while Bob looks more like the profile
than Amy does, Amy looks more like the audience the publication has
targeted”namely, college-educated, higher-income individuals. The success of
this targeting is evident from a comparison of the readership profile with the
demographic characteristics of the U.S. population as a whole. This suggests a
less naive approach to measuring an individual™s fit with the publication™s
audience by taking into account the characteristics of the general population in
addition to the characteristics of the readership. The approach measures the
extent to which a prospect differs from the general population in the same
ways that the readership does.
Compared to the population, the readership is better educated, more pro­
fessional, and better paid. In Table 4.2, the “Index” columns compare the read-
ership™s characteristics to the entire population by dividing the percent of the




Y
readership that has a particular attribute by the percent of the population that




FL
has it. Now, we see that the readership is almost three times more likely to be
college educated than the population as a whole. Similarly, they are only about
half as likely not to be college educated. By using the indexes as scores for each
AM
characteristic, Amy gets a score of 8.42 (2.86 + 2.40 + 2.21 + 0.95) versus Bob
with a score of only 3.02 (0.53 + 0.67 + 0.87 + 0.95). The scores based on indexes
correspond much better with the publication™s target audience. The new scores
TE

make more sense because they now incorporate the additional information
about how the target audience differs from the U.S. population as a whole.

Table 4.2 Calculating Scores by Taking the Proportions in the Population into Account

YES NO
READER­ US READER­ US
SHIP POP INDEX SHIP POP INDEX

College 58% 20.3% 2.86 42% 79.7% 0.53
educated

Prof or exec 46% 19.2% 2.40 54% 80.8% 0.67

Income >$75K 21% 9.5% 2.21 79% 90.5% 0.87

Income >$100K 7% 2.4% 2.92 93% 97.6% 0.95




Team-Fly®
Data Mining Applications 93


T I P When comparing customer profiles, it is important to keep in mind the
profile of the population as a whole. For this reason, using indexes is often

better than using raw values.


Chapter 11 describes a related notion of similarity based on the difference
between two angles. In that approach, each measured attribute is considered a
separate dimension. Taking the average value of each attribute as the origin,
the profile of current readers is a vector that represents how far he or she dif­
fers from the larger population and in what direction. The data representing a
prospect is also a vector. If the angle between the two vectors is small, the
prospect differs from the population in the same direction.


Measuring Fitness for Groups of Readers
The idea behind index-based scores can be extended to larger groups of peo­
ple. This is important because the particular characteristics used for measuring
the population may not be available for each customer or prospect. Fortu­
nately, and not by accident, the preceding characteristics are all demographic
characteristics that are available through the U.S. Census and can be measured
by geographical divisions such as census tract (see the sidebar, “Data by Cen­
sus Tract”).
The process here is to rate each census tract according to its fitness for the
publication. The idea is to estimate the proportion of each census tract that fits
the publication™s readership profile. For instance, if a census tract has an adult
population that is 58 percent college educated, then everyone in it gets a fit­
ness score of 1 for this characteristic. If 100 percent are college educated, then
the score is still 1”a perfect fit is the best we can do. If, however, only 5.8 per­
cent graduated from college, then the fitness score for this characteristic is 0.1.
The overall fitness score is the average of the individual scores for each char­
acteristic.
Figure 4.1 provides an example for three census tracts in Manhattan. Each
tract has a different proportion of the four characteristics being considered.
This data can be combined to get an overall fitness score for each tract. Note
that everyone in the tract gets the same score. The score represents the propor­
tion of the population in that tract that fits the profile.
94 Chapter 4



DATA BY CENSUS TRACT

The U.S. government is constitutionally mandated to carry out an enumeration
of the population every 10 years. The primary purpose of the census is to
allocate seats in the House of Representatives to each state. In the process of
satisfying this mandate, the census also provides a wealth of information about
the American population.
The U.S. Census Bureau (www.census.gov) surveys the American population
using two questionnaires, the short form and the long form (not counting
special purposes questionnaires, such as the one for military personnel). Most
people get the short form, which asks a few basic questions about gender, age,
ethnicity, and household size. Approximately 2 percent of the population gets

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