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customer-centric metrics that can be tracked, modeled, and reported.
Customer interactions should be turned into learning opportunities when­
ever possible. In particular, marketing communications should be set up as
controlled experiments. The results of these experiments are input for data
mining models used for targeting, cross-selling, and retention.
There are several approaches to incorporating data mining into a company™s
marketing and customer relationship management activities. Outsourcing is a
possibility for companies with only occasional modeling needs. When there is
an ongoing need for data mining, it is best done internally so that insights pro­
duced during mining remain within the company rather than with an outside
vendor.
A data mining group can be successful in any of several locations within the
company organization chart. Locating the group in IT puts it close to data and
technical resources. Locating it within a business unit puts it close to the busi­
ness problems. In either case, it is important to have good communication
between IT and the business units.
Choosing software for the data mining environment is important. However,
the success of the data mining group depends more on having good processes
and good people than on the particular software found on their desktops.
CHAPTER

17

Preparing Data for Mining




As a translucent amber fluid, gasoline”the power behind the transportation
industry”barely resembles the gooey black ooze pumped up through oil
wells. The difference between the two liquids is the result of multiple steps of
refinement that distill useful products from the raw material.
Data preparation is a very similar process. The raw material comes from
operational systems that have often accumulated crud, in the form of eccentric
business rules and layers of system enhancements and fixes, over the course of
time. Fields in the data are used for multiple purposes. Values become obso­
lete. Errors are fixed on an ongoing basis, so interpretations change over time.
The process of preparing data is like the process of refining oil. Valuable stuff
lurks inside the goo of operational data. Half the battle is refinement. The
other half is converting its energy to a useful form”the equivalent of running
an engine on gasoline.
The proliferation of data is a feature of modern business. Our challenge is to
make sense of the data, to refine the data so that the engines of data mining can
extract value. One of the challenges is the sheer volume of data. A customer
may call the call center several times a year, pay a bill once a month, turn the
phone on once a day, make and receive phone calls several times a day. Over
the course of time, hundreds of thousands or millions of customers are gener­
ating hundreds of millions of records of their behavior. Even on today™s com­
puters, this is a lot of data processing. Fortunately, computer systems have
become powerful enough that the problem is really one of having an adequate

539
540 Chapter 17


budget for buying hardware and software; technically, processing such vast
quantities of data is possible.
Data comes in many forms, from many systems, and in many different
types. Data is always dirty, incomplete, sometimes incomprehensible and
incompatible. This is, alas, the real world. And yet, data is the raw material for
data mining. Oil starts out as a thick tarry substance, mixed with impurities. It
is only by going through various stages of refinement that the raw material
becomes usable”whether as clear gasoline, plastic, or fertilizer. Just as the
most powerful engines cannot use crude oil as a fuel, the most powerful algo­
rithms (the engines of data mining) are unlikely to find interesting patterns in
unprepared data.
After more than a century of experimentation, the steps of refining oil are
quite well understood”better understood than the processes of preparing
data. This chapter illustrates some guidelines and principles that, based on
experience, should make the process more effective. It starts with a discussion
of what data should look like once it has been prepared, describing the cus­
tomer signature. It then dives into what data actually looks like, in terms of
data types and column roles. Since a major part of successful data mining is in
the derived variables, ideas for these are presented in some detail. The chapter
ends with a look at some of the difficulties presented by dirty data and miss­
ing values, and the computational challenge of working with large volumes of
commercial data.


What Data Should Look Like
The place to start the discussion on data is at the end: what the data should
look like. All data mining algorithms want their inputs in tabular form”the
rows and columns so common in spreadsheets and databases. Unlike spread­
sheets, though, each column must mean the same thing for all the rows.
Some algorithms need their data in a particular format. For instance, market
basket analysis (discussed in Chapter 9) usually looks at only the products pur­
chased at any given time. Also, link analysis (see Chapter 10) needs references
between records in order to connect them. However, most algorithms, and
especially decision trees, neural networks, clustering, and statistical regression,
are looking for data in a particular format called the customer signature.


The Customer Signature
The customer signature is a snapshot of customer behavior that captures both
current attributes of the customers and changes in behavior over time. Like
Preparing Data for Mining 541


a signature on a check, each customer™s signature is theoretically unique”
capturing the unique characteristics of the individual. Unlike a signature on a
check, though, the customer signature is used for analysis and not identifica­
tion; in fact, often customer signatures have no more identifying information
than a string of seemingly random digits representing a household, individ­
ual, or account number. Figure 17.1 shows that a customer signature is simply
a row of data that represents the customer and whatever might be useful for
data mining.


This column is an ID field where
the value is different in every
column. It is ignored for data
mining purposes. This column is the target,
This column is from the customer
what we want to predict.
information file.


2610000101 010377 14 A 19.1 14 Spring . .. TRUE
2610000102 103188 7 A 19.1 NULL TRUE
2610000105 041598 1 B 21.2 71 W. 19 St. FALSE
2610000171 040296 1 S 38.3 3562 Oak. . . FALSE

2610000182 051990 22 C 56.1 9672 W. 142 FALSE
2610000183 111192 45 C 56.1 NULL TRUE

These rows have
invalid customer IDs,
so they are ignored.
2620000107 080891 6 A 19.1 P.O. Box 11 FALSE
2620000108 120398 3 D 10.0 560 Robson TRUE
2620000220 022797 2 S 38.3 222 E. 11th FALSE
2620000221 021797 3 A 19.1 10122 SW 9 FALSE

2620000230 060899 1 S 38.3 NULL TRUE
2620000231 062099 10 S 38.3 RR 1729 TRUE
2620000300 032894 7 B 21.2 1920 S. 14th FALSE




This column is summarized
from transaction data.
This column is a text field with unique
values. It is ignored (although it may
be used for some derived variables).

These columns come from reference
tables, so their values are repeated
many times.
Figure 17.1 Each row in the customer signature represents one customer (the unit of data
mining) with fields describing that customer.
542 Chapter 17


It is perhaps unfortunate that there is no big database sitting around with
up-to-date customer signatures, ready for all modeling applications. Such a
system might at first sight seem very useful. However, the lack of such a sys­
tem is an opportunity because modeling efforts require understanding data.
No single customer signature works for all modeling efforts, although some
customer signatures work well for several applications
The “customer” in customer signature is the unit of data mining. This book
focuses primarily on customers, so the unit of data mining is typically an
account, an individual, or a household. There are other possibilities. Chapter
11 has a case study on clustering towns”because that was the level of action
for developing editorial zones for a newspaper. Acquisition modeling often
takes place at the geographic level, census block groups or zip codes. And
applications outside customer relationship management are even more dis­




Y
parate. Mastering Data Mining, for instance, has a case study where the signa­




FL
tures are press runs in plants that print magazines.
AM
The Columns
The columns in the data contain values that describe aspects of the customer. In
some cases, the columns come directly from existing business systems; more
TE

often, the columns are the result of some calculation”so called derived variables.
Each column contains values. The range refers to the set of allowable values
for that column. Table 17.1 shows range characteristics for typical types of data
used for data mining.

Table 17.1 Range Characteristics for Typical Types of Data Used for Data Mining

VARIABLE TYPE TYPICAL RANGE CHARACTERISTICS

Categorical variables List of acceptable values

Numeric Minimum and maximum values

Dates Earliest and latest dates, often latest date is less
than or equal to current date

Monetary amounts Greater than or equal to 0

Durations Greater than or equal to 0 (or perhaps strictly
greater than 0)

Binned or quantiled values The number of quantiles

Counts Greater than or equal to 0 (or perhaps greater than
or equal to 1)




Team-Fly®
Preparing Data for Mining 543


Histograms, such as those in Figure 17.2, shows how often each value or
range of values occurs in some set of data. The vertical axis is a count of
records, and the horizontal axis is the values in the column. The shape of this
histogram shows the distribution of the values (strictly speaking, in a distribu­
tion, the counts are divided by the total number of records so the area under
the curve is one). If we are working with a sample, and the sample is randomly
chosen, then the distribution of values in the subset should be about the same
as the distribution in the original data.


160

140
This histogram is for the month of
120
claim for a set of insurance claims.
100

80
This is an example of a typically
60
uniform distribution. That is, the
40 number of claims is roughly the
same for each month.
20

0
Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec




This histogram shows the number of
Number of Calls




telephone calls made for different
durations.

This is an example of an
exponentially decreasing
distribution.
1
4
7
10
13
16
19
22

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