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USAA is an insurance company that markets to active duty and retired mili­
tary personnel and their families. The company attributes information-based
marketing, including data mining, with a doubling of the number of products
held by the average customer. USAA keeps detailed records on its customers
and uses data mining to predict where they are in their life cycles and what
products they are likely to need.
Another company that has used data mining to improve its cross-selling
ability is Fidelity Investments. Fidelity maintains a data warehouse filled with
information on all of its retail customers. This information is used to build data
mining models that predict what other Fidelity products are likely to interest
each customer. When an existing customer calls Fidelity, the phone represen-
tative™s screen shows exactly where to lead the conversation.
In addition to improving the company™s ability to cross-sell, Fidelity™s retail
marketing data warehouse has allowed the financial services powerhouse to
build models of what makes a loyal customer and thereby increase customer
retention. Once upon a time, these models caused Fidelity to retain a margin­
ally profitable bill-paying service that would otherwise have been cut. It
turned out that people who used the service were far less likely than the aver­
age customer to take their business to a competitor. Cutting the service would
have encouraged a profitable group of loyal customers to shop around.
A central tenet of customer relationship management is that it is more prof­
itable to focus on “wallet share” or “customer share,” the amount of business
you can do with each customer, than on market share. From financial services
to heavy manufacturing, innovative companies are using data mining to
increase the value of each customer.


Holding on to Good Customers
Data mining is being used to promote customer retention in any industry
where customers are free to change suppliers at little cost and competitors are
eager to lure them away. Banks call it attrition. Wireless phone companies call
it churn. By any name, it is a big problem. By gaining an understanding of who
is likely to leave and why, a retention plan can be developed that addresses the
right issues and targets the right customers.
In a mature market, bringing in a new customer tends to cost more than
holding on to an existing one. However, the incentive offered to retain a cus­
tomer is often quite expensive. Data mining is the key to figuring out which
18 Chapter 1


customers should get the incentive, which customers will stay without the
incentive, and which customers should be allowed to walk.


Weeding out Bad Customers
In many industries, some customers cost more than they are worth. These
might be people who consume a lot of customer support resources without
buying much. Or, they might be those annoying folks who carry a credit card
they rarely use, are sure to pay off the full balance when they do, but must still
be mailed a statement every month. Even worse, they might be people who
owe you a lot of money when they declare bankruptcy.
The same data mining techniques that are used to spot the most valuable
customers can also be used to pick out those who should be turned down for
a loan, those who should be allowed to wait on hold the longest time, and
those who should always be assigned a middle seat near the engine (or is that
just our paranoia showing?).


Revolutionizing an Industry
In 1988, the idea that a credit card issuer™s most valuable asset is the informa­
tion it has about its customers was pretty revolutionary. It was an idea that
Richard Fairbank and Nigel Morris shopped around to 25 banks before Signet
Banking Corporation decided to give it a try.
Signet acquired behavioral data from many sources and used it to build pre­
dictive models. Using these models, it launched the highly successful balance
transfer program that changed the way the credit card industry works. In 1994,
Signet spun off the card operation as Capital One, which is now one of the top
10 credit card issuers. The same aggressive use of data mining technology that
fueled such rapid growth is also responsible for keeping Capital One™s loan
loss rates among the lowest in the industry. Data mining is now at the heart of
the marketing strategy of all the major credit card issuers.
Credit card divisions may have led the charge of banks into data mining, but
other divisions are not far behind. At Wachovia, a large North Carolina-based
bank, data mining techniques are used to predict which customers are likely to
be moving soon. For most people, moving to a new home in another town
means closing the old bank account and opening a new one, often with a
different company. Wachovia set out to improve retention by identifying
customers who are about to move and making it easy for them to transfer their
business to another Wachovia branch in the new location. Not only has reten­
tion improved markedly, but also a profitable relocation business has devel­
oped. In addition to setting up a bank account, Wachovia now arranges for
gas, electricity, and other services at the new location.
Why and What Is Data Mining? 19


And Just about Anything Else
These applications should give you a feel for what is possible using data min­
ing, but they do not come close to covering the full range of applications. The
data mining techniques described in this book have been used to find quasars,
design army uniforms, detect second-press olive oil masquerading as “extra
virgin,” teach machines to read aloud, and recognize handwritten letters. They
will, no doubt, be used to do many of the things your business will require to
grow and prosper for the rest of the century. In the next chapter, we turn to
how businesses make effective use of data mining, using the virtuous cycle of
data mining.


Lessons Learned
Data Mining is an important component of analytic customer relationship
management. The goal of analytic customer relationship management is to
recreate, to the extent possible, the intimate, learning relationship that a well-
run small business enjoys with its customers. A company™s interactions with
its customers generates large volumes of data. This data is initially captured in
transaction processing systems such as automatic teller machines, telephone
switch records, and supermarket scanner files. The data can then be collected,
cleaned, and summarized for inclusion in a customer data warehouse. A well-
designed customer data warehouse contains a historical record of customer
interactions that becomes the memory of the corporation. Data mining tools
can be applied to this historical record to learn things about customers that
will allow the company to serve them better in the future. The chapter pre­
sented several examples of commercial applications of data mining such as
better targeted couponing, making recommendations, cross selling, customer
retention, and credit risk reduction.
Data mining itself is the process of finding useful patterns and rules in large
volumes of data. This chapter introduced and defined six common data min­
ing tasks: classification, estimation, prediction, affinity grouping, clustering,
and profiling. The remainder of the book examines a variety of data mining
algorithms and techniques that can be applied to these six tasks. To be suc­
cessful, these techniques must become integral parts of a larger business
process. That integration is the subject of the next chapter, The Virtuous Cycle of
Data Mining.
CHAPTER

2
The Virtuous Cycle
of Data Mining




In the first part of the nineteenth century, textile mills were the industrial suc­
cess stories. These mills sprang up in the growing towns and cities along rivers
in England and New England to harness hydropower. Water, running over
water wheels, drove spinning, knitting, and weaving machines. For a century,
the symbol of the industrial revolution was water driving textile machines.
The business world has changed. Old mill towns are now quaint historical
curiosities. Long mill buildings alongside rivers are warehouses, shopping
malls, artist studios and computer companies. Even manufacturing companies
often provide more value in services than in goods. We were struck by an ad
campaign by a leading international cement manufacturer, Cemex, that pre­
sented concrete as a service. Instead of focusing on the quality of cement, its
price, or availability, the ad pictured a bridge over a river and sold the idea that
“cement” is a service that connects people by building bridges between them.
Concrete as a service? A very modern idea.
Access to electrical or mechanical power is no longer the criterion for suc­
cess. For mass-market products, data about customer interactions is the new
waterpower; knowledge drives the turbines of the service economy and, since
the line between service and manufacturing is getting blurry, much of the
manufacturing economy as well. Information from data focuses marketing
efforts by segmenting customers, improves product designs by addressing
real customer needs, and improves allocation of resources by understanding
and predicting customer preferences.

21
22 Chapter 2


Data is at the heart of most companies™ core business processes. It is generated
by transactions in operational systems regardless of industry”retail, telecom­
munications, manufacturing, utilities, transportation, insurance, credit cards, and
banking, for example. Adding to the deluge of internal data are external sources
of demographic, lifestyle, and credit information on retail customers, and credit,
financial, and marketing information on business customers. The promise of data
mining is to find the interesting patterns lurking in all these billions and trillions
of bytes. Merely finding patterns is not enough. You must respond to the patterns
and act on them, ultimately turning data into information, information into action, and
action into value. This is the virtuous cycle of data mining in a nutshell.
To achieve this promise, data mining needs to become an essential business
process, incorporated into other processes including marketing, sales, cus­
tomer support, product design, and inventory control. The virtuous cycle




Y
places data mining in the larger context of business, shifting the focus away




FL
from the discovery mechanism to the actions based on the discoveries.
Throughout this chapter and this book, we will be talking about actionable
results from data mining (and this usage of “actionable” should not be con­
AM
fused with its definition in the legal domain, where it means that some action
has grounds for legal action).
Marketing literature makes data mining seem so easy. Just apply the auto­
TE

mated algorithms created by the best minds in academia, such as neural net­
works, decision trees, and genetic algorithms, and you are on your way to
untold successes. Although algorithms are important, the data mining solu­
tion is more than just a set of powerful techniques and data structures. The
techniques have to be applied in the right areas, on the right data. The virtuous
cycle of data mining is an iterative learning process that builds on results over
time. Success in using data will transform an organization from reactive to
proactive. This is the virtuous cycle of data mining, used by the authors for
extracting maximum benefit from the techniques described later in the book.
This chapter opens with a brief case history describing an actual example of
the application of data mining techniques to a real business problem. The case
study is used to introduce the virtuous cycle of data mining. Data mining is
presented as an ongoing activity within the business with the results of one
data mining project becoming inputs to the next. Each project goes through
four major stages, which together form one trip around the virtuous cycle.
Once these stages have been introduced, they are illustrated with additional
case studies.


A Case Study in Business Data Mining
Once upon a time, there was a bank that had a business problem. One particu­
lar line of business, home equity lines of credit, was failing to attract good cus­
tomers. There are several ways that a bank can attack this problem.

Team-Fly®
The Virtuous Cycle of Data Mining 23


The bank could, for instance, lower interest rates on home equity loans. This
would bring in more customers and increase market share at the expense of
lowered margins. Existing customers might switch to the lower rates, further
depressing margins. Even worse, assuming that the initial rates were reason­
ably competitive, lowering the rates might bring in the worst customers”the
disloyal. Competitors can easily lure them away with slightly better terms.
The sidebar “Making Money or Losing Money” talks about the problems of
retaining loyal customers.
In this example, Bank of America was anxious to expand its portfolio of
home equity loans after several direct mail campaigns yielded disappointing
results. The National Consumer Assets Group (NCAG) decided to use data
mining to attack the problem, providing a good introduction to the virtuous
cycle of data mining. (We would like to thank Larry Scroggins for allowing us
to use material from a Bank of America Case Study he wrote. We also benefited
from conversations with Bob Flynn, Lounette Dyer, and Jerry Modes, who at
the time worked for Hyperparallel.)


Identifying the Business Challenge
BofA needed to do a better job of marketing home equity loans to customers.
Using common sense and business consultants, they came up with these
insights:
People with college-age children want to borrow against their home
––

equity to pay tuition bills.
People with high but variable incomes want to use home equity to
––

smooth out the peaks and valleys in their income.


MAKING MONEY OR LOSING MONEY?

Home equity loans generate revenue for banks from interest payments on the
loans, but sometimes companies grapple with services that lose money. As an
example, Fidelity Investments once put its bill-paying service on the chopping
block because this service consistently lost money. Some last-minute analysis
saved it, though, by showing that Fidelity™s most loyal and most profitable
customers used the bill paying service; although the bill paying service lost
money, Fidelity made much more money on these customers™ other accounts.
After all, customers that trust their financial institution to pay their bills have
a very high level of trust in that institution.
Cutting such value-added services may inadvertently exacerbate the
profitability problem by causing the best customers to look elsewhere for
better service.
24 Chapter 2


Marketing literature for the home equity line product reflected this view of
the likely customer, as did the lists drawn up for telemarketing. These insights
led to the disappointing results mentioned earlier.

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