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months. When working with data that has such cycles, it is a good idea to cal­
culate the “average per weekend” or “average per working day” to see how
the chosen measure is changing over time.

T I P When working with data that has weekly cycles but must be reported by
month, consider variables such as “average per weekend day” or “average per
work day” so that comparisons between months are more meaningful.
580 Chapter 17

Revolvers, Transactors, and Convenience Users:
Defining Customer Behavior
Often, business people can characterize different groups of customers based on
their behavior over time. However, translating an informal business description
into a form useful for data mining is challenging. Faced with such a challenge,
the best response is to determine measures of customer behavior that match the
business understanding.
This example is about a credit card group at a major retail bank, which has
found that profitable customers come in three flavors:
Revolvers are customers who maintain large balances on their credit

cards. These are highly profitable customers because every month they
pay interest on large balances.
Transactors are customers who have high balances every month, but pay

them off. These customers do not pay interest, but the processing fee
charged on each transaction is an important source of revenue. One
component of the transaction fee is based on a percentage of the trans­
action value.
Convenience users are customers who periodically charge large amounts,

for vacations or large purchases, for example, and then pay them off
over several months. Although not as profitable as revolvers, they are
lower risk, while still paying significant amounts of interest.
The marketing group believes that these three types of customers are moti­
vated by different needs. So, understanding future customer behavior would
allow future marketing campaigns to send the most appropriate message to
each customer segment. The group would like to predict customer behavior 6
months in the future.
The interesting part of this example is not the prediction, but the definition
of the segments. The training set needs examples where customers are already
classified into the three groups. Obtaining this classification proves to be a
Preparing Data for Mining 581

The data available for this project consisted of 18 months of billing data,
Credit limit


Interest rate


New charges made during each month


Minimum payment


Amount paid


Total balance in each month


Amount paid in interest and related charges each month


The rules for these credit cards are typical. When a customer has paid off the
balance, there is no interest on new charges (for 1 month). However, when
there is an outstanding balance, then interest is charged on both the balance
and on new charges. What does this data tell us about customers?

Segmenting by Estimating Revenue
Estimated revenue is a good way of understanding the value of customers. (By
itself, this value does not provide much insight into customer behavior, so it is
not very useful for messaging.) Basing customer value on revenue alone
assumes that the costs for all customers are the same. This is not true, but it is
a useful approximation, since a full profitability model is quite complicated,
difficult to develop, and beyond the scope of this example.
Table 17.5 illustrates 1 month of billing for six customers. The last column is
the estimated revenue, which has two components. The first is the amount of
interest paid. The second is the transaction fee on new transactions, which is
estimated to be 1 percent of the new transaction volume for this example.

Six Credit Card Customers and 1 Month of Data
Table 17.5

Customer 1 $500 14.9% $50 $400 $15 $15 $4.97 $0.50 $5.47
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Customer 2 $5,000 4.9% $0 $4,500 $135 $135 $18.38 $0.00 $18.38
Customer 3 $6,000 11.9% $100 $3,300 $99 $1,000 $32.73 $1.00 $33.73
Customer 4 $10,000 14.9% $2,500 $0 $0 $75 $0.00 $25.00 $25.00
Customer 5 $8,000 12.9% $6,500 $0 $0 $6,500 $0.00 $65.00 $65.00
Customer 6 $5,000 17.9% $0 $4,500 $135 $135 $67.13 $0.00 $67.13

Preparing Data for Mining 583

Estimated revenue is a good way to compare different customers with a sin­
gle number. The table clearly shows that someone who rarely uses the credit
card (Customer 1) has very little estimated revenue. On the other hand, those
who make many charges or pay interest create a larger revenue stream.
However, estimated revenue does not differentiate between different types
of customers. In fact, a transactor (Customer 5) has very high revenue. So, does
a revolver who has no new charges (Customer 6). This example shows that
estimated revenue has little relationship to customer behavior. Frequent users
of the credit card and infrequent users both generate a lot of revenue. And this
is to be expected, since there are different types of profitable customers.
The real world is more complicated than this simplified example. Each cus­
tomer has a risk of bankruptcy, where the outstanding balance must be writ­
ten off. Different types of cards have different rules. For instance, many
co-branded cards have the transaction fee going to the co-branded institution.
And, the cost of servicing different customers varies, depending on whether
the customer uses customer service, disputes charges, pays bills online, and
so on.
In short, estimating revenue is a good way of understanding which cus­
tomers are valuable. But, it does not provide much insight into customer

Segmentation by Potential
In addition to actual revenue, each customer has a potential revenue. This is
the maximum amount of revenue that the customer could possibly bring in
each month. The maximum revenue is easy to calculate. Simply assume that
the entire credit line is used either in new charges (hence transaction revenue)
or in carry-overs (hence interest revenue). The greater of these is the potential
Table 17.6 compares the potential revenue with the actual revenue for the
same six customers during one month. This table shows some interesting char­
acteristics. Some not-so-profitable customers are already saturating their
potential. Without increasing their credit limits or interest rate, it is not possi­
ble to increase their value.

Potential of Six Credit Card Customers
Table 17.6

Customer 1 $500 14.9% $6.21 $5.00 $6.21 $5.47 88%
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Customer 2 $5,000 4.9% $20.42 $50.00 $50.00 $18.38 37%
Customer 3 $6,000 11.9% $59.50 $60.00 $60.00 $33.73 56%
Customer 4 $10,000 14.9% $124.17 $100.00 $124.17 $25.00 20%
Customer 5 $8,000 12.9% $86.00 $80.00 $86.00 $65.00 76%
Customer 6 $5,000 17.9% $74.58 $50.00 $74.58 $67.13 90%
Preparing Data for Mining 585

There is another aspect of comparing actual revenue to potential revenue;
it normalizes the data. Without this normalization, wealthier customers appear
to have the most potential, although this potential is not fully utilized. So, the
customer with a $10,000 credit line is far from meeting his or her potential. In
fact, it is Customer 1, with the smallest credit line, who comes closest to achiev­
ing his or her potential value. Such a definition of value eliminates the wealth
effect, which may or may not be appropriate for a particular purpose.

Customer Behavior by Comparison to Ideals
Since estimating revenue and potential does not differentiate among types of
customer behavior, let™s go back and look at the definitions in more detail.
First, what is it inside the data that tells us who is a revolver? Here are some
definitions of a revolver:
Someone who pays interest every month

Someone who pays more than a certain amount of interest every month

(say, more than $10)
Someone who pays more than a certain amount of interest, almost

every month (say, more than $10 in 80 percent of the months)
All of these have an ad hoc quality (and the marketing group had histori­
cally made up definitions similar to these on the fly). What about someone
who pays very little interest, but does pay interest every month? Why $10?
Why 80 percent of the months? These definitions are all arbitrary, often the
result of one person™s best guess at a definition at a particular time.
From the customer perspective, what is a revolver? It is someone who only
makes the minimum payment every month. So far, so good. For comparing
customers, this definition is a bit tricky because the minimum payments
change from month to month and from customer to customer.
Figure 17.16 shows the actual and minimum payments made by three cus­
tomers, all of whom have a credit line of $2,000. The revolver makes payments
that are very close to the minimum payment each month. The transactor
makes payments closer to the credit line, but these monthly charges vary more
widely, depending on the amount charged during the month. The convenience
user is somewhere in between. Qualitatively, the shapes of the curves provide
insight into customer behavior.
586 Chapter 17

$2,000 Payment
A typical revolver only pays
Minimum on or near the minimum
balance every month.
This revolver has maintained
an average balance of
$1,070, with new charges of
about $200 dollars.







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