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m en
m ts
on , A
De ct Common
fin ion Metadata
iti s,
s Common Repository
of Customer

Figure 16.1 A customer-centric organization requires centralized customer data.

It is natural for different groups to have different definitions of these terms.
At one publication, the circulation department and the advertising sales
department have different views on who are the most valuable customers
because the people who pay the highest subscription prices are not necessarily
the people of most interest to the advertisers. The solution is to have an adver­
tising value and a subscription value for each customer, using ideas such as
advertising fitness introduced in Chapter 4.
At another company, the financial risk management group considers a cus­
tomer “new” for the first 4 months of tenure, and during this initial probation­
ary period any late payments are pursued aggressively. Meanwhile, the
customer loyalty group considers the customer “new” for the first 3 months
and during this welcome period the customer is treated with extra care. So which
is it: a honeymoon or a trial engagement? Without agreement within the com­
pany, the customer receives mixed messages.
For companies with several different lines of business, the problem is even
trickier. The same company may provide Internet service and telephone ser­
vice, and, of course, maintain different billing, customer service, and opera­
tional systems for the two services. Furthermore, if the ISP was recently
acquired by the telephone company, it may have no idea what the overlap is
between its existing telephone customers and its newly acquired Internet
Building the Data Mining Environment 519

Defining Customer-Centric Metrics
On September 24, 1929, Lieutenant James H. Doolittle of the U.S. Army Air
Corps made history by flying “blind” to demonstrate that with the aid of
newly invented instruments such as the artificial horizon, the directional gyro­
scope, and the barometric altimeter, it was possible to fly a precise course even
with the cockpit shrouded by a canvas hood. Before the invention of the artifi­
cial horizon, pilots flying into a cloud or fog bank would often end up flying
upside down. Now, thanks to all those gauges in the cockpit, we calmly munch
pretzels, sip coffee, and revise spreadsheets in weather that would have
grounded even Lieutenant Doolittle. Good business metrics are just as crucial
to keeping a large business flying on the proper course.
Business metrics are the signals that tell management which levers to move
and in what direction. Selecting the right metrics is crucial because a business
tends to become what it is measured by. A business that measures itself by the
number of customers it has will tend to sign up new customers without regard
to their expected tenure or prospects for future profitability. A business that
measures itself by market share will tend to increase market share at the
expense of other goals such as profitability. The challenge for companies that
want to be customer-centric is to come up with realistic customer-centric mea­
sures. It sounds great to say that the company™s goal is to increase customer
loyalty; it is harder to come up with a good way to measure that quality in cus­
tomers. Is merely having lasted a long time a sign of loyalty? Or should loyalty
be defined as being resistant to offers from competitors? If the latter, how can
it be measured?
Even seemingly simple metrics such as churn or profitability can be surpris­
ingly hard to pin down. When does churn actually occur:
On the day phone service is actually deactivated?

On the day the customer first expressed an intention to deactivate?

At the end of the first billing cycle after deactivation?

On the date when the telephone number is released for new customers?

Each of these definitions plays a role in different parts of a telephone busi­
ness. For wireless subscribers on a contract, these events may be far apart.
And, which churn events should be considered voluntary? Consider a sub­
scriber who refuses to pay in order to protest bad service and is eventually cut
off; is that voluntary or involuntary churn? What about a subscriber who stops
voluntarily and then doesn™t pay the final amount owed? These questions do
not have a right answer; they do suggest the subtleties of defining the cus­
tomer relationship.
As for profitability, which customers are considered profitable depends a
great deal on how costs are allocated.
520 Chapter 16

Collecting the Right Data
Once metrics such as loyalty, profitability, and churn have been properly
defined, the next step is to determine the data needed to calculate them cor­
rectly. This is different from simply approximating the definition using what­
ever data happens to be available. Remember, in the ideal data mining
environment, the data mining group has the power to determine what data is
made available!
Information required for managing the business should drive the addition of
new tables and fields to the data warehouse. For example, a customer-centric
company ought to be able to tell which of its customers are profitable. In many
companies this is not possible because there is not enough information avail­
able to sensibly allocate costs at the customer level. One of our clients, a wire­
less phone company, approached this problem by compiling a list of questions
that would have to be answered in order to decide what it costs to provide ser­
vice to a particular customer. They then determined what data would be
required to answer those questions and set up a project to collect it.
The list of questions was long, and included the following:
How many times per year does the customer call customer care?

Does the customer pay bills online, by check, or by credit card?

What proportion of the customer™s airtime is spent roaming?

On which outside networks does the customer roam?

What is the contractual cost for these networks?

Are the customer™s calls to customer care handled by the IVR or by

human operators?
Answering these cost-related questions required data from the call-center
system, the billing system , and a financial system. Similar exercises around
other important metrics revealed a need for call detail data, demographic data,
credit data, and Web usage data.

From Customer Interactions to Learning Opportunities
A customer-centric organization maintains a learning relationship with its cus­
tomers. Every interaction with a customer is an opportunity for learning, an
opportunity that can be siezed when there is good communication between
data miners and the various customer-facing groups within the company.
Almost any action the company takes that affects customers”a price
change, a new product introduction, a marketing campaign”can be designed
so that it is also an experiment to learn more about customers. The results of
these experiments should find their way into the data warehouse, where they
Building the Data Mining Environment 521

will be available for analysis. Often the actions themselves are suggested by
data mining.
As an example, data mining at one wireless company showed that having
had service suspended for late payment was a predictor of both voluntary and
involuntary churn. That late payment is a predictor of later nonpayment is
hardly a surprise, but the fact that late payment (or the company™s treatment
of late payers) was a predictor of voluntary churn seemed to warrant further
The observation led to the hypothesis that having had their service sus­
pended lowers a customers™ loyalty to the company and makes it more likely
that they will take their business elsewhere when presented with an opportu­
nity to do so. It was also clear from credit bureau data that some of the late
payers were financially able to pay their phone bills. This suggested an exper­
iment: Treat low-risk customers differently from high-risk customers by being
more patient with their delinquency and employing gentler methods of per­
suading them to pay before suspending them. A controlled experiment tested
whether this approach would improve customer loyalty without unacceptably
driving up bad debt. Two similar cohorts of low-risk, high-value customers
received different treatments. One was subjected to the “business as usual”
treatment, while the other got the kinder, gentler treatment. At the end of the
trial period, the two groups were compared on the basis of retention and bad
debt in order to determine the financial impact of switching to the new treat­
ment. Sure enough, the kinder, gentler treatment turned out to be worthwhile
for the lower risk customers”increasing payment rates and slightly increas­
ing long term tenure.

Mining Customer Data
When every customer interaction is generating data, there are endless oppor­
tunities for data mining. Purchasing patterns and usage patterns can be mined
to create customer segments. Response data can be mined to improve the tar­
geting of future campaigns. Multiple response models can be combined into
best next offer models. Survival analysis can be employed to forecast future
customer attrition. Churn models can spot customers at risk for attrition. Cus­
tomer value models can identify the customers worth keeping.
Of course, all this requires a data mining group and the infrastructure to
support it.

The Data Mining Group
The data mining group is specifically responsible for building models and
using data to learn about customers”as opposed to leading marketing efforts,
522 Chapter 16

devising new products, and so on. That is, this group has technical responsi­
bilities rather than business responsibilities.
We have seen data mining groups located in several different places in the
corporate hierarchy:
Outside the company as an outsourced activity

As part of IT

As part of marketing, customer relationship management, or finance


As an interdisciplinary group whose members still belong to their


home departments

Each of these structures has certain benefits and drawbacks, as discussed


Outsourcing Data Mining
Companies have varying reasons for considering outsourcing data mining.
For some, data mining is only an occasional need and so not worth investing
in an internal group. For others, data mining is an ongoing requirement, but

the skills required seem so different from the ones currently available in the
company that building this expertise from scratch would be very challenging.
Still others have their customer data hosted by an outside vendor and feel that
the analysis should take place close to the data.

Outsourcing Occasional Modeling
Some companies think they have little need for building models and using
data to understand customers. These companies generally fall into one of two
types. The first are the companies with few customers, either because the com­
pany is small or because each customer is very large. As an example, the pri­
vate banking group at a typical bank may serve a few thousand customers,
and the account representatives personally know their clients. In such an envi­
ronment, data mining may be superfluous, because people are so intimately
involved in the relationship.
However, data mining can play a role even in this environment. In particu­
lar, data mining can make it possible to understand best practices and to
spread them. For instance, some employees in the private bank may do a bet­
ter job in some way (retaining customers, encouraging customers to recom­
mend friends, family members, colleagues, and so on). These employees may
have best practices that should be spread through the organization.

T I P Data mining may be unncessary for companies where dedicated staff
maintain deep and personal long-term relationships with their customers.

Building the Data Mining Environment 523

Data mining may also seem unimportant to rapidly growing companies in a
new market. In this situation, customer acquisition drives the business, and


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