Most marketers understand the value of collecting
customer data, but also realize the challenges of leveraging this
knowledge to create intelligent, proactive pathways back to the customer.
Data mining - technologies and techniques for recognizing and tracking
patterns within data - helps businesses sift through layers of seemingly
unrelated data for meaningful relationships, where they can anticipate,
rather than simply react to, customer needs. In this accessible
introduction, Kurt Thearling
provides a business and technological overview of data mining
and outlines how, along with sound business processes and complementary
technologies, data mining can reinforce and redefine customer
relationships.
Data Mining
and Customer
Relationships
by Kurt Thearling
The way in
which companies interact with their customers has changed dramatically
over the past few years. A customer's continuing business is no longer
guaranteed. As a result, companies have found that they need to understand
their customers better, and to quickly respond to their wants and needs.
In addition, the time frame in which these responses need to be made has
been shrinking. It is no longer possible to wait until the signs of
customer dissatisfaction are obvious before action must be taken. To
succeed, companies must be proactive and anticipate what a customer
desires.
It is now a cliché that in the days of the corner
market, shopkeepers had no trouble understanding their customers and
responding quickly to their needs. The shopkeepers would simply keep track
of all of their customers in their heads, and would know what to do when a
customer walked into the store. But today's shopkeepers face a much more
complex situation. More customers, more products, more competitors, and
less time to react means that understanding your customers is now much
harder to do. A number of forces are working together to increase the
complexity of customer relationships:
Successful companies need to react to each and every one of these demands in a timely fashion. The market will not wait for your response, and customers that you have today could vanish tomorrow. Interacting with your customers is also not as simple as it has been in the past. Customers and prospective customers want to interact on their terms, meaning that you need to look at multiple criteria when evaluating how to proceed. You will need to automate:
The right offer
means managing multiple interactions with your customers, prioritizing
what the offers will be while making sure that irrelevant offers are
minimized. The right person means that not all customers are cut from the
same cloth. Your interactions with them need to move toward highly
segmented marketing campaigns that target individual wants and needs. The
right time is a result of the fact that interactions with customers now
happen on a continuous basis. This is significantly different from the
past, when quarterly mailings were cutting-edge marketing. Finally, the
right channel means that you can interact with your customers in a variety
of ways (direct mail, email, telemarketing, etc.). You need to make sure
that you are choosing the most effective medium for a particular
interaction.
The purpose of this book is to provide you with
a thorough understanding of how a technology like data mining can help
solve vexing issues in your interactions with your customers. We describe
situations in which a better understanding of your customers can provide
tangible benefits and a measurable return on investment.
It
is important to realize, though, that data mining is just a part of the
overall process. Data mining needs to work with other technologies (for
example, data warehousing and marketing automation), as well as with
established business practices. If you take nothing else from this book,
we hope that you will appreciate that data mining needs to work as part of
a larger business process (and not the other way around!).
What Is Data Mining?
Data mining, by
its simplest definition, automates the detection of relevant patterns in a
database. For example, a pattern might indicate that married males with
children are twice as likely to drive a particular sports car than married
males with no children. If you are a marketing manager for an auto
manufacturer, this somewhat surprising pattern might be quite valuable.
However, data mining is not magic. For many years,
statisticians have manually "mined" databases, looking for statistically
significant patterns.
Data mining uses well-established
statistical and machine learning techniques to build models that predict
customer behavior. Today, technology automates the mining process,
integrates it with commercial data warehouses, and presents it in a
relevant way for business users.
The leading data mining
products are now more than just modeling engines employing powerful
algorithms. Instead, they address the broader business and technical
issues, such as their integration into today's complex information
technology environments.
In the past, the hyperbole
surrounding data mining suggested that it would eliminate the need for
statistical analysts to build predictive models. However, the value that
an analyst provides cannot be automated out of existence. Analysts will
still be needed to assess model results and validate the plausibility of
the model predictions. Because data mining software lacks the human
experience and intuition to recognize the difference between a relevant
and an irrelevant correlation, statistical analysts will remain in high
demand.
An Example
Imagine that you are
a marketing manager for a regional telephone company. You are responsible
for managing the relationships with the company's cellular telephone
customers. One of your current concerns is customer attention (sometimes
known as "churn"), which has been eating severely into your margins. You
understand that the cost of keeping customers around is significantly less
than the cost of bringing them back after they leave, so you need to
figure out a cost-effective way of doing this.
The
traditional approach to solving this problem is to pick out your good
customers (that is, the ones who spend a lot of money with your company)
and try to persuade them to sign up for another year of service. This
persuasion might involve some sort of gift (possibly a new phone) or maybe
a discount calling plan. The value of the gift might be based on the
amount that a customer spends, with big spenders receiving the best
offers.
This solution is probably very wasteful. There are
undoubtedly many "good" customers who would be willing to stick around
without receiving an expensive gift. The customers to concentrate on are
the ones that will be leaving. Don't worry about the ones who will stay.
This solution to the churn problem has been turned around
from the way in which it should be perceived. Instead of providing the
customer with something that is proportional to their value to your
company, you should instead be providing the customer with something
proportional to your value to them. Give your customers what they need.
There are differences between your customers, and you need to understand
those differences in order to optimize your relationships. One big
spending customer might value the relationship because of your high
reliability, and thus wouldn't need a gift in order to continue with it.
On the other hand, a customer who takes advantage of all of the latest
features and special services might require a new phone or other gift in
order to stick around for another year. Or they might simply want a better
rate for evening calls because their employer provides the phone and they
have to pay for calls outside of business hours. The key is determining
which type of customer you're dealing with.
It is also
important to consider timing in this process. You can't wait until a week
before a customer's contract and then pitch them an offer in order to
prevent them from churning. By then, they have likely decided what they
are going to do and you are unlikely to affect their decision at such a
late date. On the other hand, you don't to start the process immediately
upon signing a customer up. It might be months before they have an
understanding of your company's value to them, so any efforts now would
also be wasted. The key is finding the correct middle ground, which could
very well come from your understanding of your market and the customers in
that market. Or, as we will discuss later, you might be using data mining
to automatically find the optimal point.
Relevance to a
Business Process
For data mining to impact a business,
it needs to have relevance to the underlying business process. Data mining
is part of a much larger series of steps that takes place between a
company and its customers. The way in which data mining impacts a business
depends on the business process, not the data mining process. Take product
marketing as an example. A marketing manager's job is to understand their
market. With this understanding comes the ability to interact with
customers in this market, using a number of channels. This involves a
number of areas, including direct marketing, print advertising,
telemarketing, and radio/television advertising, among others.
The issue that must be addressed is that the results of data
mining are different from other data-driven business processes. In most
standard interactions with customer data, nearly all of the results
presented to the user are things that they knew existed in the database
already. A report showing the breakdown of sales by product line and
region is straightforward for the user to understand because they
intuitively know that this kind of information already exists in the
database. If the company sells different products in different regions of
the county, there is no problem translating a display of this information
into a relevant understanding of the business process.
Data
mining, on the other hand, extracts information from a database that the
user did not know existed. Relationships between variables and customer
behaviors that are non-intuitive are the jewels that data mining hopes to
find. And because the user does not know beforehand what the data mining
process has discovered, it is a much bigger leap to take the output of the
system and translate it into a solution to a business problem.
This is where interaction and context comes in. Marketing
users need to understand the results of data mining before they can put
them into actions. Because data mining usually involves extracting
"hidden" patterns of customer behavior, the understanding process can get
a bit complicated. The key is to put the user in a context in which they
feel comfortable, and then let them poke and prod until they understand
what they didn't see before.
How does someone actually use
the output of data mining? The simplest way is to leave the output in the
form of a black box. If they take the black box and score a database, they
can get a list of customers to target (send them a catalog, increase their
credit limit, etc.). There's not much for the user to do other than sit
back and watch the envelopes go out. This can be a very effective
approach. Mailing costs can often be reduced by an order of magnitude
without significantly reducing the response rate.
Then
there's the more difficult way to use the results of data mining: getting
the user to actually understand what is going on so that they can take
action directly. For example, if the user is responsible for ordering a
print advertising campaign, understanding customer demographics is
critical. A data mining analysis might determine that customers in New
York City are now focused in the 30-to-35-year-old age range, whereas
previous analyses showed that these customers were primarily aged 22 to
27. This change means that the print campaign might move from the
Village Voice to the New Yorker There's no automated way to
do this. It's all in the marketing manager's head. Unless the output of
the data mining system can be understood qualitatively, it won't be of any
use.
Both of these cases are inextricably linked. The user
needs to view the output of the data mining in a context they understand.
If they can understand what has been discovered, they will trust it and
put it into use. There are two parts to this problem: 1) presenting the
output of the data mining process in a meaningful way, and 2) allowing the
user to interact with the output so that simple questions can be answered.
Creative solutions to the first part have recently been incorporated into
a number of commercial data mining products. Response rates and (probably
most importantly) financial indicators (for example, profit, cost, and
return on investment) give the user a sense of context that can quickly
ground the results in reality.
Data Mining and Customer
Relationship Management
Customer relationship management
(CRM) is a process that manages the interactions between a company and its
customers. The primary users of CRM software applications are database
marketers who are looking to automate the process of interacting with
customers.
To be successful, database marketers must first
identify market segments containing customers or prospects with
high-profit potential. They then build and execute campaigns that
favorably impact the behavior of these individuals.
The
first task, identifying market segments, requires significant data about
prospective customers and their buying behaviors. In theory, the more data
the better. In practice, however, massive data stores often impede
marketers, who struggle to sift through the minutiae to find the nuggets
of valuable information.
Recently, marketers have added a
new class of software to their targeting arsenal. Data mining applications
automate the process of searching the mountains of data to find patterns
that are good predictors of purchasing behaviors.
After
mining the data, marketers must feed the results into campaign management
software that, as the name implies, manages the campaign directed at the
defined market segments.
In the past, the link between data
mining and campaign management software was mostly manual. In the worst
cases, it involved "sneaker net," creating a physical file on tape or
disk, which someone then carried to another computer and loaded into the
marketing database.
This separation of the data mining and
campaign management software introduces considerable inefficiency and
opens the door for human errors. Tightly integrating the two disciplines
presents an opportunity for companies to gain competitive advantage.
How Data Mining Helps Database Marketing
Data mining helps marketing users to target marketing
campaigns more accurately; and also to align campaigns more closely with
the needs, wants, and attitudes of customers and prospects.
If the necessary information exists in a database, the data
mining process can model virtually any customer activity. The key is to
find patterns relevant to current business problems.
Typical
questions that data mining addresses include the following:
· Which customers are most likely to drop their cell phone
service? · What is the probability that a customer will purchase at least
$100 worth of merchandise from a particular mail-order catalog? · Which
prospects are most likely to respond to a particular offer? Answers to
these questions can help retain customers and increase campaign response
rates, which, in turn, increase buying, cross-selling, and return on
investment (ROI).
Scoring
Data mining
builds models by using inputs from a database to predict customer
behavior. This behavior might be attrition at the end of a magazine
subscription, cross-product purchasing, willingness to use an ATM card in
place of a more expensive teller transaction, and so on. The prediction
provided by a model is usually called a score.
A score
(typically a numerical value) is assigned to each record in the database
and indicates the likelihood that the customer whose record has been
scored will exhibit a particular behavior.
For example, if a
model predicts customer attrition, a high score indicates that a customer
is likely to leave, whereas a low score indicates the opposite. After
scoring a set of customers, these numerical values are used to select the
most appropriate prospects for a targeted marketing campaign.
The Role of Campaign Management Software
Database marketing software enables companies to deliver
timely, pertinent, and coordinated messages and value propositions (offers
or gifts perceived as valuable) to customers and prospects.
Today's campaign management software goes considerably
further. It manages and monitors customer communications across multiple
touch-points, such as direct mail, telemarketing, customer service, point
of sale, interactive web, branch office, and so on.
Campaign
management automates and integrates the planning, execution, assessment,
and refinement of possibly tens to hundreds of highly segmented campaigns
that run monthly, weekly, daily, or intermittently. The software can also
run campaigns with multiple "communication points," triggered by time or
customer behavior such as the opening of a new account.
Increasing Customer Lifetime Value
Consider, for example, customers of a bank who use the
institution only for a checking account. An analysis reveals that after
depositing large annual income bonuses, some customers wait for their
funds to clear before moving the money quickly into their stock-brokerage
or mutual fund accounts outside the bank. This represents a loss of
business for the bank.
To persuade these customers to keep
their money in the bank, marketing managers can use campaign management
software to immediately identify large deposits and trigger a response.
The system might automatically schedule a direct mail or telemarketing
promotion as soon as a customer's balance exceeds a predetermined amount.
Based on the size of the deposit, the triggered promotion can then provide
an appropriate incentive that encourages customers to invest their money
in the bank's other products.
Finally, by tracking responses
and following rules for attributing customer behavior, the campaign
management software can help measure the profitability and ROI of all
ongoing campaigns.
Combining Data Mining and Campaign
Management
The closer data mining and campaign
management work together, the better the business results. Today, campaign
management software uses the scores generated by the data mining model to
sharpen the focus of targeted customers or prospects, thereby increasing
response rates and campaign effectiveness. Ideally, marketers who build
campaigns should be able to apply any model logged in the campaign
management system to a defined target segment.
Evaluating
the Benefits of a Data Mining Model
Figure 1-1, which shows a
"gains chart," suggests some benefits available through data mining. The
diagonal line illustrates the number of responses expected from a randomly
selected target audience. Under this scenario, the number of responses
grows linearly with the target size.
Excerpted with permission from Building Data Mining Applications for CRM by Alex Berson, Stephen Smith, Kurt Thearling (McGraw Hill, 2000).
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