IntroductionTraditionally, organizations use data
tactically - to manage operations. For a competitive edge, strong
organizations use data strategically - to expand the business, to
improve profitability, to reduce costs, and to market more effectively.
Data mining (DM) creates information assets that an organization can
leverage to achieve these strategic objectives.
In this article, we address some of the key questions executives have about data mining. These include:
- What can it do for my organization?
- How can my organization get started?
Business Definition of Data MiningData mining is a new
component in an enterprise's decision support system (DSS) architecture.
It complements and interlocks with other DSS capabilities such as query
and reporting, on-line analytical processing (OLAP), data
visualization, and traditional statistical analysis. These other DSS
technologies are generally retrospective. They provide reports, tables,
and graphs of what happened in the past. A user who knows what she's
looking for can answer specific questions like: "How many new accounts
were opened in the Midwest region last quarter," "Which stores had the
largest change in revenues compared to the same month last year," or
"Did we meet our goal of a ten-percent increase in holiday sales?"
We
define data mining as "the data-driven discovery and modeling of hidden
patterns in large volumes of data." Data mining differs from the
retrospective technologies above because it produces models - models
that capture and represent the hidden patterns in the data. With it, a
user can discover patterns and build models automatically, without
knowing exactly what she's looking for. The models are both descriptive
and prospective. They address why things happened and what is likely to
happen next. A user can pose "what-if" questions to a data-mining model
that can not be queried directly from the database or warehouse.
Examples include: "What is the expected lifetime value of every customer
account," "Which customers are likely to open a money market account,"
or "Will this customer cancel our service if we introduce fees?"
The
information technologies associated with DM are neural networks,
genetic algorithms, fuzzy logic, and rule induction. It is outside the
scope of this article to elaborate on all of these technologies.
Instead, we will focus on business needs and how data mining solutions
for these needs can translate into dollars.
Mapping Business Needs to Solutions and ProfitsWhat
can data mining do for your organization? In the introduction, we
described several strategic opportunities for an organization to use
data for advantage: business expansion, profitability, cost reduction,
and sales and marketing. Let's consider these opportunities very
concretely through several examples where companies successfully applied
DM.
Expanding your business: Keystone Financial of Williamsport,
PA, wanted to expand their customer base and attract new accounts
through a LoanCheck offer. To initiate a loan, a recipient just had to
go to a Keystone branch and cash the LoanCheck. Keystone introduced the
$5000 LoanCheck by mailing a promotion to existing customers.
The
Keystone database tracks about 300 characteristics for each customer.
These characteristics include whether the person had already opened
loans in the past two years, the number of active credit cards, the
balance levels on those cards, and finally whether or not they responded
to the $5000 LoanCheck offer. Keystone used data mining to sift through
the 300 customer characteristics, find the most significant ones, and
build a model of response to the LoanCheck offer. Then, they applied the
model to a list of 400,000 prospects obtained from a credit bureau.
By
selectively mailing to the best-rated prospects determined by the DM
model, Keystone generated $1.6M in additional net income from 12,000 new
customers.
Reducing costs: Empire Blue Cross/Blue Shield is New
York State's largest health insurer. To compete with other healthcare
companies, Empire must provide quality service and minimize costs.
Attacking costs in the form of fraud and abuse is a cornerstone of
Empire's strategy, and it requires considerable investigative skill as
well as sophisticated information technology.
The latter includes
a data mining application that profiles each physician in the Empire
network based on patient claim records in their database. From the
profile, the application detects subtle deviations in physician behavior
relative to her/his peer group. These deviations are reported to fraud
investigators as a "suspicion index." A physician who performs a high
number of procedures per visit, charges 40% more per patient, or sees
many patients on the weekend would be flagged immediately from the
suspicion index score.
What has this DM effort returned to
Empire? In the first three years, they realized fraud-and-abuse savings
of $29M, $36M, and $39M respectively.
Improving sales
effectiveness and profitability: Pharmaceutical sales representatives
have a broad assortment of tools for promoting products to physicians.
These tools include clinical literature, product samples, dinner
meetings, teleconferences, golf outings, and more. Knowing which
promotions will be most effective with which doctors is extremely
valuable since wrong decisions can cost the company hundreds of dollars
for the sales call and even more in lost revenue.
The reps for a
large pharmaceutical company collectively make tens of thousands of
sales calls. One drug maker linked six months of promotional activity
with corresponding sales figures in a database, which they then used to
build a predictive model for each doctor. The data-mining models
revealed, for instance, that among six different promotional
alternatives, only two had a significant impact on the prescribing
behavior of physicians. Using all the knowledge embedded in the
data-mining models, the promotional mix for each doctor was customized
to maximize ROI.
Although this new program was rolled out just
recently, early responses indicate that the drug maker will exceed the
$1.4M sales increase originally projected. Given that this increase is
generated with no new promotional spending, profits are expected to
increase by a similar amount.
Looking back at this set of
examples, we must ask, "Why was data mining necessary?" For Keystone,
response to the loan offer did not exist in the new credit bureau
database of 400,000 potential customers. The model predicted the
response given the other available customer characteristics. For Empire,
the suspicion index quantified the differences between physician
practices and peer (model) behavior. Appropriate physician behavior was a
multi-variable aggregate produced by data mining - once again, not
available in the database. For the drug maker, the promotion and sales
databases contained the historical record of activity. An automated data
mining method was necessary to model each doctor and determine the best
combination of promotions to increase future sales.
Getting StartedIn
each case presented above, data mining yielded significant benefits to
the business. Some were top-line results that increased revenues or
expanded the customer base. Others were bottom-line improvements
resulting from cost-savings and enhanced productivity. The natural next
question is, "How can my organization get started and begin to realize
the competitive advantages of DM?"
In our experience, pilot
projects are the most successful vehicles for introducing data mining. A
pilot project is a short, well-planned effort to bring DM into an
organization. Good pilot projects focus on one very specific business
need, and they involve business users up front and throughout the
project. The duration of a typical pilot project is one to three months,
and it generally requires 4 to 10 people part-time.
The role of
the executive in such pilot projects is two-pronged. At the outset, the
executive participates in setting the strategic goals and objectives for
the project. During the project and prior to roll out, the executive
takes part by supervising the measurement and evaluation of results.
Lack of executive sponsorship and failure to involve business users are
two primary reasons DM initiatives stall or fall short.
In
reading this article, perhaps you've developed a vision and want to
proceed - to address a pressing business problem by sponsoring a data
mining pilot project. Twisting the old adage, we say "just because you
should doesn't mean you can." Be aware that a capability assessment
needs to be an integral component of a DM pilot project. The assessment
takes a critical look at data and data access, personnel and their
skills, equipment, and software. Organizations typically underestimate
the impact of data mining (and information technology in general) on
their people, their processes, and their corporate culture. The pilot
project provides a relatively high-reward, low-cost, and low-risk
opportunity to quantify the potential impact of DM.
Another
stumbling block for an organization is deciding to defer any data mining
activity until a data warehouse is built. Our experience indicates
that, oftentimes, DM could and should come first. The purpose of the
data warehouse is to provide users the opportunity to study customer and
market behavior both retrospectively and prospectively. A data mining
pilot project can provide important insight into the fields and
aggregates that need to be designed into the warehouse to make it really
valuable. Further, the cost savings or revenue generation provided by
DM can provide bootstrap funding for a data warehouse or related
initiatives.
Source:http://ezinearticles.com/?Digging-Up-Dollars-With-Data-Mining---An-Executives-Guide&id=6052872
