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Data Leveraging Architects: Critical to Optimal Customer Relationship
By Jim Wheaton
Principal, Wheaton Group
Original version of an article that appeared in the
March 15, 2000 issue of "Direct"
Customer Relationship Management is often
an exercise in sophisticated chaos. Massive resources are
poured into "star wars" technology. Advance-degreed data miners
develop complex algorithms to drive overall contact strategy.
Agency creatives jump into the fray with their own unique contributions.
Unfortunately, far too few companies have successfully implemented a
cutting-edge CRM program. All too often, the data miners have
little real-world business experience, and the agency creatives are suspicious
of technology and statistics. To top things off, the technologists resent
the "meddling" from both parties.
The success of any CRM initiative can be ensured by appointing a "data
leveraging architect." This is a seasoned individual with the practical
expertise and vision to coordinate the multiple functional disciplines required
for the success of any complex, data-driven strategy. The best architects
command the respect and cooperation of the technologists, data miners and
creatives. They are able to transform these disparate skill sets into an
integrated team, thereby realizing the full potential of Customer Relationship
The data leveraging architect must drive the CRM initiative from the
beginning. Participation is especially important during the design and
construction of the underlying data warehouse or mart. Without such an
architect, any CRM initiative is at significant risk of failure. At a
minimum, many months are likely to be added to the time that it takes for the
project to begin to realize a return on investment.
The first of the following case studies proves the point by providing a primer
of what not to do. The second vividly illustrates the value that an
experienced data leveraging architect can bring to any Customer Relationship
Case Study #1
A major financial institution
recognized the need for its credit card division to acquire better
customers, and to spend less in doing so. To that end, it
embarked on the construction of a massive prospecting data mart.
A well-known services firm was engaged to build and maintain the
The services firm operated a research and consulting group that provided
predictive modeling as well as quantitative and strategic consulting services
to many of its clients. Because of existing commitments, however, no one
from the group participated in the building of the mart. Also, no data
leveraging architect was appointed to supervise the project. Instead, the
technologists assumed total control.
After six months of intense work, the prospecting data mart was ready to
launch. Representatives from the financial institution, anxious to
display immediate payback to senior management, requested a two-day summit
meeting to develop a comprehensive, data-driven strategy. Several members
of the service firm's research and consulting group were asked to attend.
One hour into the meeting, the brainstorming came to an abrupt and premature
end. The technical folks, in their quest for processing efficiency, had
not included in the mart a running history of several fields that were critical
to the execution of any analytical work. Instead, the values comprising
these fields were over-written each and every month.
The incorporation of this running history necessitated a redesign of the
mart. The unfortunate result was a two-month delay, a loss of credibility
in the eyes of senior management, and a substantial decline in momentum.
At the same time, the financial institution retained a prestigious strategy
firm to review its entire operation. During the launching of the mart,
senior management was presented with the results of the seven-figure
study. An entire section of the report described in detail a
state-of-the-art CRM program.
Senior management, impressed with the strategy firm's recommendations, asked it
to implement the vision. The goal was to build a cutting-edge,
multi-faceted, statistics-based algorithm that would drive all of the
institution's prospect and customer contacts. A team of statisticians was
assigned to the project. The prospecting data mart would be a key input
to the algorithm.
Armed with impressive degrees from elite institutions, the project team's army
of statisticians descended upon the financial institution with a mandate to
revolutionize the way that business was conducted. Unfortunately, no one
on the team had any substantial real world experience. Therefore, when
constructing their cutting-edge algorithm, they neglected to consider mundane
data processing realities such as computer run times. This was a critical
issue with the prospecting data mart, which was being maintained in a legacy
The services firm that was maintaining the data mart asked to be apprised of
the project team's strategy, but to no avail. The project team was
particularly wary of the data leveraging consultants employed within the
service's firm's research and consulting group. These experts, although
privy to invaluable insights about the limitations of the systems under which
the mart was operating, were perceived to be a competitive threat.
Unfortunately, no overall data leveraging architect had been appointed to
insist on the cooperation of all parties. As a result, the project team's
statisticians worked for seven months in a virtual communications vacuum.
The lack of coordination spawned a total disaster. A benchmark test of
the cutting-edge, statistics-based algorithm indicated that it would take 360
CPU days to execute against the entire prospecting data mart. Because
mainframes process multiple jobs at once, this translated into an elapsed time
of between two to four years - an absurdly impracticable situation.
Panic-stricken, the financial institution assembled an emergency team of data
processing experts to develop a solution. Ultimately, about ninety-five
percent of the algorithm was discarded, and the balance rewritten, for the sake
of efficiency. The code that remained was just a shell of the strategy
firm's original vision. Senior management, again, was not impressed.
Case Study #2
A multi-billion dollar
company manufactured products with price points in the thousands
of dollars. These products were marketed to large businesses
primarily through a dedicated sales force, and to small firms and
consumers via direct mail and space ads. Concerned about a
recent loss of market share, the manufacturer retained an outside
consulting firm to develop a comprehensive CRM strategy. The
first step was to construct a data mart containing robust, atomic-level
purchase information and promotion history.
As with any CRM initiative that contains a business-to-business component, it
was critical that the design of the data mart reflect the way in which the
manufacturer viewed its customers. Specifically, there are three levels
at which customer transaction information can be aggregated: the company,
the location, and the individual.
Assume for the sake of simplicity that the manufacturer operated out of two
locations: an executive office at 1426 Pearl Street in Boulder, Colorado,
and a manufacturing facility at 6707 Winchester Circle. Assume also that
it had received orders from Cynthia Baughan at the Pearl Street location, and
Marilyn Margarito and David James at the Winchester Circle address.
A company-level view would consider all of the orders to comprise a single
customer, regardless of the associated individual and location. A
location-level perspective would differentiate Pearl Street from Winchester
Circle. And, an individual-level look would define as discrete entities
Cynthia Baughan, Marilyn Margarito, and David James.
The manufacturer viewed its customers primarily from a location-level
perspective. Accordingly, this was reflected in the initial design
specifications for the data mart. However, the data leveraging architect
recognized that the project scope should be expanded to enhance the mart's
ability to track activity at the individual-level. The vision was to
maintain a relationship with customers as they changed locations and companies
during the course of their careers.
Hygiene is notoriously poor with business-to-business data marts. As
employees move from location to location, and from company to company, most
marts have no mechanism to reflect these changes. Often, third class mail
is sent for years to departed employees. The amount of waste is
The data leveraging architect realized that the manufacturer's sales
representatives were privy to virtually all of their customers' career
changes. The challenge was to provide the mechanism and incentive for
them to input this information into the data mart in an accurate and timely
manner. The solution was to create a structured Graphical User Interface,
and to ensure weekly input via "carrot" and "stick" incentives. The
result was a dramatic enhancement of the CRM strategy's effectiveness.
The Graphical User Interface proved to be especially valuable whenever a loyal
customer would move to a company that had never been receptive to the
manufacturer's products. Such an event would trigger a Marketing
Action/Reaction System, which - in turn - would unleash a flurry of
hyper-targeted prospecting activity. Likewise, when an individual with a
history of hostility towards the manufacturer would jump to an historically
loyal customer, the Marketing Action/Reaction System would instantaneously
activate a series of preventive promotional contacts.
State-of-the-art Customer Relationship
Management requires "star wars" technology as well as highly trained
specialists. Individuals must be hired to work directly with
the technology. Data miners and agency creatives are required
to develop the data-driven promotional campaigns.
But it is the data leveraging architect who must assume the critical role of
combining all of these disparate components into a cost-effective and
coordinated CRM program. Without such an architect, database marketers
often have to explain to senior management why most if not all of their efforts
have been a failure.
Jim Wheaton is a Principal at Wheaton Group, and can be reached at 919-969-8859
or firstname.lastname@example.org. The firm specializes in direct marketing
consulting and data mining, data quality assessment and assurance, and the
delivery of cost-effective data warehouses and marts. Jim is also a
Co-Founder of Data University www.datauniversity.org.