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WRITTEN BY
Jeanne G. Harris
Andersen Consulting Institute
for Strategic Change
http://harris.CRMproject.com
Finding the Customer in Transaction Data
As long as computers have been used in business, organizations have generated
transaction data. Despite improvements in data collection, most businesses
still struggle to develop the very capabilities that prompted them to gather
data in the first place – the ability to aggregate, analyze and use data to make
Jeanne G. Harris is a
senior research fellow at the
informed decisions that lead to action and generate real business value.
Andersen Consulting Institute
Customer data remains one of their most underutilized assets. With the
current research focuses on
for Strategic Change. Her
growing importance of Customer Relationship Management, transforming data
extracting business value from
into meaningful insights is more critical than ever. Which are the primary
knowledge management, and
success factors essential to building broad organizational capabilities for
transaction data, customer
customer intimacy.
transforming data into knowledge and then into business results?
Realizing the Promise of Customer
Relationship Management
Most savvy executives recognize that forging longterm relationships with their key customers is the
route to success in an increasingly competitive and
dynamic marketplace. Executives have long
envisioned a world where customers would replace
products or channels as the centerpiece of their
business. Yet for many senior managers, realigning
their organization to support a customer-driven
strategy remains an elusive goal. This article
explains why transforming customer transaction data
into business results is a critical and challenging
element of any CRM strategy. It also describes how
successful companies foster an analytic capability,
which accelerates the creation and impact of customer insights. Finally it concludes with four critical
success factors essential to creating an organization
that can support a successful CRM strategy.
Data is the Lifeblood of Customer
Relationship Management
Customer Relationship Management is only as good as
the data and knowledge that it relies upon.
Organizations have devoted extensive IT resources to
implementing sales planning systems, knowledge
management systems, marketing decision support,
data warehouses, and call centers. The CRM software
market continues to grow rapidly, up to $16 billion by
2003, according to AMR market research. And the
market for analytic systems is growing just as quickly.
According to one market research organization
(survey.com), today’s market for ”business intelligence and data warehousing” is growing at an annual
growth rate of more than 43 percent, and is expected to exceed $148 billion by 2003. So insufficient
investment in technology to create more data is not
the problem. Every day, transaction data is created
which has the potential to help companies
understand their customers better, and make better
decisions to enhance customer relationships. But
simply amassing customer transaction data doesn’t
assure results. The real problem is that, despite the
terabytes of data coursing throughout organizations
today, not enough of it is actually being used to
create, enhance and sustain customer relationships
and create business value. In short, these CRM
initiatives often fail to generate business results.
It’s not that data doesn’t get turned into knowledge and results at all; nor is it that we don’t really
know how to do it. Some companies are achieving
significant benefits from improved decisions and
better customer interactions. Transforming raw data
into useful knowledge for decision-making just does
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not happen often enough, primarily because
organizations overlook the most important
step in the process – the human realm of
analyzing, interpreting and acting upon data.
Learning from Success
There are companies who have been
successful using the principles of CRM to
create tremendous value for their organizations. Andersen Consulting’s Institute for
Strategic Change recently completed a
study to learn how firms realize value from
their transaction data. The study looked at
over 100 companies to identify those that
had successfully demonstrated their ability
to transform data into knowledge and
results. We studied 20 companies that had
made significant progress towards unleashing the power of their transaction data.
These companies routinely use their
transaction data to identify new customer
segments, improve the profitability of
existing customers, improve the effectiveness of direct mailings, enhance each
customer interaction, and build customer
relationships. On the surface, these companies have little in common. They are in
different industries and have vastly
different customers and market strategies.
But what this diverse set of companies has
in common speaks volumes about what
executives need to know in order to
translate data into results. Some of the
companies who focused on generating and
acting upon customer insights included:
• First Union, a major bank, successfully
implemented a CRM strategy that enables
it to deepen its understanding of who its
customers are and react more quickly to
customer needs. First Union relationship
managers are able to react in real time to
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Analytic capabilities are discussed in
further detail at the following links:
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a significant behavior on the part of a
consumer (e.g., made a major savings
withdrawal or deposit, or purchased a
house). This strategy will contribute
$100 million annually in NIBT (net
income before taxes).
• US West, one of the world’s largest
telecommunications companies, expected
the firm would lose 35 percent of its
customer base when its monopoly was
opened to competition. This presented
the company with a major challenge –
how to grow revenues in an increasingly
competitive market? So it launched a
major CRM initiative designed to increase
sales with existing customers, retaining
the most profitable and potentially profitable ones. Its goal was to transform the
former monopoly into a truly customerdriven entity by leveraging the firm’s
transaction data and marketing information. Today, US West’s executives tout the
company’s CRM strategy to Wall Street
analysts as a key source of competitive
advantage for their firm.
• With continuing consolidation of the
banking industry in the late 1990s, the
watchword at Wachovia Bank, a major
regional bank, became ”profitability.” This
meant quality customers, quality products
and services, and quality growth potential.
An executive vice president at the bank
said, “Aggressively using customer information is the way we keep our customer at
the centerpiece of our relationships.
Wachovia’s competitive position depends
upon our ability to use information faster
and smarter than our competition.”
• Few companies can boast a closer customer relationship than Harley Davidson.
Many customers have such a close relationship to the company, its products, and
the lifestyle they represent, that they will
tattoo the company’s name on their bodies. Harley relies upon its knowledge about
its customers to guide new product
designs and marketing decisions. This
deep customer insight is based upon a
personal commitment to ”living the Harley
lifestyle.” Executives and employees spend
significant time riding with customers and
actively participate in Harley-sponsored
events, such as road rallies.
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• Kraft is a giant in the consumer packaged
goods industry. Nearly one out of every
ten products purchased in U.S. supermarkets is a Kraft product. By transforming
its vast stores of scanner and other transaction data into action-oriented business
knowledge, Kraft is moving towards its
goal of ”undisputed industry leadership.”
A recent Cannondale Associates study of
retailers showed Kraft had risen from
14th to 2nd in having the best customer
insight and the greatest ability to exploit
category information.
Building a Capability,
Not a System
What these companies have in common is a
focus on creating an analytic capability
that enables professionals to leverage customer information – through data aggregation and analysis – to gain customer
insight, make better decisions, and shape
future customer interactions. If data is the
lifeblood of CRM, it is the employees who
are the brains, arms and legs. Without a
high level of human performance, a CRM
initiative is ”brain dead.” Data may be
flowing throughout the company, but
employees lack the knowledge and
resources they need to effectively analyze
data, make decisions, and take the actions
needed to achieve results. Thus, building
an analytic capability to achieve a
well-defined set of business outcomes is
the key to a thriving CRM effort.
Unlike companies who simplistically
view CRM as an IT project, the companies in
our study recognized that they needed to
consider how CRM would impact many different aspects of their business. As one US West
vice president noted, ”It’s not a linear,
staged process. It’s more like managing a 15level chess game because there are 15 CRMrelated projects happening concurrently, and
their interdependencies must be managed.”
The companies in our study shared
four common success factors:
• Employees share a common understanding of strategic objectives and the
outcomes desired.
• Employees have the knowledge, skills,
and experience necessary to use the data.
• Senior management is committed to
building a ”fact-based culture.”
• IT infrastructure and quality data exists
to support an analytic capability.
Beginning with the End in Mind (Focus
on Strategic Objectives and Outcomes)
As Yogi Berra once noted, ”You’ve got to be
very careful if you don’t know where you
are going, because you might not get
there.” The most successful companies
focus on a few key strategic objectives,
along with clearly defined quantifiable outcomes. They place a high priority on ensuring that everyone in the organization is
”on the same page” and understands what
they are striving to accomplish. Having a
common understanding is important in
determining how to allocate resources and
how to implement customer insights. For
example, an executive at Kraft said, ”The
reason why we have done so well (in category management) is that we focus on what
is truly critical.” By maintaining a lasersharp focus on this objective, Kraft can
analyze a product category using a fraction
of the data (and time) of their competitors,
with superior results.
Most successful companies, we found,
take pains to ensure that the conclusions of
their analyses are implemented. ”These
insights only have value if the business
owner takes action,” noted a Fleet Bank
senior vice president. One way that these
organizations focus management attention
on implementing the results of their analyses
is to ensure that their performance metrics
and incentives are aligned with strategic
objectives. The right decision for the company won’t be implemented if it conflicts with
the sales force’s incentive compensation.
The Right Stuff
(Knowledge, Skills and Experience)
An unusual combination of skills and experiences are needed to transform data into
actionable decisions. Even the most sophisticated analytic software needs to be used
by employees with the right knowledge,
experiences, and insights into their company’s objectives. Nearly two-thirds of the
firms in our study cited recruiting, developing and retaining highly skilled employ-
ees with analytic capabilities as a major
challenge to effectively using transaction
data for decision-making. Having the right
mix of skills and experience is crucial, since
no single individual can ”know it all.” Most
companies are aware of the need for knowledge about using analytic software tools.
But just as important is an understanding
of statistical techniques, and how to apply
them to a given problem.
Another often overlooked but vital
expertise is an understanding of the
sources, relationships, and meaning of the
data itself. Most organizations have someone who has a deep understanding of how
their company’s data is produced and transformed. This expertise only comes after
long experience of working at a company.
”[Roger] knows the tribal lore,” said one
marketing information manager. ”We rely
on him to understand the data, and it’s all
in his head.” Such an employee would obviously be difficult and costly to replace.
Identifying, rewarding and sustaining key
individuals with this company specific
knowledge is one of the most effective
ways to speed the transformation of
customer data into meaningful insights.
Finally, the most sophisticated analysis
is useless if it is not effectively communicated to decision-makers. A good analyst
adjusts his or her communication style to fit
the needs of the decision-maker. When analysts lack effective teaming and communication skills, there can be a breakdown in
the decision-making process. Companies
with effective analytic capabilities recognize that analysts and decision-makers must
work together to achieve a common end.
”To be successful, you have to learn the
lingo and be willing to talk about what the
data is saying,” said a marketing program
manager at Hewlett-Packard. ”Now, instead
of two hour conversations, we do it in 15
minutes, and we get a better decision.”
Creating a Fact-Based Culture
By far, the greatest challenge to creating a
thriving CRM analytic capability is developing a ”fact-based culture.” In one survey
at Andersen Consulting’s Institute for
Strategic Change, more than 62 percent of
executives viewed cultural resistance as
the greatest hurdle to achieving significant return on major investments. Kraft
Foods has largely met this challenge. ”Data
is like oxygen at Kraft. Our executives
don’t question the need for it, because we
use it daily,” said the vice president of
marketing information services. But for
most companies, data-driven analysis is
not the driving force behind decision-making. Few employees would openly object to
a CRM initiative that is intended to
improve customer relationships, yet we
repeatedly found that the culture of an
organization can suffocate a CRM analytic
capability. ”At times the culture can seduce
you into thinking you’re making progress
selling CRM,” said a US West employee.
”Only to find out later that nothing has
changed. Trying to change this culture is
like hitting up against a Jell-O wall.”
So how do you create a fact-based,
analytic-friendly culture? First of all, everyone applying the data must believe in the
value of having high quality, credible data,
and in the value of analysis in decisionmaking. Secondly, decision-making processes need to be based upon evidence rather
than opinions.
Culture change of this magnitude is
not easy to accomplish. It requires a major
commitment from top management. The
CEO of Earthgrains made a concerted effort
to change the culture of his organization
by challenging anyone who presented a
recommendation that was unsupported by
facts. ”In God we trust, but all others need
data” became his creed. Senior management commitment to creating a fact-based
culture is one of the most crucial factors for
a successful CRM analytic capability,
because both decision-makers and analysts
must believe in the importance of data
analysis and in the value of having high
quality, credible data.
Creating Stronger Technical
and Data Infrastructures
Although we found that the differentiating
factor between successful and less successful firms was a more holistic approach
encompassing more than just technology
and data initiatives, technology and data
are still important first steps. At times
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companies feel that they simply don’t have
the data they need to even begin to build
a CRM analytic capability. Indeed, the first
thing a firm needs to do is to assess
whether it has a transaction data environment that is sufficiently robust and of sufficient quality to provide data for analysis.
If not, it makes sense to begin putting a
new one in place immediately. As a practical matter, however, no organization has all
the data needed to answer every question
about their customers and their relationships with them. So, instead of deferring
the creation of an analytic capability to
support CRM, our research suggests that an
organization need only have sufficient
quality data to use as a starting point.
As valuable as analyzing transaction
data can be, some of the most insightful
information and knowledge doesn’t even
reside in a transaction system. Intelligently
and selectively integrating transaction data
with externally produced data can lead to
new markets and opportunities. For example, in order to identify new customer segments, knowledge about existing customer
behavior must be compared to the characteristics of potential customers. Typically
such an analysis requires extensive external
information. Similarly, linking transaction
data to other forms of corporate knowledge
(often contained in memos, presentations,
or meetings) can produce far greater customer insight than either approach alone.
At Harley Davidson, for example, the most
valuable customer knowledge was derived
from close observation of customer behavior. Data analysis is viewed as a ”tie-breaker”
used by decision-makers when they are
unable to achieve consensus any other way.
Assuming that some quality data is
available, most companies focus the majority of their CRM resources and attention on
establishing the right technology and data
environments for analysis and decision-
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making. The companies in our study each
used different technical architectures and
tools. Choosing a specific technical architecture or product is far less important than
recognizing the need to create a technical
environment that supports all aspects of a
company’s analytic capability. Companies
generally underestimate the challenges of
establishing an analytic technical architecture that must anticipate and support a
vast array of problems, ranging from ”Who
are our best customers?” to ”How effective
was our last marketing campaign?” Creating
an analytic architecture requires different
software, tools, skills, and even methods
than a traditional transaction application
architecture. These human performance
challenges were common to even the most
successful organizations in our study.
Although performing a complex analysis may require a sophisticated and highly
integrated analytic environment, communication of the analysis to decision-makers
and customers should be in a form that is
”decision-ready.” At Kraft Foods, for example, highly skilled analysts use complex statistical tools for their category management
analyses. But the sales force receives the
results in a business-focused slide presentation that addresses the implications for each
category for an individual grocery manager.
Another ongoing challenge is creating
and maintaining a high quality data environment. Lack of confidence in the data
can fatally undermine any analytic capability. Maintaining data that is accurate,
complete, reliable, accessible, current and
comprehensive is a costly and timeconsuming effort. According to industry
estimates, 60-80 percent of the total cost
of a data warehouse project is spent on
cleaning up and integrating data.
A seemingly simple activity like determining a list of all customers is rarely as
straightforward as it seems. Within a bank,
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for example, the retail customer is the individual or household; the institutional trust
customer is often an institution or group;
the corporate lending customer is a division
or corporation; and the investment customer
is very likely another financial intermediary.
The labor-intensive process of mapping
inconsistent data often requires the time of
the very people who need to be spending
more time with customers–sales and relationship managers. As a result, the problems
of data quality are often organizational, cultural, and human performance issues, rather
than purely technical ones. Maintaining high
quality data remains a challenge for even
the most successful CRM initiatives.
Summary
As long as computers have been used in
business, organizations have generated
transaction data. Yet this data remains one
of our most underutilized assets. With the
growing importance of CRM, transforming
data into meaningful insights is more critical than ever. Despite the hype of technology vendors, computers don’t make and carry
out the decisions in an organization. Skilled
people will continue to be the heart of any
CRM initiative. Employees need to be armed
with the right skills, knowledge, and experience. They must have a clear understanding
of what outcomes they are trying to achieve,
and be supported by an organization
(culture, infrastructure, and metrics), which
effectively implements fact-based decisions.
Building an analytic capability now is
the difference between a ”brain-dead” organization and one that will thrive in the new
reality of the customer-driven marketplace.
To request a free copy of the Andersen
Consulting Institute for Strategic Change
white paper, “Data to Knowledge to Results:
Building an Analytic Capability,” please contact the author at [email protected].