1. 14 Fall 2004
Burden
By D. Randall Brandt, Kunal Gupta, and Jim Roberts
Conventional managerial wisdom holds
that attending to customer satisfaction, value, and loyalty
makes good business sense because it leads to repeat purchasing,
increased share of wallet, positive word of mouth, and a number of
other behaviors that enable companies to achieve desired business
results. In fact, faith in the preceding alleged benefits has set into
motion more than a few customer loyalty initiatives in companies
throughout the United States and around the world.
Still, in business—as in spiritual matters—faith will be put to the
test. At some point, no matter what their stated beliefs or commit-
ments, senior managers, employees, and shareholders will demand evi-
dence of the “bottom-line” impact of customer satisfaction, value, and
loyalty. They will demand evidence that investments made in managing
of proof
Reprinted with permission fromMarketing Research, Fall 2004, published by the American Marketing Association.

2. Customer-centric measures
must be linked to financial
performance.

3. relationships with customers, as well as those made in relevant
process and quality improvements, actually contribute to
growth in revenue, profitability, and other financial and mar-
ket performance outcomes. In the process, it is very likely that
the burden of proof will be placed squarely on the shoulders of
marketers and marketing researchers.
Assumptive Linkage vs. Direct Linkage
One method of demonstrating the financial or market
impact of customer satisfaction, value, and loyalty on business
results is assumptive linkage. Estimates or assumptions are
made regarding the financial/market worth of a customer.
These estimates usually are derived from such sources as his-
torical data on customer accounts, financial records, and/or
market studies. Combining such estimates with data on cus-
tomer satisfaction, dissatisfaction, and/or market behavior
intentions can help companies project the financial/market
impact of satisfaction or dissatisfaction.
As an example, consider that a provider of auto insurance
currently has 600,000 policyholders and that the current
average annual value of each policy is $1,000. Based upon
a recent survey of the company’s policyholders, assume
the following:
• During the past 12 months, 40% of the policyholders experi-
enced some service problem or failure (an estimated 240,000
of the 600,000 total customer base).
• About 80% of these policyholders reported the problem to
the company, and 20% did not (192,000 and 48,000 of the
240,000 having experienced a problem, respectively).
• Of those who contacted the company, 40% were very satis-
fied with the company’s response (76,800 of the 192,000).
• Of those who contacted the company, 35% were only par-
tially satisfied with the company’s response (67,200 of the
192,000).
• Of those who contacted the company, 25% were dissatisfied
with the company’s response (48,000 of the 192,000).
• All of the very satisfied policyholders intend to renew their
policies during the coming year.
• Of the partially satisfied policyholders, 5% say they will not
renew as a result of their problem experience (3,400 of the
67,200 partially satisfied customers).
• Of the dissatisfied policyholders, 40% say they will not
renew as a result of their problem experience (19,200 of the
48,000 dissatisfied customers).
• Of the policyholders who experienced a problem, but did
not report the problem to the company, 10% will not renew
as a result of their problem experience (4,800 of the 48,000
who did not report the problem).
Based on the data, it is projected that a total of 27,400 cus-
tomers will not renew their policies. At an average annual
value of $1,000, the projected cost of customer dissatisfaction
is $27.4 million—not counting additional revenues that may
be lost due to negative word-of-mouth communications from
the disgruntled former policyholders.
It is one thing to tell managers that 25% of customers who
reported a service problem were dissatisfied with the com-
pany’s response, but it is quite another to demonstrate that the
potential cost of customer dissatisfaction with problem han-
dling could exceed $27 million. More often than not, framing
the impact of customer satisfaction or dissatisfaction in finan-
cial terms makes the stronger statement.
As a means of demonstrating the business impact of cus-
tomer satisfaction and dissatisfaction, assumptive linkage has
at least three benefits. First, it does not require a tremendous
amount of data, and the data required are relatively easy to
obtain. Second, it is relatively straightforward and needs not
employ sophisticated or complex statistical or other computa-
tional techniques (although such techniques may be utilized).
Third, this method has high heuristic appeal because it pro-
vides a relatively simple means of enabling managers to visual-
ize the ramifications of improving customer satisfaction
and/or reducing dissatisfaction.
Despite its benefits, assumptive linkage also suffers from
several shortcomings. It is highly dependent on assumptions
regarding the financial or market worth of a customer. So, to
the extent that these assumptions are erroneous, so will be the
financial/market impact projections. The method also relies
heavily on measures of behavioral intent, and it is well-known
that what buyers say they are going to do in the future is not
always confirmed by their actual purchases or consumption
behavior. And, unless an organization makes an effort to track
actual market behaviors or financial results, it’s impossible to
confirm or refute assumptive linkage analyses. The accuracy of
such analyses becomes nearly impossible to evaluate.
Given the limitations, some managers are skeptical of
and/or hesitant to use the method of assumptive linkage. The
potential accuracy of assumptive linkage analyses may be
improved if results are presented in a probabilistic context
rather than as point estimates. Instead, these managers seek to
establish direct linkages among measures of customer satisfac-
tion, value, and loyalty, as well as actual business results.
16 Fall 2004
Executive Summar y
While customer satisfaction, value, and loyalty are gath-
ered via the same survey instruments and are often com-
bined to form a core index of customer affinity, each reflects
a somewhat distinct aspect of customer affinity toward a
brand or firm. This article furnishes an overview of two basic
approaches to building the financial case for the three
aspects. It examines the benefits and limitations of each
method, along with other factors that must be considered in
planning and executing a customer-to-financial linkage effort.

4. For example, in a national survey of business decision mak-
ers who were responsible for selecting computer hardware and
software vendors for their respective companies, each decision
maker was asked to evaluate his/her satisfaction with and
commitment to a specific vendor with whom his/her company
currently was doing business. Based on their responses to
questions regarding overall satisfaction, likelihood to recom-
mend the vendor, and desire to continue doing business with
that vendor in the future, each decision maker was placed into
one of four customer loyalty segments:
Loyal. These decision makers said they were very satisfied
with, definitely would recommend, and definitely would con-
tinue doing business with this vendor.
Satisfied. These decision makers said they were basically
satisfied with, probably would recommend, and probably
would continue doing business with this vendor.
Vulnerable. These decision makers said they were
marginally satisfied with, might or might not recommend, and
might or might not continue doing business with this vendor.
Dissatisfied. These are decision makers who said they were
dissatisfied with, probably/definitely would not recommend,
and probably/definitely would not continue doing business
with this vendor.
One year later, the same decision makers were again sur-
veyed to determine if they were still doing business with the
same vendors and, if so, whether they were doing more, less,
or about the same volume of business as the preceding year.
Results, illustrated in Exhibit 1, indicate a clear and posi-
tive relationship between customer satisfaction and loyalty
and actual customer retention. Similarly, results presented in
Exhibit 2 on page 19 show that decision makers in the “loyal”
segment were (a) less likely to have reduced and (b) more
likely to have increased a vendor’s share of business volume
than decision makers who reported lower levels of customer
satisfaction with and commitment to their respective vendors
one year earlier. In short, the greater the level of satisfaction
and commitment reported by a customer, the more likely a
vendor was to retain and gain share of that customer’s busi-
ness in the future.
Proponents of direct linkage cite at least two advantages of
this method over assumptive linkage. First, direct linkage
focuses on actual market behaviors and outcomes and, as a
result, frequently has stronger “face validity” with senior man-
agers and other stakeholders who are seeking evidence of the
financial/market impact of customer satisfaction, value, and
loyalty. Second, because it focuses on actual market behaviors
and outcomes, direct linkage provides a means of developing
predictive models and performing “what-if” simulations.
In their article, “The Missing Link,” in the Winter 2003
issue of Marketing Research, Kyle Lundby and Colleen
Rasinowich discussed some of the steps that should be under-
taken in connection with direct linkage. We will attempt to
build upon the points made by those authors by focusing on
three key issues.
1. The Right Choices
Managers may be inclined initially to focus on how mea-
sures of customer satisfaction and/or loyalty are linked to
financial and market performance. However, recent research
suggests that customer satisfaction and loyalty measures may
be more relevant in some industries than in others. (See
Reichheld, F. F. (2003), “ The One Number You Need to
Grow,” Harvard Business Review (December), 46-54; and
Brandt, D.R., K. Gupta, and J. Kershaw (2004), “Building an
Index of Customer Affinity: An Empirical Examination of
Alternative Approaches,” paper presented at the AMA
Advance Research Techniques Forum, Whistler, British
Columbia, Canada (June 13-15).)
Moreover, customer loyalty may not be the best leading
indicator of business results under certain market conditions.
For example, to the extent that buyers perceive little or no dif-
ference in the quality of products or services provided by com-
petitors in a marketplace, such products and services are
essentially viewed as commodities. Thus, price becomes the
dominant basis of competition. Under such circumstances, an
organization may have trouble linking measures of customer
loyalty to financial or market results.
In Managing Customer Value (Free Press, 1994), Brad Gale
describes how AT&T struggled during the 1980s to establish a
link between changes in the company’s index of customer sat-
isfaction and changes in customer retention, defection, and
market share. Only after it was discovered that many buyers
perceived they could get satisfactory or even equivalent service
quality from AT&T’s competitors—at comparable or even
lower prices—did the company shift its focus from an index of
customer satisfaction to a measure of customer value.
Specifically, when AT&T turned its attention to the customers’
perception of whether products/services received were “worth
what you paid for them,” the company was able to create a
“value index.” In a short time, AT&T was able to establish a
strong, positive relationship between changes in the customer
value index and changes in market share. Thus, customer-per-
ceived value—not loyalty—proved to be the right measure for
linkage analysis.
Selecting the right set of market or financial performance
measures also poses a challenge. Frequently, managers attempt
marketing research 17
Exhibit 1 Linking satisfaction to retention
Customer loyalty segment
Percentage
of retained
customers
Loyal Satisfied Vulnerable Dissatisfied
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
30%
25%
57%
88%

5. to establish a direct, positive correlation between measures of
customer satisfaction, value, and/or loyalty and measures such
as revenues or units sold per customer. However, nearly as
often, results reveal little or no direct association between
these customer and financial measures. Quite often, it’s only
possible to establish a meaningful link when critical mediating
variables are added to the analysis.
To illustrate, consider a B2B setting in which two different
customers purchase the same number of products from
Company X, generating the same amount of sales revenue—
let’s say $50,000 annually. Yet one of these two customers
might report a very high level of satisfaction and loyalty, while
the other reports only moderate satisfaction and loyalty. Such
a result might indicate that customer satisfaction/loyalty and
customer spending have little to do with
each other. However, by taking into
account Company X’s relative share of
each customer’s total purchases, the
annual revenue generated by each cus-
tomer can be properly transformed or
adjusted, producing a more meaningful
link between the customer and financial
metrics. In this case, it would not be sur-
prising to find that the $50,000 gener-
ated by the highly satisfied/loyal cus-
tomer represents nearly all of his/her
spending in the product category, while
the $50,000 generated by the less satis-
fied/loyal customer represents only a
portion of his or
her spending.
One way to increase the chances of
selecting the right measures for linkage
analysis is to build a linkage blueprint
before attempting to assemble and ana-
lyze data. (Lundby and Rasinowich refer to
such a blueprint as a “working model.”) A link-
age blueprint, typically developed by a cross-functional team
of managers and researchers, depicts expected causal relation-
ships among key customer and financial/market variables. By
building and documenting a linkage model or blueprint prior
to assembling and analyzing data, managers and researchers
are forced to “think through” the factors that ultimately shape
the strength and form of relationships among key customer
and financial or market performance metrics.
2. Relevant Data
Identifying and blueprinting appropriate customer and
financial/market performance measures is a critical first step in
linkage analysis. However, successful negotiation of this first
step is of little practical value unless relevant data can be
assembled in a form that enables researchers to “breathe life”
into the linkage blueprint.
It is not uncommon to discover that data are unavailable
for some of the variables included in the linkage blueprint.
Specifically, while many organizations may have considerable
amounts of financial and operational data, they often lack the
customer and/or other stakeholder data needed to complete
linkage analysis. Until such data are acquired, such analysis
simply cannot be performed. The “good” news is that estab-
lishing this gap between required vs. available data often cre-
ates heightened awareness of the need to conduct appropriate
marketing or customer research.
Even when data are available, they often reside in separate
databases that are not easily integrated. Also, data ownership
can present a challenge. As W. Scheimann observed in
Organizational Surveys (Jossey-Bass, 1996), “Organizations
face some significant problems when they must integrate
financial and non-financial information in order to achieve
alignment…Data from these different sources historically have
been guarded by different functions, such as finance and mar-
keting. These guardians have been reluctant to share infor-
mation that is part of their turf. After all, information has
given them power and has been their unique way of adding
value to the organization. Sharing information opens them
to a variety of risks—loss of ownership, control,
and power—that they would prefer to avoid.”
In short, data location and/or ownership can
be impediments to linkage, even when the
required data are “available.”
Another issue that must be addressed cen-
ters on identifying a unit of analysis that makes
it possible for customer and financial or market performance
data to be linked meaningfully. In our experience, the data
may be organized in relation to two basic units of analysis: (1)
individual customers or accounts and (2) aggregates or organi-
zational entities (e.g., branches, stores, districts, etc.).
The study of B2B purchase decision makers described previ-
ously illustrates the use of individual customers as units of anal-
ysis. That is, the customer satisfaction and loyalty data from
each decision maker were linked to retention and volume of
purchases reported by those same decision makers. Essentially,
the data matrix used for the customer survey and market behav-
ior data consisted of a field of columns corresponding to the sat-
isfaction and loyalty measures as well as another field of
columns corresponding to the retention and purchase volume
measures. Each row of the matrix provided the observed values
of the above measures for a given decision maker.
Quite often, organizations are structured around outlets
such as stores, branches, or other decentralized entities. For
each of these entities, it’s possible to assemble relevant opera-
tional, employee, customer, and/or financial performance data
18 Fall 2004
The greater the level of satisfaction and
commitment reported by a customer, the more likely
a vendor was to retain and gain share of that
customer’s business in the future.

6. and to conduct linkage analysis using the entities themselves as
data points or units of analysis. Such an approach was used by
Sears to demonstrate the impact of employee attitudes
and commitment, customer attitudes and buying
behavior, and financial performance.
In some situations, it’s possible to analyze data in
relation to both individual and aggregate level units
within a common analytical framework. So-called
multi-level designs are often employed to analyze data
when some linkages are examined using individuals as
units of analysis, but these individuals are nested
within regions, branches, or other organizational
structures. For example, hierarchical linear modeling
(HLM) may be used to examine how the link between
stated customer loyalty and customer purchase behav-
ior varies across different branches or markets. Use of
a multi-level approach adds sampling complications
to the design and analysis scenario, but should be con-
sidered whenever it is feasible.
An additional issue for linkage analysis centers on
the temporal frame of reference. Cross-sectional anal-
yses attempt to establish a link between customer sat-
isfaction, value, and loyalty, as well as business
results, based on data collected at a single point in
time or time frame. Longitudinal analyses attempt to
establish such a link based on data collected over mul-
tiple points in time. Generally speaking, models that
link measures of customer satisfaction, value, and loy-
alty gathered at an earlier point in time to customer
purchase or other business results data gathered at a
later point in time have greater explanatory power
than cross-sectional models (i.e., models in which cus-
tomer and business results data from a common time
frame are linked).
The study of B2B purchase decision makers
described earlier takes a longitudinal frame of refer-
ence. That is, the customer satisfaction and loyalty
survey data were gathered at an earlier point and then
linked to retention and purchase volume data that
were gathered at a later time. Exhibit 3 provides examples of
linkage that might result from other units of analysis and tem-
poral frames of reference.
The examples in Exhibit 3 don’t represent all the possible
ways to establish direct linkages among customer and finan-
cial/market metrics, but they illustrate the essence of this
method and highlight its main distinction from the method of
assumptive linkage: Direct linkage focuses on actual, as
opposed to possible and/or probable financial/market results.
3. Important Contextual Influences
A number of factors can influence an organization’s ability
to establish or demonstrate the linkage between measures of
customer satisfaction, value, and/or loyalty and financial and
marketplace performance indicators. Among these are avail-
ability of buyer options, business or market cycles, and con-
sumption cycles.
Consider results from a recent study of customer satisfaction
with cable television service, which attempted to establish a
direct link between customer satisfaction and buying behavior.
marketing research 19
Exhibit 2 Satisfaction vs. change in purchasing share
Exhibit 3 Temporal frame of reference
Customer loyalty segment
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Percentage
of customers
Loyal Satisfied Vulnerable Dissatisfied
33%
48%
20%
64%
14%
74%
8%
83%
s Increased
s Decreased
Analysis to determine if a
customer’s survey rating on
“value of services for fees paid”
is positively associated with the
number of different accounts the
customer has with a financial
services organization, across a
sample of current customers.
Analysis to determine if the prob-
ability of a customer renewing
his/her auto insurance policy at a
later time is positively related to
his/her level of satisfaction with
the insurance carrier at an earlier
time, across a sample of policy
holders surveyed six months
before their policies were due for
renewal.
Analysis to determine if a trend in
the percentage of customers who
have enlisted the services of a real
estate company as a result of a
friend’s recommendation is
positively related to the change in
the percentage of current or recent
customers who say they
“definitely would” recommend
that company to a friend.
Analysis to determine if annual
sales revenues and gross sales
margins increase as the percent-
age of buyers who say they
“definitely would buy again”
increases, across all North
American dealerships participat-
ing in annual customer
satisfaction surveys.
Cross-sectional Longitudinal

7. Compare the behavioral responses of “dissatisfied” cable
customers to those of “satisfied” ones. During the six-month
period after they were surveyed, significantly more dissatisfied
customers either discontinued or “downgraded” their use of
cable service than did satisfied customers, which is expected if
customer satisfaction and buying behavior are positively
related. However, note that a slightly higher percentage of dis-
satisfied customers actually “upgraded” their service than did
satisfied ones. In the absence of an alternative, these disgrun-
tled customers may have decided to “bite the bullet” and spend
more with the cable company despite their dissatisfaction.
Whether due to market structure, contractual obligations,
or other reasons, when buyer switching options are limited or
even unavailable, it’s often difficult to establish a direct, posi-
tive, and/or clear relationship between customer satisfaction
and buying behavior. This makes the method of assumptive
linkage the only viable approach to linkage analysis. That is,
managers must focus on what might happen if customers did
have an option to buy from more than one provider.
Business and market cycles also must be considered in
determining the best approach to linkage analysis. For exam-
ple, in some industries, buyer demand periodically outstrips
the available supply of a given product and/or the ability of
the producers to provide it. Under such “sold-out” market
conditions, it may be difficult to establish a direct link between
customer loyalty or preference and actual buying behavior. To
illustrate, buyers of industrial chemicals may well have a pre-
ferred supplier for such products. However, if the preferred
supplier is not able to provide this product in the quantities
required by the customer (or at all), these customers probably
will attempt to buy the product from an alternative supplier.
In this case, the dynamics of product demand and availability
take over, and the positive effects of customer satisfaction,
preference, and loyalty become nearly impossible to detect—
even though they might be quite evident under ordinary mar-
ket conditions. Thus, when attempting to link measures of
customer satisfaction, value, and loyalty to business results,
organizations must be acutely aware of the presence of such
market conditions at any given time in their industry. Failure
to do so could lead to the erroneous conclusion that there’s no
connection between customer satisfaction/preference and buy-
ing behavior. Such a conclusion, in turn, might discourage
management from making necessary investments in building
customer satisfaction and loyalty.
For products and services purchased and consumed on a
regular and relatively frequent basis, it’s not difficult to adopt
an approach in which measures of the customer’s satisfaction,
perceived value, and likelihood to buy again are linked to
his/her subsequent purchases. In fact, in some industries, it
may take relatively little time to establish a direct link
between customer satisfaction and business results using
such an approach.
In contrast, for products and services that have a relatively
infrequent or long-term purchase and consumption cycle, such
an approach may not be viable. If a real estate company
wishes to determine the extent to which seller satisfaction
leads to repeat business, that company must confront an inter-
esting challenge: Given that 10 or more years may pass before
the seller once again has need of the company’s services,
adopting an approach where each seller’s behavior is traced to
his/her satisfaction with the previous sale is neither practical
nor efficient. Instead, the real estate company might opt to
examine the relationship between seller satisfaction and sales
volume across different geographical areas or markets (if the
company’s organizational structure makes this possible).
Alternatively, the company could begin monitoring and corre-
lating trends in seller satisfaction with changes in some mea-
sure of business performance—such as the incidence of repeat
customers or the percentage of clients having selected the com-
pany on the basis of seller referrals.
In sum, both assumptive linkage and direct linkage offer
organizations potentially useful techniques for assessing the
financial or market impact of customer satisfaction, value, and
loyalty. The keys to using these techniques effectively are: (a)
selection of metrics that reflect the organization’s critical suc-
cess factors and business objectives, (b) the ability to recognize
critical contextual influences, and (c) assembly and analysis of
data that take both of the preceding factors into account. q
Additional Reading
Allen, D. R. and M. Wilburn (2002), Linking Customer and
Employee Satisfaction to the Bottom Line. Milwaukee: ASQ
Press.
Rucci, A S. Kirn and R. Quinn (1998), “The Employee-
Customer-Profit Chain at Sears,” Harvard Business Review,
76, 83-97.
D. Randall Brandt was formerly senior vice president, cus-
tomer loyalty and relationship management, at Burke Inc., a
Cincinnati, Ohio-based research and consulting firm. He may
be reached at randy.brandt@maritz.com. Kunal Gupta is
senior consultant, decision sciences, at Burke and may be
reached at kunal.gupta@burke.com. Jim Roberts is vice
president, decision sciences, at Burke and may be reached
at jim.roberts@burke.com.
20 Fall 2004
Exhibit 4 Satisfaction and behavior in a monopoly
Change in service level
80%
70%
60%
50%
40%
30%
20%
10%
0%
Percentage
of customers
Disconnect Downgrade Maintain Upgrade
10% 12%
7%
15%
71%
59%
12% 15%
s Satisfied
s Unsatisfied