Return on Marketing: Using Customer Equity to Focus Marketing Strategy
Author(s): Roland T. Rust, Katherine N. Lemon and Valarie A. Zeithaml
Reviewed work(s):
Source: Journal of Marketing, Vol. 68, No. 1 (Jan., 2004), pp. 109-127
Published by: American Marketing Association
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Roland T. Rust, Katherine N. Lemon, & Valarie A. Zeithaml
Return on Marketing:
Using Customer Equity to Focus
Marketing Strategy
The authors present a unified strategic framework that enables competing marketing strategy options to be traded
off on the basis of projected financial return, which is operationalized as the change in a firm's customer equity rel-
ative to the incremental expenditure necessary to produce the change. The change in the firm's customer equity is
the change in its current and future customers' lifetime values, summed across all customers in the industry. Each
customer's lifetime value results from the frequency of category purchases, average quantity of purchase, and
brand-switching patterns combined with the firm's contribution margin. The brand-switching matrix can be esti-
mated from either longitudinal panel data or cross-sectional survey data, using a logit choice model. Firms can ana-
lyze drivers that have the greatest impact, compare the drivers' performance with that of competitors' drivers, and
project return on investment from improvements in the drivers. To demonstrate how the approach can be imple-
mented in a specific corporate setting and to show the methods used to test and validate the model, the authors
illustrate a detailed application of the approach by using data from the airline industry. Their framework enables
what-if evaluation of marketing return on investment, which can include such criteria as return on quality, return on
advertising, return on loyalty programs, and even return on corporate citizenship, given a particular shift in cus-
tomer perceptions. This enables the firm to focus marketing efforts on strategic initiatives that generate the great-
est return.
The Marketing Strategy Problem
Top managers are constantly faced with the problem of
how to trade off competing strategic marketing initia-
tives. For example, should the firm increase advertis-
ing, invest in a loyalty program, improve service quality, or
Roland T. Rust is David Bruce Smith Chair in Marketing, Director of the
Center for e-Service, and Chair of the Department of Marketing, Robert H.
Smith School of Business, University of Maryland (rrust@ rhsmith.umd.
edu). Katherine N. Lemon is Associate Professor, Wallace E. Carroll
School of Management, Boston College (e-mail: lemonka@bc.edu).
Valarie A. Zeithaml is Roy and Alice H. Richards Bicentennial Professor
and Senior Associate Dean, Kenan-Flagler School of Business, University
of North Carolina, Chapel Hill (e-mail: zeithamv@bschool.unc.edu). This
research was supported by the Marketing Science Institute, University of
Maryland's Center for e-Service, and the Center for Service Marketing at
Vanderbilt University. The authors thank Northscott Grounsell, Ricardo
Erasso, and Harini Gokul for their help with data analysis, and they thank
Nevena Koukova, Samir Pathak, and Srikrishnan Venkatachari for their
help with background research.The authors are grateful for comments and
suggestions provided by executives from IBM, Sears, DuPont, General
Motors, Unilever, Siemens, Eli Lilly, R-Cubed, and Copernicus. They also
thank Kevin Clancy, Don Lehmann, Sajeev Varki, Jonathan Lee, Dennis
Gensch, Wagner Kamakura, Eric Paquette, Annie Takeuchi, and seminar
participants at Harvard Business School, INSEAD, London Business
School, University of Maryland, Cornell University, Tulane University, Uni-
versity of Pittsburgh, Emory University, University of Stockholm, Norwe-
gian School of Management, University of California at Davis, and Mon-
terrey Tech; and they thank participants in the following: American
Marketing Association (AMA) Frontiers in Services Conference, MSI Cus-
tomer Relationship Management Workshop, MSI Marketing Metrics Work-
shop, INFORMS Marketing Science Conference, AMA A/RfT Forum, AMA
Advanced School of Marketing Research, AMA Customer Relationship
Management Leadership Program, CATSCE, and QUIS 7.
none of the above? Such high-level decisions are typically
left to the judgment of the chief marketing or chief executive
officers, but these executives frequently have little to base
their decisions on other than their own experience and intu-
ition. A unified, data-driven basis for making broad, strate-
gic marketing trade-offs has not been available. In this arti-
cle, we propose that trade-offs be made on the basis of
projected financial impact, and we provide a framework that
top managers can use to do this.
Financial Accountability
Although techniques exist for evaluating the financial return
from particular marketing expenditures (e.g., advertising,
direct mailings, sales promotion) given a longitudinal his-
tory of expenditures (for a review, see Berger et al. 2002),
the approaches have not produced a practical, high-level
model that can be used to trade off marketing strategies in
general. Furthermore, the requirement of a lengthy history
of longitudinal data has made the application of return on
investment (ROI) models fairly rare in marketing. As a
result, top management has too often viewed marketing
expenditures as short-term costs rather than long-term
investments and as financially unaccountable (Schultz and
Gronstedt 1997). Leading marketing companies consider
this problem so important that the Marketing Science Insti-
tute has established its highest priority for 2002-2004 as
"Assessing Marketing Productivity (Return on Marketing)
and Marketing Metrics." We propose that firms achieve this
financial accountability by considering the effect of strategic
marketing expenditures on their customer equity and by
relating the improvement in customer equity to the expendi-
ture required to achieve it.
Journal of Marketing
Vol. 68 (January 2004), 109-127 Return on Marketing / 109
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Customer Equity
Although the marketing concept has reflected a customer-
centered viewpoint since the 1960s (e.g., Kotler 1967), mar-
keting theory and practice have become increasingly
customer-centered during the past 40 years (Vavra 1997, pp.
6-8). For example, marketing has decreased its emphasis on
short-term transactions and has increased its focus on long-
term customer relationships (e.g., ffakansson 1982; Stor-
backa 1994). The customer-centered viewpoint is reflected
in the concepts and metrics that drive marketing manage-
ment, including such metrics as customer satisfaction
(Oliver 1980), market orientation (Narver and Slater 1990),
and customer value (Bolton and Drew 1991). In recent
years, customer lifetime value (CLV) and its implications
have received increasing attention (Berger and Nasr 1998;
Mulhern 1999; Reinartz and Kumar 2000). For example,
brand equity, a fundamentally product-centered concept, has
been challenged by the customer-centered concept of cus-
tomer equity (Blattberg and Deighton 1996; Blattberg, Getz
and Thomas 2001; Rust, Zeithaml, and Lemon 2000). For
the purposes of this article, and largely consistent with Blat-
tberg and Deighton (1996) but also given the possibility of
new customers (Hogan, Lemon, and Libai 2002), we define
customer equity as the total of the discounted lifetime values
summed over all of the firm's current and potential
customers .1
Our definition suggests that customers and customer
equity are more central to many firms than brands and brand
equity are, though current management practices and met-
rics do not yet fully reflect this shift. The shift from product-
centered thinking to customer-centered thinking implies the
need for an accompanying shift from product-based strategy
to customer-based strategy (Gale 1994; Kordupleski, Rust,
and Zahorik 1993). In other words, a firm's strategic oppor-
tunities might be best viewed in terms of the firm's opportu-
nity to improve the drivers of its customer equity.
Contribution of the Article
Because our article incorporates elements from several liter-
ature streams within the marketing literature, it is useful to
point out the relative contribution of the article. Table 1
shows the contribution of this article with respect to several
streams of literature that influenced the return on marketing
conceptual framework. Table 1 shows related influential lit-
erature streams and exemplars of the stream, and it high-
lights key features that differentiate the current effort from
previous work. For example, strategic portfolio models, as
Larreche and Srinivasan (1982) exemplify, consider strate-
gic trade-offs of any potential marketing expenditures. How-
ever, the models do not project ROI from specific expendi-
tures, do not model competition, and do not model the
behavior of individual customers, their customer-level brand
switching, or their lifetime value. Our model adds to the
'For expositional simplicity, we assume throughout much of the
article that the firm has one brand and one market, and therefore we
use the terms "firm" and "brand" interchangeably. In many firms,
the firm's customer equity may result from sales of several brands
and/or several distinct goods or services.
strategic portfolio literature by incorporating those
elements.
Three related streams of literature involve CLV models
(Berger and Nasr 1998), direct marketing-motivated models
of customer equity (e.g., Blattberg and Deighton 1996; Blat-
tberg, Getz, and Thomas 2001), and longitudinal database
marketing models (e.g., Bolton, Lemon, and Verhoef 2004;
Reinartz and Kumar 2000). Our CLV model builds on these
approaches. However, the preceding models are restricted to
companies in which a longitudinal customer database exists
that contains marketing efforts that target each customer and
the associated customer responses. Unless the longitudinal
database involves panel data across several competitors, no
competitive effects can be modeled. Our model is more gen-
eral in that it does not require the existence of a longitudinal
database, and it can consider any marketing expenditure, not
only expenditures that are targeted one-to-one. We also
model competition and incorporate purchases from com-
petitors (or brand switching), in contrast to most existing
models from the direct marketing tradition.
The financial-impact element of our model is foreshad-
owed by two related literature streams. The service profit
chain (e.g., Heskett et al. 1994; Kamakura et al. 2002) and
return on quality (Rust, Zahorik, and Keiningham 1994,
1995) models both involve impact chains that relate service
quality to customer retention and profitability. The return on
quality models go a step farther and explicitly project finan-
cial return from prospective service improvements. Follow-
ing both literature streams, we also incorporate a chain of
effects that leads to financial impact. As does the return on
quality model, our model projects ROI. Unlike other mod-
els, our model facilitates strategic trade-offs of any prospec-
tive marketing expenditures (not only service improve-
ments). We explicitly model the effect of competition-an
element that does not appear in the service profit chain or
return on quality models. Also different from prior research,
our approach models customer utility, brand switching, and
lifetime value.
Finally, we compare the current article with a recent
book on customer equity (Rust, Zeithaml, and Lemon 2000)
that focuses on broad managerial issues related to customer
equity, such as building a managerial framework related to
value equity, brand equity, and relationship equity. The book
includes only one equation (which is inconsistent with the
models in this article). Our article is a necessary comple-
ment to the book, providing the statistical and implementa-
tion details necessary to implement the book's customer
equity framework in practice. The current work extends the
book's CLV conceptualization in two important ways: It
allows for heterogeneous interpurchase times, and it incor-
porates customer-specific brand-switching matrices. In sum-
mary, the current article has incorporated many influences,
but it makes a unique contribution to the literature.
Overview of the Article
In the next section, on the basis of a new model of CLV, we
describe how marketing actions link to customer equity and
financial return. The following section describes issues in
the implementation of our framework, including data
options, model input, and model estimation. We then present
110 I Journal of Marketing, January 2004
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TABLE
1
Comparing
the
Return
on
Marketing
Model
with
Existing
Marketing
Models
Type
of
Model Strategic
portfolio
CLV Direct
marketing: customer equity
Strategic Trade- offs
of
Any
Marketing
Exemplars
Expenditures
Larrech6
and
Srinivasan
(1982)
Berger
and
Nasr
(1998)
Blattberg
and
Deighton (1996); Blattberg, Getz,
and
Thomas
(2001)
ROI
Modeled
and
Calculated?
Explicitly Models
Competition?
Calculation
of
CLV?
Can
Be
Applied
to
Most
Industries?
Net
Present
Value
of
Revenues
and
Costs?
Brand
Switching Modeled
at
Customer
Statistical
Level?
Details?
No
Yes
Yes
Yes
Yes No
No
No
No
No
Yes
No
No
Yes
Yes
No
No
No
Yes
Yes
No
Yes
Yes
Yes
Longitudinal
database marketing
Bolton,
Lemon,
and
Yes
Verhoef
(2004);
Reinartz
and
Kumar
(2000)
No,
unless
panel
data
No,
unless
panel
data
Yes
Yes
No
Yes
Yes
No
No
No
No
Yes
Yes
No
No
No
Yes
Yes
Yes
No
Yes
Yes
Yes
No
No
No
No
No
Yes
Yes
Yes
Service
profit
chain
Return
on
quality
Customer
equity
book
Heskett
et
al.
(1994);
Kamakura
et
al.
(2002)
Rust,
Zahorik, and
Keiningham (1994,1995) Rust,
Zeithaml,
and
Lemon (2000)
Return
on
Current
paper
Yes
marketing
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Return on Marketing / 111
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an example application to the airline industry, showing some
of the details that arise in application, in testing and validat-
ing our choice model, and in providing some substantive
observations. We end with discussion and conclusions.
Linking Marketing Actions to
Financial Return
Conceptual Model
Figure 1 shows a broad overview of the conceptual model
that we used to evaluate return on marketing. Marketing is
viewed as an investment (Srivastava, Shervani, and Fahey
1998) that produces an improvement in a driver of customer
equity (for simplicity of exposition, we refer to an improve-
ment in only one driver, but our model also accommodates
simultaneous improvement in multiple drivers). This leads
to improved customer perceptions (Simester et al. 2000),
which result in increased customer attraction and retention
(Danaher and Rust 1996). Better attraction and retention
lead to increased CLV (Berger and Nasr 1998) and customer
equity (Blattberg and Deighton 1996). The increase in cus-
tomer equity, when considered in relation to the cost of mar-
FIGURE 1
Return on Marketing
Marketing investment
Increased CLV
Increased
customer equity
Return on marketing investment
keting investment, results in a return on marketing invest-
ment. Central to our model is a new CLV model that incor-
porates brand switching.
Brand Switching and CLV
It has long been known that the consideration of competing
brands is a central element of brand choice (Guadagni and
Little 1983). Therefore, we begin with the assumption that
competition has an impact on each customer's purchase
decisions, and we explicitly consider the relationship
between the focal brand and competitors' brands. In con-
trast, most, if not all, CLV models address the effects of
marketing actions without considering competing brands.
This is because data that are typically available to direct
marketers rarely include information about the sales or pref-
erence for competing brands. Our approach incorporates
information about not only the focal brand but competing
brands as well, which enables us to create a model that con-
tains both customer attraction and retention in the context of
brand switching. The approach considers customer flows
from one competitor to another, which is analogous to
brand-switching models in consumer packaged goods (e.g.,
Massy, Montgomery, and Morrison 1970) and migration
models (Dwyer 1997). The advantage of the approach is that
competitive effects can be modeled, thereby yielding a fuller
and truer accounting of CLV and customer equity.
When are customers gone? Customer retention histori-
cally has been treated according to two assumptions (Jack-
son 1985). First, the "lost for good" assumption uses the
customer's retention probability (often the retention rate in
the customer's segment) as the probability that a firm's cus-
tomer in one period is still the firm's customer in the fol-
lowing period. Because the retention probability is typically
less than one, the probability that the customer is retained
declines over time. The implicit assumption is that cus-
tomers are "alive" until they "die," after which they are lost
for good. Models for estimating the number of active cus-
tomers have been proposed for relationship marketing
(Schmittlein, Morrison, and Columbo 1987), customer
retention (Bolton 1998), and CLV (Reinartz 1999).
The second assumption is the "always a share" assump-
tion, in which customers may not give any firm all of their
business. Attempts have been made to model this by a
"migration model" (Berger and Nasr 1998; Dwyer 1997).
The migration model assigns a retention probability as pre-
viously, but if the customer has missed a period, a lower
probability is assigned to indicate the possibility that the
customer may return. Likewise, if the customer has been
gone for two periods, an even lower probability is assigned.
This is an incomplete model of switching because it
includes purchases from only one firm.
In one scenario (consistent with the lost-for-good
assumption) when the customer is gone, he or she is gone.
This approach systematically understates CLV to the extent
that it is possible for customers to return. In another scenario
(consistent with t