Description
The customer relationship management (CRM) literature recognizes the long-run value of potential and current customers. Increased revenues, profits, and shareholder value are the result of marketing activities directed toward developing, maintaining, and enhancing successful company–customer relationships.
MANAGING CUSTOMER RELATIONSHIPS 3
3
CHAPTER 1
MANAGING CUSTOMER RELATIONSHIPS
RUTH N. BOLTON AND CRINA O. TARASI
Abstract
The customer relationship management (CRM) literature recognizes the long-run value of po-
tential and current customers. Increased revenues, pro?ts, and shareholder value are the result
of marketing activities directed toward developing, maintaining, and enhancing successful com-
pany–customer relationships. These activities require an in-depth understanding of the underlying
sources of value that the ?rm both derives from customers, as well as delivers to customers. We
built our review from the perspective that customers are the building blocks of a ?rm. In order
to endure long-term success, the role of marketing in a ?rm is to contribute to building strong
market assets, including a valuable customer portfolio. CRM is an integral part of a company’s
strategy, and its input should be actively considered in decisions regarding the development of
organizational capabilities, the management of value creation, and the allocation of resources.
CRM principles provide a strategic and tactical focus for identifying and realizing sources of value
for the customer and the ?rm and can guide ?ve key organizational processes: making strategic
choices that foster organizational learning, creating value for customers and the ?rm, managing
sources of value, investing resources across functions, organizational units, and channels, and
globally optimizing product and customer portfolios. For each organizational process, we identify
some of the challenges facing marketing scientists and practitioners, and develop an extensive
research agenda.
Companies are increasingly focused on managing customer relationships, the customer asset, or
customer equity. Customer relationship management (CRM) explicitly recognizes the long-run
value of potential and current customers, and seeks to increase revenues, pro?ts, and shareholder
value through targeted marketing activities directed toward developing, maintaining, and enhanc-
ing successful company-customer relationships (Berry, 1983, p. 25; Morgan and Hunt, 1994, p.
22; Gronroos, 1990 p. 138). These activities require an in-depth understanding of the underlying
sources of value the ?rm both derives from customers and delivers to them.
The purpose of this chapter is to describe how companies can effectively cultivate customer
relationships and develop customer portfolios that increase shareholder value in the long run. We
review the extensive literature on customer relationship management, customer asset management,
and customer portfolio management, and summarize key ?ndings. The chapter has three major
components. First, we de?ne CRM, describe how marketing thinking about CRM has evolved over
time, and assess whether CRM principles and systems have improved business performance (to
date). Second, we examine (in detail) ?ve organizational processes that we believe are necessary
for effective CRM: making strategic choices that foster organizational learning, creating value
4 RUTH N. BOLTON AND CRINA O. TARASI
for customers and the ?rm, managing sources of value (acquisition, retention, etc.), investing
resources across functions, organizational units, and channels, and globally optimizing product
and customer portfolios. We describe each process, summarize key ?ndings, identify emerging
trends and issues, and predict likely future developments (both theoretical and methodological).
Our concluding remarks make recommendations about areas where further research is needed.
Perspective on the Evolution of Customer Relationship Management
Current De?nition of CRM
After surveying many alternative de?nitions of CRM, Payne and Frow (2005, p. 168) offer the
following comprehensive de?nition, which we will use to frame the discussion in our chapter:
CRM is a strategic approach concerned with creating improved shareholder value through the
development of appropriate relationships with key customers and customer segments. CRM
unites the potential of relationship marketing strategies and IT [information technology] to
create pro?table, long-term relationships with customers and other key stakeholders. CRM
provides enhanced opportunities to use data and information to both understand customers
and co-create value with them. This requires a cross-functional integration of processes,
people, operations and marketing capabilities that is enabled through information, technol-
ogy and applications.
Researchers have emphasized different CRM issues depending on whether they are consider-
ing a business-to-consumer or business-to-business context. However, we focus on conceptual
and methodological principles that are applicable in both contexts, highlighting noteworthy
exceptions.
CRM vis-à-vis the Domain of Marketing
Marketing theory has frequently provided guidance on how ?rms should react to opportunities,
but marketing actions are also able to change the environment and create opportunities (Zeithaml
and Zeithaml, 1984). Marketing—considered as a general management responsibility—plays
“the crucial roles of (1) navigation through effective market sensing, (2) articulation of the new
value proposition, and (3) orchestration by providing the essential glue that ensures a coherent
whole” (Hunt, 2004, p. 22). CRM enhances these capabilities because it is “the outcome of the
continuing evolution and integration of marketing ideas and newly available data, technologies
and organizational forms” (Boulding et al., 2005).
CRM principles and systems help organizations to focus on the dual creation of value: the
creation of value for shareholders (via long-term ?rm pro?tability) and the creation of value or
utility for customers (Vargo and Lusch, 2004). These objectives are congruent because relation-
ships represent market-based assets that a ?rm continuously invests in, in order to be viable in the
marketplace. Strong relationships are associated with customer loyalty and/or switching costs,
which create barriers to competition. Thus relationships provide a differential advantage by making
resources directed to customers more ef?cient. For example, loyal customers are more responsive
to marketing actions and cross-selling (Verhoef, 2003).
Marketers sometimes use the term “customer asset,” but customers and assets do not have iden-
tical features. The mind-set associated with “owning” customers is dangerous because customer
MANAGING CUSTOMER RELATIONSHIPS 5
relationships must be carefully managed and customer loyalty must be earned (Rust et al., 2004).
However, the customer base is certainly a market-based asset that should be measured, managed,
and tracked over time (Bell et al., 2002). Srivastava, Shervani, and Fahey (1998) discuss how
market-based assets, such as customer or partner relationships, can increase shareholder value by
accelerating and enhancing cash ?ows, lowering the volatility and vulnerability of cash ?ows, and
increasing the residual value of cash ?ows. Their framework links customer relationship manage-
ment with business performance metrics.
Origins in Relationship Marketing
The foundation for the development of CRM is generally considered to be relationship market-
ing, de?ned as marketing activities that attract, maintain, and enhance customer relationships
(Berry 1983). Gronroos (1990, p.138) argues for the importance of relationships in the marketing
context. He proposes a de?nition for marketing, namely, that marketing is “to establish, maintain
and enhance relationships with consumers and other partners, so that the objectives of the parties
involved are met. This is achieved by a mutual exchange and ful?llment of promises.” However,
although the terms “CRM” and “relationship marketing” are relatively new, the phenomenon is not
(Gummesson, 1994, p. 5, 2002, p. 295). Marketers have always been preoccupied with defensive
strategies aimed at increasing customer retention, thereby increasing revenues and pro?tability
(Fornell and Wernerfelt, 1987). For example, writing in the Harvard Business Review, Grant and
Schlesinger (1995 p. 61) argue that the gap between organization’s current and full-potential
pro?tability is enormous, and suggest that managers ask themselves: “How long on average do
your customers remain with the company? [and] What if they remained customers for life?” Dur-
ing the same time period, a growing literature has focused on the “service pro?t chain” linking
employee satisfaction, customer satisfaction, loyalty, and pro?tability (e.g., Heskett, Sasser, and
Schlesinger, 1997; Reichheld, 1993; Liljander, 2000).
Emergence of Customer Equity and Early Customer Relationship Models
This perspective naturally evolved and expanded to consider the management of customer equity
or the value of the customer base. Initially, researchers were primarily concerned with the alloca-
tion of resources between customer acquisition and retention (Blattberg and Deighton, 1996).
Generally, the management of customer equity requires that organizations use information about
customers and potential customers to segment them and treat them differently depending on their
future long-term pro?tability (Blattberg, Getz, and Thomas, 2001; Peppers and Rogers, 2005;
Rust, Zeithaml, and Lemon, 2000). Notably, ?rms must go beyond traditional market segmenta-
tion activities, such as customizing offerings (i.e., goods or services) and ef?ciently managing
resources to achieve pro?tability criteria. Instead, ?rms must identify and acquire customers who
are not only willing to accept the ?rm’s offer or value proposition—but also provide value for the
company when they do (e.g., Cao and Gruca, 2005; Ryals, 2005).
Marketers were quick to recognize that the value of the customer asset (i.e., the value a customer
or potential customer provides to a company) is the sum of the discounted net contribution margins
of the customer over time—that is, the revenue provided to the company less the company’s cost
associated with maintaining a relationship with the customer (Berger and Nasr, 1998). Early appli-
cations of CRM systems typically utilized models that predict (rather than explain) future customer
behavior or pro?tability. For example, in an early paper, Schmittlein and Peterson (1994) use past
purchase behavior—that is, data on the frequency, timing, and dollar value of past purchases—to
6 RUTH N. BOLTON AND CRINA O. TARASI
predict likely future purchase patterns. They were able to show that their “customer base analysis”
was effective in predicting purchase patterns for different key industrial buying groups.
For about a decade, relatively narrow CRM systems coexisted, rather uneasily, with broader, strate-
gically meaningful conceptualizations of CRM as a “strategic bridge between information technology
and marketing strategies aimed at building long term relationship and pro?tability” (Ryals and Payne
2001, p. 3). Modelers frequently applied CLV concepts in direct marketing, database marketing, or
electronic commerce contexts (Ansari and Mela, 2003; Bult and Wansbeek, 1995; Elsner, Krafft,
and Huchzermeier, 2004).
1
Progress was made toward identifying which variables are the “best”
predictors of customer lifetime pro?tability (in a given study context). For example, Reinartz and
Kumar (2003) compare traditional models that consider frequency, timing, and monetary value with
models that show how managerial decision variables in?uence the pro?tability of customers over
time—and show that the latter are superior. Nevertheless, most applications (to date) have relied on
estimates of current customer pro?tability, rather than future customer pro?tability.
Customer Relationship Management and Business Performance
Marketing Metrics
The challenges of applying CRM principles were exacerbated as managers and researchers turned
their attention to “metrics” or the measurement of the impact of marketing on business performance
(cf. Lehmann, 2004). Most popular measures of current CRM systems are outcome measures:
number of acquired customers, “churn” as a percentage of the customer base (the inverse of the
customer retention rate), the dollar value of cross-selling, the percentage increase in customer
migration to higher margin products, changes in individual customer lifetime value (CLV), and
so forth. Any single outcome measure provides an incomplete and (often) short-run assessment of
the ?rm’s success at creating value for both customers and shareholders (Boulding et al., 2005).
Most dangerously, optimizing a small number of outcome measures may lead to core rigidities
(Atuahene-Gima, 2005; Leonard-Barton, 1992) that undermine the organization’s core capabilities
and lead to business failure. For example, there are numerous stories of ?rms that have focused on
customer acquisition at the expense of customer retention activities or vice versa.
One way to assess the impact of marketing on business performance is to forecast the lifetime
value of individual customers under alternative scenarios, aggregating across customers, and iden-
tifying the “best” set of scenarios or set of organizational actions. This approach seems “doable”
but it can be challenging to move from the calculation of individual customers’ lifetime revenues to
individual customers’ pro?tability. For example, Niraj, Gupta, and Narasimhan (2001) demonstrate
this method for an intermediary in a supply chain, such as a distributor, where costs are incurred
at each step in the supply chain and there is heterogeneity in purchasing characteristics.
Initial Failure of CRM “Systems”
A constructive distinction is often missing in CRM frameworks. There is a difference between
CRM systems—software that integrates relevant customer information (sales, marketing, etc.)
with product and service information—and CRM processes, for example, the cross-functional
steps required to ensure customer retention and effectiveness of marketing initiative, such as a
continuing dialogue with customers across all contact points and personalized treatment (Day,
2000). In other words, CRM systems are intended to support CRM processes, which are meant
to enhance the value of the customer relationship.
MANAGING CUSTOMER RELATIONSHIPS 7
CRM starts from the fundamental assumption that the bounded rationality of humans charged
with initiating, maintaining, and building relationships can be supported and enhanced by speci?c
organization capabilities, namely, the intelligent utilization of databases and information technol-
ogy. However, many organizations’ initial experiences were disappointing, especially in the short
run. The Economist (2003, p. 16) describes the experiences of ?nancial services organizations
and pessimistically observes that:
The three year economic downturn has cooled even Wall Street’s ardor for fancy new IT
[information technology] gear. . . . The problem is that most IT projects are lengthy affairs
and notoriously “back loaded.” . . . Few things in technology have promised so much and
delivered so little as “customer (or client) relationship management” (CRM) software. In
implementing CRM, insiders reckon that four out of ?ve such projects fail to deliver the
goods.
These failures typically arose from a narrow application of CRM principles. For example,
Rigby, Reichheld, and Schefter (2002) identi?ed four situations that independently and together
result in failed CRM systems: (1) implementing CRM without having in place a clear customer
strategy, (2) assuming that CRM has to match organizations’ current practices, and not enhance
them, (3) assuming that CRM technology and not CRM strategy matters, and (4) using CRM to
stalk, not to woo customers. In other words, many so-called CRM systems used technology (both
hardware and software) to optimize the usage of information within functional silos, without a
relational orientation, creating obstacles to organizational learning and the dual creation of value.
Thus, it is not particularly surprising that they identi?ed solutions that were suboptimal—and
even unpro?table—in the long run.
More Nuanced Approaches to Evaluating CRM Systems and Technology
Research has established that CRM systems can improve intermediate measures of business perfor-
mance. For example, Mithas, Krishnan, and Fornell (2005) study the effect of CRM applications
on customers and ?nd out that the use of CRM systems positively impacts customer satisfaction,
both directly and through improved customer knowledge. Despite this fact—and the common
belief that more and better customer knowledge can only bene?t a ?rm and its customers—the
?nancial return on large investments in CRM technology has been questioned. For example, as
Reinartz, Krafft, and Hoyer (2004, p. 293) report, commercial studies “provide some convergent
validity that approximately 70 percent of CRM projects result in either losses or no bottom line
improvements.” Contrary to such reports, their own empirical investigation indicates that com-
panies that implemented CRM processes performed better not only in relationship maintenance
but also in relationship initiation.
A critical issue for many organizations is that the adoption of CRM technology is fraught with
implementation challenges, including information technology design, procedure, and process is-
sues, dif?culties in maintaining accurate and current information, obstacles arising from interfaces
that are not user friendly, and so forth (e.g., Johnson, Sohi, and Grewal, 2004; Meuter et al., 2005;
Morgan, Anderson, and Mittal, 2005; Winer, 2001). For this reason, we must distinguish between
technology-driven implementation—which results in user frustration—and customer-driven imple-
mentation—which has high user involvement; the latter resulted in successful operational CRM
systems. A recent study by Jayachandran and colleagues (2005) estimates an interaction effect
showing that customer relationship performance for a diverse sample of businesses is enhanced
8 RUTH N. BOLTON AND CRINA O. TARASI
by organizational information processes when a high level of technology is used. In other words,
technology use for customer relationship management—by moderating the in?uence of organiza-
tional information processes on customer relationship performance—performs a supportive role
only. They show that effective organizational information processes (i.e., effective communica-
tion, information capture, and information integration, as well as access and use of information)
enhance the effectiveness of CRM technology in achieving business success.
CRM Principles and the Role of Organizational Capabilities and Processes
After more than twenty years of research on CRM, the accumulated evidence indicates that the
application of CRM principles yields positive ?nancial outcomes. In their introduction to the
Journal of Marketing’s special section on CRM, Boulding and colleagues (2005) argue that
CRM improves business performance in a wide variety of industry settings. A striking example
is described in a case study by Ryals (2005), showing that a business unit was able to achieve a
270 percent increase in business unit pro?ts above target by implementing some straightforward
CRM procedures.
Why do ?rms experience such widely varying degrees of success from applying CRM? The
implementation of CRM systems or technology alone is doomed to fail, because the collection of
the data does not imply the existence of useful information that will be disseminated and acted
upon appropriately. Boulding and colleagues (2005) argue that, holding ?xed the level of CRM
investment, the effectiveness of CRM activities depends on (a) how CRM is integrated with the
existing processes of the ?rm and (b) the ?rm’s preexisting capabilities. In other words, orga-
nizations that have already developed learning capabilities and effective information processes
are more likely to improve their business performance by adopting CRM systems. They are able
interpret information correctly and act on it in a manner to increase value for both the customer
and the ?rm.
In a recent Harvard Business Review article, Gulati and Oldroyd (2005) observe that the
implementation or CRM systems must serve the purpose of getting closer to customers, and that
in order to succeed the company as a whole has to engage in a learning journey—learning about
the customer and about the business and how its way of doing business can be improved. If this
activity is regarded as a departmental or functional responsibility, CRM efforts will fail. The authors
identify four stages in the evolution of a successful CRM implementation: communal coordina-
tion (gathering information); serial coordination (gaining insight from customers’ past behavior);
symbiotic coordination (learning to predict future customer behavior) and integral coordination
(real time response to customer needs). This evolutionary and transformational process takes time,
resources, and patience, but the implementation of each of the stages should provide visible end
results. Harrah’s started this process under Gary Loveman’s leadership in 1998 and, after a constant
evolution that took more than seven years and involved all employee levels, it enjoyed impressive
growth compared to competitors. Furthermore, the deep understanding of the customer provided
new levers for future growth (Gulati and Oldroyd, 2005; Gupta and Lehmann, 2005).
In summary, marketing science and practice has moved away from simplistic evaluations of
investments in CRM technology or systems to consider the role of ?rms’ preexisting capabilities
and organizational processes. For this reason, the remainder of this article frames our discussion of
what we know about CRM in terms of ?ve interrelated organizational processes: making strategic
choices that foster organizational learning, creating value for customers and the ?rm, managing
sources of value (acquisition, retention, etc.), investing resources across functions, organizational
units, and channels, and globally optimizing product and customer portfolios. We discuss how each
MANAGING CUSTOMER RELATIONSHIPS 9
process in?uences the effectiveness of CRM, and describe its challenges. The processes and their
relationships are depicted in Figure 1.1; subtopics are listed in Table 1.1. We begin by describing
research regarding how organizations’ strategic choices in?uence the effectiveness of CRM in
enhancing business performance, which provides a conceptual rationale for our framework.
Strategic Choices
In a recent executive roundtable discussion, executives from IBM, Yellow-Roadway, Luxottica Retail
(Lens Crafters and Sunglass Hut), McKinsey & Company and Cisco Systems stated that that there
were immense opportunities for the transformation of organizations through the integration of busi-
ness processes and the use of technology to generate competitive advantage, cost saving ef?ciencies
and an enhanced customer experience. Executives in Europe and North America strongly believe that
successful organizations require a cross-functional process-oriented approach that positions CRM at
a strategic level (Brown, 2005; Christopher, Payne, and Ballantyne, 1991; Payne and Frow, 2005).
This notion is consistent with empirical evidence showing that ?rms’ prior strategic commitments
(as opposed to their general market orientation) have impressive effects on the performance of their
CRM investments in a retailing context (Srinivasan and Moorman, 2005).
Figure 1.1 Customer Relationship Management Processes
Figure 1.1 Customer Relationship Management Processes
Strategic Choices
Dual creation
of value
Managing
sources of
value
Investments
across
functions
Global
optimization
Existing
relational
processes
=
Table 1.1
Processes
Strategic choices • Organizational information processes
• O rganizational learning
Dual creation of value • Creating value for customers
• Valuing customers
Customer portfolio management • Acquisition
• Retention
• Increased margins from relationship expansion activities
(e.g., product usage, cross-selling)
• Divestment
Allocation of resources across • Employee selection and training
functions, channels, and • Service quality
organizational units • Customer management effort
• Managing customer contacts
• Customer equity models
Global optimization models • Segmentation
• Matching product portfolio and customer portfolio
• Risk/return management
10 RUTH N. BOLTON AND CRINA O. TARASI
Organizational Learning
Based on extensive ?eld interviews, Payne and Frow (2005) identify ?ve key cross-functional CRM
processes: a strategy development process; a value creation process; a multichannel integration pro-
cess; an information management process; and a performance assessment process. They argue that
an organization’s strategy development process—a precursor for subsequent processes—requires
a dual focus on its business strategy and customer strategy, and that how well the two interrelate
will fundamentally affect the success of its CRM strategy.
In particular, organizational information processes—information reciprocity, information
capture, information integration, information access, and information use—relevant to CRM can
play a vital role in enhancing business performance (Jayachandran et al., 2005). This observation
should not be surprising because the primary outcome of the adoption of CRM technology is the
generation of an enormous database describing customer pro?les, sales, costs, operations, and
so forth. If intelligently processed and interpreted, these data can provide information regarding
the value of customers and the effectiveness and ef?ciency of marketing actions (Berger et al.,
2002). Each customer interaction is (or should be) part of an iterative learning process both from
the customer and the company points of view (Ballantyne, 2004).
Challenges
Our review of prior research suggests two fruitful areas for future research. First, marketing
scientists and practitioners have acknowledged that CRM technology alone cannot sustain a
competitive advantage. The failure of many ?rms to reap economic rewards from investments in
CRM technology is a symptom of an underlying problem, namely, how to create a coordinated
strategy that integrates business processes and generates an enhanced customer experience (i.e.,
the creation of value for customers), competitive advantage, and cost saving ef?ciencies (i.e.,
the creation of value for the ?rm). The value a company has to offer to its customer is derived
not only from the quality of its offerings but also from its relational characteristics and supplier
characteristics (Crosby, Gronroos, and Johnson, 2002; Menon, Homburg, and Beutin, 2005;
Storbacka, Strandvik, and Gronroos, 1994). For this reason, appropriate organizational structures
and processes for a given ?rm are likely to depend on its business environment (i.e., they will be
contingency-based). Thus, there is a critical need for more research on how CRM principles can
guide strategic choices that improve business performance in different business contexts, thereby
bridging the functional silos that exist in many organizations. Otherwise, ?rms will be unable to
pro?tably exploit innovations in technology and business processes—for example, radio frequency
identi?cation technology.
Second, ?rms’ experiences in implementing CRM technology have shown that transforming
data into useful information—especially learning from past experience—is challenging for many
organizations. Ambler (2003, p. 21) points out a paradox: “Marketing is the means whereby a
company achieves its key objectives,” but quantifying the results of marketing actions is extremely
challenging. CRM systems can provide the tools for accurately measuring marketing outcomes,
where “clarity of goals and metrics separate the professional from the amateur” (Ambler, 2003,
p. 17). Gupta and Lehmann (2005) have suggested a set of metrics that is based on a pro?tability
tree and is suitable for strategic decision-making. It is important to recognize that different metrics
are required for different purposes. Hence, research is required to identify metrics linked to future
pro?tability because, without making sense of the interrelationships of marketing variables, it will
be impossible for marketing to evolve from a function in a company to a guiding principle (Hunt
MANAGING CUSTOMER RELATIONSHIPS 11
2004). In addition, research is required to show how metrics can be used to manage value creation
for customers and for the ?rm. Furthermore, at an implementation level, research is required to
develop “interlocking” metrics that coordinate decision making at strategic and tactical levels, as
well as decision making across channels and organizational units.
Dual Creation of Value
Dual creation of value requires that the ?rm simultaneously create value for customers and value
for shareholders. First, we discuss how to create value for customers. Second, we consider how
managers can assess the value of individual customers or segments, and then aggregate them to
calculate the value of the customer base to the ?rm. We identify the research challenges associ-
ated with each task.
Creating Value for Customers
A common trait of many studies is a focus on measuring CRM’s impact on the end results, such
as pro?ts and shareholder value, without studying the relations among processes and connections
among variables (Boulding et al., 2005). Return on investment is certainly a measure of success,
but—without a profound understanding of how relational processes can operate effectively—suc-
cess from CRM initiatives is elusive. Although the speci?cs will be unique to each ?rm, prior
research provides a conceptual framework for understanding how relational processes create value
for customers. Speci?cally, research on the antecedents of service quality, customer satisfaction,
trust, and commitment provide insights for managers (Berger et al., 2002; Rust, Lemon, and
Zeithaml, 2004).
Relationships with Consumers
Research on CRM is a natural evolution of marketers’ longstanding interest in understanding how
relationships with individual customers are created, built, and sustained over time (Bhattacharya and
Bolton, 2000). It began with investigations of how customers formed their assessments of products
(goods and services). This research stream is extensive; therefore an extensive discussion of the
antecedents of customer assessments (e.g., perceived service quality and customer satisfaction) as
well as the implicit bonds (e.g., legal, economic, technological, knowledge, social, etc.) (Liljander
and Strandvik, 1995) is beyond the scope of this section. Notably, customer satisfaction literature
developed around the idea that satisfaction is in?uenced by the difference between expectations
and experience (Oliver, 1980, 1999). Service quality literature developed along parallel lines (cf.,
Parasuraman, Zeithaml, and Berry, 1985, 1988). For example, Boulding and colleagues (1993)
brought together two streams of service quality research in showing that both expectations as
predictions (expectations about what will happen) and normative expectations (expectations about
what should happen, often based on communications from the service provider) are important
in determining perceived service quality. This stream of literature is extremely useful in helping
researchers build theory-based models of customer behavior (Bolton and Lemon, 1999).
Business-to-Business Relationships
Researchers focusing on CRM principles have been especially interested in interorganizational
relationships because—until the recent advent of electronic commerce with its potential for
12 RUTH N. BOLTON AND CRINA O. TARASI
precise (one-to-one) targeting of marketing activities to customers—business-to-business (B2B)
relationships have been the most fruitful context for the application of the principles of customer
relationship management. This stream of research has tended to have a strategic orientation,
re?ecting the notion that a coherent set of cross-functional activities is required to create, build,
and sustain relationships (Ford, 1990).
2
Two important focal constructs in understanding inter-
organizational relationships are trust and commitment (Morgan and Hunt, 1994). For example,
Anderson and Weitz (1992) consider how commitment depends on self-reported and perceived
“pledges” (i.e., idiosyncratic investments and contractual terms), communication, and relationship
characteristics. Their research is particularly noteworthy because they studied 378 dyads—that is,
pairs of manufacturer and industrial distributors—so that they were able to model the antecedents
and consequences of each party’s perception of the other party’s commitment. Recent research
has extended our knowledge of interorganizational relationships through studies of organizational
norms, contracting, opportunism, and so forth (Heide and Weiss, 1995; Kalwani and Narayandas,
1995; Kumar and Corsten, 2005; Narayandas and Rangan, 2004; Wuyt and Geyskens, 2005). B2B
decisions are especially complex because multiple people participate in the purchase decision (e.g.,
purchasing manager, end user, decision maker), and interactions occur at multiple levels (e.g.,
contract level, organizational unit level, ?rm level). This research stream is very helpful in building
theory-based models of organizational buying behavior. Most prior research has been conducted
at the enterprise level, using key informants; future research is required that uses information
obtained from multiple informants as well as from multiple levels within the buying organization
(Bolton, Lemon, and Bramlett, 2004).
Using Customer Assessments of Relationships to Explain Behavior
Numerous studies have shown that self-reports of customer assessments (such as satisfaction) can
explain customer behavior. Bolton (1998) models the duration of the customer–?rm relationship
at the individual level. She ?nds that prior cumulative satisfaction is weighed more heavily than
satisfaction from recent events, and that satis?ed customers have longer relationships, and generate
greater revenues and pro?ts (for contractual relationships). However, Verhoef (2003) ?nds that, if
customer assessments primarily re?ect cognition (without an affective component), it may prove
dif?cult to predict customer retention or share of the wallet. At the aggregate level, Gruca and
Rego (2005) use data from the American Customer Satisfaction Index and Compustat to show that
customer satisfaction plays a major role in increasing cash ?ow and enhancing its stability.
Challenges
CRM systems operate at the customer–?rm interface, and ?rms frequently use information from
customers to create and deliver valuable offerings to them. Customers are likely to be willing
to reveal private information if they derive “fair” value from exchanges with the ?rm. However,
?rms may behave opportunistically (extracting all economic surplus), creating mistrust among
customers, so that they act strategically when they provide information or participate in transactions
with the ?rm (Boulding et al., 2005). For example, customers might retaliate against perceived
unfairness by providing inaccurate information, generating unfavorable word of mouth, switch-
ing to the competition, or boycotting the ?rm. Consequently, successful implementation of CRM
principles requires that ?rms carefully consider issues related to privacy and fairness (Boulding et
al., 2005). Additional research is required on how these constructs in?uence business performance
in the long run.
MANAGING CUSTOMER RELATIONSHIPS 13
Mediating constructs, such as perceived fairness, satisfaction, and commitment, are important
precursors of customer behavior. Moreover, prior research has shown that self-report measures
obtained from survey data can be used to predict customer behavior (e.g., Bolton and Lemon,
1999). Researchers have also used survey measures as proxies for consumer behavior, assuming
that the antecedents of the proxy are identical to the antecedents of the target variable. However,
there is a signi?cant body of literature that shows otherwise (Chandon, Morwitz, and Reinartz,
2005; Morwitz, 1997; Morwitz and Schmittlein, 1992; Seiders et al., 2005). For example, Mittal
and Kamakura (2001) analyze the in?uence of satisfaction on behavioral intentions and actual
behavior and ?nd that the effect of satisfaction on behavioral intentions is nonlinear with decreas-
ing returns, whereas its effect on behavior is nonlinear with increasing returns. For this reason,
marketers must be cautious about using only survey data to study how relational processes create
value for customers. Hence, there is also a need for additional research to develop more longitu-
dinal models of customer behavior (Bolton, Lemon, and Verhoef 2004).
Value of Customers to the Firm
Customer Valuation
The value of the customer asset (i.e., the value that the customer provides to a company) is the sum
of the customer’s discounted net contribution margins over time—that is, the revenue provided to
the company less the company’s cost associated with maintaining a relationship with the customer
(Berger and Nasr, 1998). Naturally, a company cannot perfectly predict the cash ?ows associated
with an individual customer, but it can calculate the expected value of the cash ?ows (adjusting
for risk) associated with an individual customer conditional on the customer’s characteristics, the
company’s planned marketing actions and environmental factors (Hogan et al., 2002). For example,
Pfeifer and Bang (2005) propose a model of calculating the mean CLV taking into account the fact
that customers have not completed their purchasing cycle and therefore any mean calculation of
their value is inaccurate because it does not include future purchases. They use a nonparametric
method to compute mean CLV across all customers, to be used as guidance for the appropriate
level of investment in customers.
Gupta, Lehmann, and Stuart (2004) propose forecasting CLV by decomposing it into three
underlying sources: customer acquisition (i.e., trial), retention (repeat purchase behavior), and
gross margins (in?uenced cross-buying, cost structure, etc). They demonstrate that the basic
calculations are relatively straightforward. Research has shown that the CLV framework can
be used to generate estimates of the future pro?tability of individual customers—given certain
marketing actions and competitive conditions—and to identify optimal allocations of resources
(cf., Jain and Singh, 2002; Kumar, Ramani, and Bohling, 2004). In contrast, substantial empirical
evidence—using rigorous holdout sample procedures—indicates that measures of the past pro?t-
ability of individual customers are poor predictors of future customer pro?tability (Campbell and
Frei, 2004; Malthouse and Blattberg, 2005).
Forecasting Sources of CLV
To ensure accuracy, it is recommended that estimates of the revenue sources of CLV should be
broken down to the customer or cohort or segment level (rather than the ?rm level). Customer-level
forecasts of each source are preferable for ?ve reasons (Gupta and Lehmann, 2005, pp. 7–9). First,
customer-level pro?tability can be decomposed into its underlying sources—customer acquisition,
14 RUTH N. BOLTON AND CRINA O. TARASI
retention, and margin—which are amenable to managerial action. Second, by preparing forecasts
of each underlying source (rather than extrapolating ?rm-level historical data), managers can
explicitly account for changes over time in the underlying sources of pro?tability, thereby iden-
tifying turning points. For example, a ?rm might discover that its constant earnings over the past
few years are the net result of increases in customer acquisition rates and decreases in margins.
Further analysis might reveal that customer acquisition will slow down, causing a decline in future
earnings. Third, projected customer revenues can take into account any effects of cross-selling
(which increase margins) and word-of-mouth. Fourth, the effect of a planned marketing action
will be different for each CLV source: acquisition, retention, and margins (Bolton, Lemon, and
Verhoef 2004). For example, Thomas and Reinartz (2003) show that the amount of direct mail sent
has an effect on cross-buying opposite to that on purchase frequency. Fifth, without considering
customers’ migratory behavior, customers will be undervalued since they are considered lost when
they switch to competition and they are accounted for as new customers when they switch back
(for a model of accounting for switching behavior see Rust et al., 2004).
To calculate CLV and identify the most pro?table customers, the company must forecast the
cost to serve a customer as well as revenue sources. As Kaplan and Narayanan (2001) point out,
the cost to serve customers can vary dramatically: 20 percent of customers who are most pro?table
can account for 150 percent to 300 percent of pro?ts, while the 10 percent who are least pro?table
may lose 50 percent to 200 percent of pro?ts. Under these conditions, it is necessary to measure the
real pro?tability of customers and (if necessary) take corrective actions to forestall losses (either by
“?ring” the unpro?table customers or by adopting solutions to make the relationship pro?table).
Firm Valuation
Recent research has shown that the CLV framework (i.e., using forecasts of acquisition, reten-
tion, and margins) can be used to calculate the value of the ?rm’s current and future customer
base. Gupta, Lehmann, and Stuart (2004) use publicly available information from annual reports
and other ?nancial statements to calculate a customer-based valuation of ?ve companies. They
compare their estimates of customer value (post-tax) with the reported market value for each of
the companies. Their estimates are reasonably close to the market values for three ?rms, and
signi?cantly lower for two ?rms (Amazon and eBay). They infer that these two ?rms either are
likely to achieve higher growth rates in customers or margins than they forecast, or they have some
other large option value that the CLV framework does not capture.
Challenges
Berger and colleagues (2002) discuss four critical and interrelated actions required of ?rms that
wish to understand how their actions affect the value of their customer assets: (1) create a database;
(2) segment based on customer needs and behavior; (3) forecast CLV under alternative resource
allocation scenarios; and (4) allocate resources. Although the challenges of creating an integrated
database cannot be overestimated, they are primary related to cost and implementation issues. In
contrast, forecasting customer-level CLV is a signi?cant technical challenge for four reasons.
First, the forecasts should re?ect changes in customer behavior in response to changes in or-
ganizational decisions and the environment. To make CLV calculations tractable prior research
has made strong implicit assumptions about customer behavior and marketing programs (e.g.,
Berger and Nasr, 1998; Blattberg and Deighton, 1996; Dwyer, 1989; Rust et al., 2004). For ex-
ample, researchers frequently assume ?xed marketing programs, deterministic retention rates,
MANAGING CUSTOMER RELATIONSHIPS 15
and stable switching patterns among competitive offerings. Additional research is required to
relax these assumptions in practical situations. For example, Lewis (2005) estimated a structural
dynamic programming model that accounts for the effects of marketing variables, past purchasing
activity, consumer expectations of future promotions, and preference heterogeneity on consumer
behavior regarding online grocery purchases. The model was used to simulate customer response
to marketing programs over an extended time period, thereby providing an estimate of customer
value that is directly connected to organizational decisions. He found that, relative to a holdout
sample, the simulation-based forecasts outperformed standard methods in terms of absolute error
and were better able to account for variation in long-term values in a heterogeneous customer
base. He was also able to estimate the long-term consequences of alternative pricing and promo-
tion strategies.
Second, different customers will value the same product differently, and they will have dif-
ferent acquisition rates, retention rates, and margins (due to cross-buying); therefore, forecasting
models must account for customer heterogeneity (cf., Chintagunta and Prasad 1998; Schmittlein
and Peterson 1994). Third, it will be necessary to allocate costs to individual customers. In direct
marketing contexts, ?rms are able to assign the costs of direct communication, delivery of the
product, and promotions to individual customers (Berger and Nasr-Bechwati, 2001; Dwyer, 1989;
Keane and Wang, 1995). However, in many industries, ?rms must create methods for accurately
attributing the indirect costs of marketing actions to individual customers or customer segments.
Berger and colleagues (2002) point out that cost allocation can be particularly challenging for
?rms that invest in programmatic efforts, such as service improvement efforts or investments in
physical infrastructure.
A fourth challenge is to understand and incorporate competitive effects on customer acquisition
and retention. Accounting for competitors’ acquisition campaigns might explain customer behavior
in most markets. Optical scanner data provide competitive information in retail environments, but
information about competitive behavior is seldom available in other contexts.
Managing Sources of Value
Organizations can manage sources of value by acquiring and retaining the most desirable customers;
expanding relationships through the stimulation of usage, upgrades, and cross-buying; improving
their overall pro?tability by adjusting prices or managing costs; and managing the customer and
product portfolios. Since not all customers are equally pro?table, investments in customers should
be based on their pro?t potential, as illustrated in Table 1.2. Firms should acquire customers in the
upper-right quadrant and divest customers in the lower-left quadrant. Vulnerable customers may
defect to competitors unless the ?rm develops an appropriate marketing program to retain them;
free riders should receive lower product quality and higher prices.
These strategies require the ?rm to develop marketing programs targeted at individual
customers or segments that in?uence acquisition, retention, and margins (via cross-buying),
thereby maximizing CLV and value for customers. Marketers have developed a substantial
body of knowledge about how ?rm actions in?uence customer behavior. A useful summary of
this literature is provided by Bolton, Lemon, and Verhoef (2004), who identify six categories of
marketing decision variables that can be used to in?uence customer behavior and CLV: price,
service quality programs, direct marketing promotions, relationship marketing instruments (e.g.,
rewards programs), advertising communications, and distribution channels. In the following
paragraphs, we brie?y summarize some key considerations concerning how these marketing
actions in?uence each source of value.
16 RUTH N. BOLTON AND CRINA O. TARASI
Customer Acquisition
Customer acquisition is a ?rst step in building a customer base. Targeting, acquiring, and keeping
the “right” customers entails a consideration of ?t with current ?rm offering, future pro?tability,
and contribution to the overall business risk. Many ?rms do not employ appropriate criteria to
identify pro?table customers and their marketing programs are broadly communicated to poten-
tial customers who may or may not be pro?table. Consequently, customer acquisition can be a
costly and risky process—especially because new customers may not represent a good ?t for the
organization’s value proposition, a phenomenon that can often occur if acquisition is done outside
previously targeted segments. Customer–product ?t becomes important because campaigns aimed
toward new customers—that change the positioning of a product—can alienate existing customers.
Mittal and Kamakura (2001) discuss the nature of the relationship (or ?t) of the customer and the
brand, ?nding that customers with different characteristics have different satisfaction thresholds,
and, therefore, different probabilities of repurchase.
3
This leads to the more general observation that
customer acquisition in?uences the diversity of the customer portfolio—thereby in?uencing busi-
ness risk—but this aspect of CRM is rarely studied in marketing (Johnson and Selnes, 2005).
Lack of focus during acquisition activities is very likely to result in adverse selection—whereby
the prospects that are least likely to be pro?table are mostly likely to respond to marketing ef-
forts. For credit companies, the problem is particularly worrisome because they must verify the
suitability of all respondents, thus incurring screening costs. Cao and Gruca (2005) address the
problem of adverse selection by using data from a ?rm’s CRM system to target prospects likely
to respond and be approved. This approach increases the number of customers who are approved
while reducing the number of “bad” customers. Their analysis is post facto and the marketing
message is not altered, but their results show 30 percent to 75 percent improvements compared
to traditional models that take into account either response likelihood or approval likelihood but
not both. This method can be extended to new customer acquisition and better targeting of costly
promotions to migrate customers to higher levels of lifetime value.
Customer Retention
Even though the optimal mix of marketing programs is unique to each business model, customer
retention is often easier and cheaper than customer acquisition, especially in stable markets with
low growth rates. An organizational emphasis on customer retention also makes sense when
discount rates are low (Gupta and Lehmann, 2005). Hence, customer retention has received
considerable attention from marketers. In fact, many organizations have considered the man-
agement of CLV as equivalent to the management of customer retention, and have ignored the
Table 1.2
Comparison of Value of Customers to the Firm with Value to Customers
LOW Value to Customers HIGH Value to Customers
HIGH Value of Customers Vulnerable Customers Star Customers
LOW Value of Customers Lost Causes Free Riders
Source: Gupta and Lehmann (2005), p. 44.
MANAGING CUSTOMER RELATIONSHIPS 17
contribution of other sources of CLV.
4
Research con?rms that consumers with higher satisfaction
levels and better price perceptions have longer relationships with ?rms (e.g., Bolton, 1998). In
a B2B context, suppliers who have long-term relationships with customers are able to achieve
signi?cant sales growth and higher pro?tability through differential reductions in discretion-
ary expenses (Kalwani and Narayandas, 1995). However, customer retention and defection are
complex processes (Åkerlund, 2005).
Relationship Expansion
Organizations can increase CLV and gross margin per customer by stimulating increased product
usage or cross-buying (cf., Hogan et al., 2002). However, marketing programs designed to expand
relationships with customers have received much less attention than programs for retaining cus-
tomers. Customer loyalty and cross-buying may be simultaneously determined in some contexts.
However, in a direct mail context, Thomas and Reinartz (2003) have shown that cross-buying is
a consequence, and not an antecedent, of loyalty behaviors. Nevertheless, the effectiveness of
a ?rm’s customer retention and cross-selling efforts will certainly be jointly in?uenced by the
organization’s capabilities and systems. A few studies have investigated how service organizations
can expand their relationships with customers by increasing usage or cross-buying of additional
services (e.g., Bolton and Lemon, 1999; Kamakura et al., 2002; Kamakura, Ramaswami, and
Srivastava, 1991; von Wangenheim, 2004; Verhoef, Franses, and Hoekstra, 2001). They typically
show that experiences with currently owned products (goods or services) are an important predic-
tor of cross-buying.
Customer Divestment
Although organizations may have customers who are unpro?table to serve (“free riders”), ?ring
customers or refusing to serve them is seldom necessary. Instead, organizations can offer a less
attractive value proposition to some segments (e.g., by raising prices or offering lower product
quality). In addition, marketing campaigns can be designed to attract pro?table customers and
be unappealing to less desirable customers. Another option is to ?nd a way to make the latter
group pro?table by changing the ?rm’s business model. For example, IBM wanted to focus on
Fortune 1000 companies, but could not ignore less pro?table relationships with small business.
Hence, they developed a dealer network that could serve the medium and small businesses in a
pro?table way.
Challenges
Many ?rms use the predicted value of the customer asset (also known as customer lifetime value or
CLV) to allocate resources to customer or customer segments, thus accurate calculations are impor-
tant. CLV predictions should be based on forecasts of revenue sources and costs to serve—based
on a particular set of marketing actions and an environmental scenario—where multiple forecasts
are possible. Dynamic models to forecast the sources of CLV are required for four reasons. First,
CLV is often considered a ?xed value, when it is actually in?uenced by and in?uences marketing
strategy (Berger et al., 2002). For example, certain service attributes or marketing variables—such
as price or quality—may become more (or less) important to customers as the duration of the
relationship lengthens (Boulding et al.,1993; Mittal, Katrichis, and Kumar, 2001; Mittal, Kumar,
and Tsiros, 1999). Consequently, dynamic models are required to re?ect the evolution of customer
18 RUTH N. BOLTON AND CRINA O. TARASI
preferences and behaviors over time—so that the path-dependent nature of organizational decisions
is explicitly recognized (Rust and Chung, forthcoming; Bolton, forthcoming).
There are established streams of research that model customer acquisition and retention, but
there are fewer dynamic models that describe how relationships are expanded by stimulating us-
age, cross-buying, and word-of-mouth (WOM)—and how these sources affect CLV. Furthermore,
customer behaviors are not (typically) considered to be jointly determined within a system of
equations. For example, Hogan, Lemon, and Libai (2003, 2004) assess the impact of customer loss
due to WOM on product adoption and examine the underestimated effectiveness of advertising
due to failure to account for WOM. Subsequently, von Wangenheim and Bayón (forthcoming)
propose a model for including the effect of customer referrals CLV calculations. We believe that
much more work is required to build comprehensive, dynamic models of the multiple sources of
CLV to produce accurate estimates of CLV, especially in light of the in?uence of socialization
and networks on future behavior (see Hakanson and Snehota, 1995).
Second, forecasts of sources of CLV will depend on competitors’ activities—and these activities
will change over time. Current CRM models devote little attention to competitors and their in?u-
ence on a customer’s relationship with the target ?rm (for a notable exception see Rust, Lemon,
and Zeithaml, 2004). Failure to account for competitive effects in a dynamic manner will impair
the accuracy of estimating the impact of the marketing actions (Rust et al., 2004).
Third, it is necessary to forecast the implications of marketing actions for the long and interme-
diate term, as opposed to the short term (Lewis, 2005; Reinartz, Thomas, and Kumar, 2005; Rust
and Verhoef, forthcoming). For example, Dekimpe and Hanssens (1995) estimate the long-term
effect of marketing activity (speci?cally, media spending) on sales using persistence modeling
based on time-series observations. The long-term advertising effect is a combination of consumer
response, competitive reaction, and ?rm decision rules effects. The study shows that an advertising
medium with lower short-term impact can have a higher long-term effect. Thus, their example
demonstrates that traditional approaches can underestimate the long-term effectiveness of marketing
expenditures. In subsequent work, they also show that the strategic context is a major determinant
of marketing effectiveness and long-term pro?tability (Dekimpe and Hanssens, 1999).
Fourth, it is interesting to observe, that—from a customer portfolio management perspec-
tive—the goal of CRM is to invest in customer relationships to maximize value to the customer
and (aggregate) value for the ?rm. Maximizing the duration of a speci?c customer–?rm relation-
ship or the CLV of an individual customer may not be appropriate. This issue arises whenever
the ?rm makes decisions about which customers to acquire, retain, or divest—as well as how to
create a portfolio of customers with desirable risk/return characteristics. In other words, decisions
about individual customers cannot be made without considering the optimal characteristics of the
entire customer portfolio.
Allocating Resources Within and Across Functions, Channels, and
Organizational Units
Berger and colleagues (2002, p. 51) recommend that “?rms should manage their customers like
they manage their assets: by making pro?table investments in value-producing areas.” Marketers
have been especially interested in methods for allocating resources between customer acquisition
and retention to maximize return on investment. Unfortunately, many CLV calculations have been
characterized as “undervaluing long term customers and over-evaluating prospects” (Hogan et al.,
2002), which can lead to misallocation of resources.
In mature markets, customer retention is cheaper and easier and has more impact than customer
MANAGING CUSTOMER RELATIONSHIPS 19
acquisition (Berger and Nasr, 1998; Gupta and Lehmann, 2005; Gupta, Lehmann, and Stuart,
2004; Jain and Singh, 2002; Reinartz, Thomas, and Kumar, 2005), yet overbidding on the future
can shift the attention from retention to acquisition. Customer acquisition is vital in a growing
market because it assures the future growth of the company; yet, in a mature market, retaining
customers most often offers the best return on investment.
The problem of ?nding the equilibrium between investing in acquisition versus in retention is
exacerbated by the fact that even though customer acquisition and retention are not independent
processes, data limitations have frequently led marketers to treat them as such.
5
Thomas (2001)
?nds that naive predictions can lead to overinvestment in certain customers (e.g., due to incor-
rectly estimating the impact of add-on selling). The adoption of a long-term perspective implies
maximization of neither acquisition rate nor relationship duration, but maximization of the pro?t-
ability of the relationship over time (Reinartz, Thomas, and Kumar, 2005).
Strategic models have emerged to help ?rms allocate resources across diverse organizational
actions that in?uence customer equity. For example, Rust and colleagues (2004) develop a com-
prehensive strategic model that links strategic investments (e.g., in quality, advertising, loyalty
programs, corporate citizenship) to customer equity de?ned as the sum of current and future cus-
tomer lifetime values. They account for competition (via switching probabilities) and customer
heterogeneity. Their comprehensive model represents an important step toward understanding
the complex effect of strategic changes. However, most research has focused (more narrowly) on
resource allocation within speci?c functional areas, including employee selection and training,
service quality, customer management effort, multiple channels, customization at the customer,
cohort or segment level, loyalty or rewards programs, and the management of customer contacts
and processes. We brie?y summarize these literature streams below.
Employee Selection and Training
The “service–pro?t chain” links service operations, employee assessments and customer assessments
to ?rm pro?tability (Heskett et al., 1994). For example, Schlesinger and Heskett (1991) describe a
“cycle of failure” that occurs when ?rms minimize employee selection effort and training, so that
employees are unable to respond to customers requests, and (consequently) customers become dis-
satis?ed and do not return—yielding low pro?t margins. A signi?cant stream of research has focused
on a single link in the chain: the relationship between employees and customers. For example, Reich-
held (1993) recommends that “to build a pro?table base of faithful customers, try loyal employees.”
Subsequently, there have been numerous studies of the relationships among employee performance,
satisfaction, organizational citizenship behaviors, service climate, and customer satisfaction (de Jong,
Ruyter, and Lemmink, 2004; Donovan, Brown, and Mowen, 2004; Gruen, Summers, and Acito,
2000; Netemeyer et al., 1997; Netemeyer, Maxham, and Pullig, 2005).
The service–pro?t chain also provides an integrative framework to guide ?rms’ investments
in operations, employee selection and training, and customer management. Researchers have
modeled components of the service–pro?t chain in different industry contexts, such as banking
(Loveman, 1998; Roth and Jackson, 1995) and retailing (Rucci, Kim, and Quinn, 1998). Notably,
Kamakura and colleagues (2002) develop a comprehensive approach to the service–pro?t chain,
incorporating a strategic model estimated with structural equation modeling and an operational
analysis based on data envelopment analysis. They were able to identify ways for bank branches to
achieve superior pro?tability. Interestingly, they discovered that bank branches must be operation-
ally ef?cient (in terms of deploying employees and technology) and must achieve high customer
retention to be maximally pro?table.
20 RUTH N. BOLTON AND CRINA O. TARASI
Service Quality
The marketing literature has linked service quality to pro?tability in six ways: as a mediator of key
service attributes (e.g., responsiveness), through direct effects of service quality on pro?tability,
offensive effects, defensive effects, links between perceived service quality and purchase inten-
tions, and via customer and segment pro?tability. Zeithaml (1999) provides an excellent summary
of this vast literature, so we do not review it in this chapter. In an early paper, Rust, Zahorik, and
Keiningham (1995) provide a framework for evaluating service quality improvements. They il-
lustrate its application and show how it is possible to spend too much (or too little) on quality.
Subsequently, Rust, Moorman, and Dickson (2002) consider how ?nancial returns from quality
improvements arise from revenue expansion, cost reduction or both. On the basis of their empirical
work, they conclude that ?rms that adopt primarily a revenue expansion emphasis perform better
than ?rms that adopt a cost reduction emphasis or a combination strategy.
Customer Management Effort
Bowman and Narayandas (2004) investigate how increasing product quality and the effort dedi-
cated to customer management in?uence customer satisfaction and pro?ts. They ?nd that customer
delight “pays off,” but there are diminishing returns on customer management efforts. Moreover,
the presence of a viable competitor provides a benchmark for comparison, as well as resulting in
lower margins and lower share of wallet. A competitor’s customer management effort negatively
in?uences customer perceptions of employee performance and responsiveness. However, the focal
?rm’s customer management effort is twice as important (in terms of the magnitude of the effect)
as competitors’ actions. The size of the customer matters in three ways: margins increase with
customer size (nonlinear relationship with decreasing returns); the responsiveness of share of wallet
variables to satisfaction decreases with customer size; and larger customers are more demanding,
and thus have a lower baseline for both satisfaction and performance assessment.
Multiple Channels
The advent of e-commerce has resulted in a proliferation of businesses that use multiple channels
to reach their customers. If there is no “overlap” in customers across channels, each channel can
be treated as a separate business entity for revenue generation purposes. However, if customers
interact with the ?rm via multiple channels (e.g., browsing online but purchasing in the store) the
?rm can improve customer pro?tability by leveraging organizational information processes with
CRM systems. Friedman (2002) points out that often the most ef?cient way to generate leads
may be through direct mailing, Internet, or telechannels, while negotiation and sale closure is
best done through direct sales channels, while customer support can be done through telephone
or Internet. Only by sharing information across channels in real time can ?rms optimize the
results of multichannel customer contact. Thomas and Sullivan (2005) show how multichannel
retailers can use enterprise-level data to understand and predict their customers’ channel choices
over time. They use the information to develop strategies for targeting and communicating with
customers in a multichannel environment. Their results indicate that the ?rm bene?ts from ef?-
ciency in marketing expenditures (i.e., increasing the value of each customer), thereby increasing
customer pro?tability.
Interestingly, ?rms with extensive experience in one channel and limited experience in other
channels are handicapped when they attempt to create value for customers. For example, Srinivasan
MANAGING CUSTOMER RELATIONSHIPS 21
and Moorman (2005) show that retailers who are best at using CRM to create customer satisfaction
have medium levels of experience in either channel. An explanation for the curvilinear (inverted-
U-shape) relationship between length of experience and success of CRM implementations may
be that medium levels of experience make ?rms more committed to the implementation of CRM
because it is perceived as a tool to leverage organizational learning. Apparently, companies with
low levels of experience cannot use CRM systems to overcome their lack of experience—and lack
of involvement by users may exacerbate the situation. This study is a good example of how CRM
principles indicate that ?rms’ strategic choices should be contingency-based.
Customization at the Customer, Cohort, or Segment Level
Managerial decisions about investments in human resources, service quality, customer manage-
ment, and channels are typically made at the organizational level. However, managers must also
decide how to allocate resources across individual customers or market segments and organizational
units (e.g., geographic regions or bank branches). At the customer level, customized activities can
be based on classi?cation variables (such as demographics, or previous purchases), but also on
customer response to company-initiated campaigns, such as sales force effort or direct mailing
(Rust and Verhoef, forthcoming). Customization at the market segment level can be equally ef-
fective (Libai, Narayandas, and Humby, 2002). For organizations with customers who have both
unique and common requirements, implementation can be on a case-by-case basis, some customers
treated uniquely, some grouped within segments, to optimize the ef?ciency of the system (e.g.,
Bolton and Myers, 2003). As the relationships evolve and customers are better understood, service
can be further customized.
Loyalty or Rewards Programs
There is ample evidence that a loyalty program can stimulate purchase behavior. For example, when
Hilton Hotels introduced a guest loyalty program about a decade ago, it helped the company focus
on the most pro?table group of customers and reduced the weight of brand positioning—chang-
ing the nature of competition in the hospitality industry (Bell et al., 2002). Bolton, Kannan, and
Bramlett (2000) discovered that loyalty programs can positively reinforce purchase behavior via a
virtuous cycle: more experience with the product stimulates more usage, and more usage leads to
more experience. They observed that loyalty programs had complex effects on customer behavior.
Members of the loyalty programs were more forgiving of billing errors and exhibited more stable
behavior over time (because they were less affected by perceived losses or gains from previous
transactions). The authors concluded that loyalty reward programs have the potential to “operate
as a form of mass customization that strengthens customers’ perception of the company’s value
proposition” (p. 106). Moreover, Kivetz and Simonson (2003) found that a key factor affecting
consumers’ response to loyalty programs is their perceived relative advantage or “idiosyncratic ?t”
with consumer conditions and preferences. When consumers believe they have an effort advantage
over others, higher program requirements magnify this perception and can increase the overall
perceived value of the program.
The “dark side” of loyalty programs is that some programs fail to contribute to the creation
of customer assets or build brand loyalty. They primarily discount prices, thereby eroding future
pro?ts (Shugan 2005). Furthermore, customers who respond primarily to value propositions,
even though satis?ed, may actually provide little value for the company (Gummesson 2002).
Verhoef’s (2003) research suggests that relationship marketing efforts (i.e., direct mailings and
22 RUTH N. BOLTON AND CRINA O. TARASI
customer loyalty reward programs) increase customer retention and share of wallet when they
in?uence customers’ affective commitment, rather than their calculative commitment (which has
an economic basis).
Managing Customer Contacts and Processes
Customer-?rm contacts are sometimes called “touch points,” “critical incidents,” or “moments of
truth” (Bitner, Booms, and Mohr, 1994). Information about customer contacts resides throughout
the organization in fragments that are seldom linked, lacking the understanding of the entire pro-
cess from the customer’s perspective. These fragments are typically stored in information “silos”
according to the nature of the activity: transaction histories, sales call records, service operations
data, complaints or service requests, marketing communications (e.g., clickstream data, direct
marketing activities), community building activities (e.g., Saturn picnics), consumer responses to
loyalty programs, and so forth (Bhattacharya and Bolton, 2000; Winer, 2001).
The effects of customer–?rm contacts on customer perceptions and behavior are complex; they
depend on the number and nature of the contacts, the sequence and timing of the contacts, the channel,
whether the contacts are customer- or ?rm-initiated, and whether short- or long-run effects are assessed.
For example, Bolton and Drew (1991) develop a dynamic model of attitude change that shows that the
effect of discon?rmation is larger and the effect of prior attitudes on customer attitude is smaller im-
mediately after the service change than in a subsequent period. The effect of customer–?rm contacts on
pro?tability may be nonlinear or exhibit threshold effects. For example, Venkatesan and Kumar (2004)
found inverted-U relationships between customer pro?tability and the number of products returned,
number of customer contacts, and average time between two customer contacts.
The relationship context moderates the effect of customer–?rm contacts. Reinartz and Kumar
(2000) show that—contrary to popular opinion—the most pro?table customers of a catalog com-
pany do not have a long tenure with the company; customer pro?tability does not increase over
time, the cost to serve customers does not decrease over time, and that long-life customers do not
pay higher prices. However, in contractual settings, long-term relationships are most pro?table,
and it makes sense to focus on customer satisfaction and retention (Bolton, 1998). The important
effect of prior experiences is especially evident when the ?rm considers how to “win back” lost
customers. Thomas, Blattberg, and Fox (2004) point out that the nature and in?uence of the prior
relationship have an effect customer reacquisition and any subsequent relationship—so this feature
should be taken into account when deciding which lapsed customers to target and how to design the
?rm’s offering. For example, they ?nd that lapsed customers who are more likely to be reacquired
have a shorter second tenure with the ?rm after they have been reacquired.
Even in ongoing relationships, prior experiences have signi?cant downstream effects. Two
examples will suf?ce. Research has shown that extreme incidents—extremely satisfying or dis-
satisfying events—can affect purchase behavior and associated revenues two years later (Bolton,
Lemon, and Bramlett, 2004). Second, theoretical and empirical research shows that brands are
social entities, created as much by consumers as by marketers, implying that brand communities
are sources of value for customers and in?uence behavior (Algesheimer, Dholakia, and Herrmann,
2005; Muniz and O’Guinn, 2001).
Challenges
As discussed earlier, researchers have relied on simplifying assumptions to make it possible
to calculate CLV and to identify “optimal” solutions. Indeed, Gupta and Lehmann (2005) cor-
MANAGING CUSTOMER RELATIONSHIPS 23
rectly argue that executives can gain important strategic insights from fairly straightforward
analyses. However, our current models are stylized representations of a much more complex
reality. Prior research has established that the effects of investments in employee selection and
training, service quality, customer management effort, customization of marketing commu-
nications, loyalty programs, and the management of customer contacts on customer behavior
(and CLV) are frequently characterized by nonlinear effects, as well as interaction effects
with other decision variables and relationship context variables. Moreover, simultaneous
relationships in which organizational actions and customer behavior have feedback effects,
are frequently observed. In the future, it will be necessary to build more complex statistical
models to capture the richness of these underlying processes (e.g., systems of simultaneous
equations that accommodate sample selection bias, threshold effects, nonlinearities, etc.). In
addition, since naturally occurring data tend to provide insuf?cient variation to disentangle
simultaneous effects, ?eld and laboratory experiments will also be useful—especially when
evaluating alternative courses of action.
A better understanding is required of how companies can implement a coherent and synchro-
nized set of activities that cuts across organizational functions (e.g., marketing, operations, and
human resources), multiple channels, and an increasingly diverse set of marketing actions (brand
equity, communications activities, loyalty programs, service guarantees, etc.). For example, as
we discuss later in this chapter, we know very little about how brand equity, product portfolio
decisions, or innovation contribute to CLV. A third challenge for many organizations is account-
ing for competitive action and reaction (Boulding et al., 2005). The incorporation of competitors’
actions and reactions into CRM models—plus consumer responses to these actions—has been
largely ignored by researchers (due to the unavailability of data), although we know that competi-
tive effects can be important (Shankar and Bolton, 2004). Current approaches either assume that
competitive behavior will remain stable or that relatively straightforward forms of competitive
reaction (based on game theoretic models) will occur. In the future, it is likely that technological
progress will make it possible to collect competitive information in some study contexts, thereby
enriching our understanding of marketplace dynamics.
Global Optimization Models
One of the central tenets of recent customer equity models is that the ?rm’s portfolio of customers
is a portfolio of assets that should be managed accordingly. Not all customers are equal in terms
of the investment required to acquire or retain them, or in terms of their long-term pro?tability
(Thomas, Reinartz, and Kumar 2004). Moreover, investing in customers based on an estimate of
their current lifetime value ignores the future potential of these customers under different strate-
gies (Reinartz and Kumar, 2000, 2003). Hence, ?rms require sophisticated methods for managing
customer relationships as effectively as possible to achieve desired levels of risk and return. We
refer to these methods as “global optimization models,” despite the fact that this term implies a
degree of precision in resource allocation that is currently unattainable.
In order to be able to successfully manage customers as assets, a suitable system of CRM
metrics should be developed and used to guide resource allocation strategies. Gupta and Lehm-
ann (2005, pp. 110–115) recommend that the organization develop metrics for each element of a
“pro?tability tree” (based on sources of CLV). Alternative strategies can be analyzed by tracing
their effects through the tree. Firms will require two sets of metrics to provide diagnostic informa-
tion: customer-focused metrics, to assess value to the customer, and company-focused metrics, to
assess the value of the customer (p. 132).
24 RUTH N. BOLTON AND CRINA O. TARASI
Segmentation
Traditional market segmentation variables include geography, channel, customer cohort, demograph-
ics or “?rmographics” (e.g., industry type, growth rate, customer size), and so forth. However, to
determine the desirability of customers, Thomas, Reinartz, and Kumar (2004) propose segmentation
based on ease of acquiring and retaining customers, observing that there is a negative correlation be-
tween acquisition and retention costs and pro?tability. Boulding and colleagues (2005, p. 158) remark
on the unexpected relevance of traditional market segmentation to CRM activities as follows:
Some may equate CRM with the idea that every ?rm offer/activity should be customized
for individual consumers. However, in all [four] of the application papers [in the Journal of
Marketing Special Section on CRM], we saw the use of basic market segmentation . . . and
three of the papers identify just two segments. Admittedly, these segments were not based
on standard demographics, but instead on detailed analyses of prior observed behavior.
This observation is at odds with popular enthusiasm for one-to-one marketing and e-custom-
ization—which have been successful in some contexts (e.g., Peppers and Rogers, 2005; Ansari
and Mela, 2003). One explanation may be that customized approaches are required in dynamic
environments where choices are complex and customers have heightened expectations. Conse-
quently, marketers face two basic questions: (1) Which segmentation variables are most effective
for the implementation of CRM procedures and under what conditions? (2) To what extent should
organizational actions be standardized or customized—that is, what is the appropriate level of
aggregation of customers for organizational action?
Challenges
Economic content, resource content, and social content have to concur for a customer to engage in a
relationship characterized by commitment and trust (Morgan, 2000; Morgan and Hunt, 1994). Custom-
ers’ level of engagement with the ?rm arises from how their needs ?t with the characteristics of the
product, as well as from the supplier’s actions. Customers will expand their relationship with a ?rm if
new needs arise that require a “problem solving” approach to decision making, whereas they are likely
to maintain a less intimate relationship when needs can be met by a routine purchase. Therefore, cus-
tomers are likely to expand (or withdraw from) a relationship when their needs change. For this reason,
research is required to develop a deeper understanding of relationship dynamics and trigger points to
select the forward-looking segmentation variables that are leading indicators of future customer pro?t-
ability (Gustafsson, Johnson, and Roos, 2005). There is a need to return to basic principles of market
segmentation (Elrod and Winer, 1982), which call for the creation of market segments by aggregating
customers who have the same response function coef?cients (obtained from behavioral models). This
need is particularly critical in global environments, where the trade-off between customization and
standardization is an especially “high stakes” decision (e.g., Bolton and Myers, 2003).
Matching the Customer Portfolio and the Product Portfolio
The Customer Portfolio
The customer portfolio should include customers who have close relationships with the ?rm and
customers who have weaker relationships. Although this recommendation may seem counterintui-
MANAGING CUSTOMER RELATIONSHIPS 25
tive, the underlying rationale is that ?rms require a future-oriented perspective that recognizes that
they can strengthen weaker relationships with customers over time, yielding greater future cash
?ows, and that different levels of relationships might require different levels of service. Johnson
and Selnes (2004) illustrate this important insight by developing a stylized model and using it
to simulate the outcomes of customer portfolio decisions. Their simulations assess the impact of
organizational decisions on business outcomes, such as pro?ts and shareholder value, based on a
foundation embedded in relational processes and connections among variables.
They postulate that customers can be classi?ed into four groups: strangers, acquaintances,
friends, and partners. Strangers—potential customers—have no current relationships with the
?rm. Acquaintances are customers who have low involvement with the ?rm, can easily switch
suppliers, and are retained based merely on their satisfaction with current offerings. Friends base
the relationship with the ?rm on satisfaction and trust. Partners represent the most committed
segment, and the offering for them is customized, dedicated resources being devoted to the indi-
vidual customer. As the level of commitment increases, the value of the offering becomes more
customized and thus more dif?cult to compare to other ?rms’ offers.
Managers are accustomed to thinking in terms of a dichotomy: offensive marketing, which em-
phasizes customer acquisition, versus defensive marketing, which emphasizes customer retention
(Fornell and Wernerfelt, 1987). Johnson and Selnes (2004, 2005) demonstrate that CRM strategies
are more nuanced, arguing that “the individual relationships are the building blocks for understand-
ing the value created across an entire customer portfolio” (2004, p. 3). Firms must identify ways
to connect with their customers and create value by adapting their offer to the customer’s speci?c
needs. Assuming that all customers have the same needs, even in terms of relationship intensity,
is a naive oversimpli?cation (Gadde and Snehota, 2000). The process of dual value creation and
relationship development takes time and effort, and requires a substantial commitment to ensure
that future cash ?ow increases from the target market. Johnson and Selnes (2004, 2005) recommend
(1) balancing closer customer relationships with weaker ones and (2) balancing customers who
have stable purchasing patterns with customers who have more volatile patterns. For example, a
broader customer base that includes customers who have weaker relationships with the ?rm (e.g.,
friends and acquaintances) provides opportunities for economies of scale, insulation from cost
shocks, and more opportunities to build stronger relationships. Their approach extends conventional
notions of customer behavior-based market segmentation to explore dynamic considerations of
customer portfolio management.
The Product Portfolio
The construction of the product portfolio begins with investments in brands over time (Park, Ja-
worski, and Maclnnis, 1986). Keller (1993) de?nes customer-based brand equity as the differential
effect that knowledge about the brand has on customer response to the marketing of that brand.
This framework suggests that brand marketing activities (and investments) should be designed to
enhance brand awareness and improve the favorability, strength, or uniqueness of brand associa-
tions. Ailawadi, Lehmann, and Neslin (2003) have shown that revenue premium, an outcome-based
measure of brand equity, is stable over time and correlates with brand and category characteristics
as well as with other measures of brand equity.
In an integrating framework that builds on the work of Keller and Lehmann (2003), Ambler and
colleagues (2002) make a compelling argument regarding the synergy between brand equity and
customer equity. Recently, customer equity models have incorporated brand equity as a distinct
revenue source (cf. Rust, Lemon, and Zeithaml, 2004). Fournier (1998) argues that conceptual-
26 RUTH N. BOLTON AND CRINA O. TARASI
izing brand equity in terms of brand–consumer relationship provides more insight into the ways
that strong bonds or relationships are created, maintained, and deepened over time. Consistent
with this notion, Hogan and colleagues (2002, p. 30) point out that “the valuation of brand exten-
sion opportunities is fraught with uncertainty” because the value of the brand and its ability to be
extended depends on the “quality” (i.e., current and future value) of the customer portfolio. They
explicitly recognize the role of in?uencers with social connections to other potential customers.
A recent study of the relationship between customer satisfaction, cash ?ow, and shareholder
value by Gruca and Rego (2005) shows that the larger the brand portfolio, the less ef?cient ?rms
are in growing their cash ?ows. They also ?nd that ?rms operating in more concentrated indus-
tries (i.e., with fewer competitors) are better able to convert satisfaction into reduced cash ?ow
variability. Mizik and Jacobson (2005) ?nd that brand assets (especially measures of a brand’s
relevance and vitality to consumers) in?uence stock returns both directly and indirectly via current
earnings. These ?ndings suggest that viewing the product portfolio decision through a “relation-
ship lens” is appropriate.
Challenges
At present, there is a clash between customer-centered and product-centered views of the ?rm.
Yet, recent research suggests that simultaneously “matching” the product and customer portfolios
is crucial to long-term business performance. A matching approach is much different from current
approaches to determining the appropriate depth and breadth of a ?rm’s product portfolio (Bordley
2003). This issue is especially critical when we consider innovation or new product development
or new customer acquisition. For example, Thompson, Hamilton, and Rust (2005) show that
products are loaded with many features to stimulate trial (customer acquisition), but this strategy
can potentially decrease customer lifetime value. Additional research is required to understand
and manage the dynamic process by which customer and product portfolios should be adjusted
over time to achieve strategic objectives for the dual creation of value. This topic has important
implications for the management of innovation, investments in brand equity, the development of
new markets, and the deepening of relationships in existing markets.
Managing Risk and Return
Segmenting the market and then nurturing selected customers—thereby developing trust and
commitment to the relationship—is (potentially) a high return strategy. However, a ?rm’s targeted
customers are very likely to be vulnerable to the same business cycles and economic factors. Con-
sequently, customer satisfaction and commitment will not insulate the ?rm from market downturns,
even though customers may be less price elastic in their purchasing intentions (Anderson and
Sullivan, 1993). In other words, it is insuf?cient simply to consider future customer pro?tability.
Factors such as the customer’s industry, ?rm size, geography, and contribution to revenue volatility
should be considered when making resource allocation decisions.
Financial principles suggest that a diversi?ed customer base can help companies to dampen
the volatility of earning streams and ensure the stability of the business (Dhar and Glazer, 2003;
Srivastava, Shervani, and Fahey, 1998, 1999). A diversi?cation strategy requires a long-term assess-
ment of “desirable” customers in terms of both risk and return, as opposed to a short-term focus on
expected returns. CLV calculations incorporate both risk and return by discounting revenues and
costs to estimate net present value. Marketers have typically used a risk-adjusted rate of return—that
is, they employ a single discount rate, the weighted average cost of capital. Von Wangenheim and
MANAGING CUSTOMER RELATIONSHIPS 27
Lenz (2005) propose calculating customer beta based on volatility of purchase to account for the
risk associated with a customer. An alternative approach, called the “certainty equivalent approach,”
explicitly adjusts cash-?ow streams for various risk factors (such as the probability of defection)
and then discounts them at the risk free rate (cf. Hogan et al., 2002, p. 31).
Recent research has shown that customer satisfaction is linked to the growth and stability of
a ?rm’s future cash ?ows. Anderson, Fornell, and Mazvancheryl (2004) reported a positive as-
sociation between a ?rm’s current level of customer satisfaction and contemporaneous ?nancial
market measures such as Tobin’s Q, stock and market-to-book ratio. Subsequently, Gruca and
Rego (2005) reported that satisfaction increases shareholder value by increasing future cash
?ow and reducing its variability, and their ?ndings were robust when alternative measures of
?rm value or model speci?cations were explored. Their study was based on longitudinal data
from the American Customer Satisfaction Index and Compustat databases. They were able to
show that the relationship between satisfaction and cash ?ows is moderated by industry (e.g.,
product purchase cycle) and ?rm (e.g., magnitude of advertising expenditures) characteristics.
Not surprisingly, they ?nd that there are trade-offs between cash-?ow growth and cash-?ow
variability. These ?ndings suggest that marketers should explicitly recognize different risk fac-
tors in resource allocation decisions.
Challenges
The development of an “optimal” strategy requires a balancing of risk and return that goes beyond
current CRM practices that target the most pro?table or easy-to-serve customers. Yet, marketers do
not have appropriate procedures to adjust for the differential risk of various customers when making
resource allocation decisions. The ?nancial principles are especially challenging because—in these
situations—customers with certain characteristics (e.g., industrial buyers in speci?c industries or
consumers in speci?c geographical areas) may yield more volatile earnings streams. If marketers
wish to explicitly recognize risk factors other than defection, their CLV calculations should use a
risk-adjusted discount rate rather than a weighted average cost of capital. Hogan and colleagues
(2002) say that this task can be accomplished either by measuring the variance of returns over time for
various segments and calculating the appropriate discount rate—analogously to the evaluation of real
options (Copeland and Antikarov, 2001)—or by decomposing customer pro?tability into additional
sources. The ?nancial principles of portfolio management are well established, but marketers know
very little about how CRM principles should be applied to customer and product portfolios.
Conclusions
Based on our review of the evolving literature on CRM, we have argued that CRM is an integral part of
a company’s strategy. A company’s decisions regarding the development of organizational capabilities,
the management of value creation and its sources, and the allocation of resources across investment
opportunities are crucial elements in the array of strategic choices of the company. CRM principles
provide a strategic and tactical focus for identifying and realizing sources of value for the customer
and the ?rm. This chapter has described how CRM principles can guide ?ve key organizational pro-
cesses: making strategic choices that foster organizational learning, creating value for customers and
the ?rm, managing sources of value, investing resources across functions, organizational units, and
channels, and globally optimizing product and customer portfolios. For each organizational process,
we have identi?ed some of the challenges facing marketing scientists and practitioners. These are
summarized in our research agenda, which is displayed in Table 1.3.
28 RUTH N. BOLTON AND CRINA O. TARASI
Table 1.3
Future Research Agenda
Process Research topics
Strategic choices • Methods for achieving cross-functional coordination
• Development of organizational information processes and learning
capabilities
• Guidelines regarding the effective application of customer relationship
management (CRM)principles in strategic, cross-functional contexts
• Metrics for establishing goals and assessing outcomes
• Frameworks that link strategic-level metrics to tactical metrics for
functional groups, business processes, and business units
Dual creation of value Value for the customer
• Extension of theoretical work on how to create value for customers
• Models that predict how customers will perceive value in the future
• Studies of the antecedents and outcomes of customers’ willingness to
reveal private information, perceptions of fairness
• Research that extends current knowledge of retrospective measures
(satisfaction, purchase intentions) to leading indicators and antecedents of
future behavior
Value for the ?rm
• Better forecasts of sources of customer lifetime value (CLV): acquisition,
retention, and margin
• Decomposition of forecasts of margin into underlying components
(product usage, cross-buying, word-of-mouth [WOM])
• Research to link organizational actions to sources of value
• Research to reconcile customer-based valuation with market values for ?rms
• Dynamic forecasts that account for changes in customer preferences,
organizational actions, and competitor actions, heterogeneity across
customers
• Methods for allocating “lumpy” costs
• Improved methods for incorporating value of WOM into CLV
Managing sources of • How to create product–customer ?t
value for customers • How to recruit/divest customers and avoid “adverse selection” problems
• Further investigation of antecedents of each source of value: trial, usage,
product usage, and (especially) cross-buying and word-of mouth
• Development of dynamic models of customer behavior to accommodate
shifts in customer preferences (e.g., due to triggers), context effects,
changes in competitive behavior, short and long-run outcomes
Allocation of resources • How to maximize future customer pro?tability in the long run instead of
within and across current customer pro?tability in the short run
functions, channels, • Managing dynamics in changing customer relationships, such as fading
and organizational units and growing
• More sophisticated models that recognize simultaneity among variables
representing marketing actions and customer responses (including WOM)
• How to implement a coherent (across functions) and synchronized (over
time) set of activities
• Use of systematic experimentation so that opportunities are not overlooked
Global optimization • Segmentation should go beyond “classic” variables (geography, customer
models size, past customer behaviors) to re?ect underlying customer needs,
trigger points, and so on, that signal future behavior
• Models of how to create “balanced” customer portfolios with desired
levels of future earnings and risk
MANAGING CUSTOMER RELATIONSHIPS 29
• Conceptual models of customer–brand relationships, and models of
when and how to invest in brands to increase CLV
• Matching (new and existing) product bene?ts to (new and existing)
customer needs
• Matching the product and customer portfolio over time
• Developing a diversi?ed customer portfolio
• Adjusting for the differential risk of different customer groups
In this concluding section, we identify three features that are likely to characterize future research
on the topic of managing customer relationships. These features provide a unifying perspective
on how future research is likely to unfold.
Customer Portfolio Management as a Unifying Framework
In his famous article, “Marketing Myopia,” Levitt (1960) argues that ?rms exist to ful?ll needs, not
to sell products. In other words, customers are the building blocks of ?rms. The ultimate objective
of the ?rm is to position itself for long-term survival, and the role of marketing is to assist the
?rm in its endeavor by building strong market assets such as a valuable customer base and strong
brands (Anderson 1982). Hence, the development of a customer base is vital to ?rm survival and
should be one of the main foci of marketing (Hunt and Horn, 1983).
Wayland and Cole (1997, p. 12) claim that most businesses suffer from “a glut of products that
blur the company’s focus.” An illustrative example is the grocery store manager who attempted to
increase pro?tability by eliminating small volume items, thereby alienating customers and opening
the gates to competition (Rust, Zeithaml, and Lemon, 2000, pp. 16–22). This example shows that
an inadequate strategy for the dual creation of value—especially regarding the synchronization of
the customer and product portfolios—can impede long-term growth and pro?tability. Knowledge of
products is not enough. Knowledge of what products mean to customers and their role in building
the customer asset is vital. The role of CRM is to assist ?rms in leveraging “the information and
experience in acquisition, development and retention of a pro?table customer portfolio,” which
Wayland and Cole call customer knowledge management (1997, p. 32).
For these reasons, customer portfolio management provides a unifying framework for consid-
ering any expenditure or investment decision. Customer portfolio management frames questions
that arise within and across all ?ve organizational processes. At the strategic level, research is
required to guide ?rms in establishing goals for managing their customer portfolios, as well as
assessing business performance outcomes. This issue is closely intertwined with the need for
research to reconcile customer-based value creation with market values for ?rms, build models
to predict sources of value for both the customer and the ?rm, and develop metrics that can be
used to manage sources of value. In addition, research is required to guide ?rms in how to identify
and implement cross-functional activities, synchronized over time, to be maximally effective in
creating portfolios with desired levels of earnings and risk.
Models of Complex Systems
When the majority of ?rms have adopted CRM technology and related best practices, ?rms that use
CRM systems to connect with customers across multiple channels will no longer have a competi-
Process Research topics
Table 1.3 (continued)
30 RUTH N. BOLTON AND CRINA O. TARASI
tive advantage in their markets. As Porter (1996) observes, operational systems are not a long-term
sustainable source of competitive advantage. Consistent with this notion, research indicates that
CRM technology leverages an organization’s prior capabilities and information processes to create
value for customers and the ?rm. Day (2003, p. 77) argues that ?rms must create a superior cus-
tomer-relating capability by aligning the organization through incentives, metrics, accountabilities,
and structures. Consequently, additional research is required to investigate “how an organization
can create, communicate, and deliver value for customers by integrating and coordinating cross-
functional processes to produce coherent, mutually bene?cial outcomes” (Bolton, 2006).
Firms will require comprehensive, integrative models to guide strategic choices, allocate re-
sources to create value for customers, and manage customer acquisition, retention, cross-buying,
word-of-mouth, and divestment in ways that increase value for the ?rm. They need models of
complex systems that capture relations among organizational processes as well as connections
among strategic and tactical variables. We use the term “complex systems” to refer to systems of
equations that describe simultaneous relationships among multiple dependent variables (customer
and ?rm actions) that contribute to CLV, that capture the effects of interactions among organiza-
tional functions (e.g., employee selection and training, quality of service operations) and among
marketing variables—price, direct marketing promotions, relationship marketing instruments,
advertising communications, and distribution channels—and also the effects of competitive ac-
tions and heterogeneity across customers.
Based on past experience, purely predictive models are unlikely to provide suf?cient insight into
the path-dependent nature of the value creation process. Hence, models of complex systems must be
based on strong theory about how customer behavior changes over time, recognizing the important role
of mediating constructs (e.g., fairness, satisfaction, commitment, trust, brand preference) as precursors
of customer behavior. They must also re?ect how customers actively participate in the creation of the
customer experience (Prahalad and Ramaswamy, 2004). These features have become increasingly
important as ?rms interact with customers in “real time” where communication and interactions are
(potentially) two-way and take place across multiple channels. For these reasons, models are likely
to be based on increasingly sophisticated theory and methods (e.g., agent-based models).
A Shift in Focus Toward Contingencies or Context Effects
Throughout this chapter, we have noted many instances in which CRM research has discovered
contingency-based ?ndings. For example, Gruca and Rego (2005) found that ?rms operating in
more concentrated industries were better able to convert satisfaction into reduced cash ?ow vari-
ability (i.e., reducing the risk associated with the customer portfolio). Kamakura and colleagues
(2002) found that bank branches must be operationally ef?cient (in terms of deploying employees
and technology) and must achieve high customer retention if they are to be maximally pro?table
(i.e., increasing returns from the customer portfolio). Based on these and other ?ndings, there is
now compelling evidence that CRM research must move away from studies of marketing decision
variables in isolation.
We believe that marketing scientists should make a conscious effort to investigate how context
variables—which prior research may have considered “background factors”—moderate the effec-
tiveness of ?rm decision variables on business performance outcomes (Lynch, 1982). By moving
background factors into the foreground, marketers can begin to understand how the effectiveness
of marketing activities depends on organizational and market contingencies. There are numerous
ways to design research studies to attack broader issues (without modeling complex systems),
depending on the nature of the underlying problem.
MANAGING CUSTOMER RELATIONSHIPS 31
First, laboratory experiments can be designed to study organizational variables that tend to
covary in ?eld settings. For example, prior research indicates that: (1) activities that enhance
brand awareness and/or improve the favorability, strength, or uniqueness of brand associations,
create “brand equity” and yield a revenue premium (Ailawadi, Lehmann, and Neslin, 2003); (2)
brands are social entities, created by communities of consumers, as well as by marketers (Muniz
and O’Guinn, 2001); (3) the success of brand extensions depends on the quality of the current and
future customer portfolio (Hogan et al., 2002). In an experiment, the cognitive, social, and rela-
tional effects associated with a brand can be manipulated to help assess their relative size, as well
as the existence of interaction effects, and the net effect on the value of the customer to the ?rm.
Experiments are also a useful way to study threshold effects, nonlinear effects, and so forth.
Second, ?eld experiments are useful for studying how organizations allocate resources in situ-
ations that are characterized by endogenous or “feedback effects.” These situations are especially
prevalent in CRM settings. For example, a ?rm may use CLV estimates to target certain market
segments with a loyalty program, but the existence of the loyalty program has altered customers’
purchase behavior and consequently in?uences the ?rm’s estimate of CLV. Companies should
engage in systematic experimentation to disentangle these effects. Small-scale “pilot studies”
provide opportunities to conduct simple ?eld experiments that vary organizational actions across
customers, organizational units, and so forth. A compelling argument in favor of pilot studies is
that—without systematic experimentation—opportunities for revenue expansion are likely to be
overlooked.
A variety of quasi-experimental designs are useful when collecting cross-sectional or longitu-
dinal data. Furthermore, when suf?cient academic research has accumulated, meta-analyses can
play an important role in identifying moderating variables.
In Closing
Research on managing customer relationships has the potential to provide a unifying framework
for studying diverse marketing issues, and to contribute more broadly to business practice. It also
identi?es fruitful new areas for theoretical and methodological advances in addressing organi-
zational challenges at the cultural, strategic, and tactical levels. We know very little about how
brand equity, product portfolio decisions, or innovation contribute to customer equity, and we do
not understand the relationship dynamics that unfold to create customer value. Research on these
topics will generate new intellectual insights for marketing scientists and managers.
Notes
1. There is a long tradition of reach frequency monetary value models that use the number and timing
of previous transactions to identify customers who should be targeted with advertising and promotion (Ven-
katesan and Kumar, 2004). Interestingly, customer–?rm contacts or touch history is not considered in models
for predicting whether a customer is “dead” or “alive”—probably because ?rms do not routinely record the
occurrence of customer–?rm service encounters.
2. See the work of the Industrial Marketing and Purchasing Group (IMP) (e.g., Ford, 1990).
3. See: Abell and Hammond (1979).
4. A few database marketers have incorporated additional sources of value into their calculation of CLV
(e.g., Hughes, 1996; Wayland and Cole, 1997).
5. Thomas (2001) shows that a Tobit model with selection is better than a standard Tobit model for link-
ing customer acquisition to retention. The primary reason is that the Tobit model with selection addresses
both censoring and data truncation problems. The length of the customer’s lifetime is observed conditional
on the customer being acquired and—since the direct costs of acquisition are higher than the direct costs
32 RUTH N. BOLTON AND CRINA O. TARASI
of retention—standard Tobit model predictions can lead to overinvestment in certain customers (e.g., due
to incorrectly estimating the impact of add-on selling). Generally, selection bias may require sophisticated
analytical techniques to correct for censoring and truncation (Heckman 1979).
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doc_252557434.pdf
The customer relationship management (CRM) literature recognizes the long-run value of potential and current customers. Increased revenues, profits, and shareholder value are the result of marketing activities directed toward developing, maintaining, and enhancing successful company–customer relationships.
MANAGING CUSTOMER RELATIONSHIPS 3
3
CHAPTER 1
MANAGING CUSTOMER RELATIONSHIPS
RUTH N. BOLTON AND CRINA O. TARASI
Abstract
The customer relationship management (CRM) literature recognizes the long-run value of po-
tential and current customers. Increased revenues, pro?ts, and shareholder value are the result
of marketing activities directed toward developing, maintaining, and enhancing successful com-
pany–customer relationships. These activities require an in-depth understanding of the underlying
sources of value that the ?rm both derives from customers, as well as delivers to customers. We
built our review from the perspective that customers are the building blocks of a ?rm. In order
to endure long-term success, the role of marketing in a ?rm is to contribute to building strong
market assets, including a valuable customer portfolio. CRM is an integral part of a company’s
strategy, and its input should be actively considered in decisions regarding the development of
organizational capabilities, the management of value creation, and the allocation of resources.
CRM principles provide a strategic and tactical focus for identifying and realizing sources of value
for the customer and the ?rm and can guide ?ve key organizational processes: making strategic
choices that foster organizational learning, creating value for customers and the ?rm, managing
sources of value, investing resources across functions, organizational units, and channels, and
globally optimizing product and customer portfolios. For each organizational process, we identify
some of the challenges facing marketing scientists and practitioners, and develop an extensive
research agenda.
Companies are increasingly focused on managing customer relationships, the customer asset, or
customer equity. Customer relationship management (CRM) explicitly recognizes the long-run
value of potential and current customers, and seeks to increase revenues, pro?ts, and shareholder
value through targeted marketing activities directed toward developing, maintaining, and enhanc-
ing successful company-customer relationships (Berry, 1983, p. 25; Morgan and Hunt, 1994, p.
22; Gronroos, 1990 p. 138). These activities require an in-depth understanding of the underlying
sources of value the ?rm both derives from customers and delivers to them.
The purpose of this chapter is to describe how companies can effectively cultivate customer
relationships and develop customer portfolios that increase shareholder value in the long run. We
review the extensive literature on customer relationship management, customer asset management,
and customer portfolio management, and summarize key ?ndings. The chapter has three major
components. First, we de?ne CRM, describe how marketing thinking about CRM has evolved over
time, and assess whether CRM principles and systems have improved business performance (to
date). Second, we examine (in detail) ?ve organizational processes that we believe are necessary
for effective CRM: making strategic choices that foster organizational learning, creating value
4 RUTH N. BOLTON AND CRINA O. TARASI
for customers and the ?rm, managing sources of value (acquisition, retention, etc.), investing
resources across functions, organizational units, and channels, and globally optimizing product
and customer portfolios. We describe each process, summarize key ?ndings, identify emerging
trends and issues, and predict likely future developments (both theoretical and methodological).
Our concluding remarks make recommendations about areas where further research is needed.
Perspective on the Evolution of Customer Relationship Management
Current De?nition of CRM
After surveying many alternative de?nitions of CRM, Payne and Frow (2005, p. 168) offer the
following comprehensive de?nition, which we will use to frame the discussion in our chapter:
CRM is a strategic approach concerned with creating improved shareholder value through the
development of appropriate relationships with key customers and customer segments. CRM
unites the potential of relationship marketing strategies and IT [information technology] to
create pro?table, long-term relationships with customers and other key stakeholders. CRM
provides enhanced opportunities to use data and information to both understand customers
and co-create value with them. This requires a cross-functional integration of processes,
people, operations and marketing capabilities that is enabled through information, technol-
ogy and applications.
Researchers have emphasized different CRM issues depending on whether they are consider-
ing a business-to-consumer or business-to-business context. However, we focus on conceptual
and methodological principles that are applicable in both contexts, highlighting noteworthy
exceptions.
CRM vis-à-vis the Domain of Marketing
Marketing theory has frequently provided guidance on how ?rms should react to opportunities,
but marketing actions are also able to change the environment and create opportunities (Zeithaml
and Zeithaml, 1984). Marketing—considered as a general management responsibility—plays
“the crucial roles of (1) navigation through effective market sensing, (2) articulation of the new
value proposition, and (3) orchestration by providing the essential glue that ensures a coherent
whole” (Hunt, 2004, p. 22). CRM enhances these capabilities because it is “the outcome of the
continuing evolution and integration of marketing ideas and newly available data, technologies
and organizational forms” (Boulding et al., 2005).
CRM principles and systems help organizations to focus on the dual creation of value: the
creation of value for shareholders (via long-term ?rm pro?tability) and the creation of value or
utility for customers (Vargo and Lusch, 2004). These objectives are congruent because relation-
ships represent market-based assets that a ?rm continuously invests in, in order to be viable in the
marketplace. Strong relationships are associated with customer loyalty and/or switching costs,
which create barriers to competition. Thus relationships provide a differential advantage by making
resources directed to customers more ef?cient. For example, loyal customers are more responsive
to marketing actions and cross-selling (Verhoef, 2003).
Marketers sometimes use the term “customer asset,” but customers and assets do not have iden-
tical features. The mind-set associated with “owning” customers is dangerous because customer
MANAGING CUSTOMER RELATIONSHIPS 5
relationships must be carefully managed and customer loyalty must be earned (Rust et al., 2004).
However, the customer base is certainly a market-based asset that should be measured, managed,
and tracked over time (Bell et al., 2002). Srivastava, Shervani, and Fahey (1998) discuss how
market-based assets, such as customer or partner relationships, can increase shareholder value by
accelerating and enhancing cash ?ows, lowering the volatility and vulnerability of cash ?ows, and
increasing the residual value of cash ?ows. Their framework links customer relationship manage-
ment with business performance metrics.
Origins in Relationship Marketing
The foundation for the development of CRM is generally considered to be relationship market-
ing, de?ned as marketing activities that attract, maintain, and enhance customer relationships
(Berry 1983). Gronroos (1990, p.138) argues for the importance of relationships in the marketing
context. He proposes a de?nition for marketing, namely, that marketing is “to establish, maintain
and enhance relationships with consumers and other partners, so that the objectives of the parties
involved are met. This is achieved by a mutual exchange and ful?llment of promises.” However,
although the terms “CRM” and “relationship marketing” are relatively new, the phenomenon is not
(Gummesson, 1994, p. 5, 2002, p. 295). Marketers have always been preoccupied with defensive
strategies aimed at increasing customer retention, thereby increasing revenues and pro?tability
(Fornell and Wernerfelt, 1987). For example, writing in the Harvard Business Review, Grant and
Schlesinger (1995 p. 61) argue that the gap between organization’s current and full-potential
pro?tability is enormous, and suggest that managers ask themselves: “How long on average do
your customers remain with the company? [and] What if they remained customers for life?” Dur-
ing the same time period, a growing literature has focused on the “service pro?t chain” linking
employee satisfaction, customer satisfaction, loyalty, and pro?tability (e.g., Heskett, Sasser, and
Schlesinger, 1997; Reichheld, 1993; Liljander, 2000).
Emergence of Customer Equity and Early Customer Relationship Models
This perspective naturally evolved and expanded to consider the management of customer equity
or the value of the customer base. Initially, researchers were primarily concerned with the alloca-
tion of resources between customer acquisition and retention (Blattberg and Deighton, 1996).
Generally, the management of customer equity requires that organizations use information about
customers and potential customers to segment them and treat them differently depending on their
future long-term pro?tability (Blattberg, Getz, and Thomas, 2001; Peppers and Rogers, 2005;
Rust, Zeithaml, and Lemon, 2000). Notably, ?rms must go beyond traditional market segmenta-
tion activities, such as customizing offerings (i.e., goods or services) and ef?ciently managing
resources to achieve pro?tability criteria. Instead, ?rms must identify and acquire customers who
are not only willing to accept the ?rm’s offer or value proposition—but also provide value for the
company when they do (e.g., Cao and Gruca, 2005; Ryals, 2005).
Marketers were quick to recognize that the value of the customer asset (i.e., the value a customer
or potential customer provides to a company) is the sum of the discounted net contribution margins
of the customer over time—that is, the revenue provided to the company less the company’s cost
associated with maintaining a relationship with the customer (Berger and Nasr, 1998). Early appli-
cations of CRM systems typically utilized models that predict (rather than explain) future customer
behavior or pro?tability. For example, in an early paper, Schmittlein and Peterson (1994) use past
purchase behavior—that is, data on the frequency, timing, and dollar value of past purchases—to
6 RUTH N. BOLTON AND CRINA O. TARASI
predict likely future purchase patterns. They were able to show that their “customer base analysis”
was effective in predicting purchase patterns for different key industrial buying groups.
For about a decade, relatively narrow CRM systems coexisted, rather uneasily, with broader, strate-
gically meaningful conceptualizations of CRM as a “strategic bridge between information technology
and marketing strategies aimed at building long term relationship and pro?tability” (Ryals and Payne
2001, p. 3). Modelers frequently applied CLV concepts in direct marketing, database marketing, or
electronic commerce contexts (Ansari and Mela, 2003; Bult and Wansbeek, 1995; Elsner, Krafft,
and Huchzermeier, 2004).
1
Progress was made toward identifying which variables are the “best”
predictors of customer lifetime pro?tability (in a given study context). For example, Reinartz and
Kumar (2003) compare traditional models that consider frequency, timing, and monetary value with
models that show how managerial decision variables in?uence the pro?tability of customers over
time—and show that the latter are superior. Nevertheless, most applications (to date) have relied on
estimates of current customer pro?tability, rather than future customer pro?tability.
Customer Relationship Management and Business Performance
Marketing Metrics
The challenges of applying CRM principles were exacerbated as managers and researchers turned
their attention to “metrics” or the measurement of the impact of marketing on business performance
(cf. Lehmann, 2004). Most popular measures of current CRM systems are outcome measures:
number of acquired customers, “churn” as a percentage of the customer base (the inverse of the
customer retention rate), the dollar value of cross-selling, the percentage increase in customer
migration to higher margin products, changes in individual customer lifetime value (CLV), and
so forth. Any single outcome measure provides an incomplete and (often) short-run assessment of
the ?rm’s success at creating value for both customers and shareholders (Boulding et al., 2005).
Most dangerously, optimizing a small number of outcome measures may lead to core rigidities
(Atuahene-Gima, 2005; Leonard-Barton, 1992) that undermine the organization’s core capabilities
and lead to business failure. For example, there are numerous stories of ?rms that have focused on
customer acquisition at the expense of customer retention activities or vice versa.
One way to assess the impact of marketing on business performance is to forecast the lifetime
value of individual customers under alternative scenarios, aggregating across customers, and iden-
tifying the “best” set of scenarios or set of organizational actions. This approach seems “doable”
but it can be challenging to move from the calculation of individual customers’ lifetime revenues to
individual customers’ pro?tability. For example, Niraj, Gupta, and Narasimhan (2001) demonstrate
this method for an intermediary in a supply chain, such as a distributor, where costs are incurred
at each step in the supply chain and there is heterogeneity in purchasing characteristics.
Initial Failure of CRM “Systems”
A constructive distinction is often missing in CRM frameworks. There is a difference between
CRM systems—software that integrates relevant customer information (sales, marketing, etc.)
with product and service information—and CRM processes, for example, the cross-functional
steps required to ensure customer retention and effectiveness of marketing initiative, such as a
continuing dialogue with customers across all contact points and personalized treatment (Day,
2000). In other words, CRM systems are intended to support CRM processes, which are meant
to enhance the value of the customer relationship.
MANAGING CUSTOMER RELATIONSHIPS 7
CRM starts from the fundamental assumption that the bounded rationality of humans charged
with initiating, maintaining, and building relationships can be supported and enhanced by speci?c
organization capabilities, namely, the intelligent utilization of databases and information technol-
ogy. However, many organizations’ initial experiences were disappointing, especially in the short
run. The Economist (2003, p. 16) describes the experiences of ?nancial services organizations
and pessimistically observes that:
The three year economic downturn has cooled even Wall Street’s ardor for fancy new IT
[information technology] gear. . . . The problem is that most IT projects are lengthy affairs
and notoriously “back loaded.” . . . Few things in technology have promised so much and
delivered so little as “customer (or client) relationship management” (CRM) software. In
implementing CRM, insiders reckon that four out of ?ve such projects fail to deliver the
goods.
These failures typically arose from a narrow application of CRM principles. For example,
Rigby, Reichheld, and Schefter (2002) identi?ed four situations that independently and together
result in failed CRM systems: (1) implementing CRM without having in place a clear customer
strategy, (2) assuming that CRM has to match organizations’ current practices, and not enhance
them, (3) assuming that CRM technology and not CRM strategy matters, and (4) using CRM to
stalk, not to woo customers. In other words, many so-called CRM systems used technology (both
hardware and software) to optimize the usage of information within functional silos, without a
relational orientation, creating obstacles to organizational learning and the dual creation of value.
Thus, it is not particularly surprising that they identi?ed solutions that were suboptimal—and
even unpro?table—in the long run.
More Nuanced Approaches to Evaluating CRM Systems and Technology
Research has established that CRM systems can improve intermediate measures of business perfor-
mance. For example, Mithas, Krishnan, and Fornell (2005) study the effect of CRM applications
on customers and ?nd out that the use of CRM systems positively impacts customer satisfaction,
both directly and through improved customer knowledge. Despite this fact—and the common
belief that more and better customer knowledge can only bene?t a ?rm and its customers—the
?nancial return on large investments in CRM technology has been questioned. For example, as
Reinartz, Krafft, and Hoyer (2004, p. 293) report, commercial studies “provide some convergent
validity that approximately 70 percent of CRM projects result in either losses or no bottom line
improvements.” Contrary to such reports, their own empirical investigation indicates that com-
panies that implemented CRM processes performed better not only in relationship maintenance
but also in relationship initiation.
A critical issue for many organizations is that the adoption of CRM technology is fraught with
implementation challenges, including information technology design, procedure, and process is-
sues, dif?culties in maintaining accurate and current information, obstacles arising from interfaces
that are not user friendly, and so forth (e.g., Johnson, Sohi, and Grewal, 2004; Meuter et al., 2005;
Morgan, Anderson, and Mittal, 2005; Winer, 2001). For this reason, we must distinguish between
technology-driven implementation—which results in user frustration—and customer-driven imple-
mentation—which has high user involvement; the latter resulted in successful operational CRM
systems. A recent study by Jayachandran and colleagues (2005) estimates an interaction effect
showing that customer relationship performance for a diverse sample of businesses is enhanced
8 RUTH N. BOLTON AND CRINA O. TARASI
by organizational information processes when a high level of technology is used. In other words,
technology use for customer relationship management—by moderating the in?uence of organiza-
tional information processes on customer relationship performance—performs a supportive role
only. They show that effective organizational information processes (i.e., effective communica-
tion, information capture, and information integration, as well as access and use of information)
enhance the effectiveness of CRM technology in achieving business success.
CRM Principles and the Role of Organizational Capabilities and Processes
After more than twenty years of research on CRM, the accumulated evidence indicates that the
application of CRM principles yields positive ?nancial outcomes. In their introduction to the
Journal of Marketing’s special section on CRM, Boulding and colleagues (2005) argue that
CRM improves business performance in a wide variety of industry settings. A striking example
is described in a case study by Ryals (2005), showing that a business unit was able to achieve a
270 percent increase in business unit pro?ts above target by implementing some straightforward
CRM procedures.
Why do ?rms experience such widely varying degrees of success from applying CRM? The
implementation of CRM systems or technology alone is doomed to fail, because the collection of
the data does not imply the existence of useful information that will be disseminated and acted
upon appropriately. Boulding and colleagues (2005) argue that, holding ?xed the level of CRM
investment, the effectiveness of CRM activities depends on (a) how CRM is integrated with the
existing processes of the ?rm and (b) the ?rm’s preexisting capabilities. In other words, orga-
nizations that have already developed learning capabilities and effective information processes
are more likely to improve their business performance by adopting CRM systems. They are able
interpret information correctly and act on it in a manner to increase value for both the customer
and the ?rm.
In a recent Harvard Business Review article, Gulati and Oldroyd (2005) observe that the
implementation or CRM systems must serve the purpose of getting closer to customers, and that
in order to succeed the company as a whole has to engage in a learning journey—learning about
the customer and about the business and how its way of doing business can be improved. If this
activity is regarded as a departmental or functional responsibility, CRM efforts will fail. The authors
identify four stages in the evolution of a successful CRM implementation: communal coordina-
tion (gathering information); serial coordination (gaining insight from customers’ past behavior);
symbiotic coordination (learning to predict future customer behavior) and integral coordination
(real time response to customer needs). This evolutionary and transformational process takes time,
resources, and patience, but the implementation of each of the stages should provide visible end
results. Harrah’s started this process under Gary Loveman’s leadership in 1998 and, after a constant
evolution that took more than seven years and involved all employee levels, it enjoyed impressive
growth compared to competitors. Furthermore, the deep understanding of the customer provided
new levers for future growth (Gulati and Oldroyd, 2005; Gupta and Lehmann, 2005).
In summary, marketing science and practice has moved away from simplistic evaluations of
investments in CRM technology or systems to consider the role of ?rms’ preexisting capabilities
and organizational processes. For this reason, the remainder of this article frames our discussion of
what we know about CRM in terms of ?ve interrelated organizational processes: making strategic
choices that foster organizational learning, creating value for customers and the ?rm, managing
sources of value (acquisition, retention, etc.), investing resources across functions, organizational
units, and channels, and globally optimizing product and customer portfolios. We discuss how each
MANAGING CUSTOMER RELATIONSHIPS 9
process in?uences the effectiveness of CRM, and describe its challenges. The processes and their
relationships are depicted in Figure 1.1; subtopics are listed in Table 1.1. We begin by describing
research regarding how organizations’ strategic choices in?uence the effectiveness of CRM in
enhancing business performance, which provides a conceptual rationale for our framework.
Strategic Choices
In a recent executive roundtable discussion, executives from IBM, Yellow-Roadway, Luxottica Retail
(Lens Crafters and Sunglass Hut), McKinsey & Company and Cisco Systems stated that that there
were immense opportunities for the transformation of organizations through the integration of busi-
ness processes and the use of technology to generate competitive advantage, cost saving ef?ciencies
and an enhanced customer experience. Executives in Europe and North America strongly believe that
successful organizations require a cross-functional process-oriented approach that positions CRM at
a strategic level (Brown, 2005; Christopher, Payne, and Ballantyne, 1991; Payne and Frow, 2005).
This notion is consistent with empirical evidence showing that ?rms’ prior strategic commitments
(as opposed to their general market orientation) have impressive effects on the performance of their
CRM investments in a retailing context (Srinivasan and Moorman, 2005).
Figure 1.1 Customer Relationship Management Processes
Figure 1.1 Customer Relationship Management Processes
Strategic Choices
Dual creation
of value
Managing
sources of
value
Investments
across
functions
Global
optimization
Existing
relational
processes
=
Table 1.1
Processes
Strategic choices • Organizational information processes
• O rganizational learning
Dual creation of value • Creating value for customers
• Valuing customers
Customer portfolio management • Acquisition
• Retention
• Increased margins from relationship expansion activities
(e.g., product usage, cross-selling)
• Divestment
Allocation of resources across • Employee selection and training
functions, channels, and • Service quality
organizational units • Customer management effort
• Managing customer contacts
• Customer equity models
Global optimization models • Segmentation
• Matching product portfolio and customer portfolio
• Risk/return management
10 RUTH N. BOLTON AND CRINA O. TARASI
Organizational Learning
Based on extensive ?eld interviews, Payne and Frow (2005) identify ?ve key cross-functional CRM
processes: a strategy development process; a value creation process; a multichannel integration pro-
cess; an information management process; and a performance assessment process. They argue that
an organization’s strategy development process—a precursor for subsequent processes—requires
a dual focus on its business strategy and customer strategy, and that how well the two interrelate
will fundamentally affect the success of its CRM strategy.
In particular, organizational information processes—information reciprocity, information
capture, information integration, information access, and information use—relevant to CRM can
play a vital role in enhancing business performance (Jayachandran et al., 2005). This observation
should not be surprising because the primary outcome of the adoption of CRM technology is the
generation of an enormous database describing customer pro?les, sales, costs, operations, and
so forth. If intelligently processed and interpreted, these data can provide information regarding
the value of customers and the effectiveness and ef?ciency of marketing actions (Berger et al.,
2002). Each customer interaction is (or should be) part of an iterative learning process both from
the customer and the company points of view (Ballantyne, 2004).
Challenges
Our review of prior research suggests two fruitful areas for future research. First, marketing
scientists and practitioners have acknowledged that CRM technology alone cannot sustain a
competitive advantage. The failure of many ?rms to reap economic rewards from investments in
CRM technology is a symptom of an underlying problem, namely, how to create a coordinated
strategy that integrates business processes and generates an enhanced customer experience (i.e.,
the creation of value for customers), competitive advantage, and cost saving ef?ciencies (i.e.,
the creation of value for the ?rm). The value a company has to offer to its customer is derived
not only from the quality of its offerings but also from its relational characteristics and supplier
characteristics (Crosby, Gronroos, and Johnson, 2002; Menon, Homburg, and Beutin, 2005;
Storbacka, Strandvik, and Gronroos, 1994). For this reason, appropriate organizational structures
and processes for a given ?rm are likely to depend on its business environment (i.e., they will be
contingency-based). Thus, there is a critical need for more research on how CRM principles can
guide strategic choices that improve business performance in different business contexts, thereby
bridging the functional silos that exist in many organizations. Otherwise, ?rms will be unable to
pro?tably exploit innovations in technology and business processes—for example, radio frequency
identi?cation technology.
Second, ?rms’ experiences in implementing CRM technology have shown that transforming
data into useful information—especially learning from past experience—is challenging for many
organizations. Ambler (2003, p. 21) points out a paradox: “Marketing is the means whereby a
company achieves its key objectives,” but quantifying the results of marketing actions is extremely
challenging. CRM systems can provide the tools for accurately measuring marketing outcomes,
where “clarity of goals and metrics separate the professional from the amateur” (Ambler, 2003,
p. 17). Gupta and Lehmann (2005) have suggested a set of metrics that is based on a pro?tability
tree and is suitable for strategic decision-making. It is important to recognize that different metrics
are required for different purposes. Hence, research is required to identify metrics linked to future
pro?tability because, without making sense of the interrelationships of marketing variables, it will
be impossible for marketing to evolve from a function in a company to a guiding principle (Hunt
MANAGING CUSTOMER RELATIONSHIPS 11
2004). In addition, research is required to show how metrics can be used to manage value creation
for customers and for the ?rm. Furthermore, at an implementation level, research is required to
develop “interlocking” metrics that coordinate decision making at strategic and tactical levels, as
well as decision making across channels and organizational units.
Dual Creation of Value
Dual creation of value requires that the ?rm simultaneously create value for customers and value
for shareholders. First, we discuss how to create value for customers. Second, we consider how
managers can assess the value of individual customers or segments, and then aggregate them to
calculate the value of the customer base to the ?rm. We identify the research challenges associ-
ated with each task.
Creating Value for Customers
A common trait of many studies is a focus on measuring CRM’s impact on the end results, such
as pro?ts and shareholder value, without studying the relations among processes and connections
among variables (Boulding et al., 2005). Return on investment is certainly a measure of success,
but—without a profound understanding of how relational processes can operate effectively—suc-
cess from CRM initiatives is elusive. Although the speci?cs will be unique to each ?rm, prior
research provides a conceptual framework for understanding how relational processes create value
for customers. Speci?cally, research on the antecedents of service quality, customer satisfaction,
trust, and commitment provide insights for managers (Berger et al., 2002; Rust, Lemon, and
Zeithaml, 2004).
Relationships with Consumers
Research on CRM is a natural evolution of marketers’ longstanding interest in understanding how
relationships with individual customers are created, built, and sustained over time (Bhattacharya and
Bolton, 2000). It began with investigations of how customers formed their assessments of products
(goods and services). This research stream is extensive; therefore an extensive discussion of the
antecedents of customer assessments (e.g., perceived service quality and customer satisfaction) as
well as the implicit bonds (e.g., legal, economic, technological, knowledge, social, etc.) (Liljander
and Strandvik, 1995) is beyond the scope of this section. Notably, customer satisfaction literature
developed around the idea that satisfaction is in?uenced by the difference between expectations
and experience (Oliver, 1980, 1999). Service quality literature developed along parallel lines (cf.,
Parasuraman, Zeithaml, and Berry, 1985, 1988). For example, Boulding and colleagues (1993)
brought together two streams of service quality research in showing that both expectations as
predictions (expectations about what will happen) and normative expectations (expectations about
what should happen, often based on communications from the service provider) are important
in determining perceived service quality. This stream of literature is extremely useful in helping
researchers build theory-based models of customer behavior (Bolton and Lemon, 1999).
Business-to-Business Relationships
Researchers focusing on CRM principles have been especially interested in interorganizational
relationships because—until the recent advent of electronic commerce with its potential for
12 RUTH N. BOLTON AND CRINA O. TARASI
precise (one-to-one) targeting of marketing activities to customers—business-to-business (B2B)
relationships have been the most fruitful context for the application of the principles of customer
relationship management. This stream of research has tended to have a strategic orientation,
re?ecting the notion that a coherent set of cross-functional activities is required to create, build,
and sustain relationships (Ford, 1990).
2
Two important focal constructs in understanding inter-
organizational relationships are trust and commitment (Morgan and Hunt, 1994). For example,
Anderson and Weitz (1992) consider how commitment depends on self-reported and perceived
“pledges” (i.e., idiosyncratic investments and contractual terms), communication, and relationship
characteristics. Their research is particularly noteworthy because they studied 378 dyads—that is,
pairs of manufacturer and industrial distributors—so that they were able to model the antecedents
and consequences of each party’s perception of the other party’s commitment. Recent research
has extended our knowledge of interorganizational relationships through studies of organizational
norms, contracting, opportunism, and so forth (Heide and Weiss, 1995; Kalwani and Narayandas,
1995; Kumar and Corsten, 2005; Narayandas and Rangan, 2004; Wuyt and Geyskens, 2005). B2B
decisions are especially complex because multiple people participate in the purchase decision (e.g.,
purchasing manager, end user, decision maker), and interactions occur at multiple levels (e.g.,
contract level, organizational unit level, ?rm level). This research stream is very helpful in building
theory-based models of organizational buying behavior. Most prior research has been conducted
at the enterprise level, using key informants; future research is required that uses information
obtained from multiple informants as well as from multiple levels within the buying organization
(Bolton, Lemon, and Bramlett, 2004).
Using Customer Assessments of Relationships to Explain Behavior
Numerous studies have shown that self-reports of customer assessments (such as satisfaction) can
explain customer behavior. Bolton (1998) models the duration of the customer–?rm relationship
at the individual level. She ?nds that prior cumulative satisfaction is weighed more heavily than
satisfaction from recent events, and that satis?ed customers have longer relationships, and generate
greater revenues and pro?ts (for contractual relationships). However, Verhoef (2003) ?nds that, if
customer assessments primarily re?ect cognition (without an affective component), it may prove
dif?cult to predict customer retention or share of the wallet. At the aggregate level, Gruca and
Rego (2005) use data from the American Customer Satisfaction Index and Compustat to show that
customer satisfaction plays a major role in increasing cash ?ow and enhancing its stability.
Challenges
CRM systems operate at the customer–?rm interface, and ?rms frequently use information from
customers to create and deliver valuable offerings to them. Customers are likely to be willing
to reveal private information if they derive “fair” value from exchanges with the ?rm. However,
?rms may behave opportunistically (extracting all economic surplus), creating mistrust among
customers, so that they act strategically when they provide information or participate in transactions
with the ?rm (Boulding et al., 2005). For example, customers might retaliate against perceived
unfairness by providing inaccurate information, generating unfavorable word of mouth, switch-
ing to the competition, or boycotting the ?rm. Consequently, successful implementation of CRM
principles requires that ?rms carefully consider issues related to privacy and fairness (Boulding et
al., 2005). Additional research is required on how these constructs in?uence business performance
in the long run.
MANAGING CUSTOMER RELATIONSHIPS 13
Mediating constructs, such as perceived fairness, satisfaction, and commitment, are important
precursors of customer behavior. Moreover, prior research has shown that self-report measures
obtained from survey data can be used to predict customer behavior (e.g., Bolton and Lemon,
1999). Researchers have also used survey measures as proxies for consumer behavior, assuming
that the antecedents of the proxy are identical to the antecedents of the target variable. However,
there is a signi?cant body of literature that shows otherwise (Chandon, Morwitz, and Reinartz,
2005; Morwitz, 1997; Morwitz and Schmittlein, 1992; Seiders et al., 2005). For example, Mittal
and Kamakura (2001) analyze the in?uence of satisfaction on behavioral intentions and actual
behavior and ?nd that the effect of satisfaction on behavioral intentions is nonlinear with decreas-
ing returns, whereas its effect on behavior is nonlinear with increasing returns. For this reason,
marketers must be cautious about using only survey data to study how relational processes create
value for customers. Hence, there is also a need for additional research to develop more longitu-
dinal models of customer behavior (Bolton, Lemon, and Verhoef 2004).
Value of Customers to the Firm
Customer Valuation
The value of the customer asset (i.e., the value that the customer provides to a company) is the sum
of the customer’s discounted net contribution margins over time—that is, the revenue provided to
the company less the company’s cost associated with maintaining a relationship with the customer
(Berger and Nasr, 1998). Naturally, a company cannot perfectly predict the cash ?ows associated
with an individual customer, but it can calculate the expected value of the cash ?ows (adjusting
for risk) associated with an individual customer conditional on the customer’s characteristics, the
company’s planned marketing actions and environmental factors (Hogan et al., 2002). For example,
Pfeifer and Bang (2005) propose a model of calculating the mean CLV taking into account the fact
that customers have not completed their purchasing cycle and therefore any mean calculation of
their value is inaccurate because it does not include future purchases. They use a nonparametric
method to compute mean CLV across all customers, to be used as guidance for the appropriate
level of investment in customers.
Gupta, Lehmann, and Stuart (2004) propose forecasting CLV by decomposing it into three
underlying sources: customer acquisition (i.e., trial), retention (repeat purchase behavior), and
gross margins (in?uenced cross-buying, cost structure, etc). They demonstrate that the basic
calculations are relatively straightforward. Research has shown that the CLV framework can
be used to generate estimates of the future pro?tability of individual customers—given certain
marketing actions and competitive conditions—and to identify optimal allocations of resources
(cf., Jain and Singh, 2002; Kumar, Ramani, and Bohling, 2004). In contrast, substantial empirical
evidence—using rigorous holdout sample procedures—indicates that measures of the past pro?t-
ability of individual customers are poor predictors of future customer pro?tability (Campbell and
Frei, 2004; Malthouse and Blattberg, 2005).
Forecasting Sources of CLV
To ensure accuracy, it is recommended that estimates of the revenue sources of CLV should be
broken down to the customer or cohort or segment level (rather than the ?rm level). Customer-level
forecasts of each source are preferable for ?ve reasons (Gupta and Lehmann, 2005, pp. 7–9). First,
customer-level pro?tability can be decomposed into its underlying sources—customer acquisition,
14 RUTH N. BOLTON AND CRINA O. TARASI
retention, and margin—which are amenable to managerial action. Second, by preparing forecasts
of each underlying source (rather than extrapolating ?rm-level historical data), managers can
explicitly account for changes over time in the underlying sources of pro?tability, thereby iden-
tifying turning points. For example, a ?rm might discover that its constant earnings over the past
few years are the net result of increases in customer acquisition rates and decreases in margins.
Further analysis might reveal that customer acquisition will slow down, causing a decline in future
earnings. Third, projected customer revenues can take into account any effects of cross-selling
(which increase margins) and word-of-mouth. Fourth, the effect of a planned marketing action
will be different for each CLV source: acquisition, retention, and margins (Bolton, Lemon, and
Verhoef 2004). For example, Thomas and Reinartz (2003) show that the amount of direct mail sent
has an effect on cross-buying opposite to that on purchase frequency. Fifth, without considering
customers’ migratory behavior, customers will be undervalued since they are considered lost when
they switch to competition and they are accounted for as new customers when they switch back
(for a model of accounting for switching behavior see Rust et al., 2004).
To calculate CLV and identify the most pro?table customers, the company must forecast the
cost to serve a customer as well as revenue sources. As Kaplan and Narayanan (2001) point out,
the cost to serve customers can vary dramatically: 20 percent of customers who are most pro?table
can account for 150 percent to 300 percent of pro?ts, while the 10 percent who are least pro?table
may lose 50 percent to 200 percent of pro?ts. Under these conditions, it is necessary to measure the
real pro?tability of customers and (if necessary) take corrective actions to forestall losses (either by
“?ring” the unpro?table customers or by adopting solutions to make the relationship pro?table).
Firm Valuation
Recent research has shown that the CLV framework (i.e., using forecasts of acquisition, reten-
tion, and margins) can be used to calculate the value of the ?rm’s current and future customer
base. Gupta, Lehmann, and Stuart (2004) use publicly available information from annual reports
and other ?nancial statements to calculate a customer-based valuation of ?ve companies. They
compare their estimates of customer value (post-tax) with the reported market value for each of
the companies. Their estimates are reasonably close to the market values for three ?rms, and
signi?cantly lower for two ?rms (Amazon and eBay). They infer that these two ?rms either are
likely to achieve higher growth rates in customers or margins than they forecast, or they have some
other large option value that the CLV framework does not capture.
Challenges
Berger and colleagues (2002) discuss four critical and interrelated actions required of ?rms that
wish to understand how their actions affect the value of their customer assets: (1) create a database;
(2) segment based on customer needs and behavior; (3) forecast CLV under alternative resource
allocation scenarios; and (4) allocate resources. Although the challenges of creating an integrated
database cannot be overestimated, they are primary related to cost and implementation issues. In
contrast, forecasting customer-level CLV is a signi?cant technical challenge for four reasons.
First, the forecasts should re?ect changes in customer behavior in response to changes in or-
ganizational decisions and the environment. To make CLV calculations tractable prior research
has made strong implicit assumptions about customer behavior and marketing programs (e.g.,
Berger and Nasr, 1998; Blattberg and Deighton, 1996; Dwyer, 1989; Rust et al., 2004). For ex-
ample, researchers frequently assume ?xed marketing programs, deterministic retention rates,
MANAGING CUSTOMER RELATIONSHIPS 15
and stable switching patterns among competitive offerings. Additional research is required to
relax these assumptions in practical situations. For example, Lewis (2005) estimated a structural
dynamic programming model that accounts for the effects of marketing variables, past purchasing
activity, consumer expectations of future promotions, and preference heterogeneity on consumer
behavior regarding online grocery purchases. The model was used to simulate customer response
to marketing programs over an extended time period, thereby providing an estimate of customer
value that is directly connected to organizational decisions. He found that, relative to a holdout
sample, the simulation-based forecasts outperformed standard methods in terms of absolute error
and were better able to account for variation in long-term values in a heterogeneous customer
base. He was also able to estimate the long-term consequences of alternative pricing and promo-
tion strategies.
Second, different customers will value the same product differently, and they will have dif-
ferent acquisition rates, retention rates, and margins (due to cross-buying); therefore, forecasting
models must account for customer heterogeneity (cf., Chintagunta and Prasad 1998; Schmittlein
and Peterson 1994). Third, it will be necessary to allocate costs to individual customers. In direct
marketing contexts, ?rms are able to assign the costs of direct communication, delivery of the
product, and promotions to individual customers (Berger and Nasr-Bechwati, 2001; Dwyer, 1989;
Keane and Wang, 1995). However, in many industries, ?rms must create methods for accurately
attributing the indirect costs of marketing actions to individual customers or customer segments.
Berger and colleagues (2002) point out that cost allocation can be particularly challenging for
?rms that invest in programmatic efforts, such as service improvement efforts or investments in
physical infrastructure.
A fourth challenge is to understand and incorporate competitive effects on customer acquisition
and retention. Accounting for competitors’ acquisition campaigns might explain customer behavior
in most markets. Optical scanner data provide competitive information in retail environments, but
information about competitive behavior is seldom available in other contexts.
Managing Sources of Value
Organizations can manage sources of value by acquiring and retaining the most desirable customers;
expanding relationships through the stimulation of usage, upgrades, and cross-buying; improving
their overall pro?tability by adjusting prices or managing costs; and managing the customer and
product portfolios. Since not all customers are equally pro?table, investments in customers should
be based on their pro?t potential, as illustrated in Table 1.2. Firms should acquire customers in the
upper-right quadrant and divest customers in the lower-left quadrant. Vulnerable customers may
defect to competitors unless the ?rm develops an appropriate marketing program to retain them;
free riders should receive lower product quality and higher prices.
These strategies require the ?rm to develop marketing programs targeted at individual
customers or segments that in?uence acquisition, retention, and margins (via cross-buying),
thereby maximizing CLV and value for customers. Marketers have developed a substantial
body of knowledge about how ?rm actions in?uence customer behavior. A useful summary of
this literature is provided by Bolton, Lemon, and Verhoef (2004), who identify six categories of
marketing decision variables that can be used to in?uence customer behavior and CLV: price,
service quality programs, direct marketing promotions, relationship marketing instruments (e.g.,
rewards programs), advertising communications, and distribution channels. In the following
paragraphs, we brie?y summarize some key considerations concerning how these marketing
actions in?uence each source of value.
16 RUTH N. BOLTON AND CRINA O. TARASI
Customer Acquisition
Customer acquisition is a ?rst step in building a customer base. Targeting, acquiring, and keeping
the “right” customers entails a consideration of ?t with current ?rm offering, future pro?tability,
and contribution to the overall business risk. Many ?rms do not employ appropriate criteria to
identify pro?table customers and their marketing programs are broadly communicated to poten-
tial customers who may or may not be pro?table. Consequently, customer acquisition can be a
costly and risky process—especially because new customers may not represent a good ?t for the
organization’s value proposition, a phenomenon that can often occur if acquisition is done outside
previously targeted segments. Customer–product ?t becomes important because campaigns aimed
toward new customers—that change the positioning of a product—can alienate existing customers.
Mittal and Kamakura (2001) discuss the nature of the relationship (or ?t) of the customer and the
brand, ?nding that customers with different characteristics have different satisfaction thresholds,
and, therefore, different probabilities of repurchase.
3
This leads to the more general observation that
customer acquisition in?uences the diversity of the customer portfolio—thereby in?uencing busi-
ness risk—but this aspect of CRM is rarely studied in marketing (Johnson and Selnes, 2005).
Lack of focus during acquisition activities is very likely to result in adverse selection—whereby
the prospects that are least likely to be pro?table are mostly likely to respond to marketing ef-
forts. For credit companies, the problem is particularly worrisome because they must verify the
suitability of all respondents, thus incurring screening costs. Cao and Gruca (2005) address the
problem of adverse selection by using data from a ?rm’s CRM system to target prospects likely
to respond and be approved. This approach increases the number of customers who are approved
while reducing the number of “bad” customers. Their analysis is post facto and the marketing
message is not altered, but their results show 30 percent to 75 percent improvements compared
to traditional models that take into account either response likelihood or approval likelihood but
not both. This method can be extended to new customer acquisition and better targeting of costly
promotions to migrate customers to higher levels of lifetime value.
Customer Retention
Even though the optimal mix of marketing programs is unique to each business model, customer
retention is often easier and cheaper than customer acquisition, especially in stable markets with
low growth rates. An organizational emphasis on customer retention also makes sense when
discount rates are low (Gupta and Lehmann, 2005). Hence, customer retention has received
considerable attention from marketers. In fact, many organizations have considered the man-
agement of CLV as equivalent to the management of customer retention, and have ignored the
Table 1.2
Comparison of Value of Customers to the Firm with Value to Customers
LOW Value to Customers HIGH Value to Customers
HIGH Value of Customers Vulnerable Customers Star Customers
LOW Value of Customers Lost Causes Free Riders
Source: Gupta and Lehmann (2005), p. 44.
MANAGING CUSTOMER RELATIONSHIPS 17
contribution of other sources of CLV.
4
Research con?rms that consumers with higher satisfaction
levels and better price perceptions have longer relationships with ?rms (e.g., Bolton, 1998). In
a B2B context, suppliers who have long-term relationships with customers are able to achieve
signi?cant sales growth and higher pro?tability through differential reductions in discretion-
ary expenses (Kalwani and Narayandas, 1995). However, customer retention and defection are
complex processes (Åkerlund, 2005).
Relationship Expansion
Organizations can increase CLV and gross margin per customer by stimulating increased product
usage or cross-buying (cf., Hogan et al., 2002). However, marketing programs designed to expand
relationships with customers have received much less attention than programs for retaining cus-
tomers. Customer loyalty and cross-buying may be simultaneously determined in some contexts.
However, in a direct mail context, Thomas and Reinartz (2003) have shown that cross-buying is
a consequence, and not an antecedent, of loyalty behaviors. Nevertheless, the effectiveness of
a ?rm’s customer retention and cross-selling efforts will certainly be jointly in?uenced by the
organization’s capabilities and systems. A few studies have investigated how service organizations
can expand their relationships with customers by increasing usage or cross-buying of additional
services (e.g., Bolton and Lemon, 1999; Kamakura et al., 2002; Kamakura, Ramaswami, and
Srivastava, 1991; von Wangenheim, 2004; Verhoef, Franses, and Hoekstra, 2001). They typically
show that experiences with currently owned products (goods or services) are an important predic-
tor of cross-buying.
Customer Divestment
Although organizations may have customers who are unpro?table to serve (“free riders”), ?ring
customers or refusing to serve them is seldom necessary. Instead, organizations can offer a less
attractive value proposition to some segments (e.g., by raising prices or offering lower product
quality). In addition, marketing campaigns can be designed to attract pro?table customers and
be unappealing to less desirable customers. Another option is to ?nd a way to make the latter
group pro?table by changing the ?rm’s business model. For example, IBM wanted to focus on
Fortune 1000 companies, but could not ignore less pro?table relationships with small business.
Hence, they developed a dealer network that could serve the medium and small businesses in a
pro?table way.
Challenges
Many ?rms use the predicted value of the customer asset (also known as customer lifetime value or
CLV) to allocate resources to customer or customer segments, thus accurate calculations are impor-
tant. CLV predictions should be based on forecasts of revenue sources and costs to serve—based
on a particular set of marketing actions and an environmental scenario—where multiple forecasts
are possible. Dynamic models to forecast the sources of CLV are required for four reasons. First,
CLV is often considered a ?xed value, when it is actually in?uenced by and in?uences marketing
strategy (Berger et al., 2002). For example, certain service attributes or marketing variables—such
as price or quality—may become more (or less) important to customers as the duration of the
relationship lengthens (Boulding et al.,1993; Mittal, Katrichis, and Kumar, 2001; Mittal, Kumar,
and Tsiros, 1999). Consequently, dynamic models are required to re?ect the evolution of customer
18 RUTH N. BOLTON AND CRINA O. TARASI
preferences and behaviors over time—so that the path-dependent nature of organizational decisions
is explicitly recognized (Rust and Chung, forthcoming; Bolton, forthcoming).
There are established streams of research that model customer acquisition and retention, but
there are fewer dynamic models that describe how relationships are expanded by stimulating us-
age, cross-buying, and word-of-mouth (WOM)—and how these sources affect CLV. Furthermore,
customer behaviors are not (typically) considered to be jointly determined within a system of
equations. For example, Hogan, Lemon, and Libai (2003, 2004) assess the impact of customer loss
due to WOM on product adoption and examine the underestimated effectiveness of advertising
due to failure to account for WOM. Subsequently, von Wangenheim and Bayón (forthcoming)
propose a model for including the effect of customer referrals CLV calculations. We believe that
much more work is required to build comprehensive, dynamic models of the multiple sources of
CLV to produce accurate estimates of CLV, especially in light of the in?uence of socialization
and networks on future behavior (see Hakanson and Snehota, 1995).
Second, forecasts of sources of CLV will depend on competitors’ activities—and these activities
will change over time. Current CRM models devote little attention to competitors and their in?u-
ence on a customer’s relationship with the target ?rm (for a notable exception see Rust, Lemon,
and Zeithaml, 2004). Failure to account for competitive effects in a dynamic manner will impair
the accuracy of estimating the impact of the marketing actions (Rust et al., 2004).
Third, it is necessary to forecast the implications of marketing actions for the long and interme-
diate term, as opposed to the short term (Lewis, 2005; Reinartz, Thomas, and Kumar, 2005; Rust
and Verhoef, forthcoming). For example, Dekimpe and Hanssens (1995) estimate the long-term
effect of marketing activity (speci?cally, media spending) on sales using persistence modeling
based on time-series observations. The long-term advertising effect is a combination of consumer
response, competitive reaction, and ?rm decision rules effects. The study shows that an advertising
medium with lower short-term impact can have a higher long-term effect. Thus, their example
demonstrates that traditional approaches can underestimate the long-term effectiveness of marketing
expenditures. In subsequent work, they also show that the strategic context is a major determinant
of marketing effectiveness and long-term pro?tability (Dekimpe and Hanssens, 1999).
Fourth, it is interesting to observe, that—from a customer portfolio management perspec-
tive—the goal of CRM is to invest in customer relationships to maximize value to the customer
and (aggregate) value for the ?rm. Maximizing the duration of a speci?c customer–?rm relation-
ship or the CLV of an individual customer may not be appropriate. This issue arises whenever
the ?rm makes decisions about which customers to acquire, retain, or divest—as well as how to
create a portfolio of customers with desirable risk/return characteristics. In other words, decisions
about individual customers cannot be made without considering the optimal characteristics of the
entire customer portfolio.
Allocating Resources Within and Across Functions, Channels, and
Organizational Units
Berger and colleagues (2002, p. 51) recommend that “?rms should manage their customers like
they manage their assets: by making pro?table investments in value-producing areas.” Marketers
have been especially interested in methods for allocating resources between customer acquisition
and retention to maximize return on investment. Unfortunately, many CLV calculations have been
characterized as “undervaluing long term customers and over-evaluating prospects” (Hogan et al.,
2002), which can lead to misallocation of resources.
In mature markets, customer retention is cheaper and easier and has more impact than customer
MANAGING CUSTOMER RELATIONSHIPS 19
acquisition (Berger and Nasr, 1998; Gupta and Lehmann, 2005; Gupta, Lehmann, and Stuart,
2004; Jain and Singh, 2002; Reinartz, Thomas, and Kumar, 2005), yet overbidding on the future
can shift the attention from retention to acquisition. Customer acquisition is vital in a growing
market because it assures the future growth of the company; yet, in a mature market, retaining
customers most often offers the best return on investment.
The problem of ?nding the equilibrium between investing in acquisition versus in retention is
exacerbated by the fact that even though customer acquisition and retention are not independent
processes, data limitations have frequently led marketers to treat them as such.
5
Thomas (2001)
?nds that naive predictions can lead to overinvestment in certain customers (e.g., due to incor-
rectly estimating the impact of add-on selling). The adoption of a long-term perspective implies
maximization of neither acquisition rate nor relationship duration, but maximization of the pro?t-
ability of the relationship over time (Reinartz, Thomas, and Kumar, 2005).
Strategic models have emerged to help ?rms allocate resources across diverse organizational
actions that in?uence customer equity. For example, Rust and colleagues (2004) develop a com-
prehensive strategic model that links strategic investments (e.g., in quality, advertising, loyalty
programs, corporate citizenship) to customer equity de?ned as the sum of current and future cus-
tomer lifetime values. They account for competition (via switching probabilities) and customer
heterogeneity. Their comprehensive model represents an important step toward understanding
the complex effect of strategic changes. However, most research has focused (more narrowly) on
resource allocation within speci?c functional areas, including employee selection and training,
service quality, customer management effort, multiple channels, customization at the customer,
cohort or segment level, loyalty or rewards programs, and the management of customer contacts
and processes. We brie?y summarize these literature streams below.
Employee Selection and Training
The “service–pro?t chain” links service operations, employee assessments and customer assessments
to ?rm pro?tability (Heskett et al., 1994). For example, Schlesinger and Heskett (1991) describe a
“cycle of failure” that occurs when ?rms minimize employee selection effort and training, so that
employees are unable to respond to customers requests, and (consequently) customers become dis-
satis?ed and do not return—yielding low pro?t margins. A signi?cant stream of research has focused
on a single link in the chain: the relationship between employees and customers. For example, Reich-
held (1993) recommends that “to build a pro?table base of faithful customers, try loyal employees.”
Subsequently, there have been numerous studies of the relationships among employee performance,
satisfaction, organizational citizenship behaviors, service climate, and customer satisfaction (de Jong,
Ruyter, and Lemmink, 2004; Donovan, Brown, and Mowen, 2004; Gruen, Summers, and Acito,
2000; Netemeyer et al., 1997; Netemeyer, Maxham, and Pullig, 2005).
The service–pro?t chain also provides an integrative framework to guide ?rms’ investments
in operations, employee selection and training, and customer management. Researchers have
modeled components of the service–pro?t chain in different industry contexts, such as banking
(Loveman, 1998; Roth and Jackson, 1995) and retailing (Rucci, Kim, and Quinn, 1998). Notably,
Kamakura and colleagues (2002) develop a comprehensive approach to the service–pro?t chain,
incorporating a strategic model estimated with structural equation modeling and an operational
analysis based on data envelopment analysis. They were able to identify ways for bank branches to
achieve superior pro?tability. Interestingly, they discovered that bank branches must be operation-
ally ef?cient (in terms of deploying employees and technology) and must achieve high customer
retention to be maximally pro?table.
20 RUTH N. BOLTON AND CRINA O. TARASI
Service Quality
The marketing literature has linked service quality to pro?tability in six ways: as a mediator of key
service attributes (e.g., responsiveness), through direct effects of service quality on pro?tability,
offensive effects, defensive effects, links between perceived service quality and purchase inten-
tions, and via customer and segment pro?tability. Zeithaml (1999) provides an excellent summary
of this vast literature, so we do not review it in this chapter. In an early paper, Rust, Zahorik, and
Keiningham (1995) provide a framework for evaluating service quality improvements. They il-
lustrate its application and show how it is possible to spend too much (or too little) on quality.
Subsequently, Rust, Moorman, and Dickson (2002) consider how ?nancial returns from quality
improvements arise from revenue expansion, cost reduction or both. On the basis of their empirical
work, they conclude that ?rms that adopt primarily a revenue expansion emphasis perform better
than ?rms that adopt a cost reduction emphasis or a combination strategy.
Customer Management Effort
Bowman and Narayandas (2004) investigate how increasing product quality and the effort dedi-
cated to customer management in?uence customer satisfaction and pro?ts. They ?nd that customer
delight “pays off,” but there are diminishing returns on customer management efforts. Moreover,
the presence of a viable competitor provides a benchmark for comparison, as well as resulting in
lower margins and lower share of wallet. A competitor’s customer management effort negatively
in?uences customer perceptions of employee performance and responsiveness. However, the focal
?rm’s customer management effort is twice as important (in terms of the magnitude of the effect)
as competitors’ actions. The size of the customer matters in three ways: margins increase with
customer size (nonlinear relationship with decreasing returns); the responsiveness of share of wallet
variables to satisfaction decreases with customer size; and larger customers are more demanding,
and thus have a lower baseline for both satisfaction and performance assessment.
Multiple Channels
The advent of e-commerce has resulted in a proliferation of businesses that use multiple channels
to reach their customers. If there is no “overlap” in customers across channels, each channel can
be treated as a separate business entity for revenue generation purposes. However, if customers
interact with the ?rm via multiple channels (e.g., browsing online but purchasing in the store) the
?rm can improve customer pro?tability by leveraging organizational information processes with
CRM systems. Friedman (2002) points out that often the most ef?cient way to generate leads
may be through direct mailing, Internet, or telechannels, while negotiation and sale closure is
best done through direct sales channels, while customer support can be done through telephone
or Internet. Only by sharing information across channels in real time can ?rms optimize the
results of multichannel customer contact. Thomas and Sullivan (2005) show how multichannel
retailers can use enterprise-level data to understand and predict their customers’ channel choices
over time. They use the information to develop strategies for targeting and communicating with
customers in a multichannel environment. Their results indicate that the ?rm bene?ts from ef?-
ciency in marketing expenditures (i.e., increasing the value of each customer), thereby increasing
customer pro?tability.
Interestingly, ?rms with extensive experience in one channel and limited experience in other
channels are handicapped when they attempt to create value for customers. For example, Srinivasan
MANAGING CUSTOMER RELATIONSHIPS 21
and Moorman (2005) show that retailers who are best at using CRM to create customer satisfaction
have medium levels of experience in either channel. An explanation for the curvilinear (inverted-
U-shape) relationship between length of experience and success of CRM implementations may
be that medium levels of experience make ?rms more committed to the implementation of CRM
because it is perceived as a tool to leverage organizational learning. Apparently, companies with
low levels of experience cannot use CRM systems to overcome their lack of experience—and lack
of involvement by users may exacerbate the situation. This study is a good example of how CRM
principles indicate that ?rms’ strategic choices should be contingency-based.
Customization at the Customer, Cohort, or Segment Level
Managerial decisions about investments in human resources, service quality, customer manage-
ment, and channels are typically made at the organizational level. However, managers must also
decide how to allocate resources across individual customers or market segments and organizational
units (e.g., geographic regions or bank branches). At the customer level, customized activities can
be based on classi?cation variables (such as demographics, or previous purchases), but also on
customer response to company-initiated campaigns, such as sales force effort or direct mailing
(Rust and Verhoef, forthcoming). Customization at the market segment level can be equally ef-
fective (Libai, Narayandas, and Humby, 2002). For organizations with customers who have both
unique and common requirements, implementation can be on a case-by-case basis, some customers
treated uniquely, some grouped within segments, to optimize the ef?ciency of the system (e.g.,
Bolton and Myers, 2003). As the relationships evolve and customers are better understood, service
can be further customized.
Loyalty or Rewards Programs
There is ample evidence that a loyalty program can stimulate purchase behavior. For example, when
Hilton Hotels introduced a guest loyalty program about a decade ago, it helped the company focus
on the most pro?table group of customers and reduced the weight of brand positioning—chang-
ing the nature of competition in the hospitality industry (Bell et al., 2002). Bolton, Kannan, and
Bramlett (2000) discovered that loyalty programs can positively reinforce purchase behavior via a
virtuous cycle: more experience with the product stimulates more usage, and more usage leads to
more experience. They observed that loyalty programs had complex effects on customer behavior.
Members of the loyalty programs were more forgiving of billing errors and exhibited more stable
behavior over time (because they were less affected by perceived losses or gains from previous
transactions). The authors concluded that loyalty reward programs have the potential to “operate
as a form of mass customization that strengthens customers’ perception of the company’s value
proposition” (p. 106). Moreover, Kivetz and Simonson (2003) found that a key factor affecting
consumers’ response to loyalty programs is their perceived relative advantage or “idiosyncratic ?t”
with consumer conditions and preferences. When consumers believe they have an effort advantage
over others, higher program requirements magnify this perception and can increase the overall
perceived value of the program.
The “dark side” of loyalty programs is that some programs fail to contribute to the creation
of customer assets or build brand loyalty. They primarily discount prices, thereby eroding future
pro?ts (Shugan 2005). Furthermore, customers who respond primarily to value propositions,
even though satis?ed, may actually provide little value for the company (Gummesson 2002).
Verhoef’s (2003) research suggests that relationship marketing efforts (i.e., direct mailings and
22 RUTH N. BOLTON AND CRINA O. TARASI
customer loyalty reward programs) increase customer retention and share of wallet when they
in?uence customers’ affective commitment, rather than their calculative commitment (which has
an economic basis).
Managing Customer Contacts and Processes
Customer-?rm contacts are sometimes called “touch points,” “critical incidents,” or “moments of
truth” (Bitner, Booms, and Mohr, 1994). Information about customer contacts resides throughout
the organization in fragments that are seldom linked, lacking the understanding of the entire pro-
cess from the customer’s perspective. These fragments are typically stored in information “silos”
according to the nature of the activity: transaction histories, sales call records, service operations
data, complaints or service requests, marketing communications (e.g., clickstream data, direct
marketing activities), community building activities (e.g., Saturn picnics), consumer responses to
loyalty programs, and so forth (Bhattacharya and Bolton, 2000; Winer, 2001).
The effects of customer–?rm contacts on customer perceptions and behavior are complex; they
depend on the number and nature of the contacts, the sequence and timing of the contacts, the channel,
whether the contacts are customer- or ?rm-initiated, and whether short- or long-run effects are assessed.
For example, Bolton and Drew (1991) develop a dynamic model of attitude change that shows that the
effect of discon?rmation is larger and the effect of prior attitudes on customer attitude is smaller im-
mediately after the service change than in a subsequent period. The effect of customer–?rm contacts on
pro?tability may be nonlinear or exhibit threshold effects. For example, Venkatesan and Kumar (2004)
found inverted-U relationships between customer pro?tability and the number of products returned,
number of customer contacts, and average time between two customer contacts.
The relationship context moderates the effect of customer–?rm contacts. Reinartz and Kumar
(2000) show that—contrary to popular opinion—the most pro?table customers of a catalog com-
pany do not have a long tenure with the company; customer pro?tability does not increase over
time, the cost to serve customers does not decrease over time, and that long-life customers do not
pay higher prices. However, in contractual settings, long-term relationships are most pro?table,
and it makes sense to focus on customer satisfaction and retention (Bolton, 1998). The important
effect of prior experiences is especially evident when the ?rm considers how to “win back” lost
customers. Thomas, Blattberg, and Fox (2004) point out that the nature and in?uence of the prior
relationship have an effect customer reacquisition and any subsequent relationship—so this feature
should be taken into account when deciding which lapsed customers to target and how to design the
?rm’s offering. For example, they ?nd that lapsed customers who are more likely to be reacquired
have a shorter second tenure with the ?rm after they have been reacquired.
Even in ongoing relationships, prior experiences have signi?cant downstream effects. Two
examples will suf?ce. Research has shown that extreme incidents—extremely satisfying or dis-
satisfying events—can affect purchase behavior and associated revenues two years later (Bolton,
Lemon, and Bramlett, 2004). Second, theoretical and empirical research shows that brands are
social entities, created as much by consumers as by marketers, implying that brand communities
are sources of value for customers and in?uence behavior (Algesheimer, Dholakia, and Herrmann,
2005; Muniz and O’Guinn, 2001).
Challenges
As discussed earlier, researchers have relied on simplifying assumptions to make it possible
to calculate CLV and to identify “optimal” solutions. Indeed, Gupta and Lehmann (2005) cor-
MANAGING CUSTOMER RELATIONSHIPS 23
rectly argue that executives can gain important strategic insights from fairly straightforward
analyses. However, our current models are stylized representations of a much more complex
reality. Prior research has established that the effects of investments in employee selection and
training, service quality, customer management effort, customization of marketing commu-
nications, loyalty programs, and the management of customer contacts on customer behavior
(and CLV) are frequently characterized by nonlinear effects, as well as interaction effects
with other decision variables and relationship context variables. Moreover, simultaneous
relationships in which organizational actions and customer behavior have feedback effects,
are frequently observed. In the future, it will be necessary to build more complex statistical
models to capture the richness of these underlying processes (e.g., systems of simultaneous
equations that accommodate sample selection bias, threshold effects, nonlinearities, etc.). In
addition, since naturally occurring data tend to provide insuf?cient variation to disentangle
simultaneous effects, ?eld and laboratory experiments will also be useful—especially when
evaluating alternative courses of action.
A better understanding is required of how companies can implement a coherent and synchro-
nized set of activities that cuts across organizational functions (e.g., marketing, operations, and
human resources), multiple channels, and an increasingly diverse set of marketing actions (brand
equity, communications activities, loyalty programs, service guarantees, etc.). For example, as
we discuss later in this chapter, we know very little about how brand equity, product portfolio
decisions, or innovation contribute to CLV. A third challenge for many organizations is account-
ing for competitive action and reaction (Boulding et al., 2005). The incorporation of competitors’
actions and reactions into CRM models—plus consumer responses to these actions—has been
largely ignored by researchers (due to the unavailability of data), although we know that competi-
tive effects can be important (Shankar and Bolton, 2004). Current approaches either assume that
competitive behavior will remain stable or that relatively straightforward forms of competitive
reaction (based on game theoretic models) will occur. In the future, it is likely that technological
progress will make it possible to collect competitive information in some study contexts, thereby
enriching our understanding of marketplace dynamics.
Global Optimization Models
One of the central tenets of recent customer equity models is that the ?rm’s portfolio of customers
is a portfolio of assets that should be managed accordingly. Not all customers are equal in terms
of the investment required to acquire or retain them, or in terms of their long-term pro?tability
(Thomas, Reinartz, and Kumar 2004). Moreover, investing in customers based on an estimate of
their current lifetime value ignores the future potential of these customers under different strate-
gies (Reinartz and Kumar, 2000, 2003). Hence, ?rms require sophisticated methods for managing
customer relationships as effectively as possible to achieve desired levels of risk and return. We
refer to these methods as “global optimization models,” despite the fact that this term implies a
degree of precision in resource allocation that is currently unattainable.
In order to be able to successfully manage customers as assets, a suitable system of CRM
metrics should be developed and used to guide resource allocation strategies. Gupta and Lehm-
ann (2005, pp. 110–115) recommend that the organization develop metrics for each element of a
“pro?tability tree” (based on sources of CLV). Alternative strategies can be analyzed by tracing
their effects through the tree. Firms will require two sets of metrics to provide diagnostic informa-
tion: customer-focused metrics, to assess value to the customer, and company-focused metrics, to
assess the value of the customer (p. 132).
24 RUTH N. BOLTON AND CRINA O. TARASI
Segmentation
Traditional market segmentation variables include geography, channel, customer cohort, demograph-
ics or “?rmographics” (e.g., industry type, growth rate, customer size), and so forth. However, to
determine the desirability of customers, Thomas, Reinartz, and Kumar (2004) propose segmentation
based on ease of acquiring and retaining customers, observing that there is a negative correlation be-
tween acquisition and retention costs and pro?tability. Boulding and colleagues (2005, p. 158) remark
on the unexpected relevance of traditional market segmentation to CRM activities as follows:
Some may equate CRM with the idea that every ?rm offer/activity should be customized
for individual consumers. However, in all [four] of the application papers [in the Journal of
Marketing Special Section on CRM], we saw the use of basic market segmentation . . . and
three of the papers identify just two segments. Admittedly, these segments were not based
on standard demographics, but instead on detailed analyses of prior observed behavior.
This observation is at odds with popular enthusiasm for one-to-one marketing and e-custom-
ization—which have been successful in some contexts (e.g., Peppers and Rogers, 2005; Ansari
and Mela, 2003). One explanation may be that customized approaches are required in dynamic
environments where choices are complex and customers have heightened expectations. Conse-
quently, marketers face two basic questions: (1) Which segmentation variables are most effective
for the implementation of CRM procedures and under what conditions? (2) To what extent should
organizational actions be standardized or customized—that is, what is the appropriate level of
aggregation of customers for organizational action?
Challenges
Economic content, resource content, and social content have to concur for a customer to engage in a
relationship characterized by commitment and trust (Morgan, 2000; Morgan and Hunt, 1994). Custom-
ers’ level of engagement with the ?rm arises from how their needs ?t with the characteristics of the
product, as well as from the supplier’s actions. Customers will expand their relationship with a ?rm if
new needs arise that require a “problem solving” approach to decision making, whereas they are likely
to maintain a less intimate relationship when needs can be met by a routine purchase. Therefore, cus-
tomers are likely to expand (or withdraw from) a relationship when their needs change. For this reason,
research is required to develop a deeper understanding of relationship dynamics and trigger points to
select the forward-looking segmentation variables that are leading indicators of future customer pro?t-
ability (Gustafsson, Johnson, and Roos, 2005). There is a need to return to basic principles of market
segmentation (Elrod and Winer, 1982), which call for the creation of market segments by aggregating
customers who have the same response function coef?cients (obtained from behavioral models). This
need is particularly critical in global environments, where the trade-off between customization and
standardization is an especially “high stakes” decision (e.g., Bolton and Myers, 2003).
Matching the Customer Portfolio and the Product Portfolio
The Customer Portfolio
The customer portfolio should include customers who have close relationships with the ?rm and
customers who have weaker relationships. Although this recommendation may seem counterintui-
MANAGING CUSTOMER RELATIONSHIPS 25
tive, the underlying rationale is that ?rms require a future-oriented perspective that recognizes that
they can strengthen weaker relationships with customers over time, yielding greater future cash
?ows, and that different levels of relationships might require different levels of service. Johnson
and Selnes (2004) illustrate this important insight by developing a stylized model and using it
to simulate the outcomes of customer portfolio decisions. Their simulations assess the impact of
organizational decisions on business outcomes, such as pro?ts and shareholder value, based on a
foundation embedded in relational processes and connections among variables.
They postulate that customers can be classi?ed into four groups: strangers, acquaintances,
friends, and partners. Strangers—potential customers—have no current relationships with the
?rm. Acquaintances are customers who have low involvement with the ?rm, can easily switch
suppliers, and are retained based merely on their satisfaction with current offerings. Friends base
the relationship with the ?rm on satisfaction and trust. Partners represent the most committed
segment, and the offering for them is customized, dedicated resources being devoted to the indi-
vidual customer. As the level of commitment increases, the value of the offering becomes more
customized and thus more dif?cult to compare to other ?rms’ offers.
Managers are accustomed to thinking in terms of a dichotomy: offensive marketing, which em-
phasizes customer acquisition, versus defensive marketing, which emphasizes customer retention
(Fornell and Wernerfelt, 1987). Johnson and Selnes (2004, 2005) demonstrate that CRM strategies
are more nuanced, arguing that “the individual relationships are the building blocks for understand-
ing the value created across an entire customer portfolio” (2004, p. 3). Firms must identify ways
to connect with their customers and create value by adapting their offer to the customer’s speci?c
needs. Assuming that all customers have the same needs, even in terms of relationship intensity,
is a naive oversimpli?cation (Gadde and Snehota, 2000). The process of dual value creation and
relationship development takes time and effort, and requires a substantial commitment to ensure
that future cash ?ow increases from the target market. Johnson and Selnes (2004, 2005) recommend
(1) balancing closer customer relationships with weaker ones and (2) balancing customers who
have stable purchasing patterns with customers who have more volatile patterns. For example, a
broader customer base that includes customers who have weaker relationships with the ?rm (e.g.,
friends and acquaintances) provides opportunities for economies of scale, insulation from cost
shocks, and more opportunities to build stronger relationships. Their approach extends conventional
notions of customer behavior-based market segmentation to explore dynamic considerations of
customer portfolio management.
The Product Portfolio
The construction of the product portfolio begins with investments in brands over time (Park, Ja-
worski, and Maclnnis, 1986). Keller (1993) de?nes customer-based brand equity as the differential
effect that knowledge about the brand has on customer response to the marketing of that brand.
This framework suggests that brand marketing activities (and investments) should be designed to
enhance brand awareness and improve the favorability, strength, or uniqueness of brand associa-
tions. Ailawadi, Lehmann, and Neslin (2003) have shown that revenue premium, an outcome-based
measure of brand equity, is stable over time and correlates with brand and category characteristics
as well as with other measures of brand equity.
In an integrating framework that builds on the work of Keller and Lehmann (2003), Ambler and
colleagues (2002) make a compelling argument regarding the synergy between brand equity and
customer equity. Recently, customer equity models have incorporated brand equity as a distinct
revenue source (cf. Rust, Lemon, and Zeithaml, 2004). Fournier (1998) argues that conceptual-
26 RUTH N. BOLTON AND CRINA O. TARASI
izing brand equity in terms of brand–consumer relationship provides more insight into the ways
that strong bonds or relationships are created, maintained, and deepened over time. Consistent
with this notion, Hogan and colleagues (2002, p. 30) point out that “the valuation of brand exten-
sion opportunities is fraught with uncertainty” because the value of the brand and its ability to be
extended depends on the “quality” (i.e., current and future value) of the customer portfolio. They
explicitly recognize the role of in?uencers with social connections to other potential customers.
A recent study of the relationship between customer satisfaction, cash ?ow, and shareholder
value by Gruca and Rego (2005) shows that the larger the brand portfolio, the less ef?cient ?rms
are in growing their cash ?ows. They also ?nd that ?rms operating in more concentrated indus-
tries (i.e., with fewer competitors) are better able to convert satisfaction into reduced cash ?ow
variability. Mizik and Jacobson (2005) ?nd that brand assets (especially measures of a brand’s
relevance and vitality to consumers) in?uence stock returns both directly and indirectly via current
earnings. These ?ndings suggest that viewing the product portfolio decision through a “relation-
ship lens” is appropriate.
Challenges
At present, there is a clash between customer-centered and product-centered views of the ?rm.
Yet, recent research suggests that simultaneously “matching” the product and customer portfolios
is crucial to long-term business performance. A matching approach is much different from current
approaches to determining the appropriate depth and breadth of a ?rm’s product portfolio (Bordley
2003). This issue is especially critical when we consider innovation or new product development
or new customer acquisition. For example, Thompson, Hamilton, and Rust (2005) show that
products are loaded with many features to stimulate trial (customer acquisition), but this strategy
can potentially decrease customer lifetime value. Additional research is required to understand
and manage the dynamic process by which customer and product portfolios should be adjusted
over time to achieve strategic objectives for the dual creation of value. This topic has important
implications for the management of innovation, investments in brand equity, the development of
new markets, and the deepening of relationships in existing markets.
Managing Risk and Return
Segmenting the market and then nurturing selected customers—thereby developing trust and
commitment to the relationship—is (potentially) a high return strategy. However, a ?rm’s targeted
customers are very likely to be vulnerable to the same business cycles and economic factors. Con-
sequently, customer satisfaction and commitment will not insulate the ?rm from market downturns,
even though customers may be less price elastic in their purchasing intentions (Anderson and
Sullivan, 1993). In other words, it is insuf?cient simply to consider future customer pro?tability.
Factors such as the customer’s industry, ?rm size, geography, and contribution to revenue volatility
should be considered when making resource allocation decisions.
Financial principles suggest that a diversi?ed customer base can help companies to dampen
the volatility of earning streams and ensure the stability of the business (Dhar and Glazer, 2003;
Srivastava, Shervani, and Fahey, 1998, 1999). A diversi?cation strategy requires a long-term assess-
ment of “desirable” customers in terms of both risk and return, as opposed to a short-term focus on
expected returns. CLV calculations incorporate both risk and return by discounting revenues and
costs to estimate net present value. Marketers have typically used a risk-adjusted rate of return—that
is, they employ a single discount rate, the weighted average cost of capital. Von Wangenheim and
MANAGING CUSTOMER RELATIONSHIPS 27
Lenz (2005) propose calculating customer beta based on volatility of purchase to account for the
risk associated with a customer. An alternative approach, called the “certainty equivalent approach,”
explicitly adjusts cash-?ow streams for various risk factors (such as the probability of defection)
and then discounts them at the risk free rate (cf. Hogan et al., 2002, p. 31).
Recent research has shown that customer satisfaction is linked to the growth and stability of
a ?rm’s future cash ?ows. Anderson, Fornell, and Mazvancheryl (2004) reported a positive as-
sociation between a ?rm’s current level of customer satisfaction and contemporaneous ?nancial
market measures such as Tobin’s Q, stock and market-to-book ratio. Subsequently, Gruca and
Rego (2005) reported that satisfaction increases shareholder value by increasing future cash
?ow and reducing its variability, and their ?ndings were robust when alternative measures of
?rm value or model speci?cations were explored. Their study was based on longitudinal data
from the American Customer Satisfaction Index and Compustat databases. They were able to
show that the relationship between satisfaction and cash ?ows is moderated by industry (e.g.,
product purchase cycle) and ?rm (e.g., magnitude of advertising expenditures) characteristics.
Not surprisingly, they ?nd that there are trade-offs between cash-?ow growth and cash-?ow
variability. These ?ndings suggest that marketers should explicitly recognize different risk fac-
tors in resource allocation decisions.
Challenges
The development of an “optimal” strategy requires a balancing of risk and return that goes beyond
current CRM practices that target the most pro?table or easy-to-serve customers. Yet, marketers do
not have appropriate procedures to adjust for the differential risk of various customers when making
resource allocation decisions. The ?nancial principles are especially challenging because—in these
situations—customers with certain characteristics (e.g., industrial buyers in speci?c industries or
consumers in speci?c geographical areas) may yield more volatile earnings streams. If marketers
wish to explicitly recognize risk factors other than defection, their CLV calculations should use a
risk-adjusted discount rate rather than a weighted average cost of capital. Hogan and colleagues
(2002) say that this task can be accomplished either by measuring the variance of returns over time for
various segments and calculating the appropriate discount rate—analogously to the evaluation of real
options (Copeland and Antikarov, 2001)—or by decomposing customer pro?tability into additional
sources. The ?nancial principles of portfolio management are well established, but marketers know
very little about how CRM principles should be applied to customer and product portfolios.
Conclusions
Based on our review of the evolving literature on CRM, we have argued that CRM is an integral part of
a company’s strategy. A company’s decisions regarding the development of organizational capabilities,
the management of value creation and its sources, and the allocation of resources across investment
opportunities are crucial elements in the array of strategic choices of the company. CRM principles
provide a strategic and tactical focus for identifying and realizing sources of value for the customer
and the ?rm. This chapter has described how CRM principles can guide ?ve key organizational pro-
cesses: making strategic choices that foster organizational learning, creating value for customers and
the ?rm, managing sources of value, investing resources across functions, organizational units, and
channels, and globally optimizing product and customer portfolios. For each organizational process,
we have identi?ed some of the challenges facing marketing scientists and practitioners. These are
summarized in our research agenda, which is displayed in Table 1.3.
28 RUTH N. BOLTON AND CRINA O. TARASI
Table 1.3
Future Research Agenda
Process Research topics
Strategic choices • Methods for achieving cross-functional coordination
• Development of organizational information processes and learning
capabilities
• Guidelines regarding the effective application of customer relationship
management (CRM)principles in strategic, cross-functional contexts
• Metrics for establishing goals and assessing outcomes
• Frameworks that link strategic-level metrics to tactical metrics for
functional groups, business processes, and business units
Dual creation of value Value for the customer
• Extension of theoretical work on how to create value for customers
• Models that predict how customers will perceive value in the future
• Studies of the antecedents and outcomes of customers’ willingness to
reveal private information, perceptions of fairness
• Research that extends current knowledge of retrospective measures
(satisfaction, purchase intentions) to leading indicators and antecedents of
future behavior
Value for the ?rm
• Better forecasts of sources of customer lifetime value (CLV): acquisition,
retention, and margin
• Decomposition of forecasts of margin into underlying components
(product usage, cross-buying, word-of-mouth [WOM])
• Research to link organizational actions to sources of value
• Research to reconcile customer-based valuation with market values for ?rms
• Dynamic forecasts that account for changes in customer preferences,
organizational actions, and competitor actions, heterogeneity across
customers
• Methods for allocating “lumpy” costs
• Improved methods for incorporating value of WOM into CLV
Managing sources of • How to create product–customer ?t
value for customers • How to recruit/divest customers and avoid “adverse selection” problems
• Further investigation of antecedents of each source of value: trial, usage,
product usage, and (especially) cross-buying and word-of mouth
• Development of dynamic models of customer behavior to accommodate
shifts in customer preferences (e.g., due to triggers), context effects,
changes in competitive behavior, short and long-run outcomes
Allocation of resources • How to maximize future customer pro?tability in the long run instead of
within and across current customer pro?tability in the short run
functions, channels, • Managing dynamics in changing customer relationships, such as fading
and organizational units and growing
• More sophisticated models that recognize simultaneity among variables
representing marketing actions and customer responses (including WOM)
• How to implement a coherent (across functions) and synchronized (over
time) set of activities
• Use of systematic experimentation so that opportunities are not overlooked
Global optimization • Segmentation should go beyond “classic” variables (geography, customer
models size, past customer behaviors) to re?ect underlying customer needs,
trigger points, and so on, that signal future behavior
• Models of how to create “balanced” customer portfolios with desired
levels of future earnings and risk
MANAGING CUSTOMER RELATIONSHIPS 29
• Conceptual models of customer–brand relationships, and models of
when and how to invest in brands to increase CLV
• Matching (new and existing) product bene?ts to (new and existing)
customer needs
• Matching the product and customer portfolio over time
• Developing a diversi?ed customer portfolio
• Adjusting for the differential risk of different customer groups
In this concluding section, we identify three features that are likely to characterize future research
on the topic of managing customer relationships. These features provide a unifying perspective
on how future research is likely to unfold.
Customer Portfolio Management as a Unifying Framework
In his famous article, “Marketing Myopia,” Levitt (1960) argues that ?rms exist to ful?ll needs, not
to sell products. In other words, customers are the building blocks of ?rms. The ultimate objective
of the ?rm is to position itself for long-term survival, and the role of marketing is to assist the
?rm in its endeavor by building strong market assets such as a valuable customer base and strong
brands (Anderson 1982). Hence, the development of a customer base is vital to ?rm survival and
should be one of the main foci of marketing (Hunt and Horn, 1983).
Wayland and Cole (1997, p. 12) claim that most businesses suffer from “a glut of products that
blur the company’s focus.” An illustrative example is the grocery store manager who attempted to
increase pro?tability by eliminating small volume items, thereby alienating customers and opening
the gates to competition (Rust, Zeithaml, and Lemon, 2000, pp. 16–22). This example shows that
an inadequate strategy for the dual creation of value—especially regarding the synchronization of
the customer and product portfolios—can impede long-term growth and pro?tability. Knowledge of
products is not enough. Knowledge of what products mean to customers and their role in building
the customer asset is vital. The role of CRM is to assist ?rms in leveraging “the information and
experience in acquisition, development and retention of a pro?table customer portfolio,” which
Wayland and Cole call customer knowledge management (1997, p. 32).
For these reasons, customer portfolio management provides a unifying framework for consid-
ering any expenditure or investment decision. Customer portfolio management frames questions
that arise within and across all ?ve organizational processes. At the strategic level, research is
required to guide ?rms in establishing goals for managing their customer portfolios, as well as
assessing business performance outcomes. This issue is closely intertwined with the need for
research to reconcile customer-based value creation with market values for ?rms, build models
to predict sources of value for both the customer and the ?rm, and develop metrics that can be
used to manage sources of value. In addition, research is required to guide ?rms in how to identify
and implement cross-functional activities, synchronized over time, to be maximally effective in
creating portfolios with desired levels of earnings and risk.
Models of Complex Systems
When the majority of ?rms have adopted CRM technology and related best practices, ?rms that use
CRM systems to connect with customers across multiple channels will no longer have a competi-
Process Research topics
Table 1.3 (continued)
30 RUTH N. BOLTON AND CRINA O. TARASI
tive advantage in their markets. As Porter (1996) observes, operational systems are not a long-term
sustainable source of competitive advantage. Consistent with this notion, research indicates that
CRM technology leverages an organization’s prior capabilities and information processes to create
value for customers and the ?rm. Day (2003, p. 77) argues that ?rms must create a superior cus-
tomer-relating capability by aligning the organization through incentives, metrics, accountabilities,
and structures. Consequently, additional research is required to investigate “how an organization
can create, communicate, and deliver value for customers by integrating and coordinating cross-
functional processes to produce coherent, mutually bene?cial outcomes” (Bolton, 2006).
Firms will require comprehensive, integrative models to guide strategic choices, allocate re-
sources to create value for customers, and manage customer acquisition, retention, cross-buying,
word-of-mouth, and divestment in ways that increase value for the ?rm. They need models of
complex systems that capture relations among organizational processes as well as connections
among strategic and tactical variables. We use the term “complex systems” to refer to systems of
equations that describe simultaneous relationships among multiple dependent variables (customer
and ?rm actions) that contribute to CLV, that capture the effects of interactions among organiza-
tional functions (e.g., employee selection and training, quality of service operations) and among
marketing variables—price, direct marketing promotions, relationship marketing instruments,
advertising communications, and distribution channels—and also the effects of competitive ac-
tions and heterogeneity across customers.
Based on past experience, purely predictive models are unlikely to provide suf?cient insight into
the path-dependent nature of the value creation process. Hence, models of complex systems must be
based on strong theory about how customer behavior changes over time, recognizing the important role
of mediating constructs (e.g., fairness, satisfaction, commitment, trust, brand preference) as precursors
of customer behavior. They must also re?ect how customers actively participate in the creation of the
customer experience (Prahalad and Ramaswamy, 2004). These features have become increasingly
important as ?rms interact with customers in “real time” where communication and interactions are
(potentially) two-way and take place across multiple channels. For these reasons, models are likely
to be based on increasingly sophisticated theory and methods (e.g., agent-based models).
A Shift in Focus Toward Contingencies or Context Effects
Throughout this chapter, we have noted many instances in which CRM research has discovered
contingency-based ?ndings. For example, Gruca and Rego (2005) found that ?rms operating in
more concentrated industries were better able to convert satisfaction into reduced cash ?ow vari-
ability (i.e., reducing the risk associated with the customer portfolio). Kamakura and colleagues
(2002) found that bank branches must be operationally ef?cient (in terms of deploying employees
and technology) and must achieve high customer retention if they are to be maximally pro?table
(i.e., increasing returns from the customer portfolio). Based on these and other ?ndings, there is
now compelling evidence that CRM research must move away from studies of marketing decision
variables in isolation.
We believe that marketing scientists should make a conscious effort to investigate how context
variables—which prior research may have considered “background factors”—moderate the effec-
tiveness of ?rm decision variables on business performance outcomes (Lynch, 1982). By moving
background factors into the foreground, marketers can begin to understand how the effectiveness
of marketing activities depends on organizational and market contingencies. There are numerous
ways to design research studies to attack broader issues (without modeling complex systems),
depending on the nature of the underlying problem.
MANAGING CUSTOMER RELATIONSHIPS 31
First, laboratory experiments can be designed to study organizational variables that tend to
covary in ?eld settings. For example, prior research indicates that: (1) activities that enhance
brand awareness and/or improve the favorability, strength, or uniqueness of brand associations,
create “brand equity” and yield a revenue premium (Ailawadi, Lehmann, and Neslin, 2003); (2)
brands are social entities, created by communities of consumers, as well as by marketers (Muniz
and O’Guinn, 2001); (3) the success of brand extensions depends on the quality of the current and
future customer portfolio (Hogan et al., 2002). In an experiment, the cognitive, social, and rela-
tional effects associated with a brand can be manipulated to help assess their relative size, as well
as the existence of interaction effects, and the net effect on the value of the customer to the ?rm.
Experiments are also a useful way to study threshold effects, nonlinear effects, and so forth.
Second, ?eld experiments are useful for studying how organizations allocate resources in situ-
ations that are characterized by endogenous or “feedback effects.” These situations are especially
prevalent in CRM settings. For example, a ?rm may use CLV estimates to target certain market
segments with a loyalty program, but the existence of the loyalty program has altered customers’
purchase behavior and consequently in?uences the ?rm’s estimate of CLV. Companies should
engage in systematic experimentation to disentangle these effects. Small-scale “pilot studies”
provide opportunities to conduct simple ?eld experiments that vary organizational actions across
customers, organizational units, and so forth. A compelling argument in favor of pilot studies is
that—without systematic experimentation—opportunities for revenue expansion are likely to be
overlooked.
A variety of quasi-experimental designs are useful when collecting cross-sectional or longitu-
dinal data. Furthermore, when suf?cient academic research has accumulated, meta-analyses can
play an important role in identifying moderating variables.
In Closing
Research on managing customer relationships has the potential to provide a unifying framework
for studying diverse marketing issues, and to contribute more broadly to business practice. It also
identi?es fruitful new areas for theoretical and methodological advances in addressing organi-
zational challenges at the cultural, strategic, and tactical levels. We know very little about how
brand equity, product portfolio decisions, or innovation contribute to customer equity, and we do
not understand the relationship dynamics that unfold to create customer value. Research on these
topics will generate new intellectual insights for marketing scientists and managers.
Notes
1. There is a long tradition of reach frequency monetary value models that use the number and timing
of previous transactions to identify customers who should be targeted with advertising and promotion (Ven-
katesan and Kumar, 2004). Interestingly, customer–?rm contacts or touch history is not considered in models
for predicting whether a customer is “dead” or “alive”—probably because ?rms do not routinely record the
occurrence of customer–?rm service encounters.
2. See the work of the Industrial Marketing and Purchasing Group (IMP) (e.g., Ford, 1990).
3. See: Abell and Hammond (1979).
4. A few database marketers have incorporated additional sources of value into their calculation of CLV
(e.g., Hughes, 1996; Wayland and Cole, 1997).
5. Thomas (2001) shows that a Tobit model with selection is better than a standard Tobit model for link-
ing customer acquisition to retention. The primary reason is that the Tobit model with selection addresses
both censoring and data truncation problems. The length of the customer’s lifetime is observed conditional
on the customer being acquired and—since the direct costs of acquisition are higher than the direct costs
32 RUTH N. BOLTON AND CRINA O. TARASI
of retention—standard Tobit model predictions can lead to overinvestment in certain customers (e.g., due
to incorrectly estimating the impact of add-on selling). Generally, selection bias may require sophisticated
analytical techniques to correct for censoring and truncation (Heckman 1979).
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