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M. Rosa Llamas and M. Aránzazu Sulé
Área de Comercialización e Investigación de Mercados
Facultad de Ciencias Económicas y Empresariales
Universidad de León
Campus de Vegazana, s/n
24071 León (Spain)
M. Rosa Llamas
e-mail: [email protected]
Tl: +34 987 291455
Fax: +34 987 291454
M. Aránzazu Sulé e-mail: [email protected]
Tl: +34 987 291000 Ext. 5451
Fax: 34 987 291454
CRM strategy (Customer Relationship Management) is a business philosophy, stemming
from Relationship Marketing that joins strategy and technology, with the aim of creating
value for both customers and the company. In this paper we justify the interest of establishing
a formal system to measure CRM performance. In order to do that, we first focus on the role
of marketing performance measurement throughout the time. Then, we compare different
frameworks and metrics used to measure performance in the CRM era. Finally, challenges to
face in CRM performance measurement as well as some ideas for future research are
Keywords: CRM, marketing performance measurement.
The importance of customer relationship management as source of competitive advantages
has been recognized for decades (McKenna, 1993; Woodcock, 2000), nevertheless, it has
been in recent years, with the deployment of the information technologies, when CRM has
gained growing popularity.
This business philosophy combines strategy and technology with the aim of get to know the
customer and establishing a two-way communication and interaction in order to improve the
efficiency and effectiveness of the business processes, increasing the value for both, customer
and company. There are three issues underlying the RM concept: relationships, networks and
interaction (Gummesson, 2002).
Srivastava, Shervani and Fahey (1999) in a special number of the Journal of Marketing
entitled “Fundamental issues and directions for marketing”, point out that the CRM is one of
the three key aspects in business processes since it lets the company identify consumers,
create knowledge, build relationships with customers and model their perceptions about the
company and its products. Brown (2000) considers that managing relationships with
customers is revolutionizing marketing and redefining business models. In this sense,
Greenberg (2001, p. 6) talking about CRM, states that we are on the verge of the most
significant transformation in business.
Although CRM concept has been the central core of many articles, conferences and seminars,
so far most of them corresponds to enterprise initiatives and there is a lack of empirical
academic research (Ang and Buttle, 2002; Kim, Suh and Hwang, 2003; Plakoyiannaki and
Tzokas, 2001; Winer, 2001).
The CRM approach is simple but its implementation is complex, for that reason, a high
percentage of CRM projects fails. In this sense, authors like Grabner-Kraeuter and
Moedritscher (2000) and Woodcock (2000) and consulting firms such as Gartner (2001) and
Meta Group (2002) maintain that defining project objectives clearly and having metrics that
indicate the degree of attainment of such objectives in a dynamic way, increases the
likelihood of success of such projects.
In fact, one of the research priorities in the CRM field is the development of metrics that
enable the managers to know to what extent CRM programs are working (Winer, 2001). The
Marketing Science Institute has also echoed this problem giving the topic “Assessing
Marketing Productivity (Return on Marketing) and Marketing Metrics” its highest priority for
2002-2004 (Marketing Science Institute, 2002).
Although most of the authors propose a phase of performance measurement (Payne, 2000;
Plakoyiannaki and Tzokas, 2001; Srivastava, Shervani and Fahey, 1999; Winer, 2001;
Woodcock, 2000), there is not an accepted academic model of measurement. The increasing
interest in developing measurements which justify investments in CRM includes financial and
non-financial measures, since the latter ones are receiving more and more importance (Clark
1999; IMA 1993; 1995; 1996; Marketing Science Institute 2002; Marketing Week, 2001;
Moorman and Rust, 1999; Shaw and Mazur 1997; Schultz, 2000).
The objective of this paper is to shed light on CRM performance measurement in order to
foster empirical academic research on this field, which has become of increasing interest for
both academics and managers. First of all, we focus on the business performance
measurement, placing a particular emphasis on marketing metrics. Next, we study the
evolution of the measures used in the marketing field and compare different frameworks and
metrics used to measure performance outcomes of a CRM strategy. Finally, we discuss the
challenges to face in CRM performance measurement as well as some ideas for future
Business Performance Measurement (BPM) has a lot of branches in a wide variety of
disciplines, including accounting, economics, human resource management, marketing,
operations management, psychology and sociology. In the field of marketing, performance
measurement has not been developed all that much. In fact, it has been the target of criticisms
due to its short term orientation (Dekimpe and Haussens, 1995, 1999), its limited diagnostic
power (Day and Wensley, 1988), the lack of consensus in relation to the number of measures
and the subsequent difficulty for making comparisons (Clark, 1999; Ambler and Kokkinaki,
The reasons for this poor development of marketing accountability are the difficulties in
measurement which involves the assessment of the results derived from the implementation of
different marketing strategies. One of these barriers is the complexity to isolate the effects of
a particular marketing strategy (Bonoma and Clark, 1998). Another one, is that those effects
are perceived, in most of the cases, in the long term (Dekimpe and Hanssens, 1995).
Nevertheless, it is very useful and neccessary to measure performance in order to evaluate the
result of the different marketing strategies. It lets reinforce those with positive results and
correct others not providing the expected benefits. Furthermore, it is said that what gets
measured, gets managed. According to Metrus Group (2003) there is considerable evidence of
strategic performance measurement on strategy execution and strategic performance. This
company carried out a study about the benefits of strategic performance measurement, finding
six reasons why strategic performance measurement is so powerful in improving business
performance: (1) measurement rapidly forges increased strategic agreement; (2) measurement
provides a common language to communicate strategy and key values; (3) measurement helps
forge alignment throughout the organization; (4) measurement accelerates the rate of
successful change; (5) measurement increases a company’s predictive power and early
warning capability; (6) measurement helps provide managers with a holistic perspective.
In recent decades we are being witnesses of an important transition from an industrial society
to an information one. According to many authors, this transformation, fostered by
information revolution, is comparable to previous revolutions because of the important
economic and social effects derived from it.
This revolution involves the reformulation of the key resources for the companies. If in the
Industrial Revolution the emphasis was on the tangible assets such as equipment, raw
materials, human resources, energy, etc., in the Digital Revolution intangible resources such
as brand image, customer loyalty, market knowledge, know-how, etc. are the stars.
Nowadays, it is clear that the core of the resources on which the management lies in and
whose efficient combination is translated into benefits, has broadened including another kind
of issues under the umbrella of “intangible”, “intellectual” or “invisible”. These assets can
provide the company with an important competitive advantage, guiding its success.
This change in the focus regarding to the importance of productive resources has been
followed by an evolution in the business performance measurement orientation. Traditionally
marketing performance measurement was based on the information provided by Accounting
Department, derived from balance sheet and income statement. Those measurement systems
only had into account tangible measures such as sales, gross margin, percent value from new
products and services (Crosby and Johnson, 2001). In the 80s market share gained great
popularity as a strong predictor of cash flow and profitability (e.g. Buzzel and Gale, 1987).
During the 90s, customers are viewed as assets (Rust, Zeithaml and Lemon, 2000) or equity
of the firm (Blattberg and Deighton, 1996; Blattberg and Thomas, 2001; Rust, Zeithaml and
Lemon, 2000). This customer-centered viewpoint is reflected in the concepts and metrics that
drive marketing management, so a measurement literature arises (Berger and Nasr, 1998;
Gupta, Lehmann and Stuart, 2002; Jain and Singh, 2002; Mulhern, 1999; Reinartz and
Kumar, 2000; Rust, Lemon and Zeithaml, 2003). Furthermore, the relationship between nonfinancial measures such as customer satisfaction (e.g. Anderson, Fornell and Lehmann, 1994;
Ittner and Larcker, 1998b; Szymaski and Henard, 2001), customer loyalty (Dick and Basu,
1994), brand equity (Keller, 1998), employee equity (Amir and Lev, 1996; Srivastava,
Shervani and Fahey, 1998) and profitability was proved, and subsequently this type of
measures started to have a great deployment.
Nowadays the increasing dynamic and competitive business environment demands holistic
measurement systems which provide the company with a complete “map” of different aspects
influencing the results of companies, in order to neutralize their weaknesses, reinforce the
strengthens and create new ones. In a CRM world, companies have a great amount of data
which can be transformed into useful information by easing strategic management and control
process. Managing this information in a systematic and dynamic way can yield a competitive
According to Ambler, Kokkinaki and Puntoni (2002) the evolution of marketing metrics
seems to fit the following pattern:
Little awareness regarding the necessity of using marketing metrics at top executive
Measurement systems based exclusively on financial metrics.
Broad vision of performance measurement including non-financial metrics.
Seeking some rationale(s) to reduce the number of metrics, about 25 or less (Unilever,
Performance measurement metrics can be classified into different categories: financial versus
non-financial; one-dimensional versus multi-criteria (Grabner-Kraeuter and Moedritscher,
2002); input, management and output measures (Clark, 1999); hard versus soft (Ang and
Buttle, 2002); tangible versus intangible.
One of the most used classifications divides success measures in two broad categories:
financial and non-financial (Buckley, Hall, Benson and Buckley, 1988; Frazier and Howell,
1982). Early research in performance marketing measurement focused on financial indicators:
profit, sales and cash flow (Day and Fahey, 1988; Sevin, 1965). Recently, the non-financial
measures are receiving more and more attention. Grabner-Kraeuter and Moedritscher (2002)
point out that the inclusion of this type of measures has become the state-of-the-art in
managerial accounting and business performance measurement research. Some years before,
Kaplan and Norton (1996) pointed out that non-financial measures are a great tool providing
support to the top executives to identify potential problems and assess the success of the
company. An increasing number of authors agree with them indicating the importance of soft
measures (Clark 1999; IMA 1993; 1995; 1996; Marketing Science Institute 2002; Marketing
Week, 2001; Moorman and Rust, 1999; Schultz, 2000; Shaw and Mazur 1997).
We can wonder if the popularity of non-financial measures from an academic point of view is
accompanied by the same success from a managerial perspective. There are a few researchers
who have analysed the extent to which financial and non-financial measures are used by
practitioners. A recent study from Reinecke and Reibstein (2002) found that managers
primarily rely on quantitative performance metrics such as sales, market coverage, margin, net
profit, sales profitability, share of new customers, etc. but they increasingly include
qualitative indicators such as customer satisfaction, customer retention or brand familiarity.
These findings confirm the results of another research carried out by Ambler, Kokkinaki and
Puntoni (2002) in the United Kingdom. This study shows that top management considers
financial measures more important than any other category, so this kind of metrics are the
most frequently collected. The indicators enjoying the major popularity among the top
management are the following: profit/profitability; sales, value and/or volume; gross margin;
awareness, market share (volume or value); number of new products; relative price
(SOMValue/volume); number of consumer complaints (level of dissatisfaction); consumer
satisfaction; distribution/availability; total number of customers; marketing spend; perceived
quality/esteem; loyalty/retention; relative perceived quality.
In spite of academics think that non-financial metrics should leader performance
measurement, practitioners remain using predominantly classical ones. We can find the
explanation for this behaviour in the fact that these indicators are much easier to measure. In
addition, conventional methods have the advantage of being investment evaluation settings.
Their major drawback of evaluation is that they focus on the estimation of cash flows and
accounting criteria (Kim, Suh and Hwang, 2003). Nevertheless, traditional performance
systems do not provide a full understanding of the influences on profits. The major criticisms
to classical metrics are summarized in the following:
Accounting metrics have a focus on the short-term and take little account of the value
to the firm of long-term customer preference, or the marketing investment which
created it (e.g. Ambler, Kokkinaki and Puntoni, 2002).
They are not adequate for assessing investments whose benefits will be intangible,
indirect or strategic (e.g. Bukowitz and Petrash, 1997; Grembergen and Amelinckx,
They only report functional processes (e.g. Ittner and Larcker, 1998a).
They do not take into account the influence of marketing decisions on such variables
as inventory levels, working capital needs, and financing costs that need to be
managed for the well-being of the enterprise (e.g. Srivastava, 2004).
They do not let aggregation from an operational level to a strategic one They just look
backwards, recording historical data so their prediction power is limited (e.g.
Chakravarthy, 1986; Ittner and Larcker, 1998a; Yeniyurt, 2003).
They are not suitable for strategic decisions (e.g. Kaplan and Norton, 1992).
The do not measure the value created (e.g. Lehn and Makhija, 1996).
They provide little information on deviations (e.g. Ittner and Larcker, 1998a).
There is a high number of metrics, so researchers should find some convergence in
order to describe more with less numbers (e.g. Frigo and Krumwiede, 2000; Kaplan
and Norton, 1992).
They do not link the non-financial metrics to financial numbers (e.g. Kaplan and
Norton, 1992).
Traditionally financial and non-financial measures have been seen as opposed, but there are
many connections between these two kinds of metrics. Jutla, Craig and Bodorik (2001) state
that some metrics are function of other metrics. The results of some studies about the lead/lag
relationship between financial and non-financial metrics show that there is a strong
association between non-financial performance measures such as customer and employee
satisfaction, customer and employee retention and quality measures and financial indicators
such as profitability (Banker and Mashruwala, 2000, Banker, Potter and Srinivasan, 2001;
Ittner and Larcker 1998a and 1998b, Nagar and Rajan, 2001).
The environment in which companies deploy their activity is a complex, dynamic and
multidimensional scenario. This involves that firm performance depends on a great deal of
variables, both internal and external, which make the company adapting better or worse to
that competitive environment. Therefore, companies should possess a measurement system
including every issue creating added value in the relationships the company maintains.
Nowadays, firms make enormous investments in technology and have sophisticated data
analysis systems. However, implementing a CRM strategy means to go beyond technology
investments. The success of this strategy requires changes in corporate culture, training and
involvement of the employees, and tracking and control of the performance.
Success is a multidimensional concept and it can vary throughout the time and depending on
the analysed company, and the sector to which it belongs. So, most of the traditional measures
does not provide the expected results since they view the business world from a onedimensional point of view. In a networking economy firms need holistic systems that mirror
every relationship both inside the company and with external agents. Trends in performance
measurement point in two different directions (Yeniyurt, 2003):
1. Improving financial measures in order to enhance their explanatory power
2. Developing complete systems, including both financial and non-financial metrics,
such as scorecard methods
Regarding to the first stream, the most popular method is the Economic Added Value (EVA).
This measure is defined as the difference between a company’s net operating income after
taxes and its costs of capital (Dodd and Chen, 1997). It has been widely supported by
academics (especially in the accounting literature) and practitioners since the early 1990s. Its
strengthen lies in its emphasis on value creation but it has been accused of an excessive focus
on the short-term and undervaluation of growth potential. In addition, some authors argue that
it is only a variant of residual income and internal rate of return, developed in the 50s and 60s
(e.g. Chen and Dodd; Ittner and Larcker, 1998a).
Academics and practitioners are paying more and more attention to the second research
stream, based on developing integrative non-financial and financial performance measures
focused on the process. Some of the score cards methods are summarized in Table 1:
Table 1. Measurement methodologies
Human Capital
(Fitz-Enz, 1994)
Skandia Navigator
(Edvinsson and
Malone, 1997)
Description of measure
Sets of human capital indicators are
collected and bench-marked against a
database. Similar to HRCA.
Intellectual capital is measured through the
analysis of up to 164 metric measures (91
intellectually based and 73 traditional
metrics) that cover five components: (1)
financial; (2) customer; (3) process; (4)
renewal and development; and (5) human.
Value Chain
(Lev, 2002)
(Roos, Roos,
Dragonetti and
Edvinsson, 1997)
Intangible Asset
(Sveiby, 1997)
Value Creation Index
(Ittner, Kalafut,
Larcker, Sean Love,
Low, Park, Siesfeld
and Zito, 2000)
Balanced Score Card
(Kaplan and Norton,
A matrix of non-financial indicators
arranged in three categories according to the
cycle of development: Discovery / Learning,
Implementation, Commercialisation.
Consolidates all individual indicators
representing intellectual properties and
components into a single index. Changes in
the index are then related to changes in the
firm’s market valuation.
Management selects indicators, based on the
strategic objectives of the firm, to measure
four aspects of creating value from
intangible assets. By: (1) growth; (2)
renewal; (3) utilisation /efficiency; and (4)
risk reduction / stability.
Drivers of value are derived from an
extensive literature survey and advanced
statistics. Metrics are weighted and
combined to give a Value Creation Index.
The index is compared and combined with
financial data.
A company’s performance is measured by
indicators covering four major focus
perspectives: (1) financial perspective; (2)
customer perspective; (3) internal process
perspective; and (4) learning perspective.
The indicators are based on the strategic
objectives of the firm.
Source: Pike and Roos (2004).
These methods reflect the growing interest in measure and manage intangible assets since the
difference between market value and book value is largely attributed to these invisible issues.
All of the methodologies above consider strategy as the main aspect to assess and human
capital (experience, knowledge, competences, know-how, etc.) structural capital (processes,
information systems, databases, etc) and relationship capital (customer relationships, brands,
trademarks, etc.) as the secondary issues to manage.
The most quoted method in the marketing literature in the Balanced Score Card. Based on this
model some authors have established some variations adapting the variables to a CRM
context. One of these models was presented by Grabner-Kraeuter and Moedristcher (2002),
called CRM-SEM – System for CRM excellence measurement. It provides a multi-criteria,
quantitative and qualitative focus towards an assessment of the return on CRM-related
creation of additional value for the company (contribution to ROI, EBIT, CFROI)
increase in customer value (increase in CLV, improvement of client structure)
improvement of customer-centric processes (shorter time to delivery)
as well as in the field of organisational and human resources (higher motivation, more
On the other hand, Kim, Suh, and Hwang (2003), presented a model for evaluating the
effectiveness of CRM using the Balanced Score Card. They substituted the traditional four
perspectives by others mirroring a customer-centric philosophy in CRM evaluation. The
dimensions included in the model proposed by these authors are the following: customer
knowledge, customer interaction, customer value, and customer satisfaction.
An additional measurement criterion focusing on CRM initiatives is the Loyalty Value Added
(LVA). LVA means the increase of net cash flow that is caused by structural changes in
customer interaction (Reiter 2001). The customer LVA is the difference between actual and
prospective customer revenues. Another method that has attracted a lot of attention in recent
years is Customer Lifetime Value (CLV) (Berger and Nasr, 1998; Mulhern, 1999; Reinartz
and Kumar, 2000).
Experts on CRM such as Professor Payne (2000), states these efforts to design crossfunctional frameworks, such as the Balanced Score Card approach are a useful step forward
but are not yet well-enough developed to address the complexities of CRM. This author
suggests using four categories of metrics to measure CRM performance: strategic metrics,
customer metrics, operational metrics and output metrics. In our opinion, much work is
needed in order to incorporate intangible key performance issues such as word of mouth
(referrals), customer satisfaction, employee satisfaction, perceived quality, perceived value,
customer loyalty, commitment, empathy, trust, disposal to buy again, etc. to CRM
measurement systems.
In this dynamic business context, CRM enjoys a great popularity, so CRM investments are
enormous and are growing. Nevertheless, many of these projects are not paying the expected
results. Bearing this in mind, managers should have a perfect knowledge of the different
issues of a CRM strategy impacting on their firm performance.
According to Gummesson (2002) the core variables of the modern marketing are
relationships, networks and interaction. This network economy makes it very complex to
design a framework which mirrors the myriads of interactions taking place in a company and
the value created in each of them. The problem is not the amount of relationships since firms
have the help of sophisticated information systems and data warehouses been able to manage
a great deal of data. The challenge is to capture and measure soft and qualitative information.
For example, in the book The Experience Economy (1999), authors Joseph Pine and James
Gilmore state that what engage customers with the firm is providing them with experiences.
“Commodities are fungible, goods are tangible, services are intangible and experiences are
memorable” (Gilmore, 1998). But, how to measure the value created by those experiences?
Thusy and Morris (2004) point out that part of the challenge in building customer experiences
is that experience is intangible quality, so is different from one person to another. Feelings,
emotions, smells, colours, sounds, human contact, time and other factors are the actors on the
experience stage. These authors also stress that experiences are created by people, so
employees become partners in the customer experience, when they interact. We find
extremely relevant to take into account the human factor, what Gummesson (2002) calls hrelationships.
Much has been written about the importance of customer satisfaction, customer retention and
other parameters related to the customer, whereas the role of the employees, their satisfaction,
loyalty and the link between these variables and profitability is not often included in
marketing measurement systems.
Researchers have made progress in developing models to measure CRM performance but
much work remains. Managers are overwhelmed because of the excessive number of metrics
(most of them financial) while some issues are not assessed. It is necessary to find some
convergence and reduce the number of metrics in a framework that incorporates both financial
and non-financial measures and gives the adequate importance to human relationships and
intangible assets.
This cross-functional approach should establish linkages among the metrics, shaping a
network. Other desirable characteristics of this integrative system are to be an ongoing,
repetitive process (Johnson and Gustafsson, 2000; Marr and Schiuma, 2003) which has the
ability to adapt itself in a dynamic way (Bourne, Mills, Wilcox, Neely and Platts, 2000;
Kaplan and Norton, 2000; Kennerley and Neely, 2003; Neely, 1998; Waggoner, Neely and
Kennerley 1999)and create links among long-term CRM vision, strategy and goals to the
specific short-term tactics, measures and actions that drive CRM performance (Metrus Group,
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