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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 discussed.......

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  1. HOW TO MEASURE THE IMPACT OF A CRM STRATEGY ON THE FIRM PERFORMANCE 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: Tl: +34 987 291455 Fax: +34 987 291454 M. Aránzazu Sulé e-mail: Tl: +34 987 291000 Ext. 5451 Fax: 34 987 291454 1
  2. HOW TO MEASURE THE IMPACT OF A CRM STRATEGY ON THE FIRM PERFORMANCE ABSTRACT 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 discussed. Keywords: CRM, marketing performance measurement. 1. INTRODUCTION 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. 2
  3. 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 research. 2. MARKETING PERFORMANCE MEASUREMENT: FROM FINANCIAL METRICS TO SCORE CARD METHODS 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, 1997). 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 3
  4. 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 non- financial 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, 4
  5. 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 advantage. 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 level. - 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, 1998). 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 5
  6. 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, 2002). - 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). 6
  7. 3. PERFORMANCE MEASUREMENT IN THE CRM ERA 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 one- dimensional 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 Name Originator Description of measure Human Capital (Fitz-Enz, 1994) Sets of human capital indicators are collected Intelligence and bench-marked against a database. Similar to HRCA. Skandia Navigator (Edvinsson and Intellectual capital is measured through the Malone, 1997) 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. 7
  8. Value Chain (Lev, 2002) A matrix of non-financial indicators arranged Scoreboard in three categories according to the cycle of development: Discovery / Learning, Implementation, Commercialisation. IC-Index (Roos, Roos, Consolidates all individual indicators Dragonetti and representing intellectual properties and Edvinsson, 1997) components into a single index. Changes in the index are then related to changes in the firm’s market valuation. Intangible Asset (Sveiby, 1997) Management selects indicators, based on the Monitor 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. Value Creation Index (Ittner, Kalafut, Drivers of value are derived from an extensive Larcker, Sean Love, literature survey and advanced statistics. Low, Park, Siesfeld Metrics are weighted and combined to give a and Zito, 2000) Value Creation Index. The index is compared and combined with financial data. Balanced Score Card (Kaplan and Norton, A company’s performance is measured by 1992) 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 investments: - 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 incentives). 8
  9. 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 cross- functional 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. 4. CONCLUSION AND FUTURE RESEARCH 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 h- relationships. 9
  10. 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, 2003). REFERENCES Anderson, E.W., Fornell, C. and D. R. Lehmann (1994), “Customer Satisfaction, Market Share, and Profitability: Findings from Sweden,” Journal of Marketing, 58 (July), 53-66. Ang, L. and F.A. Buttle (2002), “ROI on CRM, a customer-journey approach”, Conference Proceedings of IMP Conference, Perth, Australia. Ambler, T. and F. Kokkinaki (1997), "Measures of Marketing Success", Journal of Marketing Management, 13 (7), 665-678. Ambler, T., Kokkinaki, F. and S. Puntoni (2002), Assessing market performance: the current state of metrics, Centre for Marketing Working Paper, num. 01-903. Amir, E. and B. Lev (1996), “Value-relevance of non-financial information: the wireless communication industry,” Journal of Accounting and Economics, (Aug-Dec), 22 (1-3), 3-30. Banker, R.D. and R. Mashruwala (2000), A Contextual Study of Links Between Employee Satisfaction, Employee Turnover, Customer Satisfaction, and Financial Performance, University of Texas, Dallas working paper. Banker, R. D., Potter, G. and D. Srinivasan (2001), “An Empirical Investigation of an Incentive Plan that Includes Non-financial Performance Measures”, Accounting Review, 75 (1). Berger, P. and N.I. Nasr (1998), “Customer lifetime value: marketing models and applications”, Journal of Interactive Marketing 12, 17-30. Blattberg, R.C. and J.S Thomas (2001), Valuing, analyzing, and managing the marketing function using customer equity principles, in Kellogg on Marketing, ed. D. Iacobucci, 302- 319. Wiley & Sons, New York. Blattberg, R.C. and Deighton, J. (1996), Manage marketing by the customer equity test. Harvard Business Review Jul-Aug, 136–144. 10
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