MACQUARIE GRADUATE SCHOOL OF MANAGEMENT MGSM WORKING PAPERS IN MANAGEMENT

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1 MACQUARIE GRADUATE SCHOOL OF MANAGEMENT MGSM WORKING PAPERS IN MANAGEMENT STRATEGIC, OPERATIONAL AND ANALYTICAL CUSTOMER RELATIONSHIP MANAGEMENT: ATTRIBUTES AND MEASURES Reiny Iriana Macquarie Graduate School of Management Macquarie University, Sydney, NSW 2109, Australia & Francis Buttle Macquarie Graduate School of Management Macquarie University, Sydney, NSW 2109, MGSM WP February 2005

2 Disclaimer Working Papers are produced as a means of disseminating work in progress to the scholarly community, in Australia and aboard. They can not be considered as the end products of research, but a step towards publication in scholarly outlets. 1 Reiny Iriana & Francis Buttle Research Office Macquarie Graduate School of Management Macquarie University Sydney NSW 2109 Australia Tel Fax research@mgsm.edu.au URL Director of Research Manager, Research Office Associate Professor John Rodwell Ms Kelly Callaghan ISSN Printed copy Online copy MGSM WP STRATEGIC, OPERATIONAL AND ANALYTICAL CUSTOMER RELATIONSHIP MANAGEMENT: ATTRIBUTES AND MEASURES Reiny Iriana & Francis Buttle Correspondence to: Ms Reiny Iriana reiny1202@yahoo.com.au 1 Ms Reiny Iriana is a doctoral candidate at MGSM. reiny1202@yahoo.com.au Dr Francis Buttle is Ms Iriana s supervisor. Tel +61 (02) ; Fax +61 (0) ; francis.buttle@mgsm.edu.au All correspondence to the first named author ii

3 STRATEGIC, OPERATIONAL AND ANALYTICAL CUSTOMER RELATIONSHIP MANAGEMENT: ATTRIBUTES AND MEASURES Abstract Customer relationship management (CRM) means different things to different people. For some, CRM is the term used to describe a set of IT applications that automate customerfacing processes in marketing, selling and service. For others it is about an organizational desire to be more customer-focussed, and includes consideration of people and processes, and perhaps, but not only technology. For another group, CRM focuses on the analysis and exploitation of customer data. One distinction that has been made is between strategic, operational and analytical CRM. This paper sets out to understand, conceptualize and operationalise these terms. We ultimately present a 14-item scale that can be used with reasonable confidence to assess an organization s orientation towards one or more of these three forms of CRM. iii

4 What is CRM? The expression Customer Relationship Management, or CRM, emerged early in the 1990s. Tom Siebel, the founder of software vendor Siebel Systems Inc., is credited with inventing the term, though the origins of today s comprehensive CRM software suites are much earlier. As Buttle (2002) noted, CRM does not have a long history and there is no clear consensus on its boundaries and content. The following descriptions clearly indicate a divergence of views about CRM. They reflect views that CRM addresses issues of organizational strategy, customer life-cycle management, technology implementation, e-commerce, communications strategy, and business process design. CRM is a business strategy combined with technology to effectively manage the complete customer life-cycle (Smith 2001) CRM is the establishment, development, maintenance and optimisation of long-term mutually valuable relationships between customers and organizations (CRM (UK) 2001) A CRM system is comprised of 2 major components: a set of functions that allows the organization to build an understanding of customer behaviour, and a second set of functions that allow us to communicate with customers, across many channels, to meet their service requirements, and to try to persuade them to behave more profitably (Forsyth 2001) CRM is an e-commerce application (Khanna 2001) CRM aligns business processes with customer strategies to build customer loyalty and increase profits over time (Rigby 2002) A good CRM program enables customers to easily access the information they need at any time and includes a 24-by-7 web-site, fast tools and the ability to discuss problems with a human being rather than an electronic answering system (Rembrandt 2002) There is even a view that there is no one correct definition of CRM. Moreover, the definition of CRM will evolve and change over time (Goldenberg 2000). 1

5 The META Group (2001) made an early effort to cluster these different perspectives on CRM into meaningful subsets. They identified 3 different forms of CRM - operational, collaborative and analytical as described below: 1. Operational CRM The business processes and technologies that can help improve the efficiency and accuracy of day-to-day customer-facing operations. Operational CRM includes sales automation, marketing automation, and service automation. 2. Collaborative CRM Collaborative CRM is concerned with the components and processes that allow an enterprise to interact and collaborate with its customers. These include voice technologies, Web store-fronts, , conferencing, and face-to-face interactions. 3. Analytical CRM Analytical CRM is that portion of the CRM ecosystem that provides analysis of customer data and behavioural patterns to improve business decisions. This includes the underlying data warehouse architecture, customer profiling/segmentation systems, reporting and analysis. Building on the META Group s representation of a CRM ecosystem, Payne (2001) developed a strategic framework for CRM, consisting of five interrelated cross-functional processes: 1. Strategic Development Process 2. Value Creation Process 3. Multi Channel Integration Process 4. Information Management Process 5. Performance Assessment Process Four of these 5 processes are subsumed within three forms of CRM strategic, operational and analytical as shown in figure 1 below. Strategic CRM encompasses both the strategy development process and the value creation process, and therefore answers questions such as what business are we in?, which customers do we serve? and how do we create and deliver value to these customers? Operational CRM focuses on the management of the virtual and physical channels through which customers and company communicate and transact. Analytical CRM is focussed on the development and exploitation of customer-related data. Payne s model shows how the three forms of CRM are interrelated. Analytical CRM, for example, supports operational CRM by feeding the right information at the right time to agents and channels interacting with customers. 2

6 More recently, Buttle (2004) has also referred to the strategic, operational, analytical CRM triptych. He defines each as follows: 1. Strategic CRM is a top down perspective on CRM, which views CRM as a core customer centric business strategy that aims at winning and keeping profitable customers. 2. Operational CRM is a perspective on CRM which focuses on major automation projects within the front-office functions of selling, marketing and service 3. Analytical CRM is a bottom up perspective, which focuses on the intelligent mining of customer data for strategic or tactical purposes. Our study examines whether these three forms of CRM are conceptually and operationally different. We are particularly interested to discover whether companies do indeed orient themselves to one of these three forms of CRM. We develop a tool that can be used to 3

7 measure a company s CRM orientation whether to strategic, operational or analytical forms (hereafter referred to collectively as SOA CRM) Literature review: Strategic, Operational and Analytical CRM Strategic CRM In Payne s (2001) view, the strategy development and value creation processes jointly represent Strategic CRM. Buttle (2004) defines Strategic CRM as a top down perspective on CRM, which views CRM as a core customer centric business strategy that aims at winning and keeping profitable customers. Business strategy reflects the corporate long term vision and values of the organization in its effort to create and deliver value to customers (Plakoyiannaki & Tzokas, 2002). In Strategic CRM, the goal is to align the broader business strategy with the customer strategy. Companies with a superior understanding of their competencies relative to competitors, and of the opportunities open to them, are better placed to develop Strategic CRM that is appropriate to their own context. Pro-active targeting of preferred market and customer groups for acquisition and retention are important sub-processes within an organization s customer strategy. In the value creation process, business and customer strategy decisions are translated into implementation programs that generate value for customers and company alike. Key considerations of the value creation process are the value the customer receives and the value the organization receives. The value the customer receives is delivered by the company s value proposition(s). Companies develop offers that they believe will meet the needs and expectations of customers more effectively or efficiently than competing offers. The value the organization receives is the company s return on investment from its customer management strategy. The Customer Lifetime Value (CLV) metric can be used to measure the net present value of a customer s or segment s profit contribution over a defined period (lifetime) of transacting with a company. Operational CRM Operational CRM is a perspective on CRM which focuses on major automation projects (Buttle 2004). Operational CRM is focussed on the automation of selling, marketing, and service functions. Marketing automation applies technology to marketing processes, sales 4

8 force automation applies technology to the management of a company s selling activities, and service automation allows companies to automate their service operations. Channel integration is the key driver of many CRM implementations. Payne (2001) suggests that the Multi-Channel Integration Process attempts to ensure consistency and high quality in the customer s experience across the company s different channel types. Channel options may be evaluated based on the ability of the company to create customer value that meets customer needs and expectations at low cost. Examples of channels are distributors, catalogues, on-line shops, electronic exchanges/auctions, and direct selling (Ang & Buttle, 2002). Fayerman (2002) adds that the automation of customer-relevant business processes is not limited to front office customer contact points but may also include back office functions such as human resources and finance, because information from the back office functions may be necessary for Operational CRM to function productively. The broad objective of Operational CRM is to improve the efficiency and effectiveness of customer management processes. In achieving this objective, companies may focus on enhancing their resources to deliver improved value to customers and themselves (Plakoyiannaki & Tzokas 2002). Analytical CRM Analytical CRM is a bottom up perspective, which focuses on the intelligent mining of customer data for strategic or tactical purposes (Buttle 2004). Analytical CRM uses technology to accumulate and analyse customer-related data, and to give direction to the strategies and tactics that are implemented in the channels and touch-points deploying Operational CRM. Payne (2001) suggests that Analytical CRM is an information management process, which involves the collection and accumulation of customer information from all customer interfaces. This customer information is used to develop customer profiles and opportunities which are then delivered to the customer interfaces for better Operational CRM applications. Customer information helps the organization to have a better understanding of customer behaviour and to be able to segment its market accordingly (Plakoyiannaki & Tzokas, 2002). Knox, Maklan, Payne, Peppard, & Ryals (2003) add that the information management process supports the strategy development process by providing information about market characteristics to develop customer strategy. Information is also needed by the organization 5

9 to assist in the value creation process, such as in determining CLV and in the development of new products and services. Standard technology applications within Analytical CRM are data warehousing and data mining solutions (Gebert, Geib, Kolbe, & Brenner, 2003). Doyle (2002) suggests that Analytical CRM applications enable companies to perform 6 major functions: 1. Analysis of the characteristics and behaviour of customers 2. Modelling to predict the behaviour of customers 3. Communication management with customers 4. Personalised communication with customers 5. Interaction management 6. Optimisation to determine the best combination of customers, products and communication channel The independent research organization, Gartner Inc., has compared the benefits of Operational CRM with Analytical CRM (Gartner 2002). They claim that Operational CRM provides a faster return on investment than Analytical CRM because it is easier to implement. However, Analytical CRM provides a continuing potential ROI from having better knowledge and understanding of customers. The data analytics organization, SAS, reports that Analytical CRM can increases an organization s revenue in a number of ways (SAS 2003): 1. Effective cross sell and up sell 2. Prediction of which customers are most likely to buy 3. Identification of high value customers 4. Increased brand awareness 5. Increased customer satisfaction, loyalty and referrals Gartner (2002) also highlights key success factors of Analytical CRM implementations, notably the sharing of customer information and strong teamwork between marketing and customer service. In addition, SAS (2003) points out several reasons why many Analytical CRM initiatives fail: 1. lack of an integrated view of customers 2. Insufficient level of customer intelligence 3. Inability to act on customer intelligence quickly 6

10 Methodology The objective of our research was to develop a clearer conceptualization of CRM, and, if feasible, to create a scale that could be used to evaluate a company s orientation towards one or more of these three forms of CRM. Our approach to creation of the scale was based on the recommendations of Churchill (1979) for the development of better marketing constructs. Phase 1: Generation of Scale Items Our literature review, summarised above, generated a set of attributes that had been used to describe and discriminate between different forms of CRM. This process resulted in the pool of 32 items listed in Table 1. The table identifies which items are associated with each form of CRM, and the position in which the items appeared in the questionnaire used during the scale refinement phase. Table 1: Initial pool of scale items. Construct Strategic CRM Measuring Items S7. An important objective of our CRM program is to enhance the lifetime value of our customers S14. An important objective of our CRM program is to improve our understanding of customer needs, expectations and preferences S19. An important objective of our CRM program is to lift customer satisfaction and retention levels S20. CRM provides the basis of our competitive advantage S21. Our CRM strategy aims to win and keep carefully chosen customers or customer segments S22. Our CRM strategy creates mutual benefits for both customers and company S26. Our company is using CRM to create a customer-focused business culture S28. Our company is using CRM to ensure that all our people understand which customers we want to serve S29. Our company is using CRM to help us be more customer focused than our competitors S30. Our company is using CRM to find better ways of offering customers more value 7

11 Operational Analytical O4. An important objective of our CRM program is to enable us to adapt our offer to suit different customers requirements O6. An important objective of our CRM program is to enable us to select the most appropriate communication channels for interactions with customers O9. An important objective of our CRM program is to help our marketing people run more effective and efficient campaigns O10. An important objective of our CRM program is to help our sales people to have more effective and efficient interactions with customers O11. An important objective of our CRM program is to improve collaboration with our customers and channel partners O15. An important objective of our CRM program is to improve the productivity of our sales people O16. An important objective of our CRM program is to reduce the cost of our customer-facing operations O18. An important objective of our CRM program is to deliver consistent customer experience across all customer touch points and channels O23. Our company uses CRM to automate customer service processes to make them more efficient and effective O24. Our company uses CRM to automate marketing processes to make them more efficient and effective O25. Our company uses CRM to automate selling processes to make them more efficient and effective A1. An important objective of our CRM program is to create a comprehensive customer-related database A2. An important objective of our CRM program is to deliver customer data to our people at the right time so that they can cross-sell and up-sell customers A3. An important objective of our CRM program is to deliver customer data to our front line staff so that they can sell, market and service our customers more effectively A5. An important objective of our CRM program is to enable us to conduct intelligent analyses of customer data to guide our marketing and sales efforts A8. An important objective of our CRM program is to ensure that analysis of customer-related data underpins all our customer interactions A12. An important objective of our CRM program is to improve our ability to conduct real time analysis of data when interacting with customers A13 An important objective of our CRM program is to improve our forecasting capabilities A17. An important part of our CRM program is the use of analytical tools to make sense of, and profit from, customer data A27. Our company is using CRM to enable us to obtain competitive advantage from customer data A31. Our company uses CRM to help us identify high value customers A32. Our company uses customer information to construct customer profiles which are used to improve the consistency of the customer s experience 8

12 Phase 2: Scale Refinement: Exploratory Factor Analysis and Item-Total Correlations A questionnaire was developed and distributed to two cohorts of Executive MBA students who had taken the elective CRM course at Macquarie Graduate School of Management. Participants were asked to reflect on Customer Management practices in their own organizations and to express their level of agreement or disagreement with the 32 scale items. Participants were advised that even though their company may not have developed a formal CRM strategy, they would certainly have a de facto CRM strategy with some people, process and technology elements focussed on the management of customer relationships. Forty-eight responses were obtained, representing a 55% response rate. A seven-point, Likert-type response format was used, where 1 indicated that the respondent strongly disagreed with the item statement and 7 indicated that the respondent strongly agreed with the item statement. The initial stage of scale reduction (purification) was performed using 2 statistical processes: exploratory factor analysis (Churchill 1979) and item-total correlations (Nunnally 1978). Exploratory Factor Analysis (EFA) All 32 items were factor analysed, using principal component analysis with varimax rotation. This generated a 7-factor solution, with each factor having an eigenvalue over 1. The 7 factors solution accounted for 77.85% of the variance. The results of the EFA are presented in Table 2. Items with loadings of 0.4 or greater on more than one of the factors were eliminated. This resulted in the removal of 7 items from Strategic CRM (S7, S14, S20, S22, S26, S29, & S30), 5 items from Operational CRM (O4, O6, O9, O10, & O11), and 2 items from Analytical CRM (A12 & A13). Eighteen items remained: 3 items for Strategic CRM, 6 items for Operational CRM, and 9 items for Analytical CRM. These remaining 18 items were again factor analysed, using principal component analysis and varimax rotation. Based on the SOA conceptualization of CRM, we made an a priori determination that three factors should be retained (Hair, Anderson, Tatham, & Black, 1998). The resulting 3-factor solution, presented in Table 3, accounted for 66.04% of the variance. 9

13 Item-total correlations To achieve a more parsimonious scale, the 9 items loading on Component 1 in Table 3 were subjected to item analysis using item-total correlations. This resulted in the removal of a further 4 items. At the end of Phase 2: Scale refinement, 14 items remained, clustered and named as follows: Factor 1: Customer Orientation. 5 items in total, made up of 3 items from the Strategic CRM inventory (S19, S21 & S28), and 2 items with the highest corrected item-total correlations, both from the Analytical CRM inventory (A31 & A32). Factor 2: Analytical CRM. 5 items in total, made up of 4 items from the Analytical CRM inventory (A1, A2, A3, & A5), and 1 item from the Operational CRM inventory (O15). Factor 3: Operational CRM. 4 items in total, made up entirely of 4 items from the Operational CRM inventory (O16, O23, O24, & O25). 10

14 Table 2. Exploratory Factor Analysis: Factor loadings for the 7-factor solution Component A S A A S O S S S O A A A A S S S O S A S O O O A O A A O O O O

15 Table 3. Exploratory Factor Analysis: Factor Loadings for the 3 factor solution Component A A A S O A S S A O A A A A O O O O Phase 3: Scale Refinement: Confirmatory Factor Analysis The next phase of scale purification deployed Confirmatory Factor Analysis (CFA) as recommended by Gerbing & Anderson (1988) to test for uni-dimensionality of the factors. Gerbing & Anderson claim that CFA provides a stricter assessment of uni-dimensionality than Exploratory Factor Analysis and item-total correlations. The Confirmatory Factor Analysis (CFA) module of the Amos 4.0 Structural Equation Modelling program (Arbuckle 1999), was employed, producing the results show in Figure 2. Analysis of Model Fit Hair et al. (1998) suggest several measures of absolute fit. Chi-square (CMIN) is the most fundamental measure of overall fit, but the chi-square statistic is sensitive to sample size. Chisquare is recommended for sample sizes between 100 and 200. For samples outside this range, including our data set, other measures are preferred. Two additional measures are suggested to replace or supplement the Chi-square statistic: the goodness-of-fit index (GFI) and the root mean square residual (RMSEA). GFI generates a statistic between 0 to 1, where 0 indicates zero fit and 1 indicates perfect fit. For RMSEA, values between 0.05 and 0.08 are acceptable. However RMSEA is a better measure for 12

16 larger samples. According to Browne and Cudeck (1993), a RMSEA greater than 0.1 indicate that the model fit is not acceptable. Incremental Fit measures compare the proposed model with the null model. The null model is a realistic model that all other models should be expected to exceed. Adjusted Goodness-offit Index (AGFI), Tucker-Lewis Index (TLI), and Normed Fit Index (NFI) are measures of incremental fit. The recommended level for these three indexes is

17 Table 4. Refined Scale and Factor Loadings Scale Items Customer Analytical Operational Orientation Loadings S19. An important objective of our CRM program 0.72 is to lift customer satisfaction and retention levels S21. Our CRM strategy aims to win and keep 0.62 carefully chosen customers or customer segments S28. Our company is using CRM to ensure that 0.63 all our people understand which customers we want to serve A31. Our company uses CRM to help us identify 0.83 high value customers A32. Our company uses customer information to 0.84 construct customer profiles which are used to improve the consistency of the customer s experience A1. An important objective of our CRM program 0.66 is to create a comprehensive customer-related database A2. An important objective of our CRM program 0.78 is to deliver customer data to our people at the right time so that they can cross-sell and up-sell customers A3. An important objective of our CRM program 0.78 is to deliver customer data to our front line staff so that they can sell, market and service our customers more effectively A5. An important objective of our CRM program 0.69 is to enable us to conduct intelligent analyses of customer data to guide our marketing and sales efforts O15. An important objective of our CRM program 0.87 is to improve the productivity of our sales people O16. An important objective of our CRM program 0.64 is to reduce the cost of our customer-facing operations O23. Our company uses CRM to automate 0.80 customer service processes to make them more efficient and effective O24. Our company uses CRM to automate 0.68 marketing processes to make them more efficient and effective O25. Our company uses CRM to automate 0.67 selling processes to make them more efficient and effective Cronbach alpha Overall alpha

18 Figure 2: Confirmatory Factor Analysis 15

19 Summary statistics for the model are shown in Table 5. Table 5: Model Fit Result CMIN P RMSEA GFI AGFI TLI NFI The model fit statistics generally lean towards an adequate level of model fit. Chi-square was 2 not statistically significant ( Χ = 84.68, d.f. = 74, p = 0.186). However, sample size means that this statistic can be an unreliable indicator of fit. The RMSEA statistic is less than 0.1, which demonstrates an acceptable level of fit. A good level of fit is indicated when RMSEA is less than 0.5. Our results approximate the good level with an RMSEA of However, neither GFI nor AGFI reach the recommended minimum of TLI and NFI statistics can range from 0 to 1. An ideal model has a fit statistic of 1. Our TLI statistic indicates a very close fit, being above the recommended minimum of 0.90, and NFI a close fit. Given that this is, to the best of our knowledge, the first attempt to operationalize different forms of CRM, less stringent standards of fit are admissible. We therefore conclude that the model provides an adequate level of fit. Scale Reliability The reliability of each component (Customer Orientation, Operational CRM and Analytical CRM) and the overall (combined) scale is assessed by computing Cronbach alpha (Gerbing & Anderson, 1988). Reliabilities of the component scales are in the range of to 0.891, with the overall Cronbach alpha for the scale being (see Table 4). These levels of reliability are satisfactory. Conclusion Our study sought to gain a better understanding of what is meant by the term CRM, and, if possible, to develop an instrument that can be used to measure a company s orientation towards one of more forms of CRM. Our results support the assertion that three forms of CRM can be identified: Strategic (labelled by us as Customer Orientation), Operational and Analytical. Strategic CRM focuses on customer orientation, with the objectives of a CRM implementation being: to lift customer satisfaction and retention levels; to win and keep carefully chosen customers or customer segments; 16

20 to ensure that all employees understand which customers the organization chooses to serve; to identify high value customers; and to construct customer profiles from customer information, which can then be used to improve the consistency of the customer s experience. Analytical CRM focuses on customer data, with the objectives of a CRM implementation being: to create a comprehensive customer-related database to deliver customer data to employees at the right time so that they can cross-sell and up-sell customers to deliver customer data to front-line staff so that they can sell, market and service our customers more effectively; to conduct intelligent analyses of customer data to guide marketing and sales efforts; and to improve the productivity of sales people. Operational CRM focuses on the automation of customer service, marketing, and selling processes, with the objective of reducing the cost of customer-facing operations. Future Research The model shows an adequate level of fit but further testing will be needed to confirm the reliability and validity of the CRM triptych. The 14 item-scale will be tested with a bigger sample, consisting of companies implementing CRM in Australia. 17

21 References Ang, L., & Buttle, F. (2002). ROI on CRM: a customer- journey approach. Retrieved 31 May 2004, Arbuckle, J. L. (1999). AMOS 4.0 User's Guide: SmallWaters Corporation, Chicago IL. Buttle, F. (2002). Is it worth it? ROI on CRM. Also at Buttle, F. (2004). Customer Relationship Management: Concepts and Tools: Elsevier. Churchill, G. A., Jr. (1979). A Paradigm for Developing Better Measures of Marketing Construct. Journal of Marketing Research, 16 (February), CRM (UK) (2000). In Fox, T, CRM: delivering the benefits Doyle, S. (2002). Software review: Communication optimisation - the new mantra of database marketing. Fad or fact? Journal of Database Marketing, 9(2), Fayerman, M. (2002). Customer Relationship Management. New Direction for Institutional Research, 113(Spring 2002). Forsyth, R. (2001). Delivering value from CRM Forsyth, Gartner, et al tell you how!! Gebert, H., Geib, M., Kolbe, L., & Brenner, W. (2003). Knowledge-enabled CRM: Integrating CRM and Knowledge Management Concepts. Journal of Knowledge Management, 7(5). Gerbing, D., & Anderson, J. (1988). An Updated Paradigm for Scale Development Incorporating Unidimensionality and Its Assessment. Journal of Marketing Research, 25 (May), Goldenberg, B. (2000). Customer relationship management: what is it all about? CRMforum.com/library/ Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate Data Analysis (5th ed.): Upper Saddle River, NJ: Prentice Hall. Herschel, G. (2002). Introduction to CRM Analytics: Gartner. Khanna, S. (2001). Measuring the CRM ROI: show the benefits. META Group. (2001). Integration: Critical Issues for Implementing of CRM Solutions: META Group. Nunnally, J. C. (1978). Psychometric Theory (2nd ed.): New York: McGraw-Hill. Plakoyiannaki, E., & Tzokas, N. (2002). Customer Relationship Management: A Capabilities Portfolio Perspective. Journal of Database Marketing, 9(3), Rembrandt, M. (2002) Outsourcing CRM function. Serverworld 16(2), Feb Rigby, D. (2002), quoted in Melymuka, K. You can avoid CRM s pitfalls. Computerworld, 36(7), Feb. SAS. (2003). Maximizing ROI from CRM Initiatives: SAS white paper Smith, K. (2001). Getting payback from CRM. Webcast on November. 18