Managing Customer Service Levels and Sustainable Growth A Model for Decision Support

Transcription

1 Managing Customer Service Levels and Sustainable Growth A Model for Decision Support Amitava Dutta School of Management MSN 5F4 George Mason University 4400 University Drive Fairfax, VA USA adutta@gmu.edu Abstract Managing service levels is known to be an important element of customer relationship management. In service industries however, there is no inventory of finished goods that can be used to buffer production and yet maintain superior service levels in the face of uncertain demand patterns. Hence capacity planning for different resources takes on special importance in achieving high customer service levels. In this paper, we focus on the human resource element in a generic service firm. Using the system dynamics methodology, we develop a model of workforce acquisition and training that is driven by target levels of customer service and customer base. The model reveals the significant effects of natural process delays on maintaining service levels, and we demonstrate how it can serve as a decision support tool to achieve growth in the customer base while maintaining service levels. Such analysis can contribute to the collection of activities needed to conduct customer relationship management. 1. Introduction The economics of customer retention and loyalty has resulted in heightened interest in Customer Relationship Management (CRM) [7]. Loyal customers tend to stay longer with their preferred providers, buy more from them and generate favorable word of mouth effects that may further benefit the preferred provider. Also, acquiring new customers can cost three to five times as much as the costs of retaining current ones. These and other advantages have increased the interest in CRM among both practitioners and researchers. There are many Rahul Roy Management Information Systems Group Indian Institute of Management Calcutta Joka, Diamond Harobor Road Calcutta India rahul@iimcal.ac.in different aspects of CRM that continue to be studied in the literature. In the Information Systems literature, for instance, substantial attention has paid to the use of technology and systems to facilitate the interaction between organizations and their customers [4]. The marketing literature has examined customer switching behavior in some detail, in an effort to guide CRM activities [2], [14]. While there are different aspects of CRM, one issue that permeates the entire area is that of customer service levels. It is known to be an important determinant of customer satisfaction and hence is a central element of CRM [17]. In this paper, we take a back end view of customer service levels, meaning that we examine this issue from the standpoint of the provider. This contributes to and complements the CRM literature which has largely examined this issue from the customer facing front end. Moreover, we examine customer service levels in the context of a generic service firm. Unlike manufacturing firms, service firms have no inventory of finished goods to buffer production from random demand variability. Hence capacity planning for different resources takes on added importance in any effort to achieve high levels of customer service [3]. Technical capacity is, of course, important. In the context of CRM however, it is critical to also explicitly focus on the human resource component of the provider s capacity. This paper models the mechanics of providing customer service from the standpoint of the provider, giving particular attention to capacity planning for the human resource component. The model provides insights into the service level issue and, at the same time, can serve as a computational tool that can contribute to the overall collection of activities needed to conduct customer relationship management /06/$20.00 (C) 2006 IEEE 1

2 The literature confirms the importance of managing the human resource element as part of CRM activities and in meeting business performance objectives [1], [2], [12], [13], [16]. However, this is a complex issue with many facets. For instance, there is an inherent conflict between high worker productivity and high levels of customer service particularly in the service sector [9]. Career path design, incentive schemes etc. all play an important role in managing the human resource. There is widespread agreement however, that recruitment and training constitute very important aspects of managing the human resource in service sector firms [5], [11]. As a first step in studying the service level issue, we therefore limit our scope to these two important aspects of managing the human resource. The remainder of this paper is organized as follows. Section 2 introduces the system dynamics methodology that is used to model the relationship between human resource capacity and customer service levels. The model itself is presented and justified in section 3. In section 4, we present results of experiments with the model which illustrate the potential of using this methodology for decision support purposes in CRM. Section 5 concludes with a discussion of ways in which the current model can be refined and extended. 2. Methodology. We choose to model the customer service level problem using the system dynamics methodology [8]. A system is simply a structured collection of components. Each component has its individual properties, but it also interacts with other components in a way that is determined by the structure of the system. The behavior of a system i.e. its dynamics - is determined both by the properties of the individual components as well as their pattern of interaction. Hence the name system dynamics (SD). The mantra of system dynamics is structure determines behavior. The aim of an SD model is to express this structure in a formal manner that lends itself to computational representation and analysis. It does so by representing a system as a collection of differential equations consisting of stock, flow and auxiliary variables [15]. For purposes of narration however, it is common to represent this same structure in a much more visually appealing and comprehensible graphical form called causal loop diagrams (CLDs). Details of the methodology may be readily found elsewhere [15] and are not repeated here. The basic structural elements will be introduced in the next section at the same time that the service level model is presented. We usually associate the word system with physical things. But a system may also be social, economic or political in nature, or even a combination thereof. For instance, in marketing, one can conceive of a system that consists of the physical attributes of the car, its price, customer s preference structure and their financial status. The properties of these individual components and the pattern of their interaction would determine the behavior of this system which would be the sales volume pattern for the car. SD has a long history of being successfully applied to analyze problems in a variety of application domains including environmental policy, corporate strategy, healthcare, operations management and change management [6]. The customer service level problem that we want to analyze here is behavior that results from the interaction of components on the supply as well as demand side of the phenomenon. On the supply side, there is the physical and human resource capacity of the service provider. On the demand side, there are customers with needs for service and who have certain expectations about the quality of service they should receive. As we mentioned earlier, service industries do not have inventory. In our situation, it is the interaction between these two demand and supply components that determines the actual service levels experienced by customers on an ongoing basis over time. Therefore SD is especially well suited to capture the structure of this system and study its behavior using computational techniques. This can help craft appropriate policies to enhance CRM performance through better management of service levels. 3. A Dynamic Model of Service Level In this section we will present a model of the mechanics by which recruiting and training activities impact customer service levels, using the SD methodology identified in the previous section. The causal loop diagram (CLD) representation will be used and the model itself appears in Figure 1. We will also introduce basic structural elements of the methodology on an as-needed basis, in the course of presenting the model. Variables will be written in italics in the following narrative, to facilitate their identification in Figure 1. Before proceeding with model details however, it may be useful to first summarize the forces at play in a 2

3 qualitative narrative. This big-picture view can serve as an architectural blueprint within which to view the more formal structural elements of the SD model that will be presented shortly. Recall that this is a generic service firm context. In the absence of inventory, service level result from the relative balance between the capacity for service and demand for the same. The difficulty is that capacity usually cannot change very much in the short run, while demand can and will. Hence capacity planning has to be done judiciously. There must be enough capacity to maintain service levels in the face of uncertain demand, yet it must not be so high as to result in excessive costs. For a service provider, there are two major elements of capacity technology and human resource levels. There is one important difference between the two in terms of their acquisition by a service firm. Technology capacity acquisition usually occurs at discrete points in time and in lumpy amounts. For instance, it is usually not feasible to buy small amounts of hardware, software and communications assets continuously over time. Rather, technology acquisitions and enhancements tend to be major purchases and they occur relatively infrequently at discrete points in time. In modeling terms, this means that the graph of technology capacity of a service firm, with respect to time, would be better represented by a multi-step function than a continuously increasing one. For purposes of this paper, we view the term technical capacity somewhat broadly to include system functionality as well as throughput. By comparison, human resource capacity may be approximated as a continuous variable. While one would not hire a fractional number of workers, the size of the workforce in most service firms is such that the changes in this value resulting from normal hiring and attrition are relatively gradual. Also, these changes occur fairly continuously over time in response to business needs. In summary, between the two components of capacity, the human resource component can change more rapidly and continuously in response to business needs, compared to technical capacity. As mentioned earlier, our emphasis will be on the mechanics of managing the human resource component. Earlier in the introduction, we noted the importance of proper training in building an effective workforce that will contribute to CRM. In modeling terms, this means there is a lag between the time a worker is hired and cost incurred by the company and the time he/she is effectively contributing to meeting customer needs. The duration of this delay is a policy variable under control of management. By spending more time, recruits can be subjected to more rigorous training which in turn should result in higher service levels once they are activated to serve customers. But a long training period also increases costs and delays the time when recruits can start serving. Apart from the issue of training, management can always decide how aggressively they will recruit. In other words, how fast will they try to ramp up the workforce? But recruiting is not an instantaneous activity. It also takes time and resources just like training. A more aggressive recruiting strategy will surely reduce hiring delays, but is also more expensive for the organization. With these two components of capacity in mind, it is easy to see that service level will depend on the relative balance between this capacity and the demand for service posed to the service organization by customers. If one proxies service level by the length of time a customer has to interact with a human agent at the service provider, it is well known from the literature that this service level has a nonlinear relationship to the utilization ratio, the latter being the ratio of service demand to service capacity [10]. At low values of utilization, service levels remain strong and deteriorate only slowly with increasing utilization. However, once a threshold is crossed, service level drops precipitously in response to small increases in utilization. The above proxy for service level is not as coarse as might appear at first glance. Indeed, there are other dimensions of service level such as the completeness of responses to user questions, courtesy, perceived willingness to help and the like. When these other dimensions of service are not well catered to, it often results in more prolonged interactions between customers and service agents stemming from repeated questions, arguments, clarifications etc. Hence we feel comfortable in using this proxy for service level as a first approximation. In the concluding section of the paper, we will discuss ways in which the model can be made richer by alternate proxies for service level which are more realistic, but which also increase model complexity substantially. The relationships stated above are pretty well understood individually. However it s not as easy to deduce their complex interaction and collective impact on service levels. In social systems it is not unusual to find that while individual cause and effect relationships are not difficult to comprehended in isolation, the dynamic complexity that results from their interaction often defies human comprehension 3

4 and at times appear counterintuitive. Hence a formal model of these interacting effects can be informative. In terms of managing service levels, management needs to understand the mechanics of human resource acquisition and, in particular, take into consideration the impact of the different delays into the planning process. Qualitatively, it is easy to state that since hiring workers and training them take time, improvements in service levels will not be instantaneous. The longer the training period, the longer the delay in meeting service level shortfall. However, without more quantitative representation of the mechanics implied by the preceding narrative, it is difficult to go beyond such obvious qualitative statements to more useful operational suggestions such as reducing the training period from twelve to eight weeks will reduce customer service levels by 3% and improve cash flow by 4%. This is where the SD methodology can be used to make a contribution by developing a computational model that can be used as a decision support tool. We now proceed to restate the preceding narrative using the causal loop diagram representation mentioned earlier in section 2. The major variable of interest in Figure 1 is Actual Service Level. In presenting the model, we will be logically working outwards from this one key variable to see how it is affected by other intermediate and policy variables. The portion of Figure 1 that lies to the left of Actual Service Level represents the demand side mechanics, while the portion to the right represents the supply side mechanics. Consistent with earlier statements, it can be seen that Figure 1 models the supply side mechanics for human resource capacity acquisition in more detail than the other components. Staff Effectiveness Rigor of Training Customers Net Inflow of Customers - Actual Service Level Aggregate Human Resource Capacity - Skilled Staff Capacity Trained Rookies - Technology Capacity Customer Retention - Service Level Expected by Customers - Service Level Shortfall Perceived by Provider Overtime Skilled Staff Time Spent in Training Aggresiveness of Recruitment Rookies Recruited Technology Acquisition Actual customer growth Additional Staff Needed to maintain Service level Additional staff needed - to achieve growth Target customer growth Technology Acquisition Aggressiveness Figure 1: Causal Loop Model of Provider Capacity and Customer Service Level 4

5 To understand the mechanics represented in Figure 1, it is now necessary to introduce the basic constructs of causal loop diagrams (CLD) as used in the system dynamics method. Variables are the first basic element in a CLD, and have the usual interpretation as in any other model. They are abstractions of relevant attributes/characteristics of the problem being studied. A causal link is a directed arc pointing away from a cause towards an effect variable. In other words, it represents a single cause-effect relationship. Each causal link must have a polarity, which is usually shown next to the arrowhead. A closed sequence of causal links constitutes a feedback loop, which itself can have a negative or positive polarity. A negative polarity loop has an odd number of negative causal links, while a positive loop has an even number of negative links. The physical interpretation of link and loop polarities is explained using the demand side causal structure in Figure 1. For our purposes, the demand side is represented by the bare essentials relevant to service level management. It essentially captures the mechanics of customer retention and growth. Notice that Customer Retention has two inbound causal links. The first, from Actual Service Level, has a positive polarity. A positive polarity means that, other factors held constant, cause and effect change in the same direction. If the cause increases, the effect increases and vice versa (note that a positive polarity does not mean that the effect only increases). As cited earlier in the introduction, there is abundant empirical evidence that customer retention improves with service level. This is the basis for the positive polarity link from Actual Service Level to Customer Retention. More generally, there must be some basis for each and every causal link shown in a causal loop diagram. The justification may lie in the actual physics of a relationship. For instance, there would be a positive link from velocity to kinetic energy when modeling the dynamics of a car. But evidence for a causal link need not only be physical. It can also be based on empirical data or experimental findings. Justification of individual links constitutes the first step in validating SD models. Due to space considerations, we will not present justifications for each and every link in Figure 1. Only selected links, which are considered crucial to the narrative, will be discussed in more detail. Moreover, some of the remaining links and their polarities are self evident or are otherwise well established in the literature. To continue with the demand side presentation, observe the negative link from Service Level Expected by Customers to Customer Retention. A negative polarity means that cause and effect change in opposite directions. The expected service level is an exogenous variable and captures the effects of competition and other customer attributes. Clearly, customers do not judge a provider s service level in a vacuum, but with reference to some perceived benchmark. If this expected level increases then, all other factors held constant, customer retention will drop. So when the cause increases, the effect decreases and vice versa. This is what a negative polarity means. As Customer Retention increases, the Net Inflow of Customers must also increase, explaining the positive link between these two variables. As the Net Inflow of Customers increases, the total number of Customers must also increase. Finally, as number of Customers increases, the Actual Service Level must decrease, all other factors held constant. This accounts for the negative link between the last two variables. Notice from the preceding discussion that a link always represents a single causal effect in isolation. The collective effect of multiple links will be determined computationally using the formalisms of SD as will be shown in the next section. Now that the individual links on the demand side have been discussed, notice from Figure 1 that they form a negative feedback loop, shown by the balance symbol inside. The balance is a visual indication that this loop behaves in a self adjusting fashion to maintain balance. The right hand side of Figure 1 is structurally more complex by design, as it captures relevant details of the workforce acquisition process. Recall from our earlier discussions that the organization s capacity has two components technological and human resource. The outermost loop on the right hand side captures enhancements in technical capacity, while the remaining structure captures enhancements in the human resource component. Starting with Actual Service Level in the middle, one can see a negative link connecting this to Service Level Shortfall Perceived by Provider. This shortfall is the difference between expected and actual customer service levels. This perceived shortfall triggers enhancements in the two capacity components. Hence there are two positive links from Service Level Shortfall Perceived by Provider to Technology acquisition and Additional Staff needed to maintain serviced level, respectively. The positive polarities of the two links follow from the physical reality that an increase in shortfall must result in an increase in one or both capacity 5

6 enhancements. Increased Technology acquisition leads to increased Technology Capacity, which in turn feeds back to increase Actual Service Level. Hence the positive links between these three variables. This outermost loop on the right hand side the one related to technology capacity can be seen to be a negative feedback loop. So technology acquisitions adjust to try and help correct for drops in actual service levels. The polarity is shown by the balance symbol inside the very top part of this feedback loop. Enhancements in the human resource capacity have more complex mechanics. In Figure 1, Additional Staff needed to maintain serviced level has a positive link leading to Rookies recruited. The polarity of this link is obvious. However, we have a second positive link inbound into Rookies recruited. This one emanates from Aggressiveness of recruiting, which is a policy variable under management control. Say the organization needs to hire fifty additional staff members to maintain service levels. This policy variable will control how rapidly this ramp up occurs. Continuing this process, Rookies recruited has a positive link to Trained rookies. The polarity of this link is obvious, since the more rookies one recruits, the more trained rookies will be produced. Notice however, that there is a double hash mark across this link. This hash symbol is used in SD to denote a delayed effect. In this case, delay arises from the training activity itself, which takes time. The duration of this delay is moderated by Rigor of Training, which is another policy variable under management control. Hence there is a negative link from Rigor of training to Trained recruits. Clearly, the more rigorous the training, the longer the training time that is needed and the fewer trained recruits produced in a given amount of time. As the number of Trained recruits increases, so does Skilled Staff Capacity, which is the number of trained staff members available to service customer needs. Figure 1 shows that the service provider can also resort to the short term measure of increased overtime in response to perceived shortfalls in service levels. Hence the positive link from Service Level Shortfall Perceived by Provider to Overtime. The human resource capacity of the organization of the provider is a function of the number of properly trained staff and the time they allocate to servicing customers. Hence Aggregate Human Resource Capacity has one positive inbound link from Skilled Staff Capacity and another from Overtime. Notice that it also has an inbound negative link from Skilled Staff Time Spent in Training. This is because the rookie training process often uses current trained staff as instructors or mentors. Thus, the larger the number of rookies, the higher the amount of expert staff time devoted to training, which in turn lowers the Aggregate Human Resource Capacity. Closing the different feedback loops on the right hand side of Figure 1, Actual Service Level has three positive inbound links, from Technology Capacity, Staff Effectiveness, and Aggregate Human Resource Capacity, respectively. Finally, Figure 1 contains one structural element designed specifically for decision support purposes. Notice the variable named Target customer growth at the bottom of Figure 1. This is a management planning variable indicating a desired increase in customer base. The Actual customer growth is compared to this desired target to determine the Additional staff needed to achieve growth. This additional staffing need has a positive link back into Rookies Recruited. By integrating this structural component into the overall mechanics of maintaining customer service level, it will enable the model to help craft sustainable customer growth policies. In the preceding narrative, we have presented the micro structure i.e. the individual links - of a causal model of customer service levels. However, it is not difficult to see the larger macro structure implied by these individual effects. The feedback loops identified in Figure 1 show that the delays associated with capacity acquisition, both technical and human, make it difficult to achieve growth while maintaining acceptable service levels. In the next section, we show how the computational model resulting from Figure 1 can help in achieving sustainable growth in the customer base. 4. Experimental Results The causal loop diagram of Figure 1 was converted to its corresponding mathematical form using standard techniques from SD [15]. The conversion essentially involves specifying the functional form of causal relationships shown in Figure 1. Some of the variables in Figure 1, such as Skilled Staff Capacity, represent accumulations over time, while others such as Net Inflow of Customers, represent rates of change. In the conversion, the former are referred to as stocks and the latter as flow 6

7 variables. The relationship between stock and flow variables is expressed as a collection of differential equations, which can then be simulated to generate system behavior under different policies and conditions. In the remainder of this section, we present computational experiments that illustrate how the model can be used to perform service level management thereby supporting CRM. Figure 2 shows a set of sensitivity analysis simulation runs, in which we examine the behavior of the variable Customers over time, in response to five different policy settings. These settings reflect five different combinations of hiring aggressiveness and training rigor. The values of the associated model parameters are also shown. More aggressive hiring implies a smaller value of hiring time, while more rigorous training implies higher values for training period. Notice that in all five runs, the value of Customers first drops before starting to climb again. This is because in all five runs, the initial value of Customers was set to a value (75,000) which was higher than the number that could be supported by the current capacity at acceptable service levels. In other words, the experiments were initiated by creating a condition in which capacity needed expansion. It then becomes possible to observe how the system reacts to different management policy alternatives. Policy Management Policy Id Hiring Training Rigor 1 Aggressive Low 2 Normal Normal 3 Conservative High 4 Conservative Low 5 Aggressive High Cusomers 1.0E05 9.0E04 8.0E04 7.0E04 6.0E04 5.0E04 4.0E04 3.0E04 2.0E04 1.0E04 0.0E Time (Weeks) Figure 2: Sensitivity of Customer Base to HR Policy Figure 2 shows, quantitatively, the magnitude of loss of Customers, as the organization ramps up its human resource capacity. This happens in all five runs, clearly showing the impact of delays inherent in the mechanics of expanding the human resource component of capacity. However, the runs also show that policy alternatives 3 and 5 outperform 1, 2 and 4 by a significant amount. In runs 3 and 5, the initial drop in Customers is quite low, after which it climbs steadily and then exceeds the initial value. These two policy alternatives involve conservative hiring/high training rigor and aggressive hiring/high training rigor. The very small difference in performance between these two alternatives suggests that a policy of rigorous training is more effective in achieving sustainable growth than one of hiring new recruits more aggressively. This is because both alternatives 3 and 5 use the high training rigor option. In contrast, notice from the legend accompanying Figure 1, that the low performing policy options 1, 2 and 4, use a low or normal training rigor option. In summary, the sensitivity runs in Figure 2 suggest that it may be more effective to allocate capacity expansion resources to do a better job of training as compared to recruiting more rapidly. However, the growth in Customers is only one aspect of business performance of the organization. We augmented the simulation model resulting from Figure 1, by adding a small module that keeps track of revenues and costs during the course of each simulation run. Revenues are generated by Customers who stay with the service provider, while costs are incurred based on the level of human resource and technical capacity acquired by the provider. The unit values for revenue and cost were scaled appropriately to reflect similar costs reported in the practitioner literature. 2.E05 1.E05 0.E00-1.E E05-3.E05-4.E05-5.E05-6.E05 Time (Weeks) Figure 3: Sensitivity of Cash Flow to HR Policy Figure 3 shows the cash flow behavior generated by each of the five alternatives discussed above.cash 7

8 flow is also an important business performance measure just like the size of the customer base. The shapes of the runs are similar to those in Figure 2 and policy alternatives 3 and 5 still outperform 1, 2 and 4. However, notice that when it comes to Cash Flow, of the two superior policy alternatives, policy 3 performs better than 5. This is the opposite of the performance seen in Figure 2 for Customers. In other words, the sensitivity analyses in Figures 2 and 3, taken together, suggest that a policy of rigorous training may be more effective in sustaining customer growth compared to one of more aggressive recruiting. However, the experiments also show that there is no single policy that is best for both the performance measures studied here. So while policies 3 and 5 are better than the other three, there is a tradeoff between the two of them. Customers 1.E05 1.E05 1.E05 1.E05 9.E04 8.E04 7.E04 6.E Time (Weeks) Figure 4: Variation of Customer Base under Alternate Capacity Adjustment Policies 1 ASL Based; 2 Customer Growth Target Based Figure 4 shows an experiment in which the model is used to explore two policies that, intuitively, could both help grow the number of Customers to targeted levels. The two alternatives are based on different mental models of customer service levels, both of which are plausible based on their qualitative rationales. The first alternative, which we will name ASL-Based, is based on a rationale that in order to grow the customer base, one must close the gap between Actual Service Level (ASL) and Expected Service Level (ESL). Hence, the number of new recruits required is a function of the gap between these two variables. The larger this gap, the This rationale usually appears when actual service levels are below that expected by customers and the provider is experiencing a loss of current customers. The second alternative, which we name Target Customer Growth, holds that the growth in staffing levels mirrors the growth in Customers. So if the customer base increases 3%, the staffing level will also be set to increase by 3%. The logic is that if the customer base is already increasing at current service levels, this growth can be maintained by expanding staffing levels at the same rate. 2.E05 1.E05 1.E05 1.E05 8.E04 6.E04 4.E04 2.E04 0.E Time (Weeks) Figure 4: Variation of Cash Flow under Alternate Capacity Adjustment Policies 1 ASL Based; 2 Customer Growth Target Based Figures 5 shows how Cash Flow behave in response to these two alternative policies, labeled 1 and 2 respectively. The response of Cash Flow is shown in Figure 5. Notice that neither policy is dominant. One performs better in terms of growth in Customers, but the other does better on the Cash Flow dimension. Based on these simulation runs, it may be easier to craft a mixed policy that balances performance on these two dimensions. The preceding experiments illustrate some of the ways in which the model can be used to better understand the dynamics of customer service levels and how that is impacted by human resource capacity. Of course, a whole variety of additional experiments can be performed using the computational model presented here. 5. Conclusions In this paper, we have used the system dynamics methodology to build a computational model linking human resource and technical capacity of a service provider to customer service levels. As mentioned in the introduction, the CRM literature has long focused on the customer facing front end. Our work focuses on back end planning activity needed to maintain service levels that are so crucial to CRM. The use of system dynamics as a methodology results in a computational model of service level provision, that can be then used as a decision support tool by management. 8

9 The model presented here is a first cut at capturing the mechanics of how customer service levels arise from the interaction of demand and capacity. As such, it can be extended and refined along several fronts. For instance, a more comprehensive proxy could be developed for customer service level than the one used here. It could be disaggregated into multiple components such as customer contact time, quality of interaction, wait time etc.. Such disaggregation would be more realistic, but will also increase modeling complexity, as it will be necessary to develop and validate functional forms that would be able to combine these components into a composite measure of service quality. Another area where the model can be refined is in the way it captures different aspects of the human resource training activity. The current model treats the service provider s staff as a homogeneous variable. But customer service needs are not homogeneous. Hence most organizations have different categories of staff to cater to these different needs. Therefore, another dimension along which the model can be refined is to capture this heterogeneity in staffing composition and service level needs. However, this refinement will increase model complexity substantially, since the recruiting and training mechanics will also need to be separated out for each of these different staffing categories. While such refinements would result in a more sophisticated decision support tool, the current model, even with its simplifying approximations, should serve to illustrate the potential of using the systems dynamics methodology to build decision support tools for Customer Relationship Management. 6. References [1] Batt R., Managing customer services: Human resource practices, quit rates, and sales growth, Academy of Management Journal, 45(3), 2002, [2] Bell S. J., Auh S. and Smalley K., Customer Relationship Dynamics: Service Quality and Customer Loyalty in the Context of Varying Levels of Customer Expertise and Switching Costs, Academy of Marketing Science, 33(2), 2005, [3] Betts A., Meadows M. and Walley P., Call centre capacity management, International Journal of Service Industry Management, 11(2), 2000, 185. [4] Bueren A., Schierholz R., Kolbe L and Brenner W. "Customer Knowledge Management Improving Performance of Customer Relationship Management with Knowledge Management," Proc. Hawaii Int l Conf. on System Sciences, 7(7), 2004, 70172b. [5] Eccles G., Forte proves link between training and customer satisfaction, Human Resource Management International Digest, 8(1), 2000, [6] Coyle R., The Practice of Systems Dynamics: Milestones, Lessons and Ideas from 30 Years of Experience, System Dynamics Review, 14(4), 1998, [7] Fjermestad J. and Romano, Jr. N. C., "E- Commerce Customer Relationship Management HICSS-38," Proc. Hawaii Int l Conf. on System Sciences, 7(7), 2005, 169. [8] Forrester J.W., Industrial Dynamics, Pegasus Communication, Waltham, MA, [9] Francis H. and D'Annunzio-Green N., HRM and the pursuit of a service culture: Managerial encounters with competing discourses, Employee Relations, 27 (1/2), 2005, [10] Gross D. and Harris C.M., Fundamentals of Queuing Theory, John Wiley, [11] Hafeez K. and Abdelmeguid H., Dynamics of human resource and knowledge management, The Journal of the Operational Research Society, 54(2), 2003, [12] Maxwell G. and Lyle G., Strategic HRM and business performance in the Hilton Group, International Journal of Contemporary Hospitality Management, 14(5), 2002, [13] McGovern T. and Panaro J., The Human Side of Customer Relationship Management, Benefits Quarterly, 20(3), 2004, [14] Nasir S. and Nasir V. A. Analyzing the Role of Customer-Base Differences in Developing Customer Relationship Management Strategies, Journal of American Academy of Business, 7 (2), 2005, [15] Richardson G.P., Modeling for Management, Dartmouth Publishing Co., Aldershot, UK,

10 [16] Viardot E., Human resources management in large information-based services companies: towards a common framework? International Journal of Technology Management, 31 (3,4), 2005, 317. [17] Winsted K. F., Service Behaviors That Lead To Satisfied Customers, European Journal of Marketing, 35, 2000,