Improving Effectiveness of Customer Service in a Cost-Efficient Way - Empirical Investigation of Service Allocation Decisions with Out-Sourced Centers
Baohong Sun1 Tepper School of Business Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA15213 Tel: 412-268-6903 Fax: 412-268-7357 Email:
[email protected]
Shibo Li Kelley School of Business Indiana University 1309 E. 10th Street Bloomington, IN 47405 Phone: 812-855-9015 Fax: 812-855-6440 Email:
[email protected]
December 2005
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Baohong Sun is Associate Professor of Marketing at Carnegie Mellon University. Shibo Li is Assistant Professor of Marketing at Indiana University. The authors thank participants at Biennial MSI Young Scholars Program and International Workshop on Customer Relationship Management: Data Mining Meets Marketing for helpful comments.
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Improving Effectiveness of Customer Service in a Cost-Efficient Way - Empirical Investigation of Service Allocation Decisions with Out-Sourced Centers Abstract Ever since the 1990s, the role of call centers has been transformed from simply dealing with customer inquiries to performing integrated marketing functions. Firms are starting to view contemporary call centers as preferred and prevalent channels to acquire and retain customers, enhance customer relationship and grow long-term revenue, rather than a cost to be minimized. The management of call centers has become an important part of customer relationship management (CRM) strategy. However, the management of call centers has always remained the research topic of operation management, which focuses on minimizing operating cost and often ignores the resulting customer reactions and financial implications. In this paper, we treat service duration as a measurement of operating cost (operating efficiency), as well as a determinant of customer retention (marketing effectiveness). Given both operating cost and marketing consequences are driven by allocation decisions, we propose a dynamic structural framework that allows the firm to learn about the heterogeneous preference of customers as well as the comparative advantages of off-shore centers, balance the trade-offs between short term cost benefit and long term customer reactions, and make optimal allocation decisions that best match customer preference for service duration and maximize long-term profit. Thus, the traditional operation management problem of call allocations is formulated as a customer relationship management problem in which the firm is allowed to incorporate marketing consequences into operating decisions to grow customer relationship. Applying the proposed framework to customer call history data provided by a high tech company that operates off-shore service centers, we first estimate the model to parameterize the relationship among call allocation, service duration, customer retention and profit as well as that between short-term cost benefit and long-term marketing consequences. Based on the estimation results, we then conduct simulations to derive the optimal service allocation decisions. We next establish the dynamic, customized, and state-dependent nature of the derived optimal operating decisions and show how they are driven by marketing consequences. Finally, we demonstrate that by taking into account long-term marketing consequences, the optimal allocation decisions derived from our framework (1) improve customer retention; (2) reduce service costs; and (3) enhance profit by growing relationships. In short, the effectiveness can be improved in an efficient way. Our findings shed new light on the understanding of marketing consequences of service operation decisions. The estimation and simulation results provide directional guidance and analytical solution to improve service allocation decisions for call-center managers who plan to accommodate customer relationship to their operating decisions. The developed adaptive learning rule and optimization solutions provide the computational algorithm to automate the implementation of service allocations for firms that can integrate their CRM system and operating system. Keywords: call center; service allocation; service outsourcing; adaptive learning; service duration; customer retention; customer profitability; customer relationship management; dynamic intervention; dynamic structural model
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“Of concern for U.S. companies considering offshore outsourcing is that 65% of American consumers would alter their buying behavior toward a company if they know or had the impression the business was using an offshore service center. As American companies consider opening call centers in other countries to serve and sell to U.S. customers, they would be wise to weigh their expected cost benefits against the possibility of potentially alienating their American customers. With this in mind, companies would be prudent to view their customer support call centers as crucial elements of their customer strategy, akin to marketing and loyalty programs.” -Call center study led by Purdue University’s Center for Customer-Driven Quality 2004
1. Introduction Call centers were born of a basic need: to answer customers' questions. In 1972 Continental Airlines asked the Rockwell Collins division of Rockwell International (now Rockwell Automation) to develop the first automated call distributor, thus launching the call-center industry. Today all Fortune 500 companies have at least one call center with an average of 4,500 employees across their sites. More than $300 billion is spent annually on call centers around the world. A total of 2.9 million agents are employed at 55,000 facilities in North America. Three million are employed overseas, and this number is predicted to increase by 10% a year. By 2007, off-shoring to foreign markets will account for 7% of the total number of positions (McKinsey Quarterly 2005). Most of the outsource operations are concentrated in the Philipines, Canada, and India among other places, due to their workers’ English-language fluency, labor quality, cost of labor, as well as multicultural flexibility and adaptability. Some argue that outsourcing call centers represents a phase-two shift in the global economy, with outsourcing manufacturing being phase one (Elmoudden 2005). As a result of attitudes stemming from the days when call centers were used just to deal with inquiries initiated by customers, the management of call centers was considered little more than a cost to be minimized. This attitude leads to the increasing popularity of outsourcing. Early adopters of outsourcing have shown savings of 40% or more, generally operating at a far greater scale. Despite the significant savings in cost, a recent survey by Purdue University has shown that both consumer and business customers give significantly lower satisfaction ratings for outsourced call centers. The top problems reported are “difficulty in understanding an agent’s accent,” “poorly trained staff,” the agents “misunderstood my accent or English,” and the agents “were unable to
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resolve my problem.” The survey further shows that 65% of American consumers would alter their buying behavior towards a firm if they knew or had the impression that the business was using an outsourced service center. More and more outsourcing firms realize that the initial focus on driving down costs is paid for by the consequence of alienating the customer. Customer defections and hidden costs outweigh any potential savings they derived from outsourcing (Offshoring Digest 2005). Recently, Dell Computer and Delta Airlines took back call-center operations from outsourced vendors because of constant customer complaints. It indicates that firms have started to show increasing concern with the placement of a key corporate asset in the hands of a third-party provider. Ever since the 1990s, with the advent of software-based routing and customer relationship management applications, the marketing possibilities of call centers have been dramatically improved. Contemporary call centers handle customer survey, telemarketing, product inquiry, sales, transaction, promotion, cross-selling, advertising, post-purchase service etc., using telephone, email, fax, or webpage. They perform an integrated marketing function and are becoming a preferred and prevalent channel for interacting with their potential and current customers to acquire and retain business (Gans, Koole and Mandelbaum 2003). In addition, statistics shows that 80% of a firm’s interaction with its customers is through call centers, and 92% of customers form their opinion about a firm based on their experience with call centers (Purdue University Center for Customer Driven Quality, 2004). Thus, the management of call centers has become an important part of customer relationship management strategy. It is among the most crucial corporate assets to acquire and retain customers, grow sales, and increase profit. Today, call center managers are faced with the imperative challenge of continuously improving service quality and enriching customer interaction, together with rigorous control of operational cost to improve profitability. It calls for research to better understand the human side of operating decisions, which provides implications on how to transform call centers into strategic assets that promote growth of customer relationship and profit. Despite the increasing importance of call centers in customer relationship management, the call center management remains the research domain of operation management. As described by Gans, Koole, and Mandelbaum (2003), operation management examines the most efficient ways to manage call routing, call waiting, queuing, etc. so as to minimize operating cost. Customer decisions such as satisfaction, retention, and repeat purchase are usually described as constant or a simple liner function of cost-saving effort. While the mature literature on operation management
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significantly advances our understanding of operating efficiency, the effectiveness as a result of improved efficiency or the human reaction and its implications on profit are ignored. Both effectiveness and efficiency – the capacity to provide the best response to customer contacts with contained cost – are important. A solution to the problem is to develop a customer service strategy that successfully balances operating costs, customer retention, and long-term profit that integrates both operational and marketing considerations. This opens up a vast agenda for multidisciplinary research. There are many interesting issues to be addressed: •
How do customers evaluate the performance of out-sourced service centers? Do off-shore centers demonstrate any comparative advantages?
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What is the relationship among service allocation, operating cost, customer retention, and longterm profitability? Or, simply, what is the relationship between operating efficiency and marketing consequences?
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How can the firm learn about the heterogeneous preference of customers as well as the comparative advantages of call centers in order to improve customer retention?
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How to improve the service effectiveness of out-sourced service centers without incurring significant cost?
In this paper, we formulate the call center allocation decisions as a dynamic control problem with adaptive learning, forward-looking and optimization of firm’s intervention decisions. The firm is modeled as a decision maker that continuously learns about customer and makes service allocation decisions to maximize long-term profit. Specifically, we treat service duration as a measurement of operation efficiency, as well as a factor that determines customer retention. Given both operating efficiency and marketing consequences are driven by allocation decisions, we propose a dynamic structural framework that allows the firm to learn about the heterogeneous preference of customers as well as the comparative advantages of outsourced centers, balance the trade-offs between short term cost benefit and long term customer reactions, and make optimal allocation decisions that best match customer preference for service duration and maximize longterm profit. Thus, the traditional operation management problem is formulated as a customer relationship management problem in which the firm is supposed to take into account the long-term marketing consequences when making cost-saving operating decisions.
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Applying the proposed framework to customer call history data provided by a high tech company that operates off-shore service centers, we first estimate the model to parameterize the relationship among call allocation, service duration, customer retention and profit as well as that between short-term cost benefit and long-term marketing consequences. Based on the estimation results, we then conduct simulations to derive the optimal service allocation decisions. We next establish the dynamic, customized, and state-dependent nature of the derived optimal operating decisions. We demonstrate how the optimal operating strategies are driven by marketing consequences. We find that customers demonstrate heterogeneous preference for service durations. Some prefer to be hand-held and the others appreciate immediate and accurate answer. We also find that off-shore centers are on average less efficient (in terms of service duration) and less effective (in terms of customer retention). However, off-shore centers demonstrate comparative advantages in handling technical questions. The derived optimal allocation decisions indicate that the higher the perceived probability of a customer belonging to the hand-holding segment, the higher the probability of being routed to off-shore centers. This positive relationship between marketing consequences and operating decisions is modified by exogenous variables such as question type and customer profit. The continuous learning of heterogeneous customer preference and comparative advantages of the centers allows the firm to customize its optimal allocation decisions. Being able to act upon long term marketing consequences allows the firm to sacrifice short term operating efficiency by allocating customers to their most preferred centers in order to increase future probability of retaining. We demonstrate that by taking into account long-term marketing consequences, the optimal allocation decisions derived from our framework (1) improve customer retention; (2) reduce service costs; and (3) enhance profit by growing relationships. In other words, we show that the effectiveness can be improved in an efficient way. The proposed solution is different from conventional ways of improving customer retention by incurring more cost to increase service quality. In summary, our findings shed new light on the understanding of the marketing consequences of service allocations, an important operating decision. The derived statistical properties of the optimal allocation decisions provide directional guidance for managers to adjust their service allocation decision to accommodate customer relationship to operating decisions. The developed adaptive learning rule and optimization solutions provide the computational algorithm to
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automate the implementation of call allocations for firms that can integrate their CRM system and operating system. In Section 2, we briefly review the related literature in operation management and marketing. In Section 3, we describe the data set. In Section 4, we develop a dynamic framework with adaptive learning and optimization. In Section 5, we discuss the estimation and simulation results, as well as general discussions on adaptive learning and implementation of the proposed solutions. Conclusion, limitation, and future research are given in Section 6.
2. Related Literature
Over the past years, call-center management has always been an important research area for operation management. Mandelbaum (2002) and Gan, Kooke and Mandelbaum (2003) provide comprehensive tutorial, review, and research prospects of call-center-related research. The majority of research concentrates on queuing performance models for multiple-server systems, queuing control models for multiple-server and multi-class systems; human resources problems associates with personnel scheduling; hiring and training of call-center agents, and service quality (measured by accessibility of agents and number of calls to solve a question). Due to the recent development of sophisticated Automatic Call Distributor (ACD, an ACD is an automated switch designed to route calls), recent research focuses on networking, “skills-based routing,” and multimedia. Belt et al. (2000) outlined how the ACD technology allows managers and supervisors to monitor and measure the progress and the flow of work done by agents, and the rate of this flow. Through the monitors, managers and supervisors can routinely collect information on each agents’ call length and the time it takes the agent to wrap up the call, and analyze a wealth of statistical model about agent and team performance. The majority of this research focuses on dynamic control of call allocations with the goal of cutting cost. The service quality and customer reaction is generally ignored. The only exceptions are Gans (2002) and Hall and Porteus (2000), which allow service quality to affect customer churn. However, these models are highly stylized and will not be able to capture the details of the customer reaction. Gan, Kooke and Mandelbaum (2003) state that “traditional operational models do not capture a number of critical aspects of call-center performance….These topics include a better understanding of the role played by human factors, as well as the better use of new technologies.” In addition, the dominant research methodology is analytical models and
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simulations that support capacity management. There is very little empirical literature on service centers except some descriptive data analysis, such as histograms, and tests for “goodness of fit” with certain parametric families of distributions. Finally, other than an exploratory survey conducted by the Purdue University Center for Customer Driven Quality (2003), there is very little research devoted to the performance of outsource service centers. In marketing literature, there is an ample amount of research documenting the relationship between service quality, customer satisfaction, retention, and financial impact. Readers interested in this line of research can see excellent review papers by Rust and Chung (2005), Kamakura et al. (2005), and Anderson and Sullivan (1993), Bearden and Teel (1983), Bolton (1991), Boulding, Kalra, Staelin, and Zeithaml (1993), Boulding, Kalra, and Staelin (1999), Churchill and Suprenant (1982), LeBarbera and Mazursky (1983), Li, Sun and Wilcox (2005), Oliver (1980), Oliver and Swan (1989), Tse and Wilton (1988), and Westbrook (1980). Models have been developed to study the link between satisfaction and the incentive scheme of the salesperson (Hauser, Simester, and Wernerfelt 1994), the relationship between measured overall service quality and subsequent usage (Danaher and Rust 1996; Bolton and Lemon 1998; Mittal and Kamakura 2001), the explanation power of customer satisfaction on duration (Bolton 1998), the relationship between customer satisfaction and the productivity level of the firm (Anderson, Fornell, and Rust 1997), and customer lifetime value analysis (Reinartz and Kumar 2000; Reinartz and Kumar 2003; Rust, Lemon and Zeithaml 2004). Recently, “Holy Grail” models of CRM have been developed to determine fully personalized levels of marketing interventions, using multiple marketing interventions over times to maximize customer lifetime value (Bult and Wansbeek 1995, Schmittlein and Peterson 1994, Venkatesan and Kumar 2004, Rust and Verhoef 2005, Netzer, Lattin and Srinivasan 2005). For example, controlling for customer heterogeneous characteristics, Gonul and Shi (1998) study the optimal direct mail policy in a dynamic environment, where customers maximize utility and the direct mailer maximizes profit. Lewis (2005) adopts a dynamic programming-based approach to derive optimal pricing policy of newspaper subscription that allows for the adjustment of discounts as customer relationship evolves. Kamakura, Mittal, de Rosa and Mazzon (2002) provide an integrative framework for understanding how a firm’s operational investments into service operations are related to customer perceptions and behaviors, and how these translate into profit. However, despite the increasing importance of call centers in developing the customer relationship,
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there is almost no research in marketing literature that specifically studies the evaluation and operation of call centers. In this paper, we define CRM intervention decisions as solutions to a stochastic optimization problem under demand uncertainty in which the firm needs to learn about the heterogeneity of customer preferences, the dynamic effect of its marketing interventions, the cost of acquisition, and long-term payoff, with the goal of maximizing “long-term” profit of each customer. Accordingly, the call center management decisions are formulated as a stochastic dynamic control problem with adaptive learning, forward-looking and optimization of firm’s intervention decisions. The proposed framework differentiates from existing studies on CRM in the following ways: First, existing literature on CRM focuses on examining customer decision process assuming firm’s decisions are given and discuss implications on firm’s CRM intervention decisions. While allowing the customer decisions to be affected by firm’s decisions, we take one step further to treat the firm as a decision maker and explicitly derive its optimal decisions. Second, most current research emphasizes on developing better approaches to model customer heterogeneity, in which segmentation is based on the pooled historical data and inference is made in an ad hoc fashion. We formulate the idea of adaptive learning in which accruing customer information is adopted and integrated into the firm’s periodical decisions. As a result, the firm continuously updates its belief on customer preference according to the feedback obtained from last execution of decision. Third, we treat the firm as a forward-looking decision maker which takes into account the long-term profit implications of customer attrition when making current decision. The future consequences are built into the derived optimal decisions. This is different from most existing literature on customer lifetime value analysis that calculates net present value of customers’ future profit and treats the value as another segmentation variable to guide targeting strategies. It mitigates the endogenization problem that firm’s intervention changes customers’ future purchase probability as pointed by Rust et al. (2001) and Rust and Verhoef (2005). In summary, with adaptive learning, forward-looking and optimization, our approach is more akin to the decision support system of CRM, in which the firm obtains more information about the customer in order to make dynamic and customized decisions with the goal of maximizing long-term profit. Methodologically, this paper is related to the dynamic structural models developed to examine consumers’ dynamic decisions regarding brand, quantity, and purchase timing by accommodating consumer learning (e.g., Erdem and Keane 1996, Erdem, Keane and Strebel 2004,
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Mehta, Rajiv and Srinivasan 2004, Akcura, Gonul and Petrova 2004) and stockpiling behavior (e.g., Golabi 1985; Assunção and Meyer 1990, 1993; Helsen and Schmittlein 1992; Krishna 1992, 1994a, 1994b; Erdem and Keane 1996; Gönül and Srinivasan 1996; Sun, Neslin and Srinivasan 2003; Erdem, Imai and Keane 2004; Sun 2005; Erdem, Keane and Sun 2005). The focus of these papers is to establish that consumers are sophisticated decision makers. They can learn about product attributes or quality through observing marketing information or use experience. They can also form expectations of future marketing activities and strategically adjust their purchase decisions to coincide with a future marketing schedule.
3. Data Description
The data is provided by a global-sourcing high tech firm that operates service centers in the United States, Indian, and other global locations.2 The firm offers technology products and services to its customers. Call centers handle customer service inquiries such as installation, software and hardware upgrades, billing, routine service, and sales. Call-center agents spend most of their time receiving calls, e-mailing and chatting with customers, and keying the outcome of the interaction into computer terminals. In the offshore call centers, the agents are usually given a codified script with written-down rules that coach them on personal traits (gregarious) and emotional behavior (enthusiastic and courteous) that they should demonstrate while interacting with customers. The calibration sample contains a 52 weeks call history of 3,159 customers who made at least two calls and an average of 4.5 calls between January 2003 and December 2003, a detailed satisfaction survey of these customers, their demographic information, and retention decisions. In the call-history panel data, we have information on each call such as time stamps, reasons for the call, call duration, who the call-center representative was, who was the manager of the call center, and at which location the call was served. The survey data contains overall satisfaction score as well as detailed sub-satisfaction scores, with rating 1 being least satisfied and 5 being most satisfied.3 Given the government regulation that at most one survey can be conducted for each customer within 3 months, most customers have participated in only one satisfaction survey. The customer 2
Due to data confidentiality, we are unable to disclose more information on the nature of the product. We do not model customer satisfaction because most customers have only one measurement of satisfaction. The static measurements cannot be modeled as consequences of periodical operation decisions. We include satisfaction score as an 3
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demographic information includes tenure with the firm, region, life-stage segment, education, and number of computers. In addition, we observe whether a customer stayed or left the firm during the observation period. Finally, the firm provides us with the price paid by each customer and some estimates of average service costs. The call routing is facilitated by the use of ACD (Automated Call Distribution) system, which automatically processes incoming calls and routes them to specific agents based on their availability and skills. When agents begin working, they are required to log into the center’s ACD system. The log-in IDs are then used to retrieve the profile of the agent and their case handling history, which describes their capabilities. The firm adopts the rule of routing the customer to the agent with the lowest average duration, which is usually termed as “skill-based” routing.
[Insert Table 1 About Here]
Table 1 lists the explanations of the variables used in our analysis and their sample statistics. To simplify things, we treat all service centers within the continent of the United States as the onshore service centers and those outside as the off-shore service centers. The most important variable is service duration, which is defined as the time an agent spends on solving a problem. We construct this variable by including the total time spent with the customer on the phone (including e-mail and Internet chat), the time during which the customer is on hold and the agent is processing the customer’s request, as well as the time after the caller hangs up that the agent continues processing the request.4 The average service duration is 30 minutes. The average satisfaction score is 3.40 with a standard deviation of 1.29. Eighty-four percent of the calls were handled by on-shore service centers, while 16% were handled by off-shore centers. On average, 16% of the customers left during the 52 weeks of the observation period. In Table 1, we also compare all the listed variables between on-shore and off-shore centers. The average durations are 20 and 38 minutes for on-shore and off-shore centers. The significant explanatory variable in the retention equation in order to take into account the intra-customer variations. Future research can explicitly examine the dynamic change of customer satisfaction. 4 Researchers in operation management have examined waiting time, queuing, and abandon, which are important under capacity constraint. Due to data limitation, we do not have information on these variables. Since this is one of the first attempts to incorporate marketing consequences into operating decisions, we only focus on service time. This is a limitation of our empirical demonstration. However, it won’t affect our proposed approach. The firm also indicated that capacity was not its big concern and that our results should not be very sensitive to these simplifications. Future research can explicitly study how these variables affect allocation decisions.
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difference (t=143.1) indicates that it generally takes longer for the off-shore centers to solve a case. The mean overall satisfaction scores are 3.46 and 3.11 for on-shore and off-shore centers. The difference is significant at the t=2.22 level, indicating that customers are less satisfied with offshore service centers. As indicated by the comparison of sub-satisfaction scores, the major factors causing the difference are difficulty in understanding each other, the lack of ability to provide clear and concise answers, the lack of ability to provide a personalized and courteous response. It seems that the reasons causing the dissatisfaction of off-shore centers are centered on the so-called “people skills,” or the courtesy aspect of customer relationship. On the contrary, the difference of the rating of technical knowledge is not very significant (3.51 and 3.50 with t = 0.71), indicating that, on average, customers are less likely to be dissatisfied when their technical questions are handled by off-shore centers. Defining FREQ_OFF as the recency weighted percentage of calls being handled by offshore centers, we classify customers as off-shore when FREQ_OFF>0.5 and on-shore otherwise. It is observed that being serviced frequently by off-shore service centers leads to higher average customer attrition (17% vs. 12%, with t = 12.6 for the difference). This is consistent with the finding of the call center study of Purdue University that customers are, overall, less satisfied with the service quality of off-shore service centers and are more likely to leave.
[Insert Table 2 About Here]
The above discussion shows that customers are less satisfied with the courtesy aspect of the customer service of off-shore centers. However, they may be less sensitive regarding technical questions being handled by off-shore centers. In order to better examine customer reaction when different types of questions are handled by on-shore and off-shore centers, we further classify cases into technical questions and courtesy questions, and report in Table 2 the average satisfaction scores and retention rate. Technical questions include services down, software problem, installation, hardware issue, line problem, and network outage. Courtesy questions include inquires on billing, email account, product news, product services, and registration. For example, the three numbers in the first column of the table report the average overall satisfaction, retention rate and service duration among all the customers who asked mostly courtesy questions and were serviced mostly by on-shore centers. The comparison shows that customers are significantly happier and less likely to leave when courtesy questions are handled by on-shore centers (t =14.26 for satisfaction rating and t
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=17.43 for retention). The differences are insignificant when their technical questions are handled by off-shore centers (t =1.79 for satisfaction rating and t =1.97 for retention). This indicates that off-shore centers have a slight comparative advantage in handling technical questions. In fact, the Purdue research shows that more than 25% of the respondents indicated there would be no impact on their purchasing behavior if their technical calls were handled by an off-shore service center. This is especially true among college educated respondents and individuals ages 18 to 25. In terms of service time, it generally takes longer for off-shore centers to handle a case. This is especially true for courtesy questions. However, given the average cost of handling a phone call per minute is roughly two times less expensive, off-shore centers demonstrate a cost advantage. This is more prominent for handling technical questions.5 In summary, while allocating calls to off-shore centers drives down operating costs, it is paid for by higher customer churns. It is unclear what will be the implications for the firm in the long run. The firm faces a trade-off between short-term cost benefit and long-term marketing consequences when assigning a case to off-shore centers. However, the customers seem to be less sensitive if their technical questions are handled by off-shore centers. This indicates a “weak” comparative advantage of off-shore centers in handling technical questions. In the next section, we will propose a framework to demonstrate how a firm can take into account the comparative advantages of off-shore centers when making allocation decisions.
4. A Dynamic Framework with Adaptive Learning
We assume that the firm operates j=1 and j=2 service centers, with j=1 representing on-shore centers and j=2 representing off-shore centers. At time t=1,…,T, customer i=1,…,I may call in with question types k=1 and k=2, with k=1 representing courtesy questions and k=2 representing technical questions. We assume there are m=1,…,M segments of customers with different preferences for service durations.6 We define time periods as weeks.
5 The average per minute cost of handling calls is provided by the firm. It is calculated based on call-center agent’s wage and other variable costs such as overtime pay. We are not allowed to release the detailed information on cost. 6 For simplicity, we only consider two service centers and two types of questions. Similarly, we also ignore the difference of service duration among agents within the same center. Our model can be generalized to incorporate multiple service centers, multiple questions and multiple agents.
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We use dummy variables Dikt for k=0, 1, and 2 to denote whether customer i calls in with question type k at time t, with k=0 representing the case when customer i does not call in. Dikt =1 if customer i calls in with question type k. It is zero otherwise. Note Dikt are notations to distinguish the call and no-call occasions. It is not a decision variable. When customer i calls in with question type k, the firm decides whether to allocate the customer to on-shore or off-shore centers in order to maximize long-term expected profit. We use dummy variables Aijt to denote the firm’s allocation decisions. Ai1t =1 if the question is allocated to an on-shore service center. Ai 2t =1 if the question is allocated to an off-shore service center. They are zeros otherwise.
4.1. Service Duration
Service duration is an important indicator for both service efficiency and service effectiveness. Before assigning a case to center j, it is important for the firm to know the total time that this center takes to solve question type k in order to measure the expected cost of an allocation decision (efficiency). Similarly, customers are likely to have different preferences for service durations. Some customers may prefer agents spending an ample amount of time to hand-hold them, and some customers may appreciate their questions being answers promptly. It is important for the firm to learn about customer preference for duration and assign them to the center with their desired level of service. If getting the desired treatment is more likely to make customers stay, assigning customers according to their individual preference may increase service effectiveness. Service duration is likely to be affected by the type of question, the capability of service centers, and customer preference. Mandelbaum et al. (2001) have shown that the call duration can be best captured by a log-normal distribution. Following this convention, we assume the log of call duration log( DURijkc (m)) of customer i of type m for all call occasions is given by
(1)
Log ( DURijkc ( m)) = α 0 ( m) + α 1 (m) log( DURijkc −1 ( m)) + α 2 (m) Ai 2 c + α 3 (m) Ai 2c Di 2 c + α 4 (m) X ijkc (m) + ξ ijkc ( m)
and
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ξ ijkc (m) − N (0, σ ξ2 )
(2)
We use index c to denote the counting index of all call occasions.7 Variable log( DURijkc −1 (m)) means the log of the total service duration of the last call. Its coefficient α1 (m) measures the persistence of customer duration preference. The coefficient of Ai 2c , or α 2 (m) , measures whether it takes offshore centers more time to solve a case compared to on-shore centers. The coefficient of Ai 2c Di 2c , or α 3 (m) indicates how having technical questions solved by off-shore centers modifies the difference in duration. If α 2 (m) >0 and α 3 (m) 1), the likelihood that customer i is perceived to belong to Pr( DURijkt −1 | m = 1) Pr( RET jt −1 | m = 1)
m=n group is revised upwards. In other words, when the observed duration and resulting retention are more likely to happen when the customer is assigned to segment m, the firm increases its belief that this customer belongs to segment m. When customers do not call in,
Pr( DURijkt | m = n) Pr( DURijkt | m = 1)
=1, the
belief is updated based on observed retention. Given the updating rule, the perceived probabilities that customer i belongs to type m at time t are given by
Prit (m = n) =
(8)
LRit (m = n) M
∑
LRit (m)
m =1
for all m=1,…,M. The learning process described above is based on the firm’s accruing information to continuously update the firm’s belief about the customer’s intrinsic type. The updated knowledge is used to adjust allocation decisions, and the resulting customer reactions are fed back to the updating process. We term this “adaptive learning,” which has the following properties: (1) accruing information is used to continuously update the firm’s knowledge of customer preference; (2) the firm’s strategic decision is adapted according to the updated knowledge; and as a result, (3) the firm can revise its belief in the next period based on the successful and unsuccessful interactions with the 19
customer. Adaptive learning offers the firm the opportunity to learn about customer preference and adapt its strategies in a real-time fashion. It is an important class of learning algorithms for a stochastic environment. With the improvement of the accuracy of the firm’s knowledge of customer preference, customers are better served and may be more likely to stay with the firm. Similar idea has been adopted in conjoint analysis to reveal consumer preference (Toubia, Simester, Hauser and Dahan 2004).
Note that the above discussion is about adaptive learning of customer heterogeneous preference. Another source of uncertainty comes from the heterogeneous capabilities of each center. This is especially true given the independent ownership of off-shore centers and the challenge of controlling its service quality. Although we do not allow the firm to explicitly learn about the center heterogeneity in an adaptive fashion, we do allow the firm to take into account the differential efficiency of on-shore and off-shore service centers in dealing with different types of questions by allowing the log duration equation (equation 1) to be center-type and question-type specific. The differential efficiency is factored into the firm’s adaptive learning of customer heterogeneity, as shown by equation (7).
4.4. Objective Function We define the profit ( PROFITit ) the firm can earn from customer i at time t as the difference between the fee paid by the consumer and the cost of serving them. It is defined as,
(9)
PROFITit =
M
∑ Pr
it
m =1
(m)[ FEEit * RETit −
∑D
ikt [ Ai1t C1 DURijkt
(m) + Ai 2t C 2 DURijkt (m)]]
k
FEEit is the fee paid by customer i at time t for the purchased product or service (e.g. subscription
fee). It represents the marginal revenue contributed by customer i of type m at time t. We assume that the fee is paid at the beginning of period t so that the customer can call in to ask for service. RETit is the dummy variable indicating whether customer i stays with the firm at time t. Whenever a
customer calls in, we assume she has paid the fee and is going to be retained for the current period. C1 and C 2 are the unit costs of service for on-shore and off-shore service centers. The profit is
weighted by the firm’s perceived probabilities of customer i belonging to type m at time t, Prit (m) , as given by equation (8). 20
For many reasons, the allocation of services may not be driven solely by profit. For example, the political and ethical considerations may motivate the firm to assign more cases to onshore centers. To take into account this possibility, we assume the firm makes allocation decisions between on-shore and off-shore centers according to the following utility function U it ,
(10)
U it =
∑A
ijt
j
λ0 + λ1 PROFITit + ∑ ( Dikt Aijtτ ijkt ) j ,k
Scalar λ0 captures the intrinsic preference for the firm to allocate a service call to on-shore centers.
λ1 measures the importance of profit in determining the firm’s allocation decisions. τ ijkt is the unobserved factors that affect allocation decisions.
4.5. Manager’s Optimal Allocation Decisions
Given the lower unit service cost of off-shore centers, a cost-minimizing firm is motivated to assign as many cases as possible to the off-shore centers. However, when customer retention is taken into account, the firm faces the consequences of more dissatisfied customers and higher attrition rate. In addition, most firms care about the total profit a customer can contribute over her lifetime with the firm (Rust and Chung 2005). The firm needs to trade off the current cost of allocation decisions (operating decision) and future customer retention (marketing consequences) in order to maximize long-term profit (financial payoff). This implies that when making allocation decisions, the firm may be willing to incur a higher cost to retain the customer for future profit. This can be formulated as a dynamic programming problem, with the firm’s maximization problem being given by
(11)
∞
δτ ∑ τ
Max E it { Aijt
U iτ } ,
−1
=t
where 0< δ
