Towards ``zero abandonments'' in call center performance

Transcription

1 European Journal of Operational Research ) 50±56 Theory and Methodology Towards ``zero abandonments'' in call center performance John C. Duder a, Moshe B. Rosenwein b, * a Internal Revenue Service, Atlanta, GA, USA b Merck-Medco Managed Care, L.L.C., 100 Parsons Pond Road, Franklin Lakes, NJ 07417, USA Received 29 June 1999; accepted 11 August 2000 Abstract The call center industry is a big business in today's global economy. Sta ng costs account for over half of a call center's total operations costs. Some large call centers, in practice, operate at very close to maximum capacity, believing that such an operations policy is e cient. However, by operating at levels close to 100% utilization, a call center is ``living dangerously''. If, for example, call volumes even slightly exceed forecasts, customer calls will queue. As queue lengths and durations increase, customers will tend to abandon their calls. We provide some ``rule-of-thumb'' formulas that evaluate the cost of abandonments. These formulas may be used to justify an investment in additional agents required to improve the quality of service and reduce abandonments. Standard Erlang-C queueing formulas imply that abandonments can be signi cantly reduced with a small investment in additional agents. Thus, by improving customer service and hiring additional sta, a call center can improve pro tability. We illustrate our analysis with realistic data, based on our work with large-scale customer service centers. Ó 2001 Elsevier Science B.V. All rights reserved. Keywords: Manpower planning; Call center planning; Marketing 1. Introduction The application of operations research modeling to the call center industry is receiving signi cant attention recently, see, for example, Mehrotra 1997). Many rms seek to di erentiate themselves from their respective competition by providing superior customer service. We focus on a largescale call center operation that provides a service * Corresponding author. Tel.: address: moshe_rosenwein@merck.com M.B. Rosenwein). function in a corporation. Such a call center, typically, handles customer calls regarding, for example, billing inquiries or information about new products and services. A small percentage of calls result in actual sales. The cost of providing trained agents on-line to answer customer inquiries accounts for over 50% of total operations costs. Many rms have used queueing models to e ciently sta their call centers. Applications of such models are described for the following industries: airlines ± Gaballa and Pearce 1979), telecommunications ± Sze 1984), the IRS ± Harris et al. 1987), laboratories ± Callahan and Khan 1993), hospitals ± Agnihothri /01/$ - see front matter Ó 2001 Elsevier Science B.V. All rights reserved. PII: S )

2 J.C. Duder, M.B. Rosenwein / European Journal of Operational Research ) 50±56 51 and Taylor 1991), and retail mail-order ± Andrews and Parsons 1993) and Quinn et al. 1991). A call center may be tempted to operate at a very high level of utilization e.g., over 98%), given its large investment in its workforce. In practice, a call center is ``living dangerously'' if it drives towards 100% utilization i.e., operating at maximum available capacity). For example, if actual call volume even slightly exceeds the forecast, large queues and unanswered calls are likely to arise. Large queues have the e ect of increasing a customer's average delay in reaching an agent which, in turn, cause customers to abandon their call prior to reaching an agent. For a call center that handles service inquiries, an abandonment may not be viewed as severely as it may be viewed by a call center that serves as a sales channel where a customer abandonment implies lost revenue). We provide some ``back-of-the-envelope'' formulas that may be used to evaluate a ``zero abandonments'' policy. Abandonments add inbound telecommunications and information systems costs. For certain operations environments, we demonstrate that reducing abandonments and the average delay of a call by hiring sta may be economically advantageous. An operations policy of ``zero abandonments'' may, thus, prove to be sound nancially as well as bene ting the customer and employee bases. The authors developed the modeling approach, described below, while supporting customer service call centers at a large US provider of telecommunication services. This operation handled 70 million calls annually and had an operating budget of approximately $500 million. The authors used the model to persuade management to abandon its plan to increase its performance targets for ASA i.e., decrease customer service) in the hopes of achieving signi cant cost savings. The modeling approach was intuitive, thus facilitating the authors' ability to in uence strategy and ``sell'' the model's results to management. Similar ndings were also reported at L.L. Bean Andrews, 1997). In Section 2, we provide an overview of a call center's operations, focusing on call handling and performance metrics. Section 3 analyzes the cost to a call center of hiring additional agents versus the bene t achieved by improving level of service. Section 4 illustrates the analysis with realistic data. 2. Call center operations analysis A call is processed as follows. A customer dials, typically, a toll-free telephone number to contact a call center, i.e., the call center is charged with all inbound telecommunication expenses. An interactive voice recognition IVR) system prompts a customer with a series of announcements and interactive menus. An IVRtree is designed with the purpose of identifying characteristics of the incoming customer call e.g., reason for calling, billing history, etc.). The IVRsystem interacts with various information systems that provide a pro le of the incoming customer. An IVRexperience may have a duration of several minutes, and the resulting inbound telecommunication charges are absorbed by the call center. In some cases, a customer inquiry may be handled by the IVRsystem and, thereby, not necessitate the intervention of a more costly) human agent. A customer may elect to exit the IVRsystem at certain points in the ``dialogue'' and speak directly with an agent. If one or more agents are idle, then a customer call is routed to the agent that has been idle for the longest duration. Otherwise, if all agents are busy, a customer call is routed to the queue with the shortest expected wait time. After choosing to speak directly with an agent, a customer call) enters a queue if no agent is immediately available. A call center measures its performance by one of the following metrics: 1) average speed of answer ASA), i.e., the average delay in queue experienced by a customer waiting for an agent, 2) percent of calls answered within X, say, 30 seconds after requesting to speak to an agent), and 3) percent abandonment, i.e., the fraction of customers that terminate their call after departing the IVRsystem but prior to reaching an agent. Andrews and Parsons 1993) developed a linear regression model that showed a strong correlation between ASA and percent abandonment R-squared value > 0.9). Using data from a major telecommunications provider, we found that the distribution of calls answered within X seconds

3 52 J.C. Duder, M.B. Rosenwein / European Journal of Operational Research ) 50±56 was exponential. That is, the majority of customers are serviced almost instantaneously e.g., within 10 seconds) and yet some su er extensive delays e.g., greater than a minute). In order to analyze call center operations, we focus on a typical quarter hour during a day. As shown in Fig. 1, a call center's peak period may extend for a long duration. For each quarter hour, a call center forecasts its call volume and average talk time ATT). Given a sta ng level for a particular quarter hour and actual call volume and actual ATT, a call center can measure the utilization or occupancy of a pool of agents by occupancy ˆ number of calls answered ATT = 15 minutes number of agents on-duty : For large teams of agents e.g., number of agents >150), maintaining occupancies of less than 98% will, typically, result in very short ASAs e.g., less than 10 seconds). As ASA decreases, the percentage of customers experiencing long delays also decreases. However, some individual customers will experience long delays even under an operations policy that maintains short ASAs. We do not consider the relationship between ASA and percent of calls answered in X seconds.) As occupancies approach 100%, ASAs, and, hence, abandonments increase dramatically. Operating a call center at close to 100% occupancy places a heavy unrealistic) reliance on generating almost exact forecasts of incoming tra c. If, for example, actual call volume exceeds the forecast for a particular quarter hour even slightly e.g., by 5%), a call center will be unable to handle the over ow of calls. In addition, operating agents at close to 100% occupancy places great stress on the workforce. Increases in absences due to illness) and attrition are likely to arise, in practice, for a workforce that works at very high occupancies for an extended period of time. In order to approximate the number of agents required to attain di erent values of ASA, given Fig. 1. Call volumes by 1/4 hour of a sample day.

4 J.C. Duder, M.B. Rosenwein / European Journal of Operational Research ) 50±56 53 expected call volumes and ATT for a particular quarter hour, a standard M/M/c queueing model is used in commercial call center sta ng systems, e.g., see Potter 1997). That is, the arrivals of inbound calls are assumed to be Poisson, agent service times are assumed to be exponential, and c agents are assumed to be available. Further, the model assumes no blocking of calls into the system i.e., there are an in nite number of ``slots'' in which a call may reside while in queue) and no reneging by customers. The last assumption is potentially di cult in a call center environment in which abandonments are strongly prevalent. We, therefore, refer to our approach as a ``rule-ofthumb'' approach. We do not attempt to measure the impact of retrials i.e., calls generated by customers who abandon an initial call and call back ``shortly'' thereafter), caused by abandonments, on the call arrival distribution. In general, the Poisson assumptions are violated since retrials imply the presence of dependent demand.) Fig. 2 provides a sensitivity analysis of number of agents required for di erent call volumes and di erent ASAs. For a base case of 400 calls per quarter hour and an ATT of 6 minutes, an additional three agents can reduce ASA from 45 to 20 seconds. However, reductions of ASA for values less than 10 seconds are costly in terms of additional agents). The sensitivity analysis highlights the importance of accurate quarter hour forecasts. For example, if actual call volume exceeds the forecast by 10%, a call center will need to immediately add 16 agents in order to maintain a 45- seconds ASA. Discrepancies between actual and forecast call volume will inevitably arise. A call center must develop a plan to manage real-time imbalances between demand and supply of agents. For example, a call center may employ a team of `` exible'' agents i.e., these agents serve as a bu er stock). These agents can be ``released'' to handle inbound calls should actual volumes exceed forecasts. Otherwise, if forecasts exceed actual call volumes, the exible agents may be assigned other work in the call center, e.g., responding to inquiries that could not be handled in real-time by an agent and were deferred for later action. Fig. 2 also illustrates the di culty of operating at an ASA greater than 5±10 seconds. If a call center desires to operate at an ASA of, say, 20 seconds, it must be aware that a slight deviation in agents' adherence to the schedule e.g., reporting for duty exactly on-time) can result in a major drop-o in performance. Customers quickly experience longer delays, for a removal of a few agents can cause congested queues. As ASAs and Fig. 2. Number of agents required to attain ASAs.

5 54 J.C. Duder, M.B. Rosenwein / European Journal of Operational Research ) 50±56 queue lengths increase, the rate of costly) customer abandonments also increases. Thus, a call center should establish a performance target of a very short ASA e.g., less than 10 seconds) for each quarter hour of every day. A short ASA will also imply a low rate of customer abandonments. 3. Labor costs versus quality of service We provide a ``rule-of-thumb'' analysis that may be used by a call center to determine if, economic arguments can also support a strategy of ``zero abandonments''. Consider a call center that is deciding whether to hire additional agents in order to reduce its ASA. Its key assumptions are: no blocking of calls; a low-to-moderate rate of abandonment; a large team of agents; forecasts of call volume and ATT by quarter hour are given. The following notation is needed. The analysis considers a particular quarter hour for a call center: b ˆ cost per agent per 1/4 hour. W ˆ desired change in ASA. m ˆ number of agents required to change ASA by W. A ˆ change in abandonment rate resulting from a change of W in ASA. V ˆ actual call volume calls answered plus calls abandoned) per 1/4 hour. h ˆ average duration of an IVRexperience. q ˆ inbound telecommunications cost per minute. d ˆ information systems cost per IVRexperience. p ˆ probability of a customer retrial given a customer abandonment. r ˆ probability of a customer switching to a competitor brand given inferior customer service. N ˆ average revenue per customer. Using the standard Erlang-C formulas that are appropriate for an M=M=c queue, see Gross and Harris 1985), a call center may determine its sta ng level for a particular quarter hour given the expected call volume and ATT for that quarter hour and given a speci ed ASA. By computing the number of agents required for both its baseline and proposed ASAs, a call center may, thus, determine m as a function of W. The cost of adding m agents for a particular quarter hour is bm. By reducing the average duration of each call and by reducing retrials, a call center may achieve three types of economic savings: 1) a reduction in inbound telecommunications costs; 2) a reduction in IVR-related charges; and 3) a reduction in erosion of a rm's customer base. By reducing the average delay duration) of each call by W where W is in minutes), a call center saves qvw dollars in inbound telecommunication costs. An improvement in service level results in a decrease in abandonments and, hence, retrials. In practice, Ho man and Harris 1985) found that the probability p of a retrial given an abandonment is 0.7. For a given value of ASA, we can use, for example, the regression model in Andrews and Parsons 1993) to predict the rate of abandonments. In general, a change in the ASA by W will result in a change in the abandonment rate by A. Thus, the number of retrials that will be saved by reducing the ASA by W is pva. For each saved retrial call, a call center achieves two types of savings. Since a retrial call must be processed by an IVRfrom ``scratch'', i.e., an IVR system cannot distinguish a retrial call from a ``new'' call, a retrial call accrues d dollars of information systems expense. These costs arise from an IVRsystem querying various databases in order to develop a customer pro le. Further, an IVR experience adds inbound telecommunications expense. Speci cally, if an average IVRduration is h say, minutes), then the inbound telecommunications cost associated with an IVRexperience is qh. Thus, by eliminating pva retrials, a call center can save pva d qh in related IVRexpenses. Finally, a rm is better positioned to retain its existing customers and acquire new customers if its call centers provide superior customer service. One of the criteria that marketing organizations use to measure call center service performance is ``time required to reach an agent''. Let us assume that a

6 J.C. Duder, M.B. Rosenwein / European Journal of Operational Research ) 50±56 55 customer that has abandoned a call has judged the ``time required to reach an agent'' to be too long and, hence, views the call center as having provided inferior service. Further, assume that a fraction r of these customers switch their brand preference. By reducing the rate of abandonments by A, a call center may reduce its opportunity cost associated with losses to its customer base) by rnav. Total savings achieved by improvements in the level of service is given by S ˆ qvw pva d qh rnav ˆ V qw pa d qhš rna : Total costs associated with hiring additional agents are given by R ˆ bm: If S > R, then a rm can improve its pro tability by hiring more agents for a particular quarter hour. 4. Example We illustrate the analysis using some representative data, based on our experience with large call centers operated by a large telecommunications service provider. Assume the following costs and parameters: b ˆ $5 per agent per 1/4 hour. q ˆ $0.06 per minute. d ˆ $0.04 per IVRexperience. N ˆ $100 of revenue per customer. h ˆ 2 minutes. p ˆ r ˆ Further, assume that a particular call center seeks to reduce its ASA by 1/2 minute. Based on the regression model of Andrews and Parsons 1993), a 30 seconds reduction in ASA results in an approximate reduction of 3.5% in abandonments. The model is valid in the range of 0±2 minutes ASA.) Using the Erlang-C formulas, four additional agents are required to achieve such an ASA savings for volumes in the range of, say, 400±800 calls per quarter hour and a 6 minute ATT. For the costs and parameters given above, a call center can determine a level of call volume that would economically justify sta ng a particular quarter hour with an additional four agents. Since the duration of a call center's peak period can be quite lengthy, the analysis that is being illustrated, here, for a particular quarter hour, may be applicable to numerous quarter hours during a day. For a call center that seeks to reduce ASA by 1/2 minute, a quarter hour volume V of 532 is su cient to justify additional sta, given the cost and operational parameters listed above. For a call center that evaluates r to be 0 that is, abandonments do not lead to customers switching brands), the break-even call volume value is Conclusions We have argued that call center operations should avoid operating at levels very close to 100% occupancy. Speci cally, such practices can lead to drastic deterioration in service e.g., due to errors in demand forecasts), agent ``burn-out'', and increases in customer abandonments. We have provided a ``rule-of-thumb'' analysis to evaluate the ``economics'' of a trade-o between sta ng costs and customer service. We have shown that, for realistic operations parameters and costs data, a call center may increase pro tability by increasing its sta ng level and signi cantly reducing customer abandonments. Acknowledgements We bene ted from suggestions that were o ered by Hanan Luss of AT&T. References Agnihothri, S.R., Taylor, P.F., Sta ng a centralized appointment scheduling department in Lordes Hospital. Interfaces 21 5), 1±11. Andrews, B., Private communication. Andrews, B., Parsons, H., Establishing telephone-agent sta ng levels through economic optimization. Interfaces 23 2), 14±20.

7 56 J.C. Duder, M.B. Rosenwein / European Journal of Operational Research ) 50±56 Callahan, B.B., Khan, M.R., Planning laboratory sta ng with a queueing model. European Journal of Operational Research 67, 321±331. Gaballa, A., Pearce, W., Telephone sales manpower planning at Qantas. Interfaces 9 3), 1±9. Gross, D., Harris, C.M., Fundamentals of Queueing Theory, second ed. Wiley, New York. Harris, C.M., Ho man, K.L., Saunders, P.B., Modeling the IRS telephone taxpayer information system. Operations Research 35, 504±523. Ho man, K.L., Harris, C.M., Estimation of caller retrial rates for a telephone information system. European Journal of Operational Research 27, 207±214. Mehrotra, V., Ringing up big business. OR/MS Today 24 4), 18±24. Potter, T.F., Computing service level and average delay in the Erlang-C model. TCS Management Group. Internal report. Quinn, P., Andrews, B., Parsons, H., Allocating Telecommunications Resources at L.L. Bean, Inc. Interfaces 21 1), 75±91. Sze, D.Y., A queueing model for telephone operator sta ng. Operations Research 32, 229±249.