Empowering Call Center Simulation

Transcription

1 White Paper Empowerig Call Ceter Simulatio I ay call ceter, the biggest worry a call ceter maager has is staff schedulig. Despite havig a lot of data measured by the call ceter ifrastructure, there are very few tools that ca utilize these for staff schedulig. As people are the most expesive resource i a call ceter, it becomes imperative that staff schedulig is as optimal as possible. Arrivig at the optimal umber of call ceter agets, the skills they should possess ad the schedule they should follow is essetial for achievig a high level of customer satisfactio ad keepig staffig costs low. Several tools i the market attempt to demystify work variatio ad arrive at the right staffig fit. These tools largely use aalytical models developed by Erlag ad Palm to arrive at staffig requiremets. These are models are ideal durig iitial operatios of a call ceter where there is little measured data available. However, oce more data is available; use of simulatio will yield more accurate results. A call ceter maager eeds a tool/solutio that hadles a call ceter i its etirety. The tool/solutio should use simulatio, to remove assumptios made i aalytical models ad factor i the complex behavior of call ceters. The tool/solutio should be capable of performig what-if aalyses towards predictive isights for better schedulig. This paper illustrates such a tool/solutio ad some key what-if aalyses, which will empower a call ceter maager schedule optimally ad drive profits.

2 About the Author Bey Mathew Bey Mathew is a Seior Scietist with TCS Iovatio Labs- Performace Egieerig, Tata Cosultacy Services (TCS). His expertise is i discrete evet simulatio i the fields of computer systems, call ceters ad busiess processes. He has also worked i the areas of performace measuremet, testig ad file systems performace, databases ad middleware. Bey Mathew holds a Masters Degree i Reliability Egieerig from Idia Istitute of Techology Bombay. 2

3 Table of Cotets 1. Itroductio 4 2. Calculatig Staffig Requiremet 5 3. Call Ceter Modelig ad Simulatio 7 4. Coclusio 11 3

4 Itroductio A call ceter is a people itesive busiess. Ceters usually work 24/7 i three shifts. Brigig i the right umber of people for each shift ad keepig them optimally employed is a tough job. Overstaffig leads to idle staff ad drives up costs, while uderstaffig leads to customer dissatisfactio ad overworked staff. This ca lead to higher turover i a attritio plagued idustry. Call ceter maagers may be familiar with the umber of factors that ifluece customer ad staff experiece at a call ceter. The call volumes are ot fixed ad chage durig the course of a day ad week. The call volumes are also depedet o seasoal aspects ad evets such as product lauches ad promotios. The call ceter staff work i shifts ad eed to be give time for breaks, meetigs, traiigs ad meals durig their workig hours. To add to this, call ceter staff have differig sets of skill ad productivity. It is importat to accurately predict customer experiece ad staff experiece takig ito cosideratio all these ifluecig factors. Call ceters have tried to come up with aget schedules based o predicted call volumes by usig plug ad play products or by buildig their ow. Before we preset the case for fie graied modelig, let us examie the commo approach to schedulig. Take a look at Figure 1 which shows the compoets of a typical call ceter. Iboud calls, iitiated by customers callig i to the ceter, are first aswered by a Iteractive Voice Respose (IVR) uit. If all truk lies are busy, the call may be blocked. Customers may be able to complete the service iteractio at the IVR. If ot, the calls are passed from the IVR to a Automatic Call Distributor (ACD). The ACD is a specialized switch desiged to route each call to a idividual aget; if o qualified aget is available, the the call is placed i a queue. A queued customer may abado call without receivig service. Agets Customer Skill Based Queues Truk Lies Telephoe Network (PSTN) PABX IVR IVR Figure 1: Call Ceter Termiology Agets 4

5 I a multi-skill call ceter, we distiguish various call types ad agets by their skill group, defied as the subset of call types they ca hadle. Skill-Based Routig (SBR), or simply routig, refers to rules (programmed i the ACD) that cotrol i real time the aget-to-call ad call-to-aget assigmets. If more tha oe aget with requisite skill is available, aget selectio criteria comes ito the picture. The selectio criteria ca also be programmed i the ACD. 1 Call metrics such as Abado Rate, Average Speed of Aswer (SoA), Average Call Hadle Time, Blockage, Aget Occupacy/Utilizatio, Staff Shrikage, Service Level, Cost per Call may play a part i schedulig. Calculatig Staffig Requiremet The required umber of staff ad the schedule they follow eeds to be calculated so as to meet the specified service level. So for calculatios, the miimum required data are service levels, projected call volumes ad the average call hadle time. The call volume projectios are usually i 30 or 60 miute graularity. The call volume projectios are based o historical work volumes ad several factors like product marketig ad campaigs, ew releases, seasoal activities or competitive activities. 1.1Aalytical Models Aalytical models are mathematical models that have a closed form solutio. Closed form solutio meas that the solutio to the equatios ca be expressed as a mathematical fuctio. Call ceters use aalytical models developed by Ager Krarup Erlag to solve problems of adequate sizig of telephoe exchages i the early part of the 20th cetury Erlag-C Formula The Erlag-C model, defies the probability that all agets will be occupied or the probability that a customer will have to wait i the queue. Wait Probability = A = ratio of arrival rate to service rate = umber of agets Erlag-A Formula Palm itroduced a simple way to model abadomet. He suggested erichig Erlag-C i the followig maer. Associated with each arrivig caller there is a expoetially# distributed patiece time. A arrivig customer ecouters a offered waitig time, which is defied as the time that this customer would have to wait, give that her or his patiece is ifiite. If the offered wait exceeds the customer s patiece time, the call is the abadoed; otherwise the customer awaits service. 1. View appedix for a explaatio of each of these metrics 5

6 Wait Probability = Where A = ratio of arrival rate to service rate = umber of agets λτ= abadomet rate μ= service rate 1.2 Limitatio of Aalytical Models Erlag-C model as well as Palm s variatio of the model called Erlag-A assumes that call arrivals follow # # Poisso distributio ad hadle times follow Expoetial distributio. Several research papers show that # these are ot valid assumptios. For example hadle times are more likely to follow LogNormal distributio. I additio to this assumptio, the followig complexities of a moder call ceter are ot take ito accout by these formulae: Icomig calls require a certai skill o the part of a aget ad ot all agets will possess all the required skills. Certai calls may be give higher priority. For example, a customer uder a gold service level will be give priority over customers who are at a silver service level. Customers reportig lost/stole credit cards may be give a higher priority. Based o traiig ad years of experiece, agets will have differet hadle times for the same type of skill. I other words, agets have differet productivity levels. A certai percetage of calls may get misdirected to a aget who does ot possess the skill to hadle the call. This problem is especially more i case of IVRs that use voice-recogitio software. The aget receivig misdirected calls eeds to redirect the call towards the right queue. This meas that some part of a aget s time may be wasted i hadlig the misdirected calls. May call ceters use advaced routig techiques to route calls to the most efficiet aget. At the ed of each period, there will be calls that are i various states of completio ad eed to be carried forward to the ext period. # Statistical Distributios: Poisso, Expoetial, Logoral are all statistical distributios. Statistical distributio iflueces the shape of the curve whe we plot data values o X axis ad the umber of times the data values occurs (frequecy) o the Y axis. For example, Normal distributio follows a bell-shaped curve. I the case of a call ceter, our data is hadle time or time betwee calls. Ay DES tool should have support for rich set of stadard distributios. The tool should also support if actual measuremets are provided i files, so that if there is difficulty i fittig the data to a stadard distributio, the measured data ca be used directly for simulatio. 6

7 1.3 Discrete Evet Simulatio (DES) Discrete Evet Simulatio (DES) ivolves buildig a model of a physical system that portrays state chages at precise poits i simulated time. This techique is used to predict the behavior of complex systems like maagemet of parts ivetory i a maufacturig uit, military combat, estimatig beds required i a hospital ad of course call ceters. Usig DES, the limitatios of aalytical model ca be addressed. The ext sectio shows how DES ca be employed for predictig performace of a call ceter. Figure 2: Queuig Network Diagram of Call Ceter Call Ceter Modelig ad Simulatio 2.1 Call Ceter Resource Models A queuig etwork represetatio of a call ceter is show i Figure 2. Each compoet show i the figure ca be modeled as a resource i a discrete evet simulator. Durig simulatio, the attributes (also called parameters i certai simulatio tools) of these resources ca be chaged. These attributes ifluece the resource s behavior i terms of time take by the resource to complete various tasks. I this case the task is icomig calls. The attributes eed be comprehesive to cover all the shortcomigs of aalytical models. Followig are the customer behavior attributes: Retrials: Percetage of customers that will retry i case their first call failed due to o availability of truk free telephoe lies. Abadomet of calls: The duratio for which the customer will wait before abadoig the call. Followig are aget attributes: Skill set: The types of calls for which a aget has requisite skill to hadle. Efficiecy: Relative efficiecy of each aget. Abseteeism: The percetage of agets that will be abset o a particular day. Time for breaks ad meetigs: The duratio ad times at which agets caot take calls as they are attedig meetigs or havig luch/tea breaks. 7

8 The call ceter ifrastructure attributes iclude capacity (truk lies), time required at IVR, queuig based o skill required ad priority. Oe mai ifrastructure attribute is the aget selectio criteria. Some optios for aget selectio criteria are: Uiform Call Distributio (UCD): A icomig call is routed to the aget who has bee idle for the logest time. Expert Aget Distributio (EAD): A icomig call is routed to the aget who is best qualified to hadle the call. Least Occupied Aget (LOA): A icomig call is routed to the aget whose utilizatio is the least. Least Skills: A icomig call is routed to the aget who has the least umber of skills. Calls are allocated preferably to a sigle skilled aget so as to preserve availability of agets who ca hadle more tha oe skill. Least Cost: Calls are allocated to a available aget, costig the least i terms of wages draw. Havig provisio for dyamically chagig the aget selectio methods based o how busy the call ceter would be ideal. For example, at low loads (say less tha 60% of agets are busy), UCD ca be used. Whe the call ceter load icreases, a advaced strategy like EAD could be used. 2.2 Ruig the Simulatio Before the simulatio ca begi, values eed to be assiged to the attributes of all call ceter resources. Here lies the stregth of simulatio. Give the large umber of attributes, ay sigle attribute or combiatio of attributes ca be chaged to carry out complex what-if aalyses. The chages made to the attributes, ifluece the results of simulatio. The mai metric that ay call ceter maager would be lookig for i the results would be the customer SLAs. The customer wait times, lost ad abadoed calls should be low ad the challege is to use the least umber of agets to achieve the same. 2.3 Cofidece i Simulatio Results Sice DES depeds upo the geeratio of pseudo-radom umbers, loger the simulatio rus, higher will be the cofidece i the simulatio results. I case of call ceters this meas that the simulatio eeds to be repeated several times util the desired cofidece iterval is met. So alog with the metrics, the solutio should also calculate the cofidece iterval. 2.4 Aalyzig Simulatio Results Oce we have the various call ceter metrics with the desired cofidece level, the report data ca be aalyzed. The metrics must be reported at short itervals, say for every 30 miute iterval. This will help us fie tue the staff schedule, meetigs, breaks. For example, if we fid that there are periods which show higher wait times for customers, we ca fid periods where agets have low utilizatio ad move their schedules to higher call periods. Some of the actios that ca be take based o a typical simulatio report are show i Table 1. 8

9 Customer Wait Time Aget Utilizatio Actio 2.5 What-if Aalysis It is crucial that the solutio should perform what-if aalyses. For istace the solutio should be able to project optimizatio for various possible scearios such as: Low High Ideal Situatio o actio required Low Low Reduce umber or agets or use this period for traiigs/meetigs High High Iadequate agets. Schedule more agets durig this period. Shift breaks, meetigs if preset to lea periods. High Low Agets with requisite skills are uavailable. Move agets with required skills to this slot ad move agets whose skills are ot required durig this period to other periods. Table1: Aalyzig Simulatio Results a sample Impact of chagig aget schedule: Schedule chages ca be doe to shift timigs of agets as well as to accommodate break ad meetig timigs. These timigs are chaged so that there are o meetigs/breaks scheduled durig peak hours. Staggerig of breaks (show i Figure 3) ca also help i improvig customer experiece. The tool should allow you to verify whether the chages have resulted i reductio i customer waitig times ad abados. Figure 3: Staggerig of Breaks 9

10 Impact of cross-traiig a few agets: Call volumes chage durig the course of a day. If there are sigleskilled agets ad we have sufficiet staff to cater to peak call volumes of each skill, these agets will be uderutilized durig o-peak hours. O the other had, multi-skilled agets will be able to keep chagig the call type they aswer based o which skill is i demad at ay momet. As show i Figure 4, it is especially helpful to cross-trai agets with a skill whose peaks coicide with troughs of their origial skill. Figure 4: Balacig Variatio i Call Volumes requirig 2 skills Impact of aget efficiecy chage: The overall efficiecy of a call ceter keeps chagig over time due to traiig, experiece gaied by agets ad also due to turover of agets (as show i Figure 5). New batch of recruits due to their lack of experiece are expected to have lower efficiecies. O the other had targeted ad itesive traiigs ca improve aget s efficiecies. If the solutio ca simulate both scearios, a maager ca take decisio o timig these appropriately, so as to avoid a fall i efficiecy. Figure5: Chagig Efficiecy Dedicated Teams v/s Queue Priority: High priority customers ca either be hadled by offerig priority while waitig i queue for free agets or their calls could be hadled by a dedicated team who oly hadle high priority customers. Both the methods (as show i Figure 6) have their advatages/disadvatages. The tool should be able to simulate these optios, so that you ca decide o what is best for your call ceter ad customers. 10

11 High Priority Calls Call Cetre Ifrastructure Regular Calls Usig Priority Queue High Priority Calls Call Cetre Ifrastructure Regular Calls Usig Dedicated teams Figure 6: Hadlig High Priority Customers 3. Coclusio DES based solutio has may advatages over the Erlag formula based methods for simulatig call ceter scearios ad predictig customer ad aget experieces. It ca support a wide rage of attributes of a call ceter ad hece helps i carryig out what-if aalyses of call ceter situatios. The tool or solutio that ca geerate huge umbers of permutatio ad combiatio will give a call ceter maager may optios to optimize operatios ad cut costs. It should also report metrics that cover several aspects of a call ceter operatio such as: customer satisfactio, aget utilizatio ad costs. Such a solutio becomes a comprehesive decisio makig tool for a call ceter maager, ad will be more accurate tha a plug ad play tool or a simple i-house scheduler. 11

12 Appedix Termiology ad Explaatios Call Ceter: A Call Ceter is a cetralized office used for the purpose of receivig ad trasmittig a large volume of requests by telephoe. A call ceter is operated by a compay to admiister icomig product support or iformatio iquiries from cosumers. Outgoig calls for telemarketig, clietele, product services, ad debt collectio are also made. I additio to call ceters, collective hadlig of letters, faxes, live chat, ad s at oe locatio is kow as cotact ceters. Call Ceter Metrics Out of the etire rage of metrics used at call ceters, oly those ifluecig staffig size is listed as follows: Blockage: Idicates what percetage of customers will ot be able to access the ceter at a give time due to isufficiet etwork facilities i place. Most ceters measure blockage by time of day or by occurreces of all truks busy situatios. Abado Rate: Percetage of calls abadoed while waitig to be aswered. Abado rate is ot typically a measure associated with commuicatios, as does ot abado the queue oce it has bee set, but it does apply to web-chat iteractios. Average Speed of Aswer (SOA): Average time (usually i secods), take for a call to be aswered by a aget. This is oe of the most importat metrics as far as customer service level is cocered. The percetile value of SOA is also sometimes referred as Time Service Factor (TSF). 80/20 TSF meas that 80 percet of the customers have less tha 20 secod SOA. Service Level: Percetage of calls aswered withi a defiite timeframe. This is same as TSF. Aget Occupacy/Utilizatio: Aget occupacy is the measure of actual time a aget is busy o customer cotacts compared with available or idle time. It is calculated as ratio of workload hours to staff hours. Staff Shrikage: The amout of time staff is uavailable for hadlig calls due to traiig, time off, breaks, etc. Average Call Hadle Time: Average time take by aget to complete a call. Cost Per Call: This is usually the cost of staff cost per call. However, some call ceters may also iclude other costs like cost of telecom ifrastructure, power ad other rets. 12

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