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Empowering Contact-Center Agentsthrough Preference-Based RoutingTM

Michael E. SisselmanCo-Founder & CEO

[email protected]

Ward WhittCo-Founder & Chief Scientist

[email protected]@columbia.edu

December 2004

Empowering Contact-Center Agents through Preference-Based Routing

SeatLink improves the overallperformance of a contact center, byallowing agents to influence therouting of inbound interactionsbased on their personal preferences.

Empowered, engagedagents drive contactcenter improvements inannual retention, dailyattendance, shiftendurance, andcontinual adherence tostaffing schedules.

By improving agentsatisfaction,corporations cansimultaneously increasethe quality of customerservice and reduceoperating costs.SeatLink is the firstcompany to make agentpreferences an integralpart of routingtechnology. In doingso, it extends traditionalload-based routing andthe more recent skill-based routing, toachieve patent-pendingPreference-BasedRoutingTM. As a result,customer interactionsare handled promptly,appropriately, and withunprecedented success.

Traditional Contact Centers.............................................2

Dissappointing Performance.............................................3

The Process is the Problem..................................................4

Who Takes the Call?............................................................5

The SeatLink Solution: Preference-Based Routing ..................7

Preference-Need Alignment.............................................8

Win-Win Value Proposition................................................10

Implementing our Vision...................................................11

About the Authors.............................................................12

Table of Contents

Copyright © 2004 SeatLink, Inc.

Traditional ContactCentersIn today’s economy, many serv-ices are largely tele-services, inthat the people receiving theservice are remote from thoseresponsible for providing it. Acontact center is a collection ofcustomer service representa-tives, or agents, that provide aninterface between the serviceprovider and its remote cus-tomers.

Usually a contact center is situ-ated in a large space, perhapspar titioned into cubicles, whererows of agents wearing head-sets sit in front of computerscreens. With the dramatictechnological advances ofrecent years, the nature of con-tact centers is evolving. Thetelephone is no longer the onlymeans of interaction; use ofalternative media such as email,

web pages and web chat areon the rise. Yesterday’s tele-phone call center has becometoday’s multimedia contact cen-ter.

More than that, communicationequipment - including privatebranch exchange (PBX), auto-matic call distributor (ACD),and Voice over IP (VoIP)Gateway - enable contact-cen-ter resources to form a cohe-sive unit without necessarilybeing under one physical roof.These so-called vir tual contactcenters range from small, inter-connected groups of traditionalcall centers possibly located ondifferent continents, to a largenumber of individual agentsworking out of their ownhomes.

A number of information tech-nology (IT) support systems areused to ensure that customersare being served adequatelyand that business objectives arebeing met. For instance, cus-tomer-relations management(CRM) systems, provided bycompanies such as Siebel, main-tain a database of previousinteractions for each customer.This puts relevant customerinformation at the agent’s fin-ger tips through screen-pops,speeding the transaction, aswell as revealing the potentialsales opportunity.

Workforce-management(WFM) systems, provided bycompanies such as IEX andBlue Pumpkin, are designed toensure that the right number of

“The nature of contact centers

is evolving, but enormous problems

remain unsolved.”

Page 2

Figure 1: Contact Center Agents


Customer Service

Customer Service


agents, with the right skills, is inthe right place, at the righttime. WFM systems utilizesophisticated algorithms to per-form forecasting, staffing andscheduling, over periods rangingfrom weeks to days to half-hour increments. These sys-tems also help manage agentperformance and compensa-tion.

As the environment of contactcenters has become increasinglycomplex, the role of agents hasexpanded. Call centers havealways handled different kindsof interactions, like salesinquiries, technical support, andcustomer service. Today’s con-tact centers, however, are morelikely to also manage differentpromotions requiring special-ized knowledge, or even tohandle service functions for anumber of very different com-panies at the same time.

Centers may even offer servicein different languages, requiringagents with language fluency. Inthis environment, it is rarelyfeasible for every agent to han-dle every type of servicerequest.

DisappointingPerformanceDespite - and perhaps par tlybecause of - advanced technol-ogy and systems, the perform-ance of contact centers oftenfalls far short of expectations.Contact centers are oftenstaffed inefficiently. Currentrouting methods cause someagents to be unproductive,while others are overworked.As a consequence, some serv-ice requests are handled ade-quately, but too many othersare not. Ultimately this leads todissatisfied customers, andreduced profits for the firm.

We have identified four com-monly reported conditionsrelated to the agents that cor-relate with lackluster contactcenter performance: excessivechurn, absenteeism, fatigue, anddeviation from scheduledassignments.

Agent-retention problems arevery common, with most con-tact centers reporting turnoverrates of 20% to 200% perannum. The costs associatedwith this “churn” are significant.Most obvious are transitioncosts - the cost of hiring tem-porary help, of recruiting andtraining new agents, of adminis-

“Four conditions are symptomatic of

lackluster contactcenter performance.”

Page 3

Figure 2: Adverse Contact Center Conditions


1 High Churn(poor retention)

2 High Absenteeism(poor attendance)

3 High Schedule Deviation(poor adherence)

4 High Fatigue(poor endurance)


trative tasks like stopping bene-fit deductions, and so for th. Ithas been estimated that transi-tion costs alone can be asmuch as 100%-200% of anagent’s annual compensation.But there are also long-termproductivity costs, which arisewhen too many agents are in alearning period at any onetime.

The impact of high turnover isactually greater than it seems,because this conditioninevitably is associated withhigh degree of uncer tainty. It isdifficult to predict the long-term effects of recruiting andtraining programs when reten-tion levels vary significantly. Tohedge against potential short-falls, firms may over-compen-sate by recruiting and trainingmore agents than are actuallyneeded.

As with high turnover, manycontact centers report high lev-els of absenteeism. Agents onthe payroll simply don’t showup for scheduled hours. Liketurnover, high absenteeismrates can be compensated forby overstaffing, but at significantcost. And again, the uncer taintyassociated with absenteeismmakes planning difficult. If wesuppose an average daily absen-tee rate of 10%, the actualnumber of no-shows may fluc-tuate between 5% and 15%.Staffing algorithms tend to beless effective when agents arenot committed to showing upin the first place.

High turnover and absenteeismare clear signs that many agentsare dissatisfied with their jobs.And unhappy workers, webelieve, tend to be less effec-tive in the work that they do -as evidenced in poor scheduleadherence and high shiftfatigue. While these agentsshow up for work, they may befrequently “absent”, in the sensethat they often take breakswhen they are supposed to behandling service requests.Similarly, they are slower andless aler t. Their productivitydrops off precipitously by themiddle of the day compared totheir peers.

The Process is theProblemDiscussions about performanceproblems often lead to criticismof the agents. Managers allegethat agents are unproductive,

“High turnover andabsenteeism are clear

signs that agents aredisenfranchised.”

Page 4


Figure 3: Staffing Challenge


lack motivation, and have apoor work ethic. They thenput even greater pressure onthe agents, by imposingByzantine shop rules, “tight”scripting of customer interac-tions, and even recording everycall. Not surprisingly, thisheavy-handed approach oftenleads to even fur ther declinesin performance, as agents feeleven more disenfranchised.

But are agents themselves reallythe root cause of the problem?Consider the history of thequality movement in manufac-turing, beginning in the 1950s.At the time, inefficiencies inmanufacturing were blamed onworker inadequacy, until pio-neers W. Edwards Deming andJoseph M. Juran proved thatimproved processes are the keyto improved performance.Their well-known 85/15 rulesays that at least 85% of prob-

lems are due to deficiencies inthe system itself, and less than15% to faults in the workers.

In our view, disappointing con-tact center performance islargely attributable to faultyprocesses and not to individualagents per se. Effective solu-tions should be based on prin-ciples of work redesign andprocess engineering rather thanreplacing the agents. We advo-cate finding ways to improvethe quality of the work envi-ronment for agents, whetherthey sit in large, factory-likecontact centers or at home.We propose that they beinvolved in decision-making andindeed every aspect of opera-tions. In our view, it all beginswith agent involvement in callrouting-the operational corner-stone of every contact center.

Who Takes the Call?Suppose that a French-speakingcustomer calls at 7:45 AM witha question pertaining to cus-tomer service. Which agentshould answer the call, and howis the inbound call routed tothe agent?

In the past, calls were assignedto agents by a system of load-based routing (LBR), designedto ensure that agents respondto service requests promptly.Typical performance targets aresuch that 80% of the requestsare answered within 20 sec-onds. Under this system, allagents belong to a single agentpool, and new service requestsare assigned to the agent who

“We advocate improving the work

environment foragents.”

Page 5


Figure 4: Customer Calls into Contact Center

Purpose:

Language:

Media:

Service

French

Telephone


has been idle the longest.Conversely, when all agents areengaged, new requests areplaced in a queue. Then whenan agent becomes free, the sys-tem routes the caller who hasbeen waiting the longest.Ultimately, the agent may beable to answer the question,but if not the call is transferredto an agent that has the requi-site skills.

LBR is adequate for companiesthat receive only one type ofcall. But most contact centerstoday handle requests for manydifferent purposes and acrossdiverse media. Modern ACDsare capable of assigning calls tothe most appropriate availableagent in the first place. Oncethe identity of the caller andthe purpose of the call aredetermined, the call is routedto the most qualified availableagent. In other words, contact

centers have gone beyond tra-ditional load-based routingmethods to a system of skill-based routing (SBR), which isdesigned to ensure that servicerequests are resolved by appro-priate agents on the first call.

In Figure 5, we illustrate howthe ACD routes new servicerequests to idle agents usingSBR. Typically agents are organ-ized into multiple skill-groups.In this example, some agents(e.g. Harry and Mary) can han-dle only one type of request,while others (e.g. Sue, Dick,Tom and Jane) can handle twotypes (at either a primary orsecondary level). One agent(Mike) is proficient in all threeskills. Agents with primary skillsin a given area are given priori-ty for requests of that type,even if another agent has beenidle for more time. When anew sales inquiry arrives, forinstance, it will be assigned tothe agent with “sales inquiries”as a primary skill who has beenidle the longest - that is, toMike. Only if Mike and Suewere both engaged would therequest be routed to a second-ary-level agent - in this case,Dick. Likewise, if there is aqueue, an agent who becomesfree is assigned the request inhis primary area that has beenwaiting longest, or in his sec-ondary area if no calls in hisprimary area are waiting.Agents do not handle requestsin areas they are not trainedfor, unless the longest waitingtime exceeds a pre-determinedthreshold.

“Conventional skills-based routing

completely ignoresagents’ discontent.”

Page 6


Figure 5: Skill-Based Routing Schematic


Though SBR is more effectivethan LBR, it completely ignoreswidespread agent discontent.Agents remain “passive” in thesense that they have no controlon the types of interactionsthey are handle. Another rout-ing technology-that directlyincorporates agents’ call-han-dling preferences in real time-isneeded to improve overall con-tact center performance in ameaningful way.

The SeatLink Solution:Preference-Based RoutingThere is no disagreement that,whenever possible, a call shouldbe routed to an agent that hasrequisite skills to handle theservice interaction. But thereare usually multiple agents thatsatisfy this condition who aresitting idle, and we can do bet-ter than random assignment.

Our central asser tion is that acall should be routed to thatagent that prefers to handle itthe most. Perhaps some agentsprefer to handle customerservice calls rather than salesinquiries. Perhaps some agentsprefer to handle French calls inthe morning, and then shiftover to English in the afternoonwhen they are tired. One thingis cer tain: preferences are asvaried as the agents themselves.They have strengths and weak-nesses, likes and dislikes. Givencross-training of skills and ade-quate staffing in the first place,why not take agent preferencesinto account, as long as overallperformance targets are met?

A simple case is illustrative.Suppose that traffic volumesare such that we need 5 agentsto speak English, and 5 agentsto speak French during a par-ticular shift. We assume thataltogether we have 10 agentsworking, of which all 10 speakboth English and French. If 5of the agents happen to preferspeaking French, while theother 5 agents happen to pre-fer speaking English, then wecan do much better than ran-dom assignment. We can meetof all of the goals of the sys-tem, while responding toagents’ preferences fully.By soliciting and then respond-ing to preferences, managersdemonstrate that agents’ contri-butions are valued and thattheir active par ticipation is criti-cal to the core operations ofthe contact center itself. Whilethis isn’t a substitute for decent

“Certainly preferencesare as varied as theagents themselves.”

Page 7


Figure 6: Agents Vary by Skills and Preferences

Jane Mary Mike

Profile*Chicago*Age 32*3 years experience

*Chicago*College Graduate*Sailing hobby

*Salt Lake City*Retired captain USAF*BS Engineering

Skill*Billing, Service*English, French, Russian*Telephone

*Sales, Technical, Service*French*Telephone, email

*Service, Billing*English, French, Spanish*Telephone, email, lM

Preference“I prefer to start myday with billing callsto meet my quota.”

“In the morning, Iprefer to answer

emails.”

“Le matin je prefereparler fracais.”


compensation and safe workingconditions, agents that areinvolved in operational issuesfeel empowered, and this ulti-mately translates to improve-ments in personal and groupperformance. As a result, cus-tomer interactions are handledpromptly, appropriately, andwith unprecedented success.

SeatLink is the first company tomake agent preferences anintegral par t of routing technol-ogy. In doing so, it extends tra-ditional load-based routing andthe more recent skill-basedrouting, to achieve patent-pending preference-based rout-ing (PBR).

To see how this works, consid-er that individual agents aredivided into groups based onboth the proficiency and thedesire to handle a par ticulartype of service request. These

“preference” groups are some-what smaller than the previousskill groups, because someagents prefer not to handlecer tain interactions. Dick andJane, for instance, do notappear in the sales-inquiry pref-erence group, Sue does notappear in the technical-supportpreference group, and Mikedoes not appear in the cus-tomer-service preferencegroup.

Priority for assigning calls withina preference group is deter-mined by a numerical prefer-ence score. In the tablesabove, for example, a new salesinquiry would now be routedto Sue instead of Mike, becauseSue’s preference score - 87 - ishighest amongst the agents inthat preference group.

Preference-NeedAlignmentWhile it is desirable to incorpo-rate agents’ preferences intorouting methods, at times itmay be impossible to do that.A case in point is whenresources on hand are insuffi-cient to handle demands on thesystem as a whole, as illustratedin the following example:Suppose, once again, that trafficvolumes are such that we need5 agents to speak English, and 5agents to speak French during apar ticular shift. Now weassume that altogether we have10 agents working, of which 5speak English only, and 5 speakboth English and French. Nomatter what the preferencesare of these bilingual agents

“Priority for assigningcalls is determined

by a numerical preference score.”

Page 8


Figure 7: Preference-Based Routing Schematic


(vis-à-vis language), we mustrequire that they speak French,since otherwise many callscould not be handled at all.This simple example demon-strates that preferences can beincorporated into routing onlyif there are adequate resourcesalready in place to handle thecall volumes.

Preference-need alignmentchanges continuously inresponse to both changingagent preferences and trafficloads. Accordingly, we viewPBR optimization more in thecontext of continuous processimprovement than a calculationof a steady-state solution. Attimes, it may also be desirablefor management to take cor-rective actions to respond toresource imbalances by provid-ing various incentives to “influ-ence” agents’ preferences.

One can envision a dynamic,interactive, feedback-loop.

Star ting at the upper left ofFigure 8 and moving clockwise,interaction-handling needsand/or incentives are communi-cated to the agents. Theseneeds and incentives may betailored to individual agents,and their expression can takedifferent forms. For instance,agents might be given detailedinformation about current net-work conditions and recentbusiness results. Alternatively,they might get only a manage-ment preference score, indicat-ing how important it is to man-agement for the agent to han-dle different kinds of interac-tions.

Agents then declare their pref-erences in response to thisinformation. These declarationscan also take different forms,from a simple ranking of tasksto a numerical agent preferencescore, showing how much theagent prefers handling one kindof interaction compared to oth-ers. By taking agents’ prefer-ences into account, the routingalgorithm is modified. This inturn affects network conditionsand the business results as well.Then once again managementre-considers its instructions tothe agents, and the wholeprocess star ts over again.

Keep in mind that while prefer-ence-based routing offers aradical new approach to con-tact center management, thisapproach does not have to be

“We envision adynamic, interactive

feedback-loopfor alignment

of preferences with need.”

Page 9


Figure 8: Preference-Need Alignment

Agents share insightsand declare preferences

NetworkConditions

Change

BusinessResults

Change

(Revised) incentivesand instructions

offered to agents

Routingalgorithmmodified


introduced in a reckless man-ner. The system should includecontrols that allow managers toswitch back to skill-based rout-ing whenever they deem itadvantageous to do so. Moregenerally, it is designed to allowmanagers to incrementallyincrease or decrease the extentto which preference-basedrouting is applied. Managers willbe able to gradually increasethe level of preference-basedrouting as the benefits becomeclear, without risking negativeoutcomes.

Win-Win ValuePropositionLBR is a simple approach torouting: all agents belong to asingle agent pool, and newservice requests are assigned tothe agent who has been idlethe longest. Unfor tunately LBRleads to inefficient use of

agents, and the inability toresolve issues on the first call.SBR, on the other hand, effi-ciently distributes calls to theagents that are best qualified tohandle them. However, SBR iscompletely ignores widespreadagent discontent.

These earlier approaches areeclipsed by a 3rd generationmethod. Only preference-based routing captures the rich-ness of actively incorporatingagents’ preferences to achieveoptimal performance andreduced costs. Empoweringagents through PBR improvescontact center efficiency.Empowered, engaged agentsdrive contact center improve-ments in annual retention, dailyattendance, shift endurance, andcontinual adherence to staffingschedules.

But no less important is thefact that empowered agentshave more meaningful interac-tions with customers, therebyimproving the real quality ofservice. The idea is not only tomake sure service requests areanswered promptly and compe-tently, but to make contact-cen-ter interactions more genuinelysatisfying for agents and cus-tomers alike.

SeatLink enhances value forstakeholders across the entireinteraction-chain. For agentswe reduce stress, anxiety,burnout and boredom.Contact supervisors benefitfrom using our powerful tool toallocate work equitably and

“Only preference-based routing

achieves optimal performance and

reduced costs.”

Page 10


Figure 9: Summary of Routing Technologies

Phase Algorithm Agents Resolution Quality

1stGeneration

Load-BasedRouting

Passive Prompt PP

2ndGeneration

Skill-BasedRouting

PassivePrompt andappropriate PPPP

3rdGeneration

Preference -BasedRouting

ActivePrompt,

appropriate,and satisfying

PPPPPP


transparently. Executives bene-fit from the higher quality ofservice and productivity thattranslate to bottom-line finan-cial results. By improving agentsatisfaction, corporations cansimultaneously increase thequality of customer service andreduce operating costs. PBRcreates a compelling, win-winvalue proposition for compa-nies and agents alike.

Implementing ourVisionSeatLink Preference Server is astand-alone software-applica-tion that manages the datacommunications between vari-ous platforms in the contactcenter (e.g. ACD, CRM andWFM) and the agents.

A web-based toolbar on theagents’ screen facilitates bi-direction communications

about incentives and prefer-ences. On the back end, thesystem incorporates agents’preferences, together with busi-ness priorities and networkconditions, to derive optimalrouting instructions. A mathe-matical program is used tomaximize the overall effective-ness of the contact center, sub-ject to constraints on the num-ber of required agents handlingeach type of call. Preference-based routing is implementedwhen these instructions areuploaded to the ACD.

“A web-based toolbar is used to

communicate incentives and

preferences withthe agents.”

Page 11Copyright © 2004 SeatLink, Inc.


(Applications)

Incentives Preferences Results Guide

“I prefer to speak French in the morning.”

Figure 10: SeatLink Agent Toolbar

About the AuthorsMichael Sisselman,Co-Founder &CEO, has workedin telecommunica-tions for over 20years. He is an

accomplished entrepreneur(e.g. HotLens, MenuFax), andhas worked as an executive inthe corporate world (e.g. AT&T,AIG Telecom, Long DistanceInternational). Michael has anMBA from Columbia University,and a M.Sc. in Econometricsfrom London School ofEconomics.

Ward Whitt,Co-Founder &Chief Scientist, is aProfessor atColumbiaUniversity in the

Department of IndustrialEngineering and OperationsResearch. He has over 25years experience as aresearcher (e.g. AT&T, Bell Labs,and Avaya). He has beenawarded 8 patents, with 4 morepending, and has published over275 ar ticles related to: contactcenters, queues, games andoptimization, simulation, andcommunication networks.Ward has a Ph.D. from CornellUniversity.

The authors wish to thank Mohd Asif,Peter Bamberger, and Michal Biron fortheir insightful comments. A warmthanks to Meira Ben-Gad and CandiClare for their production assistance.

About SeatLinkThe mission of SeatLink, is to“link” customer service “seats”everywhere to business objec-tives. By allowing agents toinfluence the routing ofinbound interactions in real-time based on their personalpreferences, SeatLink improvesthe overall performance of acontact center. Empowered,engaged agents drive improve-ments in annual retention, dailyattendance, shift endurance, andcontinual adherence to staffingschedules.

SeatLink is the first company tomake agent preferences anintegral par t of routing technol-ogy. In doing so, it extends tra-ditional load-based routing andthe more recent skill-basedrouting, to achieve patent-pending Preference-BasedRoutingTM. SeatLink servershelp corporate contact centersto realize both higher quality-of-service and increased pro-ductivity.

SeatLink, Inc. was launched in2004, and is located in NewYork. More information canbe found on the web at:

www.seatlink.net

To contact your SeatLink representative, please call:

1-877-321-SEAT


Linking Customer Service Seats EverywhereTM

Copyright © 2004 SeatLink, Inc. All rights reserved. Information in this document is subject to change without notice. SeatLink, the SeatLink logo, the globemark design,Preference-Base Routing, and Linking Customer Service Seats Everywhere, are trademarks of SeatLink, Inc. All other trademarks are property of their owners.

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