Next please

Next please

Next time you're fuming in a queue, think about the science that sits behind this part of customer relations.

It's just on noon. At the local food hall, fast-food outlets are gearing up for the lunchtime rush, the bank of registers waiting to open quickly

as the queues form. Unless you're at a too-cool-for-school nightclub, no

one likes a queue - except, maybe, a rival business.

"We have a saying: two-deep is too deep," says Jeff Fisher, CEO of

Portuguese chicken restaurant operator Oporto.

"You get your rushes where everybody comes in within a two-hour window.

Once you get more than two people in a queue, the queue is too long and

they will basically walk past you and go to the next outlet."

Queuing is an inevitable part of modern life. Whether it's the chicken

shop, the bank or the call centre, it is one of the most vexing

customer-management issues. So it is no surpise that it's become the

object of a great deal of intellectual energy and management attention.

The problem is that serving customers costs money. If you had all the

money in the world, you could serve them all at once and no one would

have to wait, but that wouldn't be very profitable. The nub of the

queuing dilemma is: how long can you afford to make your customers wait

before you lose too many and your revenue walks out the door?

How long is too long?

The first man to make a formal stab at answering this question was Agner

Krarup Erlang, a Danish mathematician and engineer who worked for the

Copenhagen Telephone Company. Erlang gave birth to the modern science of

queuing theory in 1909 when he published a paper that addressed the

question of how many circuit boards and operators were needed to provide

an acceptable telephone service.

At the beginning of the 20th century, telephony presented a classic

queuing problem. Calls came in, but in unpredictable volumes. How long

callers had to wait and how many calls were successful depended on a

number of variables: how many calls were coming in at any one time, how

long they went on for, how many circuits were available to host those

calls, and how many operators there were to switch calls between


Erlang's solution provided the foundation for the science

of queuing that is now applied to problems across the economy, and with

which we are unknowingly manipulated each day as we go about our


The fundamentals of the problem are always the same, however. Whether it

is outpatients waiting to be seen at a hospital or instructions waiting

to be processed by a computer chip, customers arrive at varying rates

throughout the day, and they are served by "parallel processors" which

take a varying amount of time to serve each.

The length of a queue is determined by the speed of arrival of the

customers and the amount of time it takes to serve them. Because both

factors - arrivals and service times - are probabilistic, rarely will

the arrival of customers exactly match the ability of the operation to

service them. And figuring out how to minimise costs while maximising

the number of customers served is a complex simultaneous equation

problem that can be approached in a number of ways

Andrew Hume, chief operating officer at Australia's largest call centre

outsourcing operation, Salesforce, says cost is at the root of it all.

"You could answer every call that came in immediately if you had enough

operators. But that is often simply not commercially viable."

Salesforce's 5500 employees around the country deal with hundreds of

thousands of calls each day, many of them involving irate customers who

have had to wait, as far as they are concerned, too long to get on to

someone who can help them.

The trick is to get customers routed as efficiently as possible to

someone with the skills to help them - which is why call centres so

often rely on Interactive Voice Response (IVR) systems ("press 1 for

mortgages, press 2 for credit cards, press 3 to be driven completely

bonkers... ).

The key to good customer management, says Hume, is to ensure that when

the call is finally put through, it is to a sales agent who can be

helpful and who is trained in handling irate customers. IVR system

design is crucial for companies that offer multiple products. Who hasn't

been lost in a maze of digits, forgotten which number relates to which

option, and screamed at the handset to deliver them a human being?

Bad IVR design is a problem that is spawning its own technological

solutions. Mitel, a Canadian company, has recently been granted a patent

on technology that puts angry callers straight through to operators with

conflict resolution skills. Anyone who pushes too hard at the phone

keys, speaks too loudly or in a stressed tone, or uses swear words

indicating anger, will be immediately connected to an operator with a

soothing manner.

Salesforce uses a workforce management system that forecasts call volume

arrivals and patterns, and matches that against the number of call

centre operators available, the number of hours they are available to

work, and their individual preferences.

"It layers all that and comes up with a brick wall of rostering times

and numbers," says Hume. "At the root of it all is Erlang."

Telephone queues are one thing, but for many organisations the dilemma

is how to manage large numbers of customers physically queuing for

service. In addition to the variables related to customer volumes,

service times, addition and attrition, these organisations need to

consider the physical attributes of the customer space, which provides

limitations or opportunities for customer service that can hinder or

help queuing issues.

Australian Customs, for example, processes tens of thousands of

passenger arrivals and departures each day. But how they do that is

determined by complex models that take into account the volume of

passenger arrivals, where they are coming from, and the shape of the

passenger arrivals and departures lobby.

"Together with the airport company, we engage external consultants to

model how we can ensure our part of the total processing is done as

efficiently as possible," says Gayle Brown, national manager, airport

operations north, for the Australian Customs Service.

As in a call centre, the most efficient way to process passengers is to

separate them as much as possible into different categories and have

them processed by specialist customer service people who focus on their

particular issues. That is why airports split customers into locals and

foreigners. Locals are processed more quickly, and those customer

service agents who are freed up after all the locals are dealt with can

then turn their attention to foreigners.

Rostering remains the most crucial piece of the puzzle. Customs changes

its rosters twice yearly to adjust for the northern summer and winter

travelling seasons. It then adjusts this roster daily to take into

account the forecast passenger numbers. The biggest challenge is the

large number of upstream variables, such as flight delays and

redirections, weather, international incidents, and fluctuating

passenger numbers that can impact on standard rostering plans.

Take a number

Nearly a decade ago, the banking sector was hit with a community

backlash against branch closures and front-line staff cuts. Growing

customer fury over queues and fees for over-the-counter transactions

eventually sparked a concerted effort behind the scenes for the banks to

improve their systems.

At the banks, long lunchtime queues to deposit cheques and withdraw cash

are being dealt with by directing customers into self-service channels -

internet banking has been a big hit, for example. But as the backlash

against branch closures attests, there are still numerous transactions

that demand the human touch. For these, banks have increasingly been

relying on technology to keep costs down.

At the National Australia Bank, managers use a software tool that

crunches centrally held data about queue lengths and waiting times -

essentially it is a rostering and management system for understanding

branch profitability. The bank's branches are managed on a

franchise-type arrangement, with local managers adjusting variables such

as rostering to manage their own productivity. But NAB's general

manager, deposits and retail transactions, Lisa Gray, says head office

lays down service standards and keeps tabs on customer volumes in each

branch by the half hour, checking that branches have the right number of

staff with the right kind of skills in place.

ANZ began installing take-a-number machines in 1999 and has since

expanded the system to 40 per cent of its branches. It also allows

managers to instantly access queuing performance at any of their


"To a certain extent you can make decisions on staffing levels

intuitively, but from an executive level the system gives me a record of

each branch and how it is tracking, and what needs improving and

changing stands out," says Louis Hawke, ANZ's managing director, retail

banking. The bank is looking to take the system into other areas of

branch performance, into teller management, enquiry counters, staff

scheduling, and to help give greater insight into what kind of

transactions happen when, as part of its branch management strategies

into the future. Just remember to take a number.


Maths or models

There are two fundamental ways to deal with a queuing problem.

Maths: You make assumptions about the underlying distribution that

governs the arrival of customers, their behaviour while they are in the

queue (will they give up and go away after they have waited for a

certain amount of time, on average, or not?), and the average amount of

time it will take to serve them. Then you solve the equations to find

out on average how long each customer will have to wait in the queue,

how much serving each customer in a timely manner is worth to you, and

therefore how many "parallel processors" you need to employ.

Simulation: In effect you build a model of your operation inside the

computer and see what happens. You don't need the complex mathematics,

but you do still need to make assumptions about the behaviour of your

customers and service agents, and you can use the model to see what

happens when you change particular variables, and play with it until you

get a feel for what the ideal service environment would be.

© Fairfax Business Media

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