October 1998
Why Cookie-Cutter Performance-Measurement Metrics Can
No Longer Stand Alone
BY DEBRA N. DISBROW, CALLCENTER TECHNOLOGY, INC.
What is performance in a call center and how should it be measured? While performance
is one of the most commonly implemented metrics, the ways in which it is measured rarely
reflect the true status of the call center. Performance can be many things to many people,
but there's one aspect most experts will agree upon: it's not measured as effectively as
it could be. This is due, in part, to the increase in the number of metrics being
generated in call centers. Most metrics, like quality monitoring evaluation scores, are
used as stand-alone indicators without regard to the synergies possible from combining
multiple metrics. The practice of utilizing single, generic metrics to define performance
of agents, groups or sites has become inadequate. Before offering a solution, let's take a
look at some typical performance measurements.
Productivity is one of the oldest and most generic measures of performance. Anyone in a
call center can tell you how to measure agent productivity. Average handle time, number of
calls handled and first call resolutions are all typical ways to assess agent
productivity. The data used for this measurement usually comes from an ACD.
At the other end of the spectrum, customer satisfaction is one of the newest measures
of performance. One might assume that customers are satisfied if wait and handle times are
short and service levels are high. Monitoring a related metric, such as sales volume,
might reveal a completely different picture. Call centers have realized that direct and
frequent measurements of customer satisfaction need to become the norm to accurately
assess the situation.
Customer satisfaction tracking by call centers is conducted in a variety of ways. Some
call centers send postcards to recent customers. Others conduct random phone calls and
request that customers complete online surveys. Some call centers select customers for
follow-up based on initial call type, agent or skill set. Still others use word-spotting
techniques from recorded conversations as the basis for generating customer callbacks.
Recognizing customer satisfaction as a valid performance metric means another measure of
performance is generated. The metrics used for customer satisfaction are much more
subjective than those used for productivity.
The experiences customers have with call centers are often measured using
cradle-to-grave call-tracking software. These products manage customer interactions,
provide agent scripting and define agent workflow. Wait times, the number of call
transfers, problem-resolution speeds, call-escalation frequency, callback times and a host
of other metrics are available from these applications to assess performance. Customer
experience variables are a new set of metrics that can be added to our growing list of
performance variables.
The measurements discussed up to this point quantify the customer experience before and
after the call. Conducting performance measurements during the call is the role of quality
monitoring systems. Quality monitoring can either be performed manually or by means of an
automated recording system. The end result of either type system is yet another metric.
Automated recording systems have recently become a widespread tool in even the smallest
call centers. They are used to silently monitor and/or record agent conversations to
enable management to generate overall performance scores. Quality monitoring scores are
yet another variable by which agent performance can be measured.
Despite the obvious value of these individual metrics, variables are rarely combined to
reflect all the elements of agent performance.
Your customer service representatives are not one-dimensional.
Why are the metrics you use to measure them?
Imagine the following situations:
- A telephone service representative (TSR) handles more calls than anyone, but he or she
forgets to verify customer order information. The calls-handled data from the ACD report
would indicate the TSR is performing well. The quality monitor evaluation scores would
indicate poor performance in verifying customer order information. The same individual
reviewing the calls-handled data would need to review the quality monitoring scores to
identify the performance problem.
- A TSR has consistently high quality monitoring scores and consistently low customer
satisfaction scores. This situation may indicate that the criteria used for quality
monitoring evaluations is questionable and needs to be reconsidered. Quality monitoring
scores and customer satisfaction survey results would need to be evaluated at the same
time to reach the conclusion that the standards used for monitoring may be suspect.
- Industry standard service level takes into account only calls offered that are answered
in a predetermined number of seconds. If the abandoned call rate is high, then a true
representation of service level to customers will not be reflected. By redefining service
level to include percent abandoned calls along with percent answered calls, a more
meaningful metric would be created.
While these may be simplistic examples of the value of combining measurements, the need
to simultaneously look at all elements of performance is obvious. It is unlikely that you
will obtain an accurate and comprehensive picture of the TSR, department, or call center
without the ability to analyze combined sets of data.
Why Cookie-Cutter Statistics Can No Longer Work
Most call centers still rely on the individual statistics. Creating new metrics that cross
multiple data sources involves resources and time - time that few have to spend and
resources that often seem scarce. It is not uncommon to see personnel sitting at a
keyboard, typing in data to a spreadsheet, in an attempt to combine data from multiple
sources. While data mining and warehousing are beginning to provide some solutions,
in-house expertise is limited and cost can be significant. If you've haven't managed to
overcome all these reasons for keeping the status quo on metrics, inertia and other human
frailties will surely guarantee that one-dimensional measurements will limp along well
into the next century.
A tremendous growth in call center technology has propagated data overload in call
centers. New customer interaction touch points, such as e-mail, Internet, fax response and
video kiosks, will funnel increasing amounts of data into call centers that are already
ill-equipped to manage their data. These new touch points will generate mountains of
additional data. And buried within, will be unique performance-related metrics. Even the
most diligent analyst would rapidly become overwhelmed.
An environment filled with uninformed decision makers is inevitable if a continued
reliance on the old standby metrics of performance persists. Failure to view the call
center holistically will negate the benefits of implementing new technologies. Ironically,
the reason for analyzing metrics in the first place, a reality check on call center
operations, will remain unsatisfied amidst the clutter of data.
Turning data into information can be simplified.
Revisit performance criteria that make sense for your operation.
If you are brave enough to redefine performance, consider doing the following:
- Be the visionary. The need to view the call center holistically and to
integrate your data will only grow. Recognize that Web and e-mail interactions have become
permanent fixtures for which performance data is collected and that having immediate
access to all your data is critical. The quality as well as the quantity of customer
interactions must become the yardsticks for performance in the future.
- Redefine all the measures of performance. Identify the source of all meaningful
data. The answer to how quickly agents respond to customer requests from all sources, how
much time at work is spent responding and determining customer satisfaction are just a few
of the many absolute performance variables (APVs) you may want to include. APVs are those
variables that are independent of call center type and are considered important in most
call centers. Typical cookie-cutter APVs include the number of calls handled, average
handle time, quality monitoring scores and schedule adherence.
There are also relative performance variables (RPVs) that call centers use when defining
performance objectives. In travel reservations, for instance, the number of tickets sold
would be considered an RPV. In teleservices, setting up sales appointments or completing
research interviews are typical examples of RPVs. Every call center has its own unique
RPVs that can be used as variables in assessing performance.
- Develop the equations that reflect your call center's priorities. The result
will look something like this:
PL = Performance level.
APV = Absolute performance variable.
RPV = Relative performance variable.
Each variable should reflect a weight factor to determine its overall importance in the
formula. For instance, if APVs carry three times the weight as RPVs, the above formula
could be rewritten as follows:
By assigning weight factors, less significant variables, in this example, the RPVs,
will make a much smaller contribution to the overall performance level.
Let's take a closer look at the way one might redefine performance level for an agent
in a reservation center using the formula above:
A single number "performance level" (PL) metric like the one above, can be
generated and used in the same manner as "service level." Service level is a
commonly accepted measure of performance that refers to the speed at which calls are
answered. Each call center develops its own service level objectives and staffs in support
of that service level. Performance levels could be generated that reflect combined
variables indicating the overall performance of your call center.
The enormous task of developing performance-level metrics should be obvious. It is
extremely difficult, time-consuming and costly to be able to rapidly combine disparate
data into new performance metrics. A further complication emerges when trying to obtain
disparate real-time data such as the data from an ACD. Necessity drives innovation,
however, and some call centers have already found ways to combine multiple data sources to
create this type of performance metric for all aspects of the call center, from agents to
the enterprise.
While the process may fall short of a mathematician's dream, it nevertheless allows
users to develop a comprehensive single-number index that reflects overall performance. By
assigning tolerances or thresholds to this index, only the out-of-tolerance performance
conditions need to be addressed, saving call centers both time and money.
Performance metrics are not the only ones that can benefit from the combination of data
from multiple sources. Dozens of new metrics will support the call center. Considering the
complexities involved, one would wonder if the agony of implementing new metrics might
ever subside. The obstacles most definitely can be overcome. Technology will continue to
drive the need for multidimensional measurement systems. Holistic call center management
can become the winning way. And if you are a visionary, clarity in this otherwise muddled
environment can be yours.
Debra Disbrow is the director of marketing for CallCenter Technology Inc. (CCTI).
CCTI provides information management software that enables managers to combine data
simultaneously from all call center sources. CCTI's products use the combined data to
generate real-time and/or historic information for display, reporting and decision
support. |