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In an effort to jump on
today's "optimization" bandwagon, a growing number of contact center
solution providers claim to deliver "optimized workforce" applications.
But many of them do not actually deliver optimization solutions that are
simple and practical to execute in the real world, where daily
fluctuations in workload and staffing levels are the rule rather than
the exception. The complexity associated with finding optimal workload
balancing solutions cannot be understated, because using inadequate
tools or methods can result in sub-optimal staffing decisions that eat
away at productivity and profits.
The primary reason for
this shortcoming is that many solution providers approach call center
workforce optimization by simply analyzing call centers' historic
workloads to estimate their future work requirements. Using these
workload guesstimates, the technologies dictate an idealized agent
schedule. This approach can lead to frustration because it creates a
speculative workforce schedule that is difficult to achieve in the real
world—where call center staffs are in a constant state of change every
day.
In fact, several
solutions that claim to optimize customer contacts are actually based
only on business rules, which only have the ability to decide future
actions based on historic averages of customer-contact results. They are
not able to proactively work with each day's actual available staffing
levels.
What's more, these
workload-projection solutions rarely have the capacity to deliver
workforce management estimates across multiple contact centers. This
significant limitation means that companies cannot leverage their actual
workforce for all of their calling campaigns—and, ultimately, it means
they will never truly optimize their entire workforce.
Fortunately, there is a
much more practical approach to achieving workforce optimization in
either a single or multiple call centers—optimization technologies
embedded with workload-balancing predictive analytics. This approach
focuses first on call centers' actual agent resources including daily
and hourly fluctuations. This sophisticated and practical form of
workforce optimization can far exceed the results of applications that
attempt to force-fit staffs to estimated workload projections.
The Three Secrets Of
Optimized Workload Balancing
To achieve real workforce optimization requires that the technology
solutions are able to factor in three critical elements: the
organization's business goals, the most profitable contact strategy for
each account, and the contact centers' actual resource capabilities and
constraints. By processing these vital components, workload optimization
solutions are able to generate optimized customer contact schedules for
each day's actions—delivering insight for simple one-step customer
contact actions and for complex series of contact actions, depending on
an organization's business needs. When searching for customer-contact
workforce optimization tools, make sure they have the ability to factor
in all three critical components.
Your Business Goals.
Every business has different goals that may change at any given time.
Your optimization solution should be able to consider and support your
goals, such as maximizing net revenue, total number of delinquent
accounts paid, cross-selling opportunities, risk reduction, or attrition
prevention. In particular, watch out for solutions that claim to
optimize, but in fact really only prioritize contact schedules—for
example, by easiest to contact, dollar amount owed, or balance level.
Many solutions claim to
deliver optimization when in fact they merely prioritize customer
contacts according to some defined metric, such as balance due or days
delinquent. The primary flaw in the prioritization methodology is that,
before you reach the bottom of the account list, some of your resources
will already be expended. As a result, this process actually
sub-optimizes your entire operation, because no matter how good your
decisions are per account, you can never achieve the highest value for
your organization without considering the contact needs of your entire
account population—and reordering them for the maximum overall outcome.
While simply prioritizing your contact list might help you reach your
immediate business goals, this process does not help you meet your
long-term productivity and profitability goals.
Each Account's Needs.
The first step in optimization is to determine, using predictive
analytics, how each account scores on all of the contact treatments
under consideration. Solutions that employ intelligent predictive
modeling will aggregate all relevant predictive data and give each
account scores that quantify their propensity to respond to each
treatment. This account-level assessment is much more powerful than
simply assigning the same action to a group of similar accounts based on
their related behavior scores, which is the solution offered by today's
rules-based applications.
What's more, predictive
analytic-based solutions don't constrict your contact strategy to a
single option for each account. Instead, by ranking each contact
treatment for each account, they open up the opportunity to choose the
treatment that makes the most profit-focused sense.
The Contact Center's
Actual Resource Constraints. Making customer-contact decisions
account by account, as many contact centers do, ensures the smartest
decisions will not be made for the entire population of customers
needing treatment because, on any given day, there are a limited amount
resources available—including agent hours and budget. Several
optimization technologies today expect you to adjust your resources to
achieve their idealized projections: for example, doubling your agent
staff during short "prime time" periods. Along with being impractical,
this methodology places new pressures on your business and adds layers
of complexity. The smarter approach is to find the highest value actions
to take on each account with the actual resource levels that you possess
each day.
For example, a particular
customer's probability of paying a delinquent account is 80% with a call
and 60% with a letter. At first glance calling the account seems like
the best decision. But by calculating the ideal contact using an
optimization engine, you find that you need all of your calling
resources that day to call a large group of accounts who scored 80% or
higher (or who scored low on other forms of communication such as a
letter or agency contact). By making the optimized decision to send a
letter to this account, based on the needs of the entire account set,
bottom-line collections dollars are increased. This example is
illustrated in the graph below.
Make The Smart
Decision—Choose Intelligent Optimization
Can your customer contact organization benefit from optimization
technology? The answer is "Yes," if your operation is facing any of
these problems:
-
You face the
pressures of delivering ever-better results. Does your company
expect constantly higher customer-contact results?
-
Your business
challenges are becoming more complex. Is it more challenging and
more critical to make the most productive, profit-generating
decisions as more data and more contact channels become available?
-
Industry pressures
are increasingly influencing your decisions. Do you feel the squeeze
from an industry for which Wall Street has higher and higher
performance expectations?
-
Competitors are
breathing down your neck. If you fail to achieve your targeted
business objectives, are you vulnerable to competitive attack?
-
Combining Workforce
Solutions with Predictive Analytics
What if your organization
has already installed a workforce optimization solution? Do you have to
abandon it wholesale in order to employ the capabilities of workload
balancing with predictive analytics? No, you do not. In fact, some of
today's workforce optimization solutions work seamlessly with
intelligent predictive solutions. The resulting workforce balancing
process includes these steps:
-
Create a preliminary
workforce schedule based on the applications workload projections
-
Refine the workload
projections according to actual staff availability
-
Employ the predictive
analytic solutions' real-time monitoring capabilities for mid-day
strategy adjustments
Conclusion
Because optimization is such an important trend in today's business
world, a growing number of companies are using the term to define their
solutions. However, many of the optimization claims made today don't
hold up under close scrutiny. As you search for ways to improve your
customer contacts and make decisions that deliver the highest possible
value, make sure the solutions you're considering deliver real workforce
optimization that is practical to implement and profitable in the long
run. In this way, you'll truly gain a powerful way to meet today's
ever-growing demands for better agent utilization, improved cost
containment, and higher profitability.
About Lois Brown:
Lois Brown is vice president of marketing at Austin Logistics
Inc., where she oversees brand
positioning, marketing communications, and new product definition for
the company's expanding line of predictive analytic and optimization
solutions.
About Austin
Logistics:
Austin Logistics' products and services are trusted by many financial services companies to dramatically increase
the value of customer interactions, simplify operations and help achieve
business-specific objectives in the areas of collections, risk
management and customer service. |
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