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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?
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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?
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Competitors are breathing down your neck. If you fail to achieve your targeted business objectives, are you vulnerable to competitive attack?
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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
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Refine the workload projections according to actual staff availability
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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. | 
