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Expert's Angle: Why Workforce Management Systems Fail

On November 28, 1979, New Zealand Airlines Flight 901 left Auckland Airport carrying 237 passengers and 20 crew members. The flight was a routine sightseeing trip bound for Antarctica. Flight Planning entered coordinates into the computerized navigation system. The pilots of Flight 901 also entered a series of latitude and longitude coordinates. Unknown to the pilots, two of the coordinates had been changed earlier that morning, altering the flight path of the aircraft approximately 30 miles to the east. The change of a couple of degrees in the two coordinates put Flight 901 on a path toward Mount Erebus, an active volcano. The routine sightseeing flight terminated on Mount Erebus in one of the deadliest crashes in aviation history.   All 257 aboard were killed.
What does an airline crash have in common with workforce management software?  Aviation navigation is based on mathematical equations, and even a small deviation in the equation can dramatically alter the course. Similarly, workforce management software uses mathematical algorithms for accurate agent forecasting and scheduling based on data exclusive to each center’s target service levels, fluctuating call volumes, agent skill set and “what if” scenario requirements.
The airline crash analogy is at the heart of why workforce management systems fail to deliver software that adequately addresses forecasting and scheduling requirements. Seventy-eight percent of Pipkins’ install base are replacement systems. For instance, a well-known retailer with 2,000 agents abandoned three different packages over a six-year period because the programs lacked critical functions and the flexibility to meet the call center’s needs. Another example involves a catalog company that switched systems because their legacy platform was unable to maintain sufficient historical call data to generate accurate forecasts.

What Determines Accurate Forecasting?

Providing the required accuracy, by taking into account all the historic and future dynamics, requires a sophisticated forecasting tool. Only the most advanced systems can perform correlated forecasting, which is forecasting for specific events, such as catalog drops or other marketing events that cause wide fluctuations in the volume of calls that must be processed.

Critical Components for Accurate Forecasting

Accurate forecasting is the foundation of call center scheduling, and without it, over- and understaffing will occur. Accurate forecasting in a skill-based routing environment is the most critical component of workforce management. Without accurate forecasting, scheduling will fail to correctly plan for anticipated workloads. Precise scheduling is based on accurate workload requirements. Critical components of workforce management software for accurate forecasting include:
  • The amount of historical data available
  • The nature of the data
  • The forecasting period
  • An infinite number of different service objectives on one or more work streams
  • Algorithms that reflect real-life customer behavior
  • Special events (e.g., mail drops, campaigns and special promotions that can be quantified)
  • Emails and faxes with service objectives reflecting the way that work is handled

The Importance of Historical Trend Analysis and Pattern Recognition

Algorithms should include curve mapping and pattern recognition. In environments where workloads regularly ebb and flow due to marketing activities and other definable variables, historical trend analysis is the only way to ensure proper staffing. This is because it is the only methodology that can incorporate complex historical trends in its calculations. Without pattern matching to predict different customer behavior for various events, the risk of over- or understaffing increases dramatically. Historical trend analysis not only accurately predicts the continuation of trends, but the more advanced algorithms also incorporate pattern recognition to fine-tune forecasts for special events like promotional mailings or national holidays. Each time a particular event reoccurs, the forecasted call volume is automatically adjusted to reflect the increase or decline in incoming work caused by comparable occurrences in the past, such as a historical forty percent drop in volume on the Fourth of July.
An important component of accurate forecasting is having an integrated approach to support multi-skilled issues. It is necessary to have forecasting algorithms that directly calculate requirements in a multi-skilled environment, while avoiding repetitive analytical simulations. A single forecasted set of requirements should be generated for all interwoven skilled activities, regardless of the type of work being offered, such as email and chat. Recognizing secondary skills and accounting for call overflow to available secondarily skilled agents will help eliminate overstaffing. Forecasts that are based solely on primary skills will generally overstaff, since overflow cannot be considered as a factor.

Assigning Attributes to Specific Events

To further enhance accuracy, some forecasting tools also make it possible to describe each event in detail through the use of attributes. For example, one catalog drop might consist of 10,000 pieces sent to women between the ages of 20 and 35 in Southern California, while another might involve 5,000 pieces directed at women above the age of 35 in the Midwest. By logging these characteristics into the system, analysts ensure that the differing call patterns produced by each drop will be “remembered” and used in forecasting call volumes the next time similar mailings go out.
The most advanced systems can search for historic trends that parallel upcoming events both by specific match (e.g., the specific guest host on a TV shopping channel) and by a range of values (e.g., products between $50 and $100). This aids in correlating past and future events. There will be a substantial difference in response to a piece of jewelry that sells for $200 and one that sells for $2,000, and only a tool that allows this information to be recorded can factor in that difference when creating a forecast.
Workforce management is based on science and, thus, should be approached from a scientific perspective. Marketing hype cannot change the fact that if forecasting is inaccurate, everything else will be off balance. Many workforce management systems purchased by call centers have resulted in dissatisfaction and, ultimately, replacement. Avoid being a statistic of failed workforce management systems: Carefully investigate the algorithms in your system or a system you’re considering to determine if they do/will produce accurate forecasts.