The usefulness of the staff schedule created by workforce management software will rise or fall on the reliability of the predictions it makes about the expected volume of incoming work. Without the right assumptions about the workload to be handled, the software’s ability to accurately calculate staffing needs is doomed. The effect on the bottom line can be as chilling as a bad day on Wall Street on an investor’s stock portfolio.
If the forecasted work volume is too high, too many agents will be assigned, leading to boredom, a customer-irritating ‘don’t-bother-me’ attitude and wasted labor expense. If the forecast is too low, the call center will be understaffed, resulting in longer average speed of answer, a high number of abandoned calls and a proportionate level of lost sales. Either way, staffing mistakes can drive customers away and cause lasting business damage.
The problem: not all forecasting tools are created equal. Only the most sophisticated systems can perform correlated forecasting; that is, forecasting for specific events such as catalog drops that cause wide fluctuations in the volume of calls (and e-mails, fax and/or online chat sessions where applicable) that must be processed. For this reason, the forecasting capabilities of a workforce management software package should be a central consideration in any purchasing decision. Here are some factors to consider, the steps involved in maximizing the accuracy of the forecast and the impact the right forecasting tool can have on call center revenues.
The Importance Of Pattern Recognition
There are two basic methodologies used to forecast workload in a call center: Exponential Weighted Moving Average and Historical Trend Analysis. Both use historical data collected from the call center’s ACD and both take growth trends into account in their calculations.
The Exponential Weighted Moving Average calculates the average call volume over a specific time period and then bases its projections on a formula that assigns more weight to recent activity. This technique is effective for contact centers where there is little fluctuation in call volume and patterns, such as help desks and technical support organizations, but it has shortcomings when trends change. It is unable to predict a continuation of trends during periods of generally increasing or decreasing volume, or to associate changes in volume and/or call arrival patterns with specific events (pattern recognition).
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 recurs, 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 40 percent drop in volume on the Fourth of July.
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 because it is the only methodology that can incorporate complex historical trends in its calculations. Without pattern matching to predict different customer behavior for different events, the risk of over- or understaffing increases dramatically.
Mapping Historical Data To Special Events
A key step in using a workforce management program that employs pattern recognition is regular data validation. Analysts must review the data collected by the ACD, preferably on a daily basis and not less frequently than weekly, to determine if there is an identifiable cause for all spikes and drops in call volume.
Most unusual patterns will be related to recognizable events such as direct mail campaigns, catalog drops, TV advertorials, discount offers, competitors’ promotions, pay periods, billing cycles or holidays. Some may even be traceable to external factors such as the Super Bowl, the Olympics or a snowstorm.
If a given fluctuation was triggered by a recurring special event, analysts instruct the system to interpret that data set accordingly when producing a forecast. Conversely, if a given deviation was the result of a one-time anomaly like a product mention on the Oprah show, analysts can tell the system to ignore that data set when forecasting. These instructions are vital in producing the most accurate forecast possible.
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. One catalog drop might consist of 10,000 pieces sent to women between the ages of 20 and 35 in Southern California, for example, while another might involve 5,000 pieces directed at older women 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, for example, and only a tool that allows this information to be recorded can factor in that difference when creating a forecast.
The Impact On Call Center Revenue
With historical patterns identified, attributes assigned and upcoming special events entered into the system, call volumes can be forecast with a far greater degree of accuracy than with tools lacking these capabilities. Staffing requirements, in turn, can be predicted far more precisely. The significance can be seen by considering the consequences of a poor forecast.
Let’s say that a workforce management package has underestimated call volume and therefore staffing needs so substantially that 100 callers out of 1,000 hang up before they speak to an agent. In a sales environment in which the average order is just $50, that means $5,000 in lost revenues per day, $150,000 per month, or a staggering $1.8 million per year. At best, these lost sales cut into a call center’s profits; at worst, they can ruin a business.
There are, of course, many other components in the equation that dictate the effectiveness of a given workforce management software package. These include the software’s inherent sensitivity to agent skill sets and work rules, its real-time adherence capabilities, and its ability to calculate staffing requirements based on highly specific user-defined service levels ranging from mean time to answer to the percentage of busies and abandoned calls that will be tolerated.
But all that is moot if the software’s forecasting tool doesn’t meet the call center’s needs. Since all agent assignments are based on anticipated call volumes, a package with inadequate forecasting capabilities is like a weatherman with old technology. Both will issue a disproportionate number of wrong predictions. For call centers that rely on proper staffing to do their work, choosing the right forecasting solution can make all the difference ‘ and ward off an almanac’s worth of rainy days.
Bob Webb is vice president of sales of Pipkins, Inc. (www.pipkins.com), a worldwide supplier of workforce management software and services to the call center industry.
Originally published on TMCnet/Customer Inter@ction Solutions Magazine.