Does your company’s call volume fluctuate according to seasonal buying, catalog drops, or other special events? That means your workforce management software needs forecasting capabilities if you aim to have the right number of agents in their chairs at the right times.
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 whether 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 such as 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.
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 living 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. Since all agent assignments are based on anticipated call volumes, a package with inadequate forecasting capabilities will result in a disproportionate number of wrong predictions.
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 where 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 dictates the effectiveness of a workforce management software package. These include agent skill sets and work rules, real-time adherence capabilities, and the 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. For call centers that rely on proper staffing to do their work, choosing the right forecasting solution can make or break the bottom line.