Due to the recession, companies everywhere are cutting staffing in their call centers. At the same time, they are asking call center managers to maintain, if not improve customer service — not only to impress new customers but also to retain existing customers.
That means call center managers are under more pressure than ever to accurately schedule agents and hold down operating costs. Considering that labor is the single biggest cost facing any call center, it only makes sense that it would be the main place to look for cost efficiencies. As a result, many companies are now ditching their spreadsheets and other manual systems for scheduling agents and adopting workforce management software.
Today's workforce management systems – which are increasingly being offered on a hosted or "software-as-a-service" basis – offer advanced analytics capabilities that enable call center managers to accurately forecast how many agents will be needed for any given shift. This capability is achieved through integration with the call center's ACD or automated call distributor. By leveraging the historical data captured by the ACD, and analyzing past volume patterns, a workforce management system can predict, with surprising accuracy, how many calls will come in during any given hour of any given day at any given time of year.
This is a capability that spreadsheets and other manual systems simply cannot deliver – and it is essential for accurately balancing the number of agents with incoming call volume: Over-schedule for any given shift and you'll have agents sitting around idly doing nothing – a horrible waste of company money and resources – under-schedule and hold wait times will increase, customer service will erode, and customer satisfaction and loyalty will plummet.
But forecast accuracy involves more than just analyzing historical call data. As call center workforce management solutions provider GMT points out in a recent white paper, "Improving Bottom Line Performance through Precision Forecasting and Scheduling," "being able to create forecasts based on regular events by themselves is not enough. A catalog retailer, for example, would need the flexibility to forecast the increase in calls soon after a catalog drop. An entertainment ticketing company would need the ability to predict the demand that might generated when tickets go on sale for a leading musical act. A public safety agency would need a forecast that accounts for an increase in calls as a result of a major convention or sporting event."
So, in order to arrive at truly accurate forecasting, you must have the ability to include these "non-recurring" or "irregular" events in the creation of your forecast. As the white paper points out, "many times, inaccurate forecasts are a product of not fully appreciating the impact of these irregular or nonrecurring events."