Company leverages Elevēo forecasting and scheduling tools to achieve SLAs and enhance brand reputation among internal and externalcustomers.
From his office at the Resource P.O.S. headquarters in Chicago, Help Desk Director David Mayerchin manages help-desk agents in that city and Bosnia. ReSource P.O.S. delivers customizable, end-to-end P.O.S. and I.T. solutions. Its agents offer customers general assistance and provide troubleshooting services.
Though the industry may classify his 35-agent contact center as ‘small’, Mayerchin faces a forecasting and scheduling challenge common to virtually every contact center manager, regardless of staff size: Ensuring that the optimal number of agents, possessing the right combination of skills, are available for customer interaction at any given time.
Constrained by functional limitations of a cloud-based business analytics application he used for forecasting, Mayerchin was basing resource forecasts on rolling call volume averages and estimates of critical performance metrics, like AHT. “It required a lot of effort and wasn’t agile enough to allow us to spot new trends quickly,” he says. He points out that it often took two to three weeks to compile and analyze historical data and find the source of chronic issues, like excessive hold times.
“There were too many time gaps in our forecasts, and we were consistently over-scheduling or under-scheduling our help-desk agent resources,” he says. “We had enough agents, just not always enough at the right times.” This lack of simplified forecasting capabilities, combined with an Excel-based scheduling process for Bosnian agents and a 3rd party H.R. and Payroll software-based process for Chicago agents, hampered Resource P.O.S.’s ability to consistently meet a crucial SLA - a live answer in under 90 seconds. “This impacted our brand reputation with internal and external customers.”
Improving forecast accuracy and scheduling agility
Resource P.O.S. deployed Elevēo WFM for forecasting call volumes and scheduling help desk agents. “As soon as we started using Elevēo WFM, we were able to identify the root causes of our shift coverage problems,” Mayerchin recalls. “We fixed those periods by adjusting shifts on our new single schedule for both teams using the historical data Elevēo captures from our UCM platform.”
According to Mayerchin, COVID-19 put Elevēo’s integrated forecasting functionality and intuitive drag-and-drop scheduling tool to the test. “We were heavily affected on the retail side of our business, and because we could use call data from the previous two weeks, we reacted quickly to call volume fluctuations and improved forecasting accuracy. We were able to identify agents who could be moved to new shifts or furloughed and made informed decisions about when to bring them back. Internally, our help-desk’s reputation improved. We increased call coverage, lowered average handle times, and escalations pretty much dried up. We can now automatically publish one schedule for both distributed teams, accurately forecast and better maintain our S.L.A.s. Most importantly, our customers are happier.”