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          How Simultaneous Interactions Affect Staffing Requirements

          by Nick Martin
          May 31, 2021
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          This blog is the third in a series on digital channel management. In the previous installment, we talked about how the proliferation of digital channels is calling long-held assumptions about workforce management into question.

          Digital channels bring with them significant new challenges for the traditional WFM process of collecting data, calculating staffing requirements and optimizing schedules, then doing it all again as conditions evolve. Organizations have long relied on a key assumption in the contact center – namely, that work is sequential and contiguous – an assumption that no longer necessarily holds true.

          In the past, before the widespread proliferation of digital sales, service and support channels, organizations planned WFM processes around a queue of voice calls that logically followed the assumption of sequential and contiguous work streams. An agent handled the first call in the queue from beginning to end, then moved on to the next call only after concluding the first call in the queue.

          A unique feature of many digital channels, however, is that they permit agents and employees to handle more than one customer interaction simultaneously. Chats are one example of this, though certainly not the only one: An agent may shift between several different customer chats concurrently, and this simultaneous capability can bring substantial benefit to the contact center by adding an additional dimension to efficiency.

          Fully realizing these efficiencies, however, requires resolving some open questions that aren’t fully answered by the traditional WFM process:

          • How should AHT be defined? You can take into account either the full elapsed time of an interaction or only time that a given interaction is in focus.
          • How much wait time should be accounted for an agent/employee?
          • How should speed of response be considered?
          • How many concurrent interactions should an agent/employee handle?

          The answers to these questions can have a significant impact on staffing requirements calculations because they drive how AHT is calculated – and this, in turn, drives staffing calculations. Beyond calculating for staffing, you need to consider the effect on service objectives, because this too will raise or lower staffing levels.

          And, because no two agents are the same, you also need to consider the handling limits (or saturation level) for each given agent, which affects schedule optimization. Some people can easily handle three chats at a time or even thrive with more than three, while others become less efficient once they reach this same threshold. The differences can best be explained by the relationship between stress and performance, which has been well documented in the Yerkes-Dodson law. According to the theory, performance increases with stress but only up to a point; too much stress will degrade performance.

          For accurate forecasts and optimal schedules, a WFM process must consider all these elements – at both the team and the individual level. That requires tools able to appropriately consider AHT, service objectives and individual variances in saturation levels. To stay competitive, businesses need a WFM solution that is ready for interactions that can go beyond the traditional. Learn more about NICE WFM with machine learning 2.0 for digital channel management.

          About the Author

          Nick Martin

          Nick Martin is the Product Director, CXone Mpower WFM. He brings more than 6 years of experience in software product management specifically around WFM solutions and has worked on feature across the entire WFM lifecycle. Nick is credited as an inventor on several patent-pending and two granted-patents pertaining to WFM. Additionally, Nick has previously held roles involving multiple business functions with P&L responsibility. Prior to his corporate experience, Nick served as a Military Intelligence Officer in the U.S Army. Nick graduated with a B.S. in Human Factors Engineering from the United States Military Academy at West Point. Additionally, Nick earned his M.B.A. from Southern Methodist University and professional certificate in Data Analytics from Cornell University.

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