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          © 2025 NiCE

          The Right Way to Forecast and Schedule in a Digital World

          by Nick Martin
          June 22, 2021
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          This blog is the fifth in a series on digital channel management. In the previous installment, we talked about the challenges asynchronous interactions pose 

          Customers today have a choice in how they communicate with your business, and they’re increasingly reaching out via a wide variety of channels that range from chat and social channels to Apple business chat, WhatsApp, Talkdesk, Kik, Viber and more. These digital channels add a layer of complexity to the WFM process that can impact AHT and requirements calculations. 

          Much of the complexity is rooted in the nature of the interactions – digital interactions are often simultaneous, interrupted or asynchronous, for example. The sheer number of digital channels alone, however, adds to the complexity, raising questions around forecasting and scheduling. 

          For many years, contact centers focused mostly on calls and emails. Chats did take place, but at a volume that had minimal impact on staffing. Prioritization was straightforward, with only two categories: immediate response and deferrable work. And agents rarely if ever moved a customer from one channel to another. 

          The proliferation of digital channels – which increased in velocity due to the pandemic – turned all that on its head. In the digital world, contact centers often handle interactions on significantly more channels and with enough volume to impact staffing requirements and complicate prioritization. Among the key questions to consider:

          • When should one chat interrupt another?
          • When should one channel interrupt another?
          • What happens when an interaction changes modes (an escalation), such as going from a chat to a call or face-to-face video? 

          To solve these challenges – and the impact they have on the contact center – organizations often take a few common approaches to forecasting and scheduling:

          • By the type of interaction (e.g., calls, email or chats).
          • By customer experience (e.g., customer A, B or C).
          • Using a hybrid approach (e.g., emails, calls and chats in the same customer interaction and social media separately). 

          Note that this is not an exhaustive list — there are many other ways contact centers approach forecasting and scheduling with digital channels. None of these approaches are wrong; the best approach depends on your contact center’s unique needs. You need to consider the volumes and KPIs along each channel customers interact with to decide on an approach for your organization. The right way to schedule and forecast in a digital world, then, is the one that works for you. 

          There are, however, a few WFM requirements that are consistent across contact centers. The

          WFM solution in use needs to be flexible enough to offer choices that enable businesses to decide on the right approach. It should serve as a tool to solve a problem rather than restricting businesses to a single approach to managing digital channels. And just as a WFM solution should offer ways to address the characteristics of a digital channel, it should also offer ways to forecast and schedule in a digital environment. 

          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 Director of Product Management for CXone 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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