Workforce Engagement Management

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  • 1.  Forecasted Calculation

    Posted 02-15-2023 14:18

    Hello!

    We have a question about the Scheduled and Forecasted section in the Schedules screen of Workforce Management. How is Genesys calculating the Forecast for the number of reps? This number does not seem accurate based on our call volume. We noticed some days it is really low and some days it is a lot higher. Is there somewhere this is pulling from or a calculation?


    #Genesys Cloud CX
    #WorkforceManagement
    #Forecasting
    #Scheduling
    #Performance Management
    #Other/NotSure

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    Addison Hild
    Athene
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  • 2.  RE: Forecasted Calculation

    Posted 02-17-2023 07:43

    Addison we have the same issue. It seems to be, for us, that we have many queues (planning groups) and agents that are a part of many queues/skills. Being a smaller (<100) center 80% of oour agents are cross trained and have many if not all queues and skills. Maybe it works better for larger centers whrere each agent is only in 1 queue/skill. 

    Our only work around on this was to modify the forecast AHT of email to force the # of agents down to the correct value. We determined the correct forecast adjustment by looking at historical SLA to verify we are hitting SLA at 0 difference. 



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    Greg Barrett
    Outdoor Network LLC
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  • 3.  RE: Forecasted Calculation

    GENESYS
    Posted 02-17-2023 08:48

    I recently contributed the following article that may be of interest: https://help.mypurecloud.com/faqs/why-are-staffing-requirements-higher-or-lower-than-expected/

    If you have configured routing such that there are many planning groups with very low forecast volume (<10 per hour), then the issue of 'small numbers' comes into play. In an ideal world you have homogenous agents in terms of capabilities and homogenous interactions in terms of requirements. Obviously it is not possible to do so. However, the less homogenous either of those are, the less economies of scale you get. The further the extreme the further the non-optimal staffing requirements.



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    Jay Langsford
    VP, R&D
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