1 minute read
By Nicole Nevulis
Posted in Customer Engagement
Aging doesn’t just affect people. Predictive Work Aging tracks the status of individual work items against service goals, predicting which items are or will be at risk of missing service goals. To effectively predict if items will meet service goals, managers expect their workforce management solutions to consider:
These elements help ensure that back-office operations meet service goals and provide excellent customer service. So how does predictive work aging, well, work? Predictive Work Aging Analytics is part of the Verint Impact 360 for Back-Office Operations Workforce Management (WFM) solution. Within it, the work item tracking (WIT) feature provides analytics that track individual work items against their service goals. Managers can see the current work aging status of all items—by employee, queue or work type.
Predictive Work Aging Analytics takes WIT one step further and gives managers a crystal ball into the future. Now instead of just a “current state” view of work item aging, the solution peeks into the future—and based on existing backlog, forecasted new volumes, and current and future scheduled resources—it predicts if work items can be completed within service goals. Predictive Analytics categorizes the work into these categories:
Armed with this data, managers and employees can reprioritize work items and scheduled activities to ensure the at-risk items are completed before lower priority ones. Given today’s regulatory environment, with more and more emphasis being placed on meeting industry standards for service goals, this capability greatly helps companies meet their service requirements.
Based on the rules configured by your organization, alerts can be sent to managers as well as the employee calling out details of work items that require attention. Predictive Work Aging Analytics (first time we have used the words in this order – we should be consistent) makes it easier for managers to focus their efforts where they are needed most.
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