Contact Center supervisors responsible for daily operation are continually burdened by the duel responsibility of managing staffing requirements and service level mandates. Adding to the complexity contact centers have embraced additional UC technologies including Video, IM, Collaboration, and Application Sharing services. Predictive UC Analytics provides flexible, yet simple, analytic interfaces and real-time statistical trending to address the needs of executives, supervisors and agents in a single solution.
What does Predictive UC Analytics provide?
We recognize that contact centers utilize more than traditional voice services to support both internal and external customers. Predictive UC Analytics provides distinct levels of visibility into the operation of the organization as well as the all UC activity in a single, yet flexible framework. Whether the requirement involves interactive wall boards in graphical or tabular format on queue and agent performance or dockable, hover activated desktop displays for agents where screen real-estate is valuable, Predictive UC Analytics provides both real-time and historical reports ideal for improving the service level efficiency of your team.
For many organizations balancing staffing requirements and resource skill sets to support inbound call activity and call-back requirements is a perpetual challenge. Predictive UC Analytics's Service Level modeling provides flexible tools taking the guess work out of the equation. By providing interactive modeling functionality TeleMate ensures operational efficiency.
How does Service Level Modeling work?
Service Level Reporting provides supervisors an interactive modeling interface to review current and historical service levels by queue. Modeling enables staffing requirements to be tuned to actual call volume by queue and skill set thus improving the operational efficiency of the call center while keep costs down. Default service levels are presented for 30, 60, 90, 120, and 180 seconds but can be changed to determine optimal resource allocations.
Predictive analytics can also provide a statistical models of contact center activity providing both short-term and long-term views staffing and queue management.
Abandon sessions are the number one cause of customer dissatisfaction. Yet, businesses struggle to gain insight into reasons why calls or sessions are terminated. In this situation the more information collected about each session the better a business can handle it and keep service levels high. Predictive UC Analytics provides visibility into communication matrices including time to answer, talk time, work time, disconnect reason, and cradle-to-grave linking of session transfers so you can see who and what caused the ball to be dropped.
How does Predictive UC Analytics provide Abandon Session Analysis?
Based on the datsosurce Predictive UC Analytics is collecting session detail from, Abandon Session analysis can be perform using automated reports, dashboards, maps, and monitors that alert staff when thresholds are met. Enhanced detail is available in Contact Center datasources that provide enhanced detail attributes on disconnect reasons, reason codes, agent state changes, and often custom system handlers. Filters exist for all attributes providing granular visibility by queue and agent for both incoming sessions and callbacks.