Contact Center Routing Logic for Predictive Agent Training
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Solution Overview
Problem
Current contact center approaches fail to predictively assess and balance the distribution of customer contacts and agent training notifications in real-time, leading to inefficient service and lack of relevant information, as they depend on human resources and aggregate only past work, without anticipating impending contacts or delivering notifications independently.
Innovation Solution
The contact center workflow routing logic is used to assign both customer and intra-enterprise contacts to agents, evaluating the impact of notifications against overall service objectives, allowing for real-time delivery of training content as work items, and blending customer and notification queues to balance resource training and service goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If training notifications are delivered to agents during lulls in contact demand, then agent training can occur without interrupting customer service, but the system cannot predictively assess the impact of notifications against total work occurring in real-time
Solution Approach 1:
The system performs preliminary assessment of agent availability and work queue status before delivering training notifications. The routing engine evaluates predicted wait times, agent skills, and current workload to determine the optimal timing for notification delivery, ensuring training occurs during appropriate lulls without compromising service levels.
Solution Approach 2:
The notification delivery system dynamically adjusts based on real-time contact center conditions. The routing engine continuously monitors work queue depth, agent availability, and service level metrics, adapting the timing and targeting of training notifications to match current operational demands rather than using static scheduling.
2Reliability
If offline coaching sessions are used to address process violations, then agents receive comprehensive training, but current call processing is interrupted at the expense of real-time service goals
Solution Approach 1:
Instead of removing agents from their calls for comprehensive offline coaching, the system delivers targeted training notifications during brief intervals between calls or during natural pauses in the workflow. This partial action approach provides essential training content without completely interrupting call processing, maintaining service levels while improving agent performance.
Solution Approach 2:
The system introduces an intermediary notification mechanism that bridges offline training content with real-time call processing. Training materials are delivered as notifications during agent availability windows, serving as an intermediary between comprehensive offline coaching and continuous call handling, allowing agents to absorb training information without leaving their workstations or interrupting service flow.
3Ease of operation
If the system aggregates only work that has already occurred, then historical analysis is simplified, but the system cannot anticipate work that is about to occur or deliver notifications independent of past contacts
Solution Approach 1:
The routing engine performs preliminary analysis of incoming contact patterns, agent skills, and queue status to predict future work demands before they materialize. By assessing the work queue in advance and identifying upcoming contact types and volumes, the system can proactively schedule training notifications to coincide with predicted lulls, rather than merely reacting to historical data.
Solution Approach 2:
The system implements a feedback loop where routing decisions and notification deliveries are continuously evaluated against actual service outcomes. This feedback mechanism allows the system to learn from both historical performance and real-time conditions, improving its predictive accuracy over time while maintaining operational simplicity through automated adjustments.
Data Source
AI summary
In one embodiment, a contact center is provided that includes:(a) a work item queue 208, 250 comprising an intra-enterprise contact associated with at least first and second internal endpoints of an enterprise; and(b) a selector operable to select and assign a work item to an agent. In selecting the work item, the selector considers both a customer contact and the intra-enterprise contact. The intra-enterprise contact, for example, can be a training notification.