Dynamic Contact Center Routing via Predicted Wait Time and Customer Patience
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Solution Overview
Problem
Traditional skill-based routing in contact centers is static and requires manual effort, failing to adapt to real-time changes and resulting in inefficient agent interaction allocation, leading to increased costs and suboptimal customer experiences.
Innovation Solution
A system that dynamically routes interactions to contact center agents by calculating a predicted wait time and estimated reward for each agent, considering availability, customer patience, and agent preferences, using a processor and memory to optimize the assignment and signal the routing device for efficient interaction handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional skill-based routing with explicit skill models is used, then routing decisions can be made based on agent skills, but the models are static and do not dynamically adapt to real-time changes
Solution Approach 1:
The patent transforms the static skill-based routing model into a dynamic system that continuously adapts to real-time changes. The routing model incorporates real-time agent availability, customer patience levels, and dynamic skill assessments, allowing the system to adjust routing decisions based on current conditions rather than relying on predetermined static models.
2Measurement precision
If refined skill models are constructed manually, then routing precision improves, but the cost and manual effort increase significantly
Solution Approach 1:
The system implements self-service by automatically constructing and maintaining skill models through machine learning algorithms that analyze interaction data, agent performance, and customer feedback. This eliminates the need for manual model construction and refinement, allowing the system to continuously improve routing precision autonomously based on observed patterns and outcomes.
Solution Approach 2:
The patent incorporates feedback loops where routing outcomes, customer satisfaction metrics, and agent performance data are continuously collected and used to refine the skill models. This feedback mechanism enables the system to automatically adjust and improve routing precision over time without requiring manual intervention for model construction.
3Adaptability or versatility
If more agents are included in the candidate pool based on equivalent skills, then routing flexibility increases, but the predicted wait time and customer patience management becomes more complex
Solution Approach 1:
The patent simplifies the complexity of managing large agent pools by introducing dynamic parameter adjustments based on customer patience levels and real-time agent availability. The system adjusts routing parameters such as wait time thresholds and agent selection criteria dynamically, allowing flexible routing decisions without requiring complex manual management of the candidate pool.
Data Source
AI summary
A system and method for routing interactions to contact center agents. The system is adapted to concurrently identify a plurality of interactions waiting to be routed, and identify a plurality of candidate agents viable for handling the plurality of interactions. The system is also adapted to calculate a predicted wait time associated with each of the candidate agents. For each agent of the plurality of candidate agents, the system is adapted to estimate an expected value to be obtained by routing each of the plurality of the interaction to the agent. The expected value is a function of the predicted wait time. The system is further adapted to assign each of the plurality of the interactions to one of the plurality of candidate agents based on the estimated reward, and signal a routing device for routing each of the plurality of the interactions to the agent assigned to the interaction.