Virtual Interaction Server for Contact Center Call Routing
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Solution Overview
Problem
Contact centers face delays and inefficiencies in routing calls due to varying call volumes and agent availability, leading to prolonged wait times for callers, especially when all agents are engaged.
Innovation Solution
The implementation of Virtual Interactions (VIs), which are predicted or pre-configured interactions that anticipate future customer needs, allowing for quicker routing to available agents, reducing the need for real-time call processing and minimizing delays by pre-selecting the best agent for incoming calls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time call routing is used to connect callers to agents, then call routing accuracy can be maintained, but call processing latency increases and agent utilization decreases during peak times
Solution Approach 1:
The system creates virtual interactions in advance of actual customer calls by predicting future call volumes and customer needs. These virtual interactions are pre-configured with routing information and agent assignments, so when a real call arrives, it can be quickly matched to an existing virtual interaction rather than being routed in real-time from scratch. This preliminary preparation reduces call processing latency while maintaining routing accuracy.
Solution Approach 2:
The call routing process is segmented into two distinct phases: an offline prediction phase where virtual interactions are created in advance based on historical data and forecasting models, and an online execution phase where incoming calls are quickly matched to pre-configured virtual interactions. This segmentation allows complex routing decisions to be made beforehand, freeing up real-time resources for rapid call connection.
2Productivity
If more agents are deployed to handle peak call volumes, then customer service quality improves, but operational costs and system complexity increase
Solution Approach 1:
The system employs automated prediction algorithms and machine learning models that independently analyze historical call data, forecast future volumes, and automatically create virtual interactions without human intervention. The system self-adjusts routing configurations based on predicted patterns, eliminating the need for manual agent scheduling and reducing operational complexity while maintaining high service quality.
Solution Approach 2:
The system continuously monitors actual call patterns and compares them against predictions, using this feedback to refine and update virtual interactions in real-time. This feedback loop allows the system to adapt to changing conditions and optimize agent utilization dynamically, improving service quality without requiring fixed over-provisioning of agents.
3Productivity
If traditional call routing is used during peak times, then all incoming calls can be handled, but caller wait times increase significantly when all agents are engaged
Solution Approach 1:
Virtual interactions are created in advance of peak call periods, pre-assigning agents to handle anticipated call volumes. When peak times arrive, incoming calls are rapidly matched to these pre-configured virtual interactions, bypassing traditional queuing and reducing caller wait times while maintaining full call handling capacity.
Solution Approach 2:
The system dynamically adjusts virtual interaction configurations based on real-time agent availability and predicted call patterns. During peak times, the system can rapidly create new virtual interactions or reassign existing ones to available agents, providing flexible, adaptive call routing that responds to changing conditions without increasing wait times.
Data Source
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AI summary
A contact center has channels for receiving communications requesting interaction with an agent, agent stations providing connection ability to received communications, and a Virtual Interaction (VI) Server connected to the LAN and executing VI software on a processor from a non-transitory medium. The VI Server creates and stores a Virtual Interaction object based on determination of an expected receipt of a future communication, the VI associating a source, an intent, a set of data regarding the source, and an agent station destination, and the created VI is stored and matched with a real incoming communication from the source associated in the VI, and the VI is then instantiated as a live interaction by connecting the source of the incoming call with the agent destination associated in the VI.