Dynamic Contact Center Routing Rules Based on Forecasted Load
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Solution Overview
Problem
Current contact center systems lack flexibility in routing interactions, as their routing strategies are often monolithic and centralized, leading to inefficiencies and additional costs due to mispredicted interaction loads and resource commitments.
Innovation Solution
A system that schedules contact center resources and routing rules dynamically based on forecasted and real-time interaction loads, using programmable software agents to propagate and execute routing rules, allowing for intelligent interaction routing and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If routing strategies are fixed and centralized, then system simplicity is maintained, but flexibility and adaptability to changing interaction loads deteriorate
Solution Approach 1:
The patent segments routing strategies into individually schedulable rules that can be independently activated or deactivated based on forecasted interaction loads. Each routing rule can be scheduled for specific time periods, allowing the system to adapt to changing conditions without requiring a complete routing strategy change, thus resolving the contradiction between system simplicity and routing flexibility.
Solution Approach 2:
The patent introduces dynamic scheduling of routing rules based on forecasted interaction arrivals. The system transitions from static, fixed routing strategies to dynamic routing rule selection that adapts to predicted workload patterns. This allows the routing system to flexibly respond to changing interaction loads while maintaining operational simplicity through automated scheduling.
2Ease of manufacture
If monolithic routing strategies are used, then implementation simplicity is maintained, but ability to optimize for different interaction types and time periods deteriorates
Solution Approach 1:
The patent divides monolithic routing strategies into discrete, schedulable routing rules. Each rule can be independently configured for specific interaction types, skill requirements, and time periods. This segmentation enables fine-grained optimization of workflow efficiency for different scenarios while maintaining ease of implementation through the standardized scheduling framework.
Solution Approach 2:
The patent applies different routing rules to different interaction types, agent skills, and time periods based on local requirements. Instead of a single monolithic strategy, the system selects appropriate routing rules locally for each interaction context, optimizing workflow efficiency for diverse interaction types while maintaining simple rule-based implementation.
3Adaptability or versatility
If routing rules are scheduled dynamically based on forecasts, then adaptability to interaction load changes improves, but system complexity increases
Solution Approach 1:
The patent schedules routing rules in advance based on forecasted interaction arrivals. By performing preliminary scheduling actions using forecast data, the system achieves dynamic adaptability to predicted workload patterns without requiring complex real-time decision-making. This reduces the operational complexity while maintaining high routing adaptability to changing interaction loads.
Solution Approach 2:
The patent introduces a scheduling application as an intermediary between forecast data and routing rule execution. This intermediary component simplifies the overall system architecture by centralizing the scheduling logic and automatically selecting appropriate routing rules based on forecasts, reducing the complexity burden on other system components while enabling flexible routing adaptation.
4Productivity
If forecast-based resource scheduling is used, then resource allocation efficiency improves, but costs of scheduling changes and re-quantified commitments increase
Solution Approach 1:
The patent schedules routing rules and resource commitments in advance based on forecasted interaction patterns. By performing preliminary scheduling actions, the system optimizes resource allocation efficiency for predicted workloads while minimizing the need for frequent scheduling changes and re-quantified commitments, thereby reducing the associated costs and operational disruptions.
Data Source
AI summary
A system for scheduling resources and rules for routing includes a server connected to a network, a scheduling application executable from the server, and at least one programmable software agent for scheduling routing rules. The scheduling application receives statistics about forecast arrival rates for incoming interactions and current resource availability data and schedules resources and routing rules according to the forecast requirements the software agent propagating the portion of scheduling relative to the routing rules.