Call Distribution System Using Simulation Model for Business KPI Routing
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Solution Overview
Problem
Existing automatic call distribution systems are limited in their ability to route calls based on timely business key performance indicators (KPI) information and fail to incorporate business operational objectives, such as increasing sales, into their routing decisions, leading to suboptimal performance and lack of integration with overall business cost processes.
Innovation Solution
A call distribution system and method that utilizes a simulation model to determine impacts on KPIs and assign routings across multiple business objectives, allowing for closed-loop simulation of contact center and business operational objectives, enabling programmable models to simulate call routing based on timely business information and integrate with business systems for optimal decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing automatic call distribution systems route calls based on traditional algorithms (availability, idle time), then call distribution is simple and reliable, but the system cannot effectively incorporate timely business KPI information or simulate impacts on business objectives
Solution Approach 1:
The patent introduces a simulation model as an intermediary component between the call distribution system and business KPI information. This simulation model acts as a mediator that translates business objectives and KPIs into routing decisions, allowing the system to incorporate complex business information without directly complicating the core call distribution logic. The simulation model processes business data and generates routing recommendations that the ACD system can then execute.
Solution Approach 2:
The system performs preliminary simulation of call routing scenarios before actual calls are routed. By running simulations that predict the impact of different routing decisions on business KPIs, the system can prepare optimal routing strategies in advance. This preliminary action allows complex business information to be processed and translated into actionable routing decisions without adding real-time complexity to the call distribution process.
2Reliability
If the system incorporates simulation models and business KPI information for call routing, then call handling quality and alignment with business objectives improve, but processing time and computational resources increase
Solution Approach 1:
The simulation model performs preliminary calculations and scenario analysis before actual call routing decisions are made. By pre-processing business KPI information and simulating potential routing outcomes in advance, the system can reduce the time required for real-time routing decisions. The simulation model prepares routing recommendations that can be quickly implemented when calls actually occur.
Solution Approach 2:
The system incorporates feedback loops that continuously monitor actual call outcomes and compare them against simulated predictions and business objectives. This feedback mechanism allows the system to refine its simulation models and routing algorithms over time, improving accuracy and reducing the computational burden for future routing decisions. The feedback from actual performance data is used to adjust and optimize the simulation model parameters.
3Reliability
If agents are segregated into groups to serve particular call targets, then call handling consistency improves, but the system cannot flexibly route calls based on diverse business objectives and KPIs
Solution Approach 1:
The patent implements dynamic routing capabilities that allow the system to adapt call distribution strategies based on real-time business conditions and KPI performance. Rather than static agent groupings, the system can dynamically adjust routing decisions based on simulated impacts on business objectives. This dynamic approach maintains call handling consistency through structured routing logic while providing flexibility to respond to changing business requirements and optimize performance across multiple objectives.
Solution Approach 2:
The simulation model serves multiple functions: it evaluates different routing scenarios, predicts impacts on various business KPIs, and generates optimized routing decisions. This multi-functional capability allows the system to handle diverse business objectives and KPI requirements through a single integrated platform, providing both consistency and flexibility without requiring separate systems for different routing purposes.
4Adaptability or versatility
If the system integrates with business systems for automatic calculation of results, then overall business cost process integration improves, but system complexity and integration requirements increase
Solution Approach 1:
The simulation model acts as an intermediary layer between the call distribution system and business systems. It translates call routing data into business-relevant metrics and KPI information, then communicates with business systems through standardized interfaces. This intermediary approach simplifies integration by providing a clear data transformation layer that bridges the gap between telephony systems and business management systems, reducing the complexity of direct integration requirements.
Solution Approach 2:
The simulation model provides universal interfaces that can connect with various business systems through standardized protocols and data formats. It serves multiple integration purposes: reporting KPIs to business systems, receiving business data for simulation inputs, and generating routing decisions that align with business objectives. This multi-functional integration capability allows the system to work with diverse business systems without requiring custom integration solutions for each connection.
Data Source
AI summary
A method and apparatus are provided for automatic call distributors that route calls based in part on timely business information. The method includes providing a simulation model for calculating information and using this information from the model together with key process indicators information to generate decisions for routing calls.