Call Mapping Using Variance Algorithms for Contact Center Routing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional contact center routing systems often rely on random or round-robin methods to connect callers with available agents, which can lead to inefficient matching and suboptimal customer service experiences, as they do not consider agent performance or caller propensity effectively.
Innovation Solution
A method that involves obtaining agent performance data, ranking agents, partitioning callers based on criteria, calculating outcome value difference indicators, and matching agents with callers to optimize the likelihood of successful interactions by assigning higher performing agents to callers with higher propensity for desired outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If round-robin or random routing methods are used to connect callers with agents, then the routing process is simple and fast, but the matching efficiency and customer service quality deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing agent performance metrics and caller propensity scores before routing occurs. This allows the routing system to make informed decisions without real-time computation delays, maintaining both speed and matching quality
Solution Approach 2:
The patent replaces the mechanical round-robin routing mechanism with an intelligent algorithmic system that uses variance algorithms and distribution compensation. This substitution enables the system to consider multiple factors (agent performance, caller propensity, partition characteristics) while maintaining computational efficiency through optimized algorithms
2Reliability
If agent performance data and caller propensity analysis are incorporated into routing decisions, then matching quality improves, but system complexity increases
Solution Approach 1:
The system segments callers into different partitions based on their propensity characteristics and segments agents into performance categories. This segmentation simplifies the routing decision by reducing the search space and enabling targeted matching strategies for different segments
Solution Approach 2:
The patent transforms complex performance and propensity data into simplified parameters such as variance metrics and distribution characteristics. These transformed parameters enable efficient comparison and matching while capturing the essential information needed for high-quality routing decisions
3Productivity
If high-performing agents are assigned to high-propensity callers, then desired outcomes (sales, satisfaction) increase, but the complexity of calculating outcome value differences increases
Solution Approach 1:
The system calculates outcome value differences selectively rather than for all possible agent-caller pairs. By focusing calculations only on relevant partitions and using approximation methods when appropriate, the system achieves high-quality matching without the full computational burden of exhaustive analysis
Data Source
AI summary
Method, system and program product, comprising obtaining agent performance data; ranking, agents based the agent performance data; dividing agents into agent performance ranges; partitioning callers based on criteria into a set of partitions; determining for each partition an outcome value for a first agent performance range and a second agent performance range; calculating for the partitions a respective outcome value difference indicator based on the outcome value for the first agent performance range and the outcome value for the second agent performance range for the partition; matching a respective agent to a respective caller in one of the partitions, based on the outcome value difference indicators for the partitions.