Dynamic Caller Routing via Pattern Matching
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Solution Overview
Problem
Conventional contact center routing systems rely on random or round-robin methods to connect callers with agents, failing to consider individual caller and agent characteristics, leading to suboptimal interactions and increased wait times.
Innovation Solution
Implementing a system that uses pattern matching algorithms and computer models to match callers with agents based on demographic, psychographic, and business-relevant data, allowing for skipping or reordering in queues, and pooling callers to optimize interactions for increased revenue generation, reduced contact duration, and improved customer satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If round-robin routing is used to connect callers to agents, then callers are routed in a systematic sequence, but the matching between caller characteristics and agent capabilities is random and suboptimal
Solution Approach 1:
The patent changes the routing parameters from simple sequential ordering to multi-dimensional characteristics including caller demographics, psychographics, agent capabilities, and historical performance data. This allows the system to optimize both routing efficiency and interaction quality simultaneously by matching callers with agents based on compatible characteristics rather than random assignment.
Solution Approach 2:
The system incorporates feedback loops that continuously monitor interaction outcomes, caller satisfaction, and agent performance. This feedback is used to refine and update the matching algorithms, improving the reliability of interactions over time while maintaining high routing efficiency through learned patterns and optimized matching criteria.
2Loss of time
If callers are routed strictly in queue order, then wait time fairness is maintained, but optimal matching opportunities are lost and wait times increase
Solution Approach 1:
The patent implements dynamic queue management where caller priority positions are not fixed but can change based on real-time matching opportunities. When an agent becomes available, the system dynamically evaluates all waiting callers against the agent's capabilities and reorders the queue to prioritize the best match, thereby reducing overall wait times while increasing interaction success rates.
Solution Approach 2:
The system performs preliminary analysis of caller characteristics and agent capabilities before actual routing occurs. By pre-evaluating compatibility metrics and maintaining ranked lists of potential matches, the system can quickly connect callers with suitable agents without sacrificing matching quality, thus reducing wait times while maintaining high interaction success rates.
3Reliability
If pattern matching algorithms are implemented to match callers with agents based on detailed data, then interaction quality improves, but system complexity increases
Solution Approach 1:
The patent segments the complex matching process into distinct modular components: data collection modules, pattern matching engines, decision logic layers, and execution interfaces. Each component handles specific aspects of the matching process independently, making the overall system more manageable and maintainable while still achieving high interaction quality through coordinated operation of these specialized modules.
4Measurement precision
If caller data is collected and analyzed for routing decisions, then matching accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data collection and preprocessing operations before routing decisions are needed. Caller characteristics, agent capabilities, and historical data are pre-analyzed and stored in optimized formats, allowing rapid retrieval and comparison during actual routing events. This reduces real-time processing time while maintaining high matching accuracy through pre-computed compatibility metrics.
Data Source
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AI summary
Methods and systems are provided for routing callers to agents in a call-center routing environment. An exemplary method includes identifying caller data for at least one caller in a queue of callers, and skipping a caller at the front of the queue of callers for another caller based on the identified caller data. The caller data may include one or both of demographic data and psychographic data. Skipping the caller may be further based on comparing caller data with agent data associated with an agent via a pattern matching algorithm such as a correlation algorithm, hi one example, if the caller at the front of the queue has been skipped a predetermined number of times the caller at the front is the next routed (and cannot be skipped again).