Benchmarking Pairing Strategies in Task Assignment Systems
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Solution Overview
Problem
Task assignment systems face challenges in measuring performance changes over time when switching between pairing strategies, as the value of task assignments is often realized only after months or years, leading to difficulties in evaluating the effectiveness of alternative strategies.
Innovation Solution
A method for benchmarking pairing strategies involves determining historical task assignments for multiple cohorts over time, tracking performance differences between strategies, and generating reports to assess long-term value, allowing for fair statistical treatment of tasks irrespective of earlier cohort presence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If performance-based routing is used to maximize expected outcome of each task assignment, then task assignment performance is improved, but agent utilization uniformity deteriorates
Solution Approach 1:
The system changes the parameter of pairing strategy from static (FIFO) to dynamic (PBR), adjusting agent selection based on performance metrics. This allows maximizing task assignment outcomes while the patent addresses the uniformity issue through separate benchmarking mechanisms that monitor and adjust for agent utilization distribution.
2Productivity
If alternative pairing strategies are implemented to improve task assignment outcomes, then long-term value is improved, but measurement difficulty increases
Solution Approach 1:
The patent implements preliminary action by establishing baseline cohorts before switching pairing strategies. These baseline groups are created in advance with known characteristics, allowing future performance measurements to be compared against a predetermined reference point. This enables accurate long-term value measurement despite strategy changes by having pre-established comparison groups.
Solution Approach 2:
The system creates copies of baseline cohorts that are tracked over time alongside actual performance data. These cohort copies serve as reference representations that can be compared against actual outcomes, enabling measurement of long-term value without being confounded by strategy changes. The benchmarking system essentially compares actual performance against copied baseline expectations.
3Ease of operation
If FIFO strategy is used for task assignment, then agent utilization uniformity is improved, but task assignment performance optimization deteriorates
Solution Approach 1:
The patent transitions from static FIFO assignment to dynamic PBR assignment, where the pairing strategy adapts based on agent performance metrics and task characteristics. This dynamic approach allows the system to optimize task assignment performance while the benchmarking framework separately monitors utilization uniformity, enabling both goals to be addressed through different mechanisms.
Data Source
AI summary
Techniques for benchmarking pairing strategies in a task assignment system are disclosed. In one particular embodiment, the techniques may be realized as a method for benchmarking pairing strategies in a task assignment system comprising determining a first base cohort of a first plurality of historical task assignments for at least two pairing strategies for a first base period, determining a first performance difference between the at least two pairing strategies after a first measurement period based on the first base cohort, and outputting the first performance difference.