Agent Call Time Prediction Data Store for Routing
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Solution Overview
Problem
Call centers face challenges in accurately predicting agent response times, leading to inefficient routing of high-value calls and prolonged wait times for customers.
Innovation Solution
A data store system that prompts agents for estimates of remaining call service times, compares these estimates to actual times, and uses the accuracy statistics to route high-value calls to the most reliable agents, thereby optimizing call handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If call centers use traditional routing methods without agent prediction accuracy data, then routing decisions are made without optimization, but call handling efficiency deteriorates and wait times increase
Solution Approach 1:
The system collects and stores agent service time estimates and actual completion times in advance, building a historical data foundation before routing decisions are needed. This preliminary data accumulation enables accurate prediction of agent availability and performance, allowing optimized routing decisions to be made without increasing customer wait times
Solution Approach 2:
The system implements a feedback mechanism where agents provide service time estimates during calls, the system tracks actual completion times, and compares these against predictions. This continuous feedback loop refines prediction accuracy over time, enabling progressively better routing decisions that improve call handling efficiency while reducing wait times
2Reliability
If call centers implement comprehensive data collection and analysis systems for agent prediction accuracy, then routing optimization improves, but system complexity increases
Solution Approach 1:
The system segments the data collection and analysis function into discrete, manageable components: individual agent estimate records, actual completion time records, and computed accuracy statistics. This segmentation allows the complex data store to be built incrementally through simple, repeatable data entry and processing operations, reducing implementation complexity while maintaining high routing decision accuracy
Solution Approach 2:
The system automatically computes agent prediction accuracy statistics by comparing stored estimates with actual completion times, eliminating the need for manual analysis. This self-service automation reduces the operational complexity of maintaining accurate routing data while improving the reliability of routing decisions through consistent, error-free calculations
Data Source
AI summary
A data store (e.g., a relational database, an object-oriented database, an unstructured file system, an in-memory data structure, etc.) and associated methods are disclosed for predicting the accuracy of agent responses in a call center. In accordance with the illustrative embodiment, agents in a call center are prompted at different times during their workday for estimates of how much longer they will be servicing their current call, and the estimates are stored in the data store along with the actual times required for completion of the call. Statistics for each agent's prediction accuracy are computed and stored in the data store along with the raw data, and can then be used to assess the accuracy of subsequent agent responses to queries.