Context-Aware Estimated Wait Time Calculation for Contact Centers
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Solution Overview
Problem
Existing contact center systems inaccurately estimate wait times due to reliance on generic averages and unaccounted variations in contact complexity and agent efficacy, leading to unreliable wait time predictions.
Innovation Solution
An Estimated Wait Time (EWT) computing system that analyzes incoming contacts to determine attributes, categorizes them based on past history and agent availability, and computes wait times using a categorization and computing module to provide accurate and context-based wait time estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If generic averaging algorithms are used to calculate Estimated Wait Time, then the calculation process is simple, but the accuracy of wait time estimation deteriorates due to unaccounted variations in contact complexity and agent efficacy
Solution Approach 1:
The patent segments the contact center operations into multiple dimensions including contact attributes (new/existing customer, contact type), agent attributes (skill level, efficacy ratings), and contextual factors (time of day, queue position). This segmentation allows the system to move from generic averaging to dimension-specific calculations, improving accuracy while maintaining manageable complexity through structured data organization.
Solution Approach 2:
The patent changes the parameters used in wait time calculation from single generic averages to multiple dynamic parameters including contact complexity scores, agent efficacy ratings, real-time queue conditions, and historical performance data. These parameter changes enable the system to account for variations in contact complexity and agent efficacy, directly addressing the accuracy problem while using computational methods that balance complexity and precision.
2Device complexity
If agent efficacy variations are not accounted for in EWT calculation, then the calculation method is simple, but the reliability of wait time predictions deteriorates when skilled agents are unavailable
Solution Approach 1:
The patent performs preliminary actions by pre-categorizing contacts into complexity levels and pre-rating agent efficacy before the actual wait time calculation. The system pre-processes contact attributes and agent performance data to create lookup tables and baseline metrics, which are then applied during real-time calculations. This preliminary preparation ensures that agent efficacy variations are already accounted for in the calculation framework, improving reliability without adding significant complexity during the actual wait time computation.
3Device complexity
If manual updates of agent progress are used, then the system implementation is simple, but the accuracy of estimated wait time deteriorates due to delays and human error
Solution Approach 1:
The patent implements automated feedback mechanisms that continuously monitor agent progress through system-integrated indicators such as contact status changes, wrap-up completions, and queue position updates. This feedback loop automatically updates the Estimated Wait Time calculations in real-time without manual intervention, eliminating delays and human errors while maintaining reasonable system complexity through automated data collection and processing.
4Device complexity
If generically averaged values are used for all contacts, then the data processing is simple, but the accuracy deteriorates for specific contact categories and individual customers
Solution Approach 1:
The patent applies local quality by providing customized wait time estimates for different contact categories and individual customers rather than using uniform generic averages. The system calculates separate EWT values based on specific contact attributes (new vs. existing customers, contact types), agent availability for specific skill sets, and individual contact histories. This localized approach ensures each contact receives an accurate estimate tailored to its specific characteristics while the underlying data processing remains structured and manageable.
Data Source
AI summary
An Estimated Wait Time (EWT) computing system for computing estimated wait time for customers in a contact center is provided. The EWT computing system includes an analysis module for analyzing each incoming contact to determine attributes corresponding to the incoming contact. The EWT computing system further includes a categorization module for categorizing the incoming contact based on the determined attributes and a past history of the incoming contact or similar contacts. The EWT computing system further includes a computing module for computing an estimated wait time for the incoming contact based on a category of the incoming contact and availability of suitable agents for handling the incoming contact. The EWT computing system further includes a reporting module for reporting the estimated wait time to the incoming contact.