Dynamic Wrap-Up Time Adjustment via AI Analysis
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Solution Overview
Problem
Current contact center systems rely on static wrap-up times, which do not accurately reflect the actual time needed by agents for post-communication work, leading to resource wastage and inefficiencies.
Innovation Solution
An external system dynamically determines wrap-up times by considering both internal and external data, including AI insights and real-time information, to provide updated wrap-up times that can be adjusted during communication sessions, improving resource utilization and customer experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static wrap-up times are used, then system simplicity is maintained, but resource utilization deteriorates due to inaccurate time allocation
Solution Approach 1:
The patent implements dynamic wrap-up time adjustment by integrating AI systems that continuously analyze communication characteristics and automatically modify wrap-up times during active communication sessions. This transforms the static wrap-up time system into a dynamic one that adapts to real-time conditions, optimizing resource utilization without requiring complete system redesign
Solution Approach 2:
The system changes the wrap-up time parameter based on AI analysis of communication properties, agent performance metrics, and queue characteristics. This parameter adjustment allows the system to optimize resource allocation by allocating longer wrap-up times when needed and shorter times when sufficient, directly addressing the resource utilization issue
2Ease of manufacture
If static wrap-up times are used, then implementation ease is maintained, but productivity deteriorates due to agent violations and inefficiencies
Solution Approach 1:
The AI system automatically monitors communication progress and autonomously adjusts wrap-up times without requiring manual intervention from agents or supervisors. This self-service capability maintains implementation ease while significantly improving productivity by eliminating wrap-up time violations and ensuring adequate time for post-communication tasks
Solution Approach 2:
The system implements continuous feedback loops where AI analyzes agent behavior patterns, communication outcomes, and wrap-up time utilization data to dynamically adjust future wrap-up time allocations. This feedback mechanism improves productivity by learning from past performance while maintaining ease of implementation through automated adjustments
3Measurement precision
If external AI systems are integrated to dynamically adjust wrap-up times, then wrap-up time accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary AI layer that sits between the contact center system and wrap-up time management. This intermediary analyzes communication data and translates it into actionable wrap-up time adjustments, improving accuracy while managing system complexity through a dedicated intermediate component rather than direct integration across the entire system
Solution Approach 2:
The system segments wrap-up time management into distinct functional modules: AI analysis component, decision-making component, and execution component. This segmentation improves measurement precision by allowing specialized processing in each module while reducing overall system complexity through modular architecture that can be implemented incrementally
Data Source
AI summary
The present disclosure provides, among other things, a method of managing a wrap-up time in a contact center, the method including: receiving, by an agent of the contact center, a communication having a variable associated with the communication; receiving an input from a source external to the contact center; determining that the variable is related to the input; based on the relation of the variable to the input, determining an updated wrap-up time; storing the updated wrap-up time and the input in a database including timing variables; enabling a machine learning process to analyze the database; providing the updated wrap-up time to the agent as an amount of time rendered on a display to the agent; and updating a data model used to automatically determine wrap-up times based on the analysis of the machine learning process.