Session Stage Analysis for Agent Workload Balancing
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Solution Overview
Problem
During peak periods, online agents face challenges in maximizing service capacity and reducing user queue times due to the limitations in handling multiple user consultations simultaneously, leading to inefficiencies in resource allocation and increased waiting times.
Innovation Solution
An information processing method that determines session stages of multiple sessions, allocates additional sessions to busy agents based on preset conditions, such as sessions nearing completion, to optimize resource utilization and reduce waiting times by connecting queued users to available agents immediately after existing sessions end.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If an online agent serves multiple users simultaneously, then service capacity increases, but service quality and user experience deteriorate due to agent overload
Solution Approach 1:
The system performs preliminary analysis of session stages before making allocation decisions. By evaluating which sessions are nearing completion or can be temporarily paused, the system proactively prepares for optimal agent assignment rather than reactively assigning users to any available agent, thus maintaining service quality while maximizing capacity
Solution Approach 2:
The system dynamically adjusts agent capacity utilization based on real-time session stages. Instead of using a fixed threshold for agent availability, the system continuously monitors session progression and adapts allocation decisions according to the current state of ongoing sessions, enabling flexible optimization of both service capacity and quality
2Productivity
If agents are assigned to more sessions during peak periods, then service carrying capacity increases, but overall user queue time increases due to resource allocation inefficiency
Solution Approach 1:
The system implements a feedback mechanism that continuously monitors session stages and uses this information to guide allocation decisions. By analyzing which sessions are close to completion or can be paused, the system receives feedback on actual agent workload and capacity, enabling it to optimize future allocations and reduce queue times while maintaining high service capacity
Solution Approach 2:
The system changes the parameter of agent availability by considering sessions at different stages (ongoing, pausable, near-completion) as having different effective capacities. This transforms the static concept of agent availability into a dynamic parameter that varies based on session context, allowing more efficient utilization of agent capacity and reduced user waiting time
Data Source
AI summary
An information processing method includes determining at least two sessions corresponding to a first session object and having one-to-one correspondence with at least two second session objects, analyzing the at least two sessions, to obtain session stages of the at least two sessions currently in corresponding session processes, determining that at least one session satisfies a preset condition based on the session stages of the at least two sessions currently in the corresponding session processes, and allocating at least one second session object to be connected to the first session object.