AI Campaign Pacing for Balanced Agent Workload
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Solution Overview
Problem
Existing messaging campaign systems face challenges with uneven user response inflows, leading to agent imbalances and delayed user assistance, resulting in user dissatisfaction and reduced campaign effectiveness.
Innovation Solution
A campaign pacing system that integrates minimal agent dashboard integration, utilizes AI and machine learning to analyze message feeds, determines agent overload, and dynamically balances message distribution based on real-time and historical data to optimize agent workload and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple messages are sent to a large number of users simultaneously, then campaign reach and promotion effectiveness are improved, but agent workload becomes unbalanced and user response time increases
Solution Approach 1:
The system dynamically adjusts the message send rate based on real-time agent availability and user response patterns. The campaign pacing module monitors agent workload and automatically modulates the messaging rate to match agent capacity, preventing overload while maintaining campaign reach and reducing user response time.
Solution Approach 2:
The system implements continuous feedback loops where agent performance data and user response patterns are monitored and fed back into the campaign pacing module. This feedback mechanism enables real-time optimization of message send rates to align with agent availability, resolving the contradiction between campaign reach and response time.
2Reliability
If agents are assigned to handle user responses immediately, then user satisfaction is improved, but system complexity increases due to need for real-time monitoring and adjustment
Solution Approach 1:
The campaign pacing module operates autonomously to manage message distribution based on agent availability. The system self-regulates the send rate without requiring complex manual intervention or real-time manual monitoring, reducing operational complexity while maintaining high user satisfaction through reliable agent assignment.
Solution Approach 2:
The system performs preliminary analysis of historical data and agent capacity before executing the campaign. By pre-calculating optimal send rates and anticipating agent availability patterns, the system reduces the need for complex real-time adjustments, simplifying system architecture while ensuring reliable user satisfaction.
3Productivity
If the message send rate is increased to maximize campaign efficiency, then campaign effectiveness is improved, but agent overload occurs and response quality deteriorates
Solution Approach 1:
The system continuously monitors agent workload parameters and adjusts the message send rate parameter accordingly. When agent capacity is approached, the system automatically reduces the send rate to maintain response quality. This dynamic parameter adjustment resolves the contradiction between campaign efficiency and response quality by aligning messaging velocity with agent capacity.
Data Source
AI summary
The present invention discloses a method (300) and a system (100) for campaign pacing. The method (300) comprises receiving campaign data from a first device. The campaign data comprises information associated with a plurality of target users and a campaign message associated with a product. Further, the method (300) comprises analyzing the campaign message using an artificial intelligence technique. Upon analysis, the method (300) comprises determining a priority of the campaign message and a pacing rate of sending the campaign message based on the analysis of the campaign message. The method (300) further comprises transmitting the campaign message to a plurality of user devices (102, 202) associated with the plurality of target users based on the determined pacing rate via a communication channel (204).