Proactive Customer Intervention Using Predicted User Actions
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Solution Overview
Problem
Existing automated systems for customer interaction are passive, waiting for user-initiated actions, leading to inefficient use of higher cost communication channels.
Innovation Solution
A system that proactively generates intervening messages based on user activity prediction models, identifying anticipated actions and transmitting messages through lower cost channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system uses a passive approach waiting for user-initiated actions, then customer service can address user needs accurately, but higher cost communication channels are overloaded and resource utilization is inefficient
Solution Approach 1:
The system performs preliminary actions by proactively analyzing user activity data and predicting future user actions before they occur. The prediction model processes current user activities to anticipate what the user will do next, allowing the system to prepare and send automated messages in advance, thereby preventing users from needing to contact customer service through higher cost channels.
Solution Approach 2:
The system implements feedback by continuously monitoring user activity data and using the prediction model to generate insights about user behavior patterns. This feedback loop enables the system to adapt to user preferences and adjust its proactive intervention strategy, improving both service accuracy and channel utilization efficiency over time.
2Productivity
If the system proactively generates intervening messages based on prediction, then lower cost channels can be utilized more effectively, but the system complexity increases due to prediction models and activity analysis
Solution Approach 1:
The system enables self-service by using automated prediction and message generation capabilities to handle customer interactions without requiring complex human-in-the-loop decision-making. The prediction model and automated messaging system work together to provide service autonomously, reducing the need for complex system architecture while improving cost efficiency.
3Productivity
If automated messages are sent proactively to anticipate user needs, then customer service load on high cost channels is reduced, but there is a risk of sending unwanted or irrelevant messages to users
Solution Approach 1:
The system applies local quality by personalizing the proactive messages based on individual user characteristics, preferences, and behavior patterns. The prediction model analyzes user-specific data to determine what messages are relevant to each user, ensuring that only personalized and contextually appropriate messages are sent, thereby avoiding user experience degradation.
Data Source
AI summary
Embodiments disclosed herein generally relate to a system and method for proactively generating an intervening message for a remote client device in response to an anticipated user action. A computing system receives one or more streams of user activity. The one or more streams of user activity include interaction with a server of an organization via an application executing on the remote client device. The computing system inputs the one or more streams of user activity into a prediction model. The computing system identifies an anticipated user action based on a prediction output from the prediction model. The computing system determines, based on a solution model, a proposed solution to the anticipated user action. The computing system generates an anticipated message to be transmitted to the remote client device of the user. The computing system transmits the anticipated message to the remote client device of the user.