Network Service Session Prediction for Negative User Experience
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Solution Overview
Problem
Users of network application services often experience negative experiences that lead to dissatisfaction, causing them to switch services, reduce usage, or abandon them, which service entities struggle to identify and address due to lack of feedback from dissatisfied users.
Innovation Solution
Implementing an AI-driven system that analyzes user and provider representations to predict negative experiences, deploying preventative and mitigative tools such as notifications, service benefits, and automatic re-matching to enhance user engagement and prevent abandonment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If rating queries or feedback solicitation are used to identify negative user experiences, then the service entity can obtain user feedback, but dissatisfied users are unlikely to provide feedback and the service entity cannot reacquire those users
Solution Approach 1:
The system performs preliminary analysis of user behavior patterns, service performance metrics, and interaction data to identify users at risk of dissatisfaction before they actually become dissatisfied. This proactive approach allows the service entity to intervene with preventive measures (such as offering service improvements or compensation) before the user decides to leave, thereby maintaining user retention while still capturing information about potential issues.
2Loss of information
If the service entity waits for user feedback to identify negative experiences, then feedback can be collected, but users who abandon the service never provide feedback
Solution Approach 1:
The system implements a closed-loop feedback mechanism where user behavior data, service performance metrics, and interaction patterns are continuously collected and analyzed. This feedback loop enables the service entity to automatically identify negative experiences, trigger appropriate responses, and measure the effectiveness of interventions, creating a continuous improvement cycle that captures information about user experiences even from users who might otherwise abandon the service.
3Reliability
If proactive prediction and prevention systems are implemented, then user engagement and satisfaction can be enhanced, but system complexity increases
Solution Approach 1:
The system employs automated self-service mechanisms where algorithms automatically analyze user data, predict dissatisfaction risks, generate appropriate responses, and implement interventions without requiring manual service entity involvement. This automation reduces the operational complexity burden on the service entity while maintaining high user satisfaction through consistent, timely interventions.
Solution Approach 2:
The system introduces an intermediary intelligence layer (AI/ML models) that sits between raw user data and service entity actions. This intermediary automatically processes complex data patterns, identifies negative experiences, and recommends or executes appropriate responses, thereby managing system complexity by delegating analytical and decision-making functions to specialized intermediary components rather than requiring the service entity to directly manage all aspects of user experience monitoring and intervention.
Data Source
AI summary
A system can monitor event data corresponding to a current user experience of a requesting user during a current application session with a network service. Based on the event data, the system generates one or more representations corresponding to the current user experience of the requesting user, and executes a machine learning model to process the one or more representations in order to predict a negative user experience for the requesting user within a future time frame during the current application session. In response to predicting the negative user experience, the system implements one or more corrective actions during the current application session through the service application to prevent or mitigate the predicted negative user experience.