Visitor Engagement Detection for Online Listing Platforms
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In online sessions, such as online shopping, it is challenging for administrators to accurately track and maintain visitor engagement due to the remote and diverse nature of visitors, making it difficult to prevent disengagement and increase interaction effectively.
Innovation Solution
A visitor engagement system that uses a server computing device to monitor user actions at predetermined intervals, generates engagement scores, and initiates communication with low-engagement visitors to increase their interaction levels by providing relevant content, thereby enhancing engagement and preventing disengagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an administrator manually monitors visitor engagement in an online session, then engagement levels can be tracked, but the system becomes inadequate for large numbers of remote visitors from diverse backgrounds
Solution Approach 1:
The system uses automated computer-implemented techniques to monitor visitor engagement without requiring human administrators to manually track each visitor. The system self-services by automatically detecting engagement levels, identifying low-engagement visitors, and initiating communications to re-engage them, thereby scaling to handle large numbers of remote visitors from diverse backgrounds while maintaining tracking accuracy.
Solution Approach 2:
The patent replaces manual mechanical monitoring methods with automated computer-based systems. Instead of administrators physically observing or manually tracking visitor behavior, the system uses automated detection mechanisms including monitoring user actions, generating engagement scores, and using communication systems to interact with visitors, thereby achieving both precision and scalability.
2Measurement precision
If the system monitors all user actions in real-time, then engagement levels can be accurately determined, but the processing complexity and resource consumption increase
Solution Approach 1:
The system extracts only the most relevant user actions and engagement indicators needed to determine engagement levels, rather than processing all possible data. By focusing on key metrics such as user interactions, time spent, and specific engagement behaviors, the system achieves accurate engagement measurement while reducing processing complexity and resource consumption.
Solution Approach 2:
The system applies different monitoring and analysis methods to different aspects of visitor behavior based on their relevance to engagement. Rather than uniformly processing all user actions with the same complexity, the system tailors its analysis to specific engagement-critical behaviors, thereby achieving accurate measurement with optimized processing requirements.
3Reliability
If the system initiates communication with low-engagement visitors, then visitor disengagement can be prevented, but the communication infrastructure complexity increases
Solution Approach 1:
The system uses an intermediary communication module that automatically manages interactions with low-engagement visitors. This intermediary component handles the complexity of initiating communications, selecting appropriate messages, and coordinating with communication channels, thereby preventing visitor disengagement while keeping the overall system architecture manageable through specialized modular design.
4Productivity
If the system processes user data at predetermined time intervals, then engagement scores can be generated efficiently, but real-time responsiveness may be reduced
Solution Approach 1:
The system uses periodic time intervals to process user data and generate engagement scores, which balances efficiency with responsiveness. By processing data at optimized intervals rather than continuously, the system achieves efficient score generation while still maintaining adequate real-time responsiveness to detect and address low-engagement situations as they occur.
Data Source
AI summary
In implementations of a visitor engagement detection and control system for online sessions with a listing platform, a computing device implements a visitor engagement system to monitor user actions by the visitors with items listed by the listing platform, and map the user actions and the items listed by the listing platform to a timeline of the online session. An engagement module determines levels of visitor engagement in the online session based on the mapping of the user actions and the items, and initiates communication with a low-engagement visitor during the online session to increase an engagement level of the low-engagement visitor. In some cases, a communication is then established between the low-engagement visitor and a different low-engagement visitor, an engaged visitor, or an administrator of the listing platform.