Identifying Common Action Flows in User Session Data
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Solution Overview
Problem
Existing methods lack an efficient way to identify and analyze common user action flows in applications, which hinders performance optimization and testing of user interactions.
Innovation Solution
A system comprising a session engine, flow engine, and reporting engine processes user interaction data to identify common action flows by filtering and analyzing sequences of actions, generating scripts for testing and revealing bottlenecks or feature usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis of user interaction data is performed, then understanding of user behavior is limited, but automation complexity is low
Solution Approach 1:
The system segments user interaction analysis into distinct functional modules: session engine for data collection, flow engine for pattern recognition, and reporting engine for results presentation. Each module handles specific aspects of the analysis process, improving overall efficiency while maintaining manageable complexity through modular design.
Solution Approach 2:
The system enables self-service automated analysis by having the flow engine automatically discover and identify common action flows without manual intervention. The system processes user interaction data autonomously, generating insights about user behavior patterns, bottlenecks, and feature usage automatically.
2Measurement precision
If comprehensive user interaction data is collected, then accuracy of flow identification improves, but data processing time increases
Solution Approach 1:
The session engine performs preliminary actions by continuously collecting and organizing user interaction data into structured sessions before detailed analysis is needed. This pre-processing organizes the data in advance, allowing the flow engine to quickly identify patterns when analysis is required, improving both accuracy and efficiency.
Solution Approach 2:
The system maintains continuous data collection and processing operations rather than batch processing. The session engine continuously captures user interactions, and the flow engine continuously analyzes patterns, ensuring that comprehensive data is always available for accurate flow identification without significant delays.
3Loss of information
If detailed analysis of each user action is performed, then understanding of user behavior improves, but system resource consumption increases
Solution Approach 1:
The flow engine extracts only the essential and meaningful patterns from detailed user action data, rather than processing every individual action in full detail. It identifies common action flows and significant deviations, extracting the critical information needed for understanding user behavior while filtering out redundant data, thus reducing resource consumption.
Solution Approach 2:
The system performs partial analysis by focusing on identifying common patterns and significant deviations rather than analyzing every single user action in exhaustive detail. This selective approach provides sufficient user behavior understanding for most purposes while consuming fewer system resources.
Data Source
AI summary
A common action flow for an application is identified by processing session data maintained for a plurality of users to identify a plurality of action flows. Each action flow represents a series of actions taken by one of the users navigating the application's user interface during a session. A data structure is generated from the plurality of action flows. That data structure is indicative of a plurality of candidate sub-flows. The data structure is analyzed to identify a selected one of the candidate sub-flows repeated in multiple ones of the plurality of action flows. That identified sub-flow is the common action flow. Data representative of the identified common action flow can then be communicated.