Website Session Analysis for Pinpointing Abandonment Attributes
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Solution Overview
Problem
Existing methods for identifying issues in digital interactions, such as software bugs or interface flaws, are inefficient and inaccurate, relying on user feedback and activity monitoring, which fail to pinpoint specific webpage attributes causing user abandonment.
Innovation Solution
A detection system analyzes suspect sessions to identify underperforming stages and attributes by comparing conversion rates between suspect and similar sessions, calculating under-conversion rates, and presenting suspect attributes correlated with abandonment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user feedback and activity monitoring are used to identify issues, then overall website performance can be tracked, but specific webpage attributes causing user abandonment cannot be accurately determined
Solution Approach 1:
The patent segments user sessions into suspect sessions (where abandonment occurred) and similar sessions (where completion occurred), allowing comparison at the session level to identify specific webpage attributes causing issues. This segmentation enables precise identification of problematic attributes without requiring complex analysis of entire website activity.
Solution Approach 2:
The patent focuses analysis on specific local attributes of webpages (such as particular interface elements, loading times, or design features) rather than treating the entire website uniformly. By identifying which specific attributes correlate with abandonment in suspect sessions versus similar sessions, the system achieves high measurement precision for problematic areas without needing to analyze every aspect of the website.
2Loss of information
If traditional user feedback methods are used, then user likes and dislikes can be identified, but technical details of issues cannot be explained due to lack of user technical knowledge
Solution Approach 1:
The patent replaces the mechanical system of user feedback collection and analysis with an automated detection system that programmatically analyzes session data, webpage attributes, and conversion rates. This substitution eliminates the need for users to articulate technical issues while preserving all relevant technical detail information through automated measurement and comparison of suspect versus similar sessions.
3Productivity
If activity monitoring across entire webpage is performed, then which webpages causing issues can be identified, but efficiency and speed of issue identification are insufficient
Solution Approach 1:
The patent segments the monitoring scope from entire webpage activity to specifically suspect sessions versus similar sessions, enabling faster identification by focusing computational resources on comparative analysis of sessions with different outcomes. This segmentation maintains measurement precision by directly comparing attributes between sessions that ended in abandonment versus sessions that completed conversion.
Solution Approach 2:
The patent changes the monitoring parameters from general activity tracking to specific conversion rate measurements and attribute correlation analysis. By measuring conversion rates for suspect sessions versus similar sessions and identifying which attribute changes correlate with abandonment, the system achieves both high productivity in issue identification and high measurement precision for specific problematic attributes.
Data Source
AI summary
Techniques are described herein for identifying issues related to digital interactions. For example, a detection system may be provided to analyze suspect sessions to determine if one or more stages associated with the suspect sessions are underperforming compared to corresponding stages associated with similar sessions. The detection system may provide a user interface that allows a user to select one or more attributes that may be associated with one or more sessions. Selection of the one or more attributes may identify multiple sessions (referred to as suspect sessions herein). The one or more suspect sessions may be analyzed to determine whether one or more stages associated with the one or more suspect sessions are underperforming compared to corresponding stages associated with one or more other sessions determined to be similar to the one or more suspect sessions.