Bayesian Model for UI Treatment Effect Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
User interface designers lack reliable systems to make data-driven decisions regarding changes to user interfaces due to variability in user interactions across different groups and over time, making it difficult to determine the impact of changes on user activity.
Innovation Solution
A computer system that monitors user interactions before and after a change, divides users into treated and control groups, generates time series for result metrics, and uses a Bayesian machine learning model to estimate a conditional distribution and counterfactual behavior, determining the treatment effect of the change on user interface data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user interactions are monitored and analyzed using traditional methods, then some feedback on user behavior can be obtained, but the measurement precision of treatment effect is insufficient due to variability across user groups and time
Solution Approach 1:
The patent segments users into treated and control groups based on their interaction with specific UI elements, and further partitions the control group into multiple subgroups. This segmentation allows for more precise comparison and isolation of treatment effects from confounding variables, directly addressing the measurement precision problem by creating comparable groups with reduced variability.
Solution Approach 2:
The patent performs preliminary partitioning of the control group into multiple subgroups before analyzing treatment effects. This preliminary action creates a more refined baseline for comparison, enabling more accurate attribution of changes in user behavior to specific UI modifications rather than general trends or confounding factors.
2Quantity of substance
If multiple user groups and time periods are considered, then more comprehensive user behavior data is collected, but it becomes more difficult to determine the specific impact of UI changes
Solution Approach 1:
By segmenting both the treated and control groups into meaningful subgroups, the patent reduces the complexity of analyzing comprehensive user behavior data. Each subgroup can be analyzed independently for specific treatment effects, making it easier to isolate the impact of UI changes from other variables even when large quantities of data are collected across multiple groups and time periods.
Solution Approach 2:
The patent introduces partitioned control groups as intermediaries between the treated group and the overall control group. These intermediate subgroups serve as mediators for comparison, allowing the system to handle large quantities of user behavior data by breaking down the analysis into manageable segments while maintaining the ability to detect specific treatment effects.
3Loss of information
If anecdotal feedback from test groups is used, then some guidance on user interaction can be obtained, but the scope and reliability of feedback is limited
Solution Approach 1:
The patent segments user feedback data by creating distinct treated and control groups, with the control group further divided into multiple subgroups. This segmentation enables the system to capture and analyze feedback information across different user segments, reducing information loss by maintaining granular data that can be adapted to specific user groups rather than providing only aggregate anecdotal feedback.
Data Source
Figure 1
Figure 2~3
Figure 4~5
AI summary
Examples described herein generally relate to a computer device including a memory, and at least one processor configured to evaluate a change to a user interface. The computer device monitor user interactions with the user interface prior to and after a change to the user interface. The monitoring includes collecting result metric data per user. The computer device divides users into a treated group and a control group based on whether each user engages in a particular interaction. The computer device generates a result metric time series for the treated group and a partitioned result metric time series for the control group. The computer device estimates a conditional distribution of the result metric and a counterfactual behavior using a Bayesian machine learning model based on the result metric time series. The computer device determines a treatment effect of the change to the user interface on the result metric data.