Propensity Analysis for Website Channel Optimization
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Solution Overview
Problem
It is challenging for website operators to determine the propensities of digital channels and entry pages to drive users to perform specific target user activities, making it difficult to identify opportunities for revising these elements to increase their effectiveness.
Innovation Solution
Systems and methods that analyze website activity data to determine the propensities of digital channels and entry pages to drive target user activities, and generate recommendations for changes to improve their effectiveness, using statistical analysis and machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If website operators manually analyze digital channels and entry pages to determine their effectiveness, then they can identify opportunities for revision, but the process is time-consuming and difficult to perform accurately
Solution Approach 1:
The patent replaces manual mechanical analysis with automated computational systems. A propensity analysis system automatically processes website activity data, digital channel information, and entry page characteristics to calculate propensity scores, eliminating the need for time-consuming manual analysis while maintaining or improving accuracy through systematic statistical methods
Solution Approach 2:
The system creates a mathematical model (propensity model) that copies and simulates the complex relationships between digital channels, entry pages, and user activities. This model can be repeatedly applied to different datasets without additional time investment, enabling rapid iteration and analysis of multiple scenarios
2Productivity
If website operators focus resources on all digital channels and entry pages equally, then they maintain comprehensive coverage, but they cannot prioritize high-performing elements effectively
Solution Approach 1:
The patent applies local quality by assigning different propensity scores to different digital channels and entry pages based on their specific performance characteristics. This allows operators to identify which specific channels and pages have higher effectiveness and allocate resources accordingly, rather than treating all elements uniformly
Solution Approach 2:
The system transforms raw website activity data into a new parameter (propensity score) that quantifies the effectiveness of each digital channel and entry page. This parameter transformation enables direct comparison and prioritization of different elements, converting qualitative performance differences into actionable quantitative metrics
3Adaptability or versatility
If website operators revise digital channels and entry pages without data-driven insights, then they can make changes, but they cannot determine which changes will increase effectiveness
Solution Approach 1:
The patent implements feedback by using the propensity analysis results to guide revision decisions. The system continuously monitors website activity data, calculates propensity scores, and uses these results to identify which digital channels and entry pages should be revised. This creates a closed-loop system where measurement informs action, and results can be re-measured to assess improvement
Data Source
AI summary
Users may arrive at a website via different digital channels. Users may also arrive at the website on different entry pages of the website. A propensity analyzer can, based on website activity data associated with a website, determine propensities of one or more of the digital channels, and/or one or more of the entry pages, to drive users to perform a target user activity during visits to the website. The propensity analyzer can generate recommendations for revising digital channels and/or entry pages to increase their propensities to drive users to perform the target user activity.