Propensity Analysis for Digital Channel Optimization
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Solution Overview
Problem
It is challenging for website operators to determine the propensities of different digital channels and entry pages to drive users to perform specific target user activities, making it difficult to identify opportunities for revising web pages and digital channels to increase their effectiveness.
Innovation Solution
The system and method described determine propensities of digital channels, entry pages, digital systems, and non-digital systems to drive users to perform a target user activity by analyzing activity data and using statistical models, such as logistic regression, to identify which channels and pages are most effective.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If website operators manually analyze user activity data to determine channel effectiveness, then they can identify some performance patterns, but the analysis precision and reliability are insufficient to accurately determine propensities of different digital channels and entry pages
Solution Approach 1:
The patent introduces a propensity analysis system as an intermediary between raw user activity data and operational decision-making. This system includes proprietary data structures, statistical models, and computing infrastructure that mediate the transformation of complex user behavior data into actionable propensity metrics for different digital channels and entry pages, thereby achieving accurate measurement without requiring operators to directly manage the analytical complexity
Solution Approach 2:
The patent replaces manual, mechanical analysis methods with automated computational systems. By using proprietary data structures and statistical models implemented on computing infrastructure, the system automatically calculates propensity scores for different digital channels and entry pages, eliminating the need for manual data analysis while significantly improving measurement precision and reliability
2Productivity
If operators allocate resources uniformly across all digital channels and entry pages, then resource distribution is simple to manage, but productivity and user engagement are suboptimal because high-propensity channels are not prioritized
Solution Approach 1:
The patent changes the parameter of resource allocation from uniform distribution to propensity-based distribution. By calculating propensity scores for each digital channel and entry page using statistical models, the system enables operators to dynamically adjust resource allocation parameters according to measured effectiveness, thereby maximizing target user activity generation while maintaining manageable operational complexity through automated calculations
3Reliability
If operators revise web pages based on intuition rather than data-driven propensity analysis, then implementation is quick and easy, but the effectiveness of revisions is uncertain and reliability is low
Solution Approach 1:
The patent performs preliminary propensity analysis on existing digital channels and entry pages before revision decisions are made. By pre-calculating propensity scores and identifying specific performance gaps using statistical models, the system enables operators to make targeted, data-driven revision decisions that are more likely to be effective, while the preliminary analysis phase structures the information needed for efficient revision planning and execution
Data Source
AI summary
Users may engage in a target user activity via digital systems, such as a website, and/or non-digital systems. Users may user various digital channels to arrive at the digital systems or non-digital systems. Users may also arrive at digital systems, such as the website, via different entry pages. A propensity analyzer can, based on activity data associated with users, determine propensities of one or more of the digital channels, digital systems, non-digital channels, and/or entry pages to drive users to perform a target user activity. The propensity analyzer can generate recommendations for revising digital channels, digital systems, non-digital channels, and/or entry pages to increase their propensities to drive users to perform the target user activity.