ML Personalization Rules for Audience Segmentation
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Solution Overview
Problem
Existing content management systems (CMS) fail to effectively personalize web content by not engaging users in determining how content should be personalized and fail to discover different variants for different audiences, limiting their ability to maximize business goals such as visitor engagement and e-commerce purchases.
Innovation Solution
The use of machine learning methodologies to discover audience segments and suggest personalized content based on accumulated customer data, allowing for the generation of personalized experiences that can be edited or dismissed by users, and integration with databases like the xDB to provide unique customization and end-to-end business optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If prior art systems use content tests to select a single winner for all visitors, then the system is simple to implement and operate, but it fails to discover different variants for different audiences and limits personalization capability
Solution Approach 1:
The patent segments the audience into different user segments based on behavior patterns and preferences. Instead of applying a single content test result to all visitors, the system divides the audience into segments (e.g., based on geographic location, behavior, demographics) and applies different content variants to each segment, thereby enabling personalized content delivery while maintaining manageable system complexity through automated segmentation algorithms.
Solution Approach 2:
The patent implements dynamic content selection where the system continuously learns from user interactions and adjusts content recommendations in real-time. The machine learning models dynamically adapt to changing user preferences and update segmentations automatically, allowing the system to respond to individual user needs without requiring manual reconfiguration, thus improving personalization while managing complexity through automation.
2Ease of operation
If prior art systems present only one winning variant to users, then the system is easy to operate, but it fails to engage users in determining how content should be personalized
Solution Approach 1:
The patent enables users to self-serve by allowing them to interact with the system through simple interfaces to provide preferences or feedback. Users can manually adjust content settings, select preferred categories, or provide feedback on content relevance, and the machine learning system automatically incorporates this input to refine personalization. This self-service approach maintains ease of operation while significantly improving personalization capability through user-driven data collection.
Solution Approach 2:
The patent implements feedback mechanisms where user interactions with content (such as viewing, clicking, purchasing, or explicitly rating content) are captured and fed back into the machine learning models. This feedback loop allows the system to continuously improve its understanding of user preferences and adjust content recommendations accordingly. The feedback mechanism operates automatically in the background, maintaining ease of operation while enhancing personalization through data-driven learning from user behavior.
3Reliability
If prior art systems accumulate statistics to determine a single winner, then the system is reliable in its selection process, but it cannot discover personalization rules for different audience segments
Solution Approach 1:
The patent applies segmentation to divide the audience into distinct user segments based on analyzed behavior patterns. The system accumulates statistics for each segment separately rather than treating all users uniformly, allowing reliable determination of content preferences within each segment while simultaneously discovering different personalization rules for different audiences. This segmented approach maintains statistical reliability for each subgroup while enabling diverse personalization strategies across segments.
Solution Approach 2:
The patent changes the parameters of analysis from a single aggregate population to multiple sub-populations defined by segment-specific characteristics. By adjusting the analysis parameters to account for segment differences (such as geographic location, behavior type, or demographic factors), the system maintains reliable statistical foundations for each segment while discovering segment-specific personalization rules. This parameter adjustment enables both reliability and adaptability simultaneously.
Data Source
AI summary
A system, method, and apparatus provide the ability to generate and deliver personalized digital content. Multiple content tests are performed by presenting different variants of content to a set of different consumers of one or more consumers. A machine learning (ML model is generated and trained based on an analysis of results of the multiple content tests. Based on the ML model, personalization rules, that specify a certain variance for a defined set of facts, are output. The personalization rules are exposed to an administrative user who selects one or more of the personalization rules. A request for content is received from a requesting consumer. Based on similarities between the defined set of facts and the requesting consumer, a subset of the selected personalization rules are selected. The content is personalized and delivered to the requesting consumer based on the further selected personalization rules.