Machine Learning Engagement Strategy Generation
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Solution Overview
Problem
Conventional digital advertising systems rely heavily on human involvement for creating strategies, which leads to incomplete, biased, or inaccurate content delivery due to the vast amount of data that humans cannot process, resulting in suboptimal conversion optimization.
Innovation Solution
A learning-based engagement system generates multi-step engagement strategies using machine-learning models trained on historical user interactions, allowing for data-driven decision-making and real-time optimization of content delivery sequences to achieve conversion objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human users create engagement strategies manually, then the process is simple and controllable, but the strategies are based on incomplete or biased information due to inability to process vast user data
Solution Approach 1:
The patent introduces machine learning models as intermediaries between the vast user data and the engagement strategy generation. These models process the complex data that human users cannot handle, transforming raw data into actionable insights while maintaining human oversight for strategy validation and adjustment.
Solution Approach 2:
The patent replaces the manual mechanical process of human strategy creation with an automated system using machine learning algorithms. This substitution enables processing of vast datasets at scale, generating data-driven engagement strategies that would be impossible for humans to create manually.
2Productivity
If machine learning models are used to generate engagement strategies, then conversion optimization is improved through data-driven decisions, but the system complexity increases significantly
Solution Approach 1:
The patent segments the engagement strategy generation process into distinct machine learning models, each responsible for specific aspects such as content selection, timing optimization, and user segmentation. This modular approach manages system complexity by dividing the overall task into smaller, more manageable components.
Solution Approach 2:
The patent implements feedback loops where the machine learning models continuously learn from campaign performance data and user interactions. This feedback mechanism allows the system to self-optimize and improve conversion rates over time while adapting to changing user behaviors and campaign outcomes.
3Measurement precision
If comprehensive user data is processed to create personalized strategies, then conversion optimization improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and segmenting user data before campaigns begin, creating ready-to-use user profiles and preferences. This advance preparation reduces the computational burden during active campaigns, enabling rapid strategy generation while maintaining high personalization accuracy.
Solution Approach 2:
The patent dynamically adjusts processing parameters such as data sampling rates, model complexity, and personalization depth based on campaign priorities and resource availability. This allows the system to balance personalization accuracy with processing time by modifying parameters in real-time according to specific campaign needs.
Data Source
AI summary
Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users). Once the multi-step engagement strategies are generated, the learning-based engagement system may also generate visualizations of the strategies that can be output for display.