Multi-Step Engagement Strategy Adaptation Using Real-Time ML Feedback
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Solution Overview
Problem
Conventional digital advertising systems rely heavily on human involvement for managing strategies, which leads to incomplete, biased, or inaccurate data processing due to the vast amount of user interaction data, resulting in a failure to adjust content delivery strategies in real-time and optimize conversion effectively.
Innovation Solution
A machine-learning based multi-step engagement strategy modification system that leverages data from user interactions to modify and optimize content delivery strategies in real-time, using machine-learning models to adjust aspects such as content delivery frequency, channels, and timing based on actual user behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human users manually manage and modify content delivery strategies, then strategy creation and adjustment is straightforward and controllable, but the system cannot process vast amounts of real-time data leading to incomplete and biased optimization
Solution Approach 1:
The patent replaces manual human management of content delivery strategies with an automated machine-learning-based system. The machine-learning model processes vast amounts of real-time data about user interactions and automatically modifies engagement strategies, eliminating the limitations of human data processing capacity while maintaining strategic control through the system's intelligent algorithms.
2Productivity
If the system processes vast amounts of real-time data to optimize conversion, then conversion optimization accuracy improves, but the complexity of data processing and system operation increases significantly
Solution Approach 1:
The machine-learning system automatically processes data and modifies engagement strategies without requiring manual intervention. The system self-adjusts content delivery parameters based on real-time user interaction data, enabling continuous optimization of conversion rates while the system manages its own operational complexity through automated algorithms.
Solution Approach 2:
The system continuously collects data on user interactions with delivered content and uses this feedback to automatically modify engagement strategies. This closed-loop feedback mechanism enables real-time optimization of conversion rates by adapting strategies based on actual performance data, with the machine-learning model processing the feedback automatically.
3Adaptability or versatility
If human users create and adjust content delivery strategies, then strategy customization is possible, but the strategies become biased or inaccurate due to limited data processing capability
Solution Approach 1:
The patent replaces human strategy creation with an automated machine-learning system that processes vast amounts of data to generate highly accurate and unbiased strategies. The system maintains adaptability by continuously learning from user interaction data and automatically adjusting strategies to optimize conversion, eliminating the biases inherent in manual strategy development.
Data Source
AI summary
Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users. Based on data describing the interactions of recipients with deliveries served according to both the user-created and random multi-step engagement strategies, the machine-learning models generate a modified multi-step engagement strategy.