Content Presentation Strategy Optimization via Segmentation
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Solution Overview
Problem
Organizations face challenges in effectively presenting content to diverse customer bases over the internet, as they need to decide which subsets of customers to provide specific content to, and how to schedule it, while minimizing resource consumption and maximizing user interest and desired actions.
Innovation Solution
A system that uses machine learning and multi-arm bandit algorithms to optimize content presentation strategies by identifying the most interested customer subsets and adjusting content delivery based on feedback metrics, reducing computation and communication resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If content is presented to all potential customers, then maximum user coverage is achieved, but resource consumption increases and user interest decreases
Solution Approach 1:
The patent segments the customer base into distinct subsets based on their interests and preferences using machine learning algorithms. Instead of presenting content to all customers uniformly, the system divides the population into targeted groups and presents relevant content to each segment, thereby reducing overall resource consumption while maintaining effective user coverage.
Solution Approach 2:
The patent applies local quality by customizing content presentation strategies for different customer subsets rather than using a uniform approach. Each segment receives content tailored to their specific interests and characteristics, which optimizes resource allocation by focusing computational and communication resources on the most relevant audience segments.
2Quantity of substance
If content is presented to all potential customers, then maximum user coverage is achieved, but user interest and desired actions decrease
Solution Approach 1:
By segmenting customers into interest-based subsets, the system ensures that each user receives content relevant to their preferences. This targeted approach maintains broad user coverage while significantly improving engagement metrics and desired actions, as customers are more likely to interact with content that matches their interests.
Solution Approach 2:
The patent changes the parameters of content presentation by adjusting which content is shown to which customer subset based on analyzed preferences. This dynamic parameter adjustment optimizes both user coverage and engagement by matching content characteristics to customer interests rather than using a static one-size-fits-all approach.
3Ease of operation
If traditional content presentation methods are used, then implementation simplicity is maintained, but resource consumption increases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically segment customers and optimize content presentation without manual intervention. Machine learning algorithms autonomously analyze customer data, identify subsets, and determine optimal content strategies, reducing the need for manual configuration while minimizing resource consumption through intelligent automation.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor content presentation effectiveness and resource consumption. This feedback loop enables the system to automatically adjust segmentation strategies and content allocation to optimize the balance between implementation simplicity and resource efficiency, learning from observed outcomes to improve future presentations.
Data Source
AI summary
Strategies for an objective associated with an offering set are obtained. A strategy assigns respective selection probabilities of receiving content associated with the offering set to users of a user population. Strategy optimization iterations are performed with respect to a sub-sample of the population and a subset of the strategies. In a given iteration, weights assigned to the strategies are used to determine aggregated selection probabilities for users, content pertaining to the offering set is presented to users selected based on the aggregated probabilities, and the weights are adjusted based on feedback metrics and an exploration-exploitation tradeoff parameter. Based on weights updated in the iterations, content associated with the offering set is presented to users which were not in the sub-sample.