AI Persona Modeling for Social Media Campaign Response Prediction

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Solution Overview

Problem

Traditional social media marketing campaigns rely on historical customer purchasing behavior, failing to account for diverse audience attributes and often result in ineffective advertising that annoys or fails to engage target audiences, leading to wasted resources.

Innovation Solution

Utilizing artificial intelligence (AI) models to generate AI personas representing different segments of the target audience, predicting responses, ratings, and feedback to marketing campaigns through a combination of large language models, transformer models, decision tree-based models, and natural language processing, enabling fine-tuning before campaign release.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If traditional methods use historical purchasing behavior to create marketing campaigns, then campaigns can be generated with simple data requirements, but the campaigns fail to engage diverse audience segments and waste advertising resources

Engineering Contradiction:

Improveease of campaign creationVSAvoidcampaign effectiveness

Core Design Contradiction:

Ease of manufactureVSProductivity

Solution Approach 1:

The patent segments the target audience into multiple AI personas representing different demographic groups, interests, and behaviors. Each persona is evaluated separately through A/B testing, allowing the system to identify which segments respond positively to the campaign content. This segmentation enables targeted optimization without requiring complex manual audience research.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary A/B testing with AI personas before full campaign deployment. Multiple variations of campaign content are tested against different AI persona representations of target audiences in advance, allowing businesses to predict effectiveness and optimize content before committing significant advertising resources.

Inventive Principle:

Principle #10Preliminary action

2Device complexity

If traditional methods advertise similar products to past purchasers, then the approach requires minimal analysis, but it fails to account for audience diversity and interests in other styles

Engineering Contradiction:

Improvecomplexity of marketing analysisVSAvoidaudience preference coverage

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The AI persona system serves multiple functions simultaneously: it represents diverse audience segments, predicts campaign effectiveness, guides A/B testing strategies, and provides actionable insights for optimization. This multi-functional approach consolidates what would otherwise require separate analytical tools and processes into a single versatile system.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system changes key parameters of campaign evaluation by introducing AI-generated persona attributes (demographics, interests, behaviors) as new variables. Instead of relying solely on historical purchase data, the system incorporates multiple persona parameters to predict how different audience segments might respond to various campaign variations.

Inventive Principle:

Principle #35Parameter changes

3Speed

If businesses invest in traditional marketing campaigns without prediction capability, then campaigns can be launched quickly, but money is wasted on content that is ignored or annoys the target audience

Engineering Contradiction:

Improvecampaign launch speedVSAvoidadvertising budget waste

Core Design Contradiction:

SpeedVSLoss of energy

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system provides feedback loops where A/B testing results from AI persona evaluations inform campaign optimization. Businesses receive actionable insights about which content variations perform best with different audience segments, enabling data-driven adjustments that reduce the risk of wasting advertising budget on ineffective content.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12493897B2Artificial intelligence-based methods and systems for generating responses, ratings, and feedback of social media marketing campaigns

Publication Date: 2025.12.09 INTUIT INC

AI summary

Certain aspects provide a computer-implemented method for evaluating social media marketing campaigns using artificial intelligence (AI). The method comprises using a large language model (LLM) to generate a plurality of AI personas. Each AI persona represents a different segment of a target audience of a marketing campaign. The method uses a transformer model, a decision tree-based model, and a natural language processing (NLP) model to predict a response, a rating, and a feedback to the marketing campaign for each AI persona that represents a different segment of the target audience. The predicted responses, ratings, and feedback for the AI personas that represent different segments of the target audience are aggregated to form an evaluation of the marketing campaign for each segment of the target audience. The method sends the evaluation of the marketing campaign to a user.