Social Media Content Fluctuation Analysis for Advertising Optimization

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Solution Overview

Problem

Current methods lack effective means to quantify and analyze fluctuations in social audience engagement and advertising effectiveness across custom author crowds on social media platforms, limiting targeted marketing strategies and campaign optimization.

Innovation Solution

A system and method that analyze and compare fluctuations in content generated by custom author crowds, determining a fluctuation magnitude to assess engagement and advertising effectiveness, and provide recommendations for future content, actions, or behaviors based on historical data, enabling targeted marketing and campaign optimization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional advertising methods are used on social media platforms, then advertising reach is achieved, but measurement of advertising effectiveness and audience engagement is insufficient

Engineering Contradiction:

Improveadvertising effectiveness measurementVSAvoidaudience engagement data

Core Design Contradiction:

Measurement precisionVSLoss of information

Solution Approach 1:

The system implements feedback by continuously monitoring and analyzing user interactions with advertising content, measuring engagement metrics such as likes, shares, and comments, and using this feedback to optimize future advertising campaigns and content recommendations

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary analysis of user profiles, historical behavior patterns, and content preferences before delivering advertising content, enabling targeted advertising that is more likely to engage the intended audience and measure effectiveness accurately

Inventive Principle:

Principle #10Preliminary action

2Productivity

If generic advertising content is displayed to broad audiences, then advertising reach is maximized, but engagement and responsiveness are reduced

Engineering Contradiction:

Improveadvertising reachVSAvoidaudience engagement

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The system applies local quality by customizing advertising content based on individual user characteristics, preferences, and behavior patterns, delivering locally optimized content to each user while maintaining overall campaign coherence and broad reach potential

Inventive Principle:

Principle #3Local quality

3Loss of information

If manual analysis of social media engagement is performed, then detailed insights can be obtained, but time and resource consumption increase

Engineering Contradiction:

Improveengagement insightsVSAvoidanalysis time

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system replaces manual mechanical analysis with automated computational algorithms that process social media engagement data, user behavior patterns, and content performance metrics, delivering comprehensive insights rapidly without manual intervention

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS10628845B2Systems and methods for automating design transformations based on user preference and activity data

Publication Date: 2020.04.21 VIZIT LABS INC

AI summary

A method includes determining a recommended aspect for content that includes at least one image. The recommended aspect is determined at least in part based on activity data that indicates aspects of other content authored by or interacted with by a plurality of authors in at least one social network, website, application software, or mobile application software (app). The method further includes generating the content including the at least one image according to the recommended aspect. The recommended aspect is an aspect of the at least one image.