Video Content Prediction System Using Competitor Data Analysis
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Solution Overview
Problem
The video marketing industry faces inefficiencies due to high upfront costs and low success rates, as current methods rely on guesswork and costly trial-and-error approaches for video content optimization, leading to significant waste in investments across television and digital video advertising.
Innovation Solution
A system that predicts winning video content attributes without requiring upfront investments, using processors to analyze data streams, compute performance scores, and provide recommendations for attributes likely to succeed in achieving performance objectives, leveraging competitor and third-party data to infer effective video characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional trial-and-error video testing is used to optimize video content, then video performance can be improved through testing and learning, but the cost-to-learn ratio becomes extremely high and most video investments fail to achieve objectives
Solution Approach 1:
The system performs preliminary analysis of video attributes and competitor data before video production and testing. By analyzing historical video performance data, competitor videos, and attributing features using machine learning models, the system predicts which video attributes are likely to succeed before any production costs are incurred, eliminating the need for costly trial-and-error testing
Solution Approach 2:
The system copies and analyzes competitor video content and performance data to infer effective video characteristics. By examining competitor videos that have already been tested and proven successful in the market, the system replicates their effective attributes without requiring the user to spend millions on their own testing, thereby reducing the cost-to-learn ratio
2Productivity
If large upfront investments are made in video production and media buying, then video content can be created and distributed, but most investments fail to achieve intended goals resulting in wasted dollars
Solution Approach 1:
The system performs preliminary prediction of video success attributes before production investment. By analyzing historical data and competitor videos to identify winning attributes in advance, the system enables users to create video content with higher predicted success rates, improving the reliability of achieving marketing objectives while maintaining productivity
Solution Approach 2:
The system uses feedback from historical video performance data and competitor results to continuously improve predictions. By analyzing what attributes have succeeded in the past and what competitors are doing successfully, the system refines its predictions to improve the likelihood that new video investments will achieve their intended goals
3Ease of manufacture
If expert speculation and guesswork are used to create video content, then video production can proceed without data-driven insights, but the result is that hundreds of millions of dollars are wasted on unsuccessful ads
Solution Approach 1:
The system copies successful patterns from competitor video content and performance data. By analyzing what attributes have worked for competitors in the market and replicating those effective characteristics, the system replaces expert speculation with data-driven insights, reducing wasted investments while maintaining ease of video production
Solution Approach 2:
The system replaces the mechanical process of expert speculation and guesswork with automated machine learning analysis. By using algorithms to analyze historical data and competitor videos objectively, the system eliminates human bias and intuition-based decision-making, leading to more reliable predictions and reduced waste
Data Source
AI summary
The invention discloses a system and methods for predicting attributes of effective television ads, digital video, and other audiovisual content before production. Output comprises instructions for producing videos optimized to achieve at least one performance objective. Some embodiments predict performance of existing video, such as predicting winning Super Bowl ads before they air. The system comprises one or more processors and a memory configured to receive at least one data stream of audiovisual content in at least one public and/or a private domain; analyze said data streams to determine one or more attributes associated with said data streams; analyze said data streams to determine one or more performance scores associated with said data streams; attribute at least a portion of one said performance score to at least a portion of one said attribute associated with said data streams, and output, to a memory, any or all combinations thereof.