Two-Stage Creative Analysis Engine for KPI Prediction Accuracy
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Solution Overview
Problem
Existing content effectiveness testing methods are inefficient and costly, lacking the ability to analyze vast amounts of data for accurate KPI predictions and creative aspect modifications, and advertisers often lack access to platform-specific data for improved ad campaigns.
Innovation Solution
A two-stage machine learning engine is employed to predict KPI values based on operational and creative features, using historical data to generate precise predictions and recommendations for creative enhancements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual control group testing is used to evaluate content effectiveness, then human subjectivity and limited scalability are avoided, but the testing process becomes costly and time-consuming with limited data input
Solution Approach 1:
The patent replaces manual human testing with an automated machine learning system that processes vast amounts of data. The ML engine substitutes the mechanical process of human evaluation, automatically analyzing content effectiveness metrics without human intervention, thereby improving productivity while handling large data volumes efficiently
Solution Approach 2:
The system creates virtual models and simulations of user interactions with content, using ML algorithms to predict effectiveness outcomes. Instead of physically testing with real users, the system uses computational copies and models to evaluate content performance, enabling scalable analysis without proportional increases in testing costs
2Measurement precision
If advertisers use limited control groups for testing, then testing costs are reduced, but the representativeness and accuracy of effectiveness predictions deteriorate
Solution Approach 1:
The patent transforms the testing approach by changing key parameters: instead of using small control groups, the ML system analyzes multiple variables simultaneously including user demographics, engagement patterns, and content features. This parameter transformation enables accurate predictions without requiring large-scale manual testing infrastructure
Solution Approach 2:
The machine learning engine acts as an intermediary between the advertiser and the vast platform data. Rather than directly managing complex testing infrastructure, the ML system mediates by automatically processing data, isolating creative aspect impacts, and providing actionable insights, thereby simplifying the overall testing system complexity
3Loss of information
If vast amounts of platform data are collected for analysis, then prediction accuracy improves, but the complexity of data processing and analysis increases beyond manual capability
Solution Approach 1:
The patent segments the vast platform data into manageable components that the ML system can process efficiently. The data is divided into relevant features such as user attributes, content characteristics, and interaction metrics. This segmentation allows complete data utilization while maintaining processing feasibility through structured analysis
Solution Approach 2:
The machine learning system performs self-service data processing by automatically collecting, cleaning, and analyzing platform data without external intervention. The system independently handles the complexity of data processing, identifying patterns and generating predictions autonomously, thereby managing data complexity at scale without requiring proportional human resources
Data Source
AI summary
Systems and methods for creative analysis using a two-stage machine learning creative analysis engine are provided. The two-stage machine learning creative analysis engine includes a first stage machine learning model that predicts a KPI value for a creative based upon the operational features of the creative. Residuals of the first stage machine learning model are provided as target variables to a second stage machine learning model that predicts the residuals using creative features of the creative.