Predicting Social Media Influencer Results via Machine Learning
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Solution Overview
Problem
Current systems fail to effectively predict the success of social media influencer channels due to the combination of structured and unstructured data types, where unstructured data such as audio and video clips are not well-understood in contributing to the success of influencer channels.
Innovation Solution
A method using successive rounds of machine learning, where each round constructs a model that takes both structured and unstructured data inputs, applying different techniques based on the data type, to predict views, clicks, and conversions for media items posted on influencer channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional advertising systems and tools are used for product placement, then conventional advertising can be managed, but influencer channels with unstructured data cannot be effectively analyzed
Solution Approach 1:
The patent introduces machine learning models as intermediary components that bridge the gap between traditional advertising systems and influencer channel analysis. These models process unstructured data from influencer channels (videos, images, audio) and convert them into structured insights, enabling traditional advertising systems to effectively analyze and predict influencer channel performance
Solution Approach 2:
The system transforms unstructured influencer data into structured parameters that can be analyzed by advertising systems. By changing the state of data from unstructured to structured through feature extraction and model processing, the system enables precise measurement and prediction of influencer channel effectiveness
2Ease of manufacture
If only structured data techniques are used, then structured data analysis is simple, but unstructured data characteristics cannot be extracted
Solution Approach 1:
The patent segments the data analysis process into distinct stages: first processing structured data using traditional techniques, then processing unstructured data separately using machine learning models, and finally combining the results. This segmentation allows each data type to be handled with appropriate methods while maintaining overall system simplicity
Solution Approach 2:
The system merges structured data analysis results with unstructured data analysis results to create a comprehensive prediction model. By combining both data types, the system retains simplicity for structured data processing while successfully extracting features from unstructured data through machine learning
3Ease of operation
If manual feature extraction is used for unstructured data, then interpretability is maintained, but automation and accuracy are reduced
Solution Approach 1:
The machine learning models perform self-service by automatically extracting features from unstructured influencer data without requiring manual intervention. The models learn patterns and characteristics directly from the data, enabling automated analysis while maintaining the ability to interpret results through model explanations and feature importance metrics
Data Source
AI summary
This invention predicts results for a media clip posted to a social media influencer channel by maintaining a database of results data for media clips where an influencer channel includes media clips that include unstructured data, and structured data, and then provide to a first machine learning model a first set of channel data, extracting a first set of features, predicting a value for the first target variable, providing to a second machine learning model a second set of channel data including a second selection of structured data, and the predicted value of the first target variable, extracting a second set of features, and predicting a value for the second target variable.