Performing Artist Contract Evaluation via ML Follower Segmentation
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Solution Overview
Problem
Existing talent scouting techniques for performing artists rely heavily on manual analysis of social media metrics, which can be biased and fail to consider the engagement behavior of different follower segments, leading to inaccurate assessment of contract worthiness.
Innovation Solution
A method and system using machine learning (ML) to analyze consolidated data from multiple platforms, classify topics, determine user engagement behavior segments, and calculate a contract worthiness score based on KPIs, leveraging an ML sentiment analysis model and a trained scoring model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of social media metrics is used for talent scouting, then A&R experts can perform contract worthiness analysis based on available data, but the assessment result becomes biased and inaccurate due to equal weighting of all aspects and inability to differentiate follower segments
Solution Approach 1:
The patent segments followers into different types (fans, non-fans, neutral users) based on their engagement behavior and sentiment analysis. This segmentation allows the system to differentiate between meaningful engagement and superficial metrics, resolving the contradiction by enabling precise measurement of contract worthiness through categorized follower analysis rather than treating all followers equally.
Solution Approach 2:
The patent replaces manual mechanical analysis by A&R experts with an automated machine learning system that performs sentiment analysis, follower segmentation, and contract worthiness scoring. This substitution eliminates human bias and subjectivity while providing consistent, data-driven assessments, thereby improving measurement precision without proportionally increasing complexity.
2Measurement precision
If comprehensive data from multiple platforms is analyzed to improve contract worthiness evaluation, then assessment accuracy improves, but data processing complexity and time requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models on historical data and pre-segmenting follower types based on engagement patterns. These pre-computed models and segments are then reused during actual contract worthiness evaluations, allowing comprehensive multi-platform data analysis to be performed efficiently without repeating the entire analysis process from scratch each time.
Solution Approach 2:
The patent uses automated machine learning pipelines and natural language processing to handle comprehensive data from multiple platforms, replacing manual data collection and analysis processes. This automation significantly reduces the time required to process extensive multi-platform data while maintaining or improving assessment accuracy through consistent application of analytical models.
3Reliability
If traditional scouting methods focusing on follower count and basic metrics are used, then the process remains simple and quick, but it fails to identify truly talented artists with high commercial potential
Solution Approach 1:
The patent replaces traditional scouting methods based on simple metrics like follower count with an automated machine learning system that analyzes engagement quality, sentiment, and commercial potential indicators. This substitution improves talent identification reliability by objectively evaluating multiple dimensions of artist performance and audience engagement, while the automated nature of the system prevents complexity from becoming a barrier to implementation.
Solution Approach 2:
The patent fundamentally changes the evaluation parameters from superficial metrics (follower count, total views) to meaningful indicators of commercial potential (engagement rate, sentiment scores, fan segmentation, purchase intent signals). This parameter transformation enables reliable identification of talented artists with high commercial value, as the system evaluates qualities that actually correlate with successful music careers rather than just popularity metrics.
Data Source
AI summary
A method and system for evaluating contract-worthiness of performing artists is disclosed. The method includes receiving consolidated data corresponding to each of a set of performing artists. The method further includes processing the consolidated data by classifying a set of topics in the text data into a plurality of categories through a ML classification model, determining user engagement behavior segments based on sentiment scores associated with the text data, and determining a plurality KPIs based on the plurality of metrics, the plurality of categories, and the user engagement behavior segments. Further, the method includes calculating a contract worthiness score for the set of performing artists based on the plurality of KPIs using a trained ML contract worthiness scoring model and evaluating one or more of the set of performing artists for their contract worthiness based on the contract worthiness score and a threshold contract worthiness score.