Skip Behavior Analysis for Unbiased Song Version A/B Testing
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Solution Overview
Problem
Current music production technologies lack a method to unbiasedly test different versions of a song, making it difficult for composers to determine which versions are less likely to be skipped by listeners, as existing tools do not provide a mechanism for comparing popularity effectively.
Innovation Solution
A system and method that randomly selects and delivers different versions of a song to listeners, gathering information on play and skipping behavior, and calculates the distribution of skipping behavior to present differences between versions, allowing composers to efficiently determine audience preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple versions of a song are released to test popularity, then audience preferences can be determined, but the comparison becomes biased due to release date, title, and random factors
Solution Approach 1:
The patent creates identical copies of the same song with different titles and release dates, distributing them to different user groups. This allows comparison of popularity metrics while controlling for content variables, isolating the effect of title and release timing on skip behavior.
Solution Approach 2:
The patent segments the user base into different groups, with each group exposed to a specific version of the song. This segmentation enables controlled comparison across versions while maintaining randomization within groups, reducing bias from individual listener preferences.
2Measurement precision
If all possible versions of a song are produced and released, then the most popular version can be identified, but the process becomes impractical due to the large number of combinations
Solution Approach 1:
The patent tests a representative subset of possible song versions rather than all combinations. By selecting key variations in title, structure, and instrumentation, the system achieves sufficient statistical power to identify the optimal version without the computational and resource burden of exhaustive testing.
Solution Approach 2:
The patent implements a feedback loop where skip behavior data from each version is collected and analyzed, then used to inform subsequent version iterations. This allows the testing process to converge on the optimal version more efficiently by learning from each test round.
3Loss of information
If different versions of a song are compared, then audience preferences can be determined, but existing technology lacks tools to conduct unbiased testing
Solution Approach 1:
The patent creates a multi-functional system that not only distributes song versions but also tracks skip behavior, collects metadata, and analyzes results. This universal platform handles the entire A/B testing workflow, making unbiased song version comparison accessible without requiring separate tools for each function.
Solution Approach 2:
The patent introduces an intermediary system between the song versions and the audience that randomly assigns versions, tracks listening behavior, and collects skip data. This intermediary layer ensures unbiased distribution and accurate data collection, filling the gap in existing technology.
Data Source
AI summary
A skip behavior analyzer is part of a media delivery system that allows for unbiased A/B testing of a plurality of versions of a song. The media delivery system stores a plurality of versions of a song and randomly selects, for each requesting device, a version of the song to associate with that device. Each time the device requests the song, thereafter, the media delivery system will provide the same version of the song for consistency. The media delivery system then gathers song play and skip information, calculates the differences in distribution of the skip behavior, and provides the skip information to allow a music composer to better determine which version of a song is more popular and why that is so.