Pex

TikTok Sound ID Report

Turning Fragmented Usage Data into Actionable Claims

Designed a reporting workflow that transformed messy TikTok Sound ID data into a defensible signal clients could act on, helping uncover payout gaps and increase artist compensation.

Highlights

Outcomes

~20 Sound IDs per Song

Analyzed 141K songs and found that each song mapped to roughly 20 Sound IDs on average, revealing widespread attribution fragmentation.

830M+ Videos Uncovered

Connected 141K matched songs to more than 830 million videos, revealing how TikTok's existing payment model was underpaying artists.

$50M+ in New Royalties

Helped unlock more than $50 million in additional artist royalties after licensing terms were renegotiated using this report data.

Impact

Market-Level Visibility

Made the scale of Sound ID fragmentation visible, reinforcing the need for updated artist payment rules.

Claim Verification at Scale

Gave rights teams a trusted way to verify usage and identify misattributed Sound IDs across massive datasets.

Compensation Transparency

Created defensible evidence that strengthened payout and licensing conversations with TikTok.

Background

Context

TikTok had become a primary channel for music discovery, but attribution and payouts were difficult for rights holders to validate. Pex had matching infrastructure capable of identifying usage at scale, but clients needed a productized reporting layer they could trust and act on.

Role

Senior Lead Product Designer at Pex. Led end-to-end design of the reporting workflow, including customer research, wireframing data views, prototyping workflows, designing interactions and interfaces, and usability testing with rights teams.

Stakeholders

  • Artists, labels, publishers, and rights-management teams
  • Pex product, data, and engineering partners
  • Client legal/compliance and licensing teams

Challenges

  • Music usage on TikTok appeared under-attributed across many catalog tracks.
  • Sound IDs were fragmented across variants and re-uploads, reducing reporting clarity.
  • Claims needed to happen quickly because missed windows could mean missed payment.
  • Clients needed evidence strong enough to support licensing and payout conversations.

Objectives

Quantify the Gap

Measure the real scale of Sound ID fragmentation and reported usage discrepancies.

Drive Client Action

Deliver a report format clients could use immediately for validation and claims.

Market Transparency

Turn music detection reporting into an industry narrative around compensation on TikTok.

Research and Discovery

What We Found

  • 141,000 songs mapped to 2.8 million unique Sound IDs.
  • Those Sound IDs tied to 830 million-plus videos.
  • An average of roughly 20 Sound IDs per song made payout visibility difficult.

Implication

The core problem was not detection quality. It was translation: clients lacked a product layer that converted noisy, unstructured matching data into decision-ready evidence.

Methods

  • Large-scale catalog analysis across major and independent rights-holder data
  • Sound ID matching and clustering to detect duplicate/fragmented attribution paths
  • Workflow interviews with internal and client-side rights teams
TikTok Sound ID report summary page showing claimable IDs, claimable creations, misattributed creations, and misattributed views
Report summary converted raw matching output into a clear snapshot of claimable IDs, claimable creations, and misattributed usage.
TikTok Sound ID report chart showing top claimable Sound IDs by matched creations
Claimable Sound ID analysis ranked the highest-value attribution opportunities so rights teams could prioritize claims.
TikTok Sound ID report chart of misattributed creations by track
Misattributed creation trends surfaced where payout leakage was concentrated across key catalog tracks.

Design Approach

Signal Prioritization

Structured report views around the highest-value claim and payout risks first.

Narrative Framing

Paired quantitative findings with clear language for legal, licensing, and business teams.

Actionable Outputs

Designed report hierarchy to support verification, escalation, and faster decision-making.

Cross-team Validation

Iterated with internal experts and client workflows to ensure real-world usability.

Solution

I led creation of the TikTok Sound ID Report as a productized transparency layer: a clear, repeatable reporting experience that surfaced hidden usage patterns, helped clients verify attribution, and equipped them to pursue fairer compensation.

Detailed table of claimable Sound IDs including URL, artist, title, and creations
Detailed claimable Sound ID tables paired each match with supporting metadata for fast verification and submission.
Detailed table of misattributed TikTok creations with creation URL and view counts
Misattributed creation tables gave teams concrete evidence to escalate corrections with platform stakeholders.
Next steps page showing claimable IDs and misattributed creation workflow guidance
Action-oriented next steps translated findings into a repeatable claim and remediation workflow.

Reflections

This work demonstrated how design can translate complex data into business leverage. By making the attribution gap visible and actionable, the report helped support broader market pressure for payout reform, reflected in TikTok's announced 2026 payment-rule changes.