Overview
Trace analyzes inconsistencies between ecommerce revenue signals and advertising platform attribution claims across Shopify, Meta Ads, Instagram Ads, and Google Ads.
Advertising platforms report what they claim to have driven. Revenue data shows what actually happened. Trace makes the divergence visible—not to assign “correct” attribution (unsolvable), but to systematically verify consistency and understand where platforms diverge from reality.
System Focus
Attribution Integrity – Verify what each platform claims it drove versus what revenue data shows.
Signal Reconciliation – Normalize data from multiple sources and identify divergence patterns.
Structural Analysis – Understand why platforms diverge (measurement methodology, incentive alignment, data quality).
Monitored Platforms
- Shopify – ecommerce transaction source
- Meta Ads – advertising claims
- Google Ads – advertising claims
- Instagram Ads – advertising claims
Intelligence Layers
Layer 1: Signal Reconciliation – Align data across platforms, identify basic divergences.
Layer 2: Consistency Analysis – Which platforms agree? Where do they diverge?
Layer 3: Anomaly Detection – Identify structural patterns in divergence (incentive misalignment, methodology changes, data quality issues).
Research Context
Trace emerges from Project NIRV research on systems intelligence and platform dynamics. Advertising platforms are systems with specific incentive structures—their attribution claims reflect these incentives. Trace applies rigorous systems analysis to make this visible and operational.
Development Status
Current: Active development Stability: Core reconciliation and analysis pipelines operational Future: Expanded platform coverage, refined anomaly detection, operational reporting frameworks
Repository
Trace is part of Vectorium , the applied research ecosystem of Project NIRV.
Repository: github.com/Reuxbite/Trace