What is Attribution Health?
Attribution health measures how completely your marketing touchpoints are being captured and connected to customer purchases. High attribution rates mean you can confidently analyze which channels drive revenue; low rates mean you’re flying blind.If you’re seeing high rates of
(direct) / (none) in your attribution data, this section will help you diagnose and fix the gaps.Common Attribution Gaps
Cookie/Tracking Blockers
Ad blockers, iOS privacy features, and browser restrictions prevent traditional tracking from capturing touchpoints.
Cross-Domain Issues
Customers moving between domains (ads → landing page → checkout) can lose UTM parameters along the way.
Missing UTM Parameters
Marketing campaigns without proper UTM tagging result in traffic appearing as direct/none.
Delayed Attribution
Long consideration cycles mean the original touchpoint expires before purchase.
Attribution Health Toolkit
The following guides cover different strategies for improving your attribution coverage. Most brands benefit from implementing multiple approaches.First-Party Attributes & Tracking
UTM Capture via Checkout Attributes
Shopify Plus — Capture UTM parameters at checkout using Checkout UI Extensions, bypassing cookie blockers.
UTM Setup
All Platforms — Standardize UTM tagging so last-click attribution stays consistent.
Improve Last-Click Attribution
All Platforms — Best practices for UTM naming conventions and tracking hygiene.
Why (direct) / (none) happens
Learn what
(direct) / (none) means and how to reduce it.Zero-Party Data Methods
Zero-Party Attribution
Learn how self-reported survey answers complement tracking-based attribution.
Post-Purchase Survey (HDYHAU)
All Platforms — Ask customers directly how they heard about you. Complements tracking-based attribution.
Fairing Integration
Set up Fairing for automated post-purchase surveys with order tagging.
KnoCommerce Integration
Set up KnoCommerce for zero-party data collection and attribution.
Choosing the Right Approach
| Method | Best For | Limitations |
|---|---|---|
| Checkout Attributes | High ad blocker rates, Shopify Plus stores | Doesn’t work with accelerated checkout (Apple Pay, etc.) |
| HDYHAU Surveys | Understanding top-of-funnel discovery | Self-reported data can be inaccurate |
| UTM Best Practices | Prevention—catching gaps before they happen | Doesn’t fix historical data |
| Fairing/KnoCommerce | Automated survey collection at scale | Requires additional integration |
Measuring Your Attribution Health
SourceMedium’s MTA dashboard includes an Attribution Health module that shows:- Percentage of orders with attributable touchpoints
- Attribution rates by channel and time period
- Trends in your attribution coverage
MTA Attribution Health Module
Learn how to interpret your attribution health metrics in the MTA dashboard.
Warehouse Debugging
When the store-level health metric tells you something is wrong, move to row-level order analysis in BigQuery.Order Attribution Signals Table
Trace every evidence row on an order, inspect the winning traffic source, and compare raw captured values against the final channel mapping.
Related Resources
Attribution Source Hierarchy
How SourceMedium prioritizes attribution data from multiple sources.
Channel Mapping
Organize your attributed orders into meaningful channel groupings.