> ## Documentation Index > Fetch the complete documentation index at: https://docs.sourcemedium.com/docs/llms.txt > Use this file to discover all available pages before exploring further. # Attribution Health > Strategies and tools for improving your attribution coverage and data quality ## 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 channel reporting is less reliable. If you're seeing high rates of direct or no-source fallback values such as `(direct) / (none)` or `(none) / (none)` in your attribution data, this section will help you diagnose and fix the gaps. *** ## Common Attribution Gaps Ad blockers, iOS privacy features, and browser restrictions prevent traditional tracking from capturing touchpoints. Customers moving between domains (ads → landing page → checkout) can lose UTM parameters along the way. Marketing campaigns without proper UTM tagging can increase direct or no-source fallback 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 **Shopify Plus** — Capture UTM parameters at checkout using Checkout UI Extensions, bypassing cookie blockers. **All Platforms** — Standardize UTM tagging so last-click attribution stays consistent. **All Platforms** — Best practices for UTM naming conventions and tracking hygiene. Learn what `(direct) / (none)` means and how to reduce it. ### Zero-Party Data Methods Learn how self-reported survey answers complement tracking-based attribution. **All Platforms** — Ask customers directly how they heard about you. Complements tracking-based attribution. Set up Fairing for automated post-purchase surveys with order tagging. 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 | **Recommended approach**: Implement UTM best practices as your foundation, add checkout attribute capture for robust input, and use HDYHAU surveys to validate and supplement your tracking-based data. *** ## 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 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. Trace every evidence row on an order, inspect the winning traffic source, and compare raw captured values against the final channel mapping. Run copy-paste diagnostics for UTM coverage, fallback signals, winning evidence sources, and single-order attribution traces. *** ## Related Resources How SourceMedium prioritizes attribution data from multiple sources. Organize your attributed orders into meaningful channel groupings.