Zero-party data complements tracking-based attribution by capturing channels that are hard to track: word of mouth, podcasts, influencers, and offline media.
Prerequisites
To use this module, you need:- A post-purchase survey tool (Fairing, KnoCommerce, or similar)
- Order tagging enabled (survey responses tagged to orders)
- Consistent tag format (e.g.,
HDYHAU-Facebook,PPS-TikTok)
Key Metrics
| Metric | Definition |
|---|---|
| Response Rate | Orders with survey response / Total orders |
| Channel Distribution | % of responses attributed to each channel |
| Revenue by Channel | Revenue from orders tagged with each survey response |
| New Customer Distribution | Survey responses from first-time buyers only |
Module Sections
Survey Response Distribution
Shows the breakdown of how customers say they discovered your brand:- Bar chart: Response counts by channel
- Pie chart: Percentage distribution
- Table: Detailed breakdown with revenue
Response Rate Trends
Track survey completion over time:- Are response rates consistent?
- Did a site change affect survey visibility?
- Seasonal patterns in discovery channels?
Revenue Attribution
Connect survey responses to business outcomes:- Which discovery channels drive the most revenue?
- What’s the average order value by discovery channel?
- How does new customer LTV vary by discovery channel?
Common Analyses
1. Tracking vs Survey Comparison
Compare what tracking says vs what customers say:| Channel | Tracking Attribution | Survey Attribution | Gap |
|---|---|---|---|
| Meta | 45% | 25% | +20% over-credited |
| Podcast | 0% | 12% | -12% under-credited |
| Word of Mouth | 0% | 18% | -18% invisible to tracking |
Gaps don’t mean either source is “wrong”—they measure different things. Tracking captures last-touch interactions; surveys capture initial discovery.
2. New Customer Discovery
Filter to first-time buyers only to understand:- Where are new customers coming from?
- Which channels drive acquisition vs re-engagement?
3. Channel Quality Analysis
Go beyond volume to measure channel quality:- AOV by channel: Do podcast customers spend more?
- Repeat rate by channel: Do referral customers have higher retention?
- LTV by channel: Which discovery channels drive the best long-term customers?
Interpreting Survey Data
Expected Patterns
| Channel | Typical Survey % | Notes |
|---|---|---|
| Social (Meta, TikTok, IG) | 20-40% | Often primary for DTC brands |
| Word of Mouth / Referral | 10-25% | Strong indicator of brand health |
| Search (Google) | 5-15% | Usually lower than tracking shows |
| 3-8% | Rarely “first” discovery | |
| Podcast / Influencer | 5-15% | Highly variable by brand |
| ”I don’t remember” | 10-20% | Expected; indicates honest responses |
Red Flags
Filtering & Segmentation
Use these filters to slice survey data:| Filter | Use Case |
|---|---|
| Date range | Seasonal discovery patterns |
| Customer type | New vs returning customer discovery |
| Order value | High-value customer discovery |
| Product | Product-specific discovery channels |
| Geography | Regional marketing effectiveness |
Combining with Other Data
Zero-Party + First-Party Attribution
For the most complete picture:- Survey data: “How did you first hear about us?” (awareness)
- UTM/tracking data: Last touchpoint before purchase (conversion)
- MTA data: Multi-touch credit across the journey
Related Resources
Survey Best Practices
How to set up effective HDYHAU surveys
Attribution Health
Improve your overall attribution coverage
New Customer Analysis
Deeper analysis of new customer behavior
Fairing Integration
Connect Fairing to SourceMedium