The Post-Purchase Survey Module surfaces zero-party attribution data—self-reported responses from “How Did You Hear About Us?” (HDYHAU) surveys.
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)
See Post-Purchase Survey Best Practices for setup guidance.
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
Compare survey attribution to your tracking-based attribution. Large gaps may indicate tracking blind spots or channels you’re under-crediting.
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 |
| Email | 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
Watch for these data quality issues:
- Control channel > 5%: Customers may be clicking randomly
- “I don’t remember” < 5%: Survey may be forcing responses
- Response rate < 10%: Survey placement may need adjustment
- One channel > 60%: Consider if options are too limited
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
Use survey data to inform your MTA model weights. If surveys show 15% podcast discovery but tracking shows 0%, consider adding podcast as a valid touchpoint.
