Who can use this feature?
- Available with Anywhere: Warehouse, an add-on for Business, Advanced, and Enterprise plans.
- Requires an Admin or Architect role to configure.
For Fullstory customers with Anywhere: Warehouse, this guide unlocks a complete view of the customer journey by stitching anonymous acquisition data (e.g., from Google Analytics) with known user behavior from Fullstory. While the example below uses Snowflake as a destination, the core principles can be adapted to any data warehouse. Use this guide as both a practical starting point and inspiration for your own solution.
Introduction
Goal - Create a 360° view of the customer lifecycle you can trust.
Augment what happened from existing analytics, like Google Analytics, with the how and why from Fullstory’s deep behavioral context to paint the comprehensive picture. Stitch anonymous behavior with known post-login activity using key business data from your e-comm platform, Fullcapture™ session signals, and even your own custom-defined frustration signals, all in your warehouse, in 4 steps.
What makes this uniquely possible with Warehouse:
Google Analytics tells you about website traffic, page visits, and basic conversion counts. But what if key events are being missed, or worse, what if users encounter significant roadblocks that you never find out about?
Warehouse doesn't just give you more data; it gives you different, richer data. By bringing Fullstory's detailed behavioral recordings (every click, scroll, error, what users saw but didn't click) into your Snowflake warehouse next to your GA4 and e-comm data, you can:
- Validate Data You Rely On: Confidently check if your primary analytics capture all critical conversion events by comparing them directly against Fullstory's comprehensive data in your warehouse.
- See the Whole Journey: Uncover what users do before they sign up – which marketing channels truly brought them in, what content they explored anonymously – and link it to their eventual customer behavior. This is often a black box with other tools.
- Understand the "Why" Behind Outcomes: Go beyond what users did, and see the specific friction points or errors they encountered along their journey. Analyze how these experiences impacted their likelihood to purchase and their long-term value.
- Make Strategic Calls Based on Complete History: Base your product, marketing UX strategies on full customer lifecycles over months or years, not just recent snapshots.
Who’s Involved:
- Data Engineering /Admin (Sets up Warehouse sync, ensures source data availability)
- Data Analyst (Writes SQL to analyze funnel, isolate friction points, build BI dashboards)
- Product/Eng/Design team (Consume insights, prioritize fixes, design new solutions)
Step-by-Step Use Case
Step 1: Connect the Pipes: Get Fullstory’s long-term data flowing
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Business View: First, send Fullstory's rich behavioral data (like where users click, scroll, and encounter errors) automatically to your company's central data storage where you have your Google Analytics & Shopify data, and preserve it for however long you want. Think of it as connecting the Fullstory hosepipe to your main data tank.
- Why: Creates a powerful, centralized, and historically complete single source of truth - the essential first step to validate tracking accuracy, build complete user histories, and connect them to your business outcomes.
Technical Preview: Fullstory's Ready-to-Analyze Views tables, GA4, and Shopify order data are co-located and ready for validation, journey stitching, & enriched analysis.
Step 2: Compare core event counts & surface ‘Why’ signals
- Business View: Perform a "reality check" by combining Fullstory & Google Analytics data to ensure critical business events (like "Order Placed") are counted consistently in Snowflake. Where you see discrepancies or want a deeper understanding than GA4 provides, use Fullstory's unique data to pinpoint page URLs where users most often encounter behavioral frustration (like Rage Clicks) and specific technical errors.
Why: Build confidence in your core metrics by validating them and creating a high-level and tailored POV of where users struggle.
Optional, but powerful: Go beyond Fullcapture's signals to define and count specific sequences of granular behavioral data to create your own Why Signals. Examples:
- ‘Navigation Pogo-sticking’: User navigates back and forth between two specific pages multiple times in a session.
- ‘Help-Seeking Dead End’: User visits multiple FAQ pages, but immediately exits without converting.
- ‘Erratic Page Scans’: Users exhibit unfocused scrolling up and down a complex page and extensive mouse thrashing without meaningful clicks.
- Technical Preview: SQL queries in Snowflake validate event count and list pages with high counts of behavioral frustration/ technical errors from Fullstory data.
Step 3: Stitch the friction points through the journey from anonymous to known
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Business View: Leveraging the validated and comprehensive Fullstory data in Snowflake, you can now connect each user's entire interaction history – linking their anonymous visits (how they found you, what they explored before signing up) to their behavior as a known customer.
- Why: Reveals the full path to becoming a customer, including early influences and friction siloed analytics (eg GA or even Fullstory alone) can’t easily surface.
- Technical Preview: A new view in Snowflake, built on Fullstory’s persistent user ID, reconstructs user journeys, links anonymous to identified activity, and flags friction.
Step 4: Enrich full journeys with business outcomes & visualize strategic insights
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Business View: Enrich these complete, friction-aware user journeys by combining them in Snowflake with details from your E-commerce platform (eg. purchase history). Your BI team then uses Looker (or your BI tool of choice) to build dashboards that answer strategic questions like: "Which early anonymous marketing channels led to high-value customers who experienced minimal technical issues?".
- Why: Provides a holistic view that connects the dots across the full behavioral story to business value, and identifies how friction impacts concrete outcomes.
Technical Preview: Custom BI dashboards enable exploration of complete, error-inclusive, enriched user journeys from Snowflake.
Step 5: (Bonus) - Fuel advanced analytics & predictive models
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Business View: With this complete, validated, and stitched historical customer journey data in Snowflake, your data scientists can build sophisticated predictive models that use early browsing patterns to predict high-value customers, conduct years-worth of cohort analyses, and perform unique research on long-term behavioral shifts.
- Why: Transforms deep historical understanding into predictive power, making Warehouse a powerful asset for strategic growth initiatives.
Technical View: Custom ML models lead to more accurate predictions of customer behavior and enable sophisticated, data-driven strategic decisions.
Alternative Foundational B2C Use Cases (Ideas):
Long-Term Feature Adoption Trends: Track key product feature usage via Warehouse over 12+ months to identify features with declining engagement. Visualize historical trends in BI.
Historical Device/Browser Error Monitoring : Aggregate specific errors from Warehouse data grouped by device/browser over months/quarters to identify persistent platform-specific bugs. Visualize hotspots in BI.
Basic User Segmentation Behavior Comparison: Join Warehouse data with your CRM segments to compare historical engagement/friction metrics (eg. average rage clicks per session, features used) across different customer tiers or personas. Visualize differences in BI.