- The way a CRO platform handles analytics data is the single most important architectural decision that affects data accuracy, privacy, and cost
- Two fundamentally different approaches: proprietary data collection (VWO, Optimizely) vs. analytics integration (Varify.io)
- Proprietary data creates discrepancies, duplicate tracking costs, and vendor lock-in. Integration creates a single source of truth.
- Varify.io's integration architecture means experiment data lives in your analytics — not in a vendor's silo
How an optimization platform handles analytics data determines almost everything about its practical value: data accuracy, privacy compliance, total cost, scalability, and data portability. Yet this is rarely discussed in CRO tool evaluations, which focus on UI features instead of data architecture. The architecture decision is binary: does the tool collect its own analytics data, or does it integrate with your existing analytics?
This technical evaluation compares these approaches and explains why integration-first architecture (as used by Varify.io) produces better outcomes for most organizations. For a broader integration comparison, see our CRO analytics integrations guide.
Two data architectures — and why the choice matters
Proprietary data collection
Platforms like VWO and Optimizely deploy their own tracking scripts alongside their testing scripts. These collect visitor behavior data independently from your analytics tool. The advantage: richer out-of-box analytics without depending on third-party tools. The disadvantages: additional JavaScript on your pages, additional cookies, separate consent requirements, and a second data source that inevitably disagrees with your primary analytics.
Analytics integration
Platforms like Varify.io don't collect their own analytics data. Instead, they send experiment assignment events to your existing analytics tool (GA4, BigQuery, Matomo) and read results back. The advantage: single source of truth, no additional privacy footprint, no data discrepancies. The trade-off: depends on your analytics tool's quality and configuration.
The discrepancy problem
When two systems independently track the same visitor actions, they always produce different numbers. Different session definitions, different attribution windows, different sampling methods, different time zones — the sources of discrepancy are endless. Teams spend hours reconciling "VWO says +5%, GA4 says +2%" instead of acting on results. Integration eliminates this problem entirely.
Data flow comparison
| Dimension | Proprietary (VWO/Optimizely) | Integration (Varify.io) |
|---|---|---|
| Data collection | Own tracking script + cookies | Your analytics tool only |
| Data storage | Vendor's servers (often US) | Your infrastructure |
| Source of truth | Two sources (tool + analytics) | One source (your analytics) |
| Privacy impact | Additional cookies + consent | Zero additional footprint |
| Data portability | Locked in vendor system | In your GA4/BigQuery forever |
| Scaling cost | Grows with traffic (MTU pricing) | Flat (€149/mo regardless) |
Source: Claude Research, May 2026
On every dimension except "out-of-box richness," the integration approach produces better outcomes for the organization using the tool.
Technical deep-dive: how Varify's integration works
Varify's data flow is deliberately simple:
- Step 1 — Assignment: Varify's 11.5 KB snippet assigns the visitor to a variant client-side. No server round-trip, no Varify cookie.
- Step 2 — Event dispatch: The snippet sends an experiment participation event to your connected analytics tool (e.g., GA4 custom event or Matomo custom variable).
- Step 3 — Evaluation: When you view experiment results in Varify, the platform queries your analytics tool for conversion data segmented by variant. The calculation happens using your analytics data — not Varify's.
- Step 4 — Reporting: Varify displays the results in its dashboard: conversion rates per variant, confidence interval, statistical significance. The underlying data is always your analytics data.
This architecture means Varify's infrastructure costs don't scale with your traffic — explaining why flat-rate pricing works. It also means experiment data is permanently in your analytics, surviving any tool switch.
One source of truth. Zero data discrepancies.
Your analytics tool evaluates every experiment. Varify handles the rest.
Choosing a CRO platform based on data architecture
Your data architecture preference should drive your CRO platform choice:
- You value single source of truth: Integration-first (Varify). One set of numbers. No reconciliation. Your analytics is the authority.
- You need standalone analytics: Proprietary (VWO). If you don't have GA4/Matomo and want heatmaps + analytics + testing in one tool, proprietary tracking bundles everything. Accept the privacy and discrepancy trade-offs.
- You need data warehouse access: Varify + BigQuery. Raw event-level experiment data in your warehouse. Custom SQL analysis. No vendor data limitations.
- You need maximum privacy: Varify + Matomo (self-hosted). Zero third-party data access. Zero cookies. Zero consent requirements for testing. Complete EU compliance.
For most organizations that already have an analytics tool in place, integration-first architecture delivers better data quality, lower costs, and stronger privacy compliance.