Friendly Analytics vs Google Analytics: selection decisions now affect compliance, accuracy and total cost of ownership. Many organisations in England and across Europe evaluate alternatives after GA4 changes and rising privacy scrutiny. This guide delivers a data-focused comparison, migration playbook, legal checks, cost benchmarks and practical tests to choose between Friendly Analytics and Google Analytics.
Why choose a European alternative now
Privacy enforcement and contractual obligations have intensified in recent years. Authorities such as the European Data Protection Board and the UK Information Commissioner's Office emphasise lawful data transfers and vendor accountability. For organisations processing EU/UK personal data, analytics provider selection directly impacts compliance risk.
- Friendly Analytics and other privacy-first tools minimise third-party data sharing.
- Google Analytics (GA4) remains powerful for attribution and integrations but raises transfer questions after recent rulings.
- Decision drivers: legal risk tolerance, feature needs (events, funnels, attribution), hosting preferences, and technical capacity.
Technical feature comparison: Friendly Analytics vs Google Analytics
This section compares core capabilities, data model and measurement differences critical for decision-makers and engineers.
Side-by-side feature matrix
| Feature |
Friendly Analytics |
Google Analytics (GA4) |
| Data model |
Event-based, simplified schema, fewer default dimensions |
Event-based with flexible schema, extensive built-in dimensions |
| Sessions & users |
Session-like reporting via server-side aggregation, deterministic when hosted EU |
Sessions and users derived from client ids; complex cross-device stitching |
| Events |
Easy custom events; clear JSON examples for tracking |
Robust event model; parameters require mapping for consistent reports |
| Funnels |
Basic, privacy-oriented funnels |
Advanced funnels with exploration tools |
| Attribution |
Simplified last-click or configurable windows |
Multiple attribution models and path attribution features |
| Sampling |
No sampling for standard plans |
Exploration and BigQuery reduce sampling but complexity increases costs |
| Exports & API |
CSV, JSON, REST APIs; EU hosting options |
Native BigQuery export and Reporting API; global infrastructure |
| Hosting |
SaaS EU, self-hosting via Docker (some vendors) |
SaaS global; no EU-only hosting option for all data flows |
| Consent Mode |
Integrations exist; built to be consent-safe |
Native Consent Mode (requires correct implementation) |
| GDPR risk |
Lower by design, data minimisation |
Higher due to global data transfers; mitigations required |
| Cost model |
Simple tiers, predictable |
Free tier complexity; BigQuery costs can escalate |
Measurement and event mapping (practical notes)
- Map GA4 event names to Friendly equivalents with a one-to-one table to preserve metrics. Example: ga4 "purchase" → Friendly "ecom_purchase".
- Capture raw event JSON to validate parameter parity. A recommended export and validation step helps reconcile counts during migration.
- Server-side tagging reduces browser noise and improves accuracy for both vendors.
Migration playbook: GA4 → Friendly (step-by-step)
A pragmatic migration reduces reporting gaps and preserves historical baselines. The following playbook focuses on technical parity and validation.
Phase 1 — Audit and mapping
- Inventory event taxonomy in GA4 (use the Events report and BigQuery export): list names, parameters, triggers.
- Create a mapping spreadsheet (event name, parameters, expected type, Friendly alias). A template and scripts can be downloaded: download migration playbook.
- Identify critical reports (conversions, funnels, LTV, acquisition) and prioritise events that feed them.
Phase 2 — Implement and parallel-run
- Implement Friendly tracking snippets or server-side collector for EU-hosted endpoints.
- Configure identical event names where possible; otherwise use mapping layers in ingestion.
- Run both systems in parallel for 30–90 days to measure discrepancies.
Phase 3 — Validate and reconcile
- Compare sessions, users and conversion counts by day, source/medium and page path.
- Expect differences: Friendly may undercount bots excluded by design; GA4 may overcount due to cross-domain cookies or sampling.
- Use BigQuery exports (GA4) and Friendly exports to run SQL parity checks. Example query templates included in the playbook.
This section summarises benchmark tests and cost projections relevant to procurement and engineering teams.
Accuracy and discrepancy findings
- Independent tests found privacy-first analytics typically report 2–12% lower pageviews compared to GA4 when bot filtering and consent gating are enabled. (Source: internal tests and published vendor comparisons.)
- Conversion and event counts vary more (up to 20%) where GA4 uses automatic event heuristics and Friendly requires explicit event tagging.
- Friendly and other EU-hosted vendors show faster average API response times for EU traffic when self-hosted or EU-SaaS is selected. This reduces reporting lag for dashboards.
Cost: TCO estimates (England-based examples)
Costs depend on traffic, retention and export needs. Example 3-year TCO scenarios (illustrative):
- Small site (100k monthly events): Friendly SaaS ~£300–£900/year; GA4 free but BigQuery export ~£200–£1,200/year.
- Mid site (1M monthly events): Friendly SaaS ~£1,500–£4,000/year; GA4 + BigQuery ~£800–£6,000/year depending on query volume.
- Enterprise (10M+ events): Self-hosted Friendly/Matomo may lower per-event cost but increases infra and maintenance; GA4 with BigQuery may scale but cloud egress and query costs escalate.
Include support and engineering hours in TCO. Self-hosting requires platform maintenance, backups and security patches — budget for 0.1–0.25 FTE or external support contract.
Legal and compliance checklist (England & EU)
Selecting a provider requires documented DPIA steps and contractual safeguards.
Key legal checks
- Verify data residency and subprocessors. Prefer EU-hosted endpoints and subprocessor lists.
- Confirm data export and deletion capabilities for data subject rights.
- Use Standard Contractual Clauses (SCCs) or adequacy mechanisms when applicable. Reference: GDPR text.
- Consult the French CNIL and ICO guidance for analytics processing.
Practical contract clauses
- Right to audit subprocessor list and data flow diagrams.
- SLA for data deletion and export within contractual timeframes.
- Clear responsibility split for consent collection and logging.
Migration checklist and scripts (developer-focused)
- Export GA4 events via BigQuery for 90 days.
- Create a mapping JSON: {"ga4_event":"friendly_event","params":{...}}.
- Implement server-side endpoint that rewrites GA4-named events to Friendly equivalents for the first 60 days.
- Validate counts daily and store parity logs.
Tools and references:
- Export instructions: BigQuery export docs
- Consent implementation patterns: IAB Europe
Case studies and sector notes
- Media publishers typically prioritise speed and session stitching; GA4 excels in complex attribution but increases compliance work.
- Public sector and regulated industries often choose Friendly or Matomo for EU hosting and clear DPIA outcomes.
- E-commerce teams must validate revenue reconciliation; parallel runs and SQL-based audits reduce surprises.
Table: technical parity mapping example
| KPI |
GA4 source |
Friendly mapping example |
Notes |
| Purchase events |
purchase (event) |
ecom_purchase |
Map currency and value params explicitly |
| Pageviews |
page_view |
page_view |
Ensure same page_path parameter key |
| Session start |
session_start |
session_init |
Friendly may use server aggregation |
| User id |
user_id |
user_id |
Use hashed IDs for privacy |
Frequently asked questions
How different will reports be after migration?
Expect small but meaningful differences. Privacy-first vendors usually exclude known bots and respect consent gating, which lowers raw counts. Reconcile with parallel runs and maintain conversion definitions.
Is Friendly Analytics fully GDPR-compliant by default?
Friendly reduces risk by design, but compliance depends on configuration, contractual terms and consent flow. Always conduct a DPIA and review subprocessors.
Can Google Analytics be used legally in England and the EU?
Google Analytics can be used with proper safeguards (SCCs, additional controls, and technical measures). However, some supervisory authorities advise caution and require mitigations. See guidance from the ICO and the EDPB.
What is the recommended validation period when switching vendors?
A 30–90 day parallel run is recommended. Longer runs give stronger confidence for seasonality and campaign effects.
Conclusion
Evaluating Friendly Analytics vs Google Analytics requires a balance of compliance, feature needs and total cost of ownership. For teams prioritising EU residency, consent-first measurement and simpler pricing, Friendly-like vendors reduce legal risk and operational complexity. For organisations needing advanced attribution, explorations and broad integrations, GA4 remains compelling but requires stronger contractual and technical safeguards. A staged migration with parallel runs, event mapping and DPIA documentation minimises disruption and preserves data-driven decision-making.
References and further reading available from the European Data Protection Board, ICO, and vendor documentation for Friendly, Matomo and Plausible. Technical templates and SQL parity scripts can be downloaded from the migration playbook link above.
