5 Rookie Mistakes Analysts Make With GA4 (And How to Avoid Them)
GA4 has been around long enough that most analysts have “used” it — but many still misunderstand how it actually works. The result? Dashboards that look fine on the surface but quietly mislead teams underneath.
If you’re a beginner or intermediate analyst, these mistakes are extremely common. The good news: once you’re aware of them, they’re easy to avoid.
1. Treating GA4 Like Universal Analytics
GA4 is not UA with a new interface. It’s an event-based system, not a session-based one. Many analysts still try to force GA4 data into old mental models — sessions, bounce rate logic, and pageview-centric thinking.
This leads to incorrect funnels, misleading conversion rates, and confusion when numbers don’t align with expectations.
How to avoid it: Learn GA4 as a new model. Focus on events, parameters, and user journeys instead of pageviews and sessions.
2. Not Understanding Event Scope
One of the biggest GA4 rookie mistakes is mixing event-scoped and user-scoped data without realizing it.
For example, counting purchases as if they were sessions, or joining user properties directly to event counts without understanding duplication.
How to avoid it: Always ask: “Is this metric event-level, user-level, or session-derived?” If you can’t answer that, you shouldn’t be reporting it.
3. Blindly Trusting Default Reports
GA4’s default reports are designed to be generic. They are not tailored to your business logic, funnel structure, or measurement goals.
Many analysts assume that because a report exists, it must be “correct.” In reality, default reports often hide assumptions, filters, and sampling behaviors.
How to avoid it: Use default reports as a starting point — not a source of truth. Validate important KPIs using explorations or BigQuery exports.
4. Ignoring Consent Mode and Data Gaps
With consent mode and privacy regulations, GA4 data is no longer complete by default. Some users are modeled, some are excluded, and some events never fire at all.
Many analysts forget to account for this when reporting trends or comparing periods.
How to avoid it: Understand your consent implementation. Clearly communicate when metrics are partially modeled or incomplete — especially to stakeholders.
5. Skipping BigQuery Validation
GA4’s interface is convenient — but it hides complexity. Analysts who never validate GA4 data in BigQuery often miss duplicated events, missing parameters, or logic errors.
This becomes a serious problem when dashboards are built purely on UI-based assumptions.
How to avoid it: Even basic BigQuery checks can dramatically improve confidence:
- Count raw events vs reported events
- Check parameter population rates
- Validate key conversions independently
Final Thoughts
GA4 is powerful — but only if you respect its complexity. Most GA4 mistakes don’t come from bad intentions; they come from assumptions carried over from the past.
If you slow down, validate your logic, and understand how data is actually collected and scoped, you move from being a report builder to a real analyst.
Written with support from AI tools and edited by Hisham Ghanayem. All insights reflect real-world analytics experience.
