Why GA4 Is Not Enough for Product Decisions

And why most startups are flying blind without realizing it

Google Analytics 4 is everywhere.

It's installed. It's collecting data. Dashboards look active.

So founders assume:

"We have analytics. We're data-driven."

In reality, GA4 answers marketing questions — not product questions.

And using it as a product decision engine leads to false confidence, slow learning, and expensive mistakes.

---

The Core Problem: GA4 Was Never Designed for Product Thinking

GA4 is optimized for:

• traffic acquisition
• attribution
• campaigns
• channels
• conversions at the edge of the funnel

Product decisions require something else entirely.

They require:

• understanding behavior, not visits
• tracking users over time, not sessions
• measuring flows, not pages
• analyzing decisions, not clicks

GA4 is not broken. It's just solving a different problem.

---

What Founders Think GA4 Tells Them (But Doesn't)

Most teams believe GA4 answers questions like:

• "Which features drive retention?"
• "Where do users get stuck?"
• "What behavior predicts churn?"
• "What actually causes conversion?"

GA4 cannot reliably answer these.

Not because you configured it wrong — but because of how it models data.

---

The Structural Limitations of GA4 (Not Configuration Issues)

1. Event Soup Without Product Context

GA4 is event-based, but:

• events are flat
• context is shallow
• relationships are weak

You can track:

• clicks
• page views
• scrolls

But you can't easily answer:

• what sequence led to success
• which actions matter vs noise
• how behavior evolves across weeks

Product decisions depend on state and progression. GA4 sees isolated moments.

---

2. Sessions Are Gone — But Mental Models Didn't Improve

GA4 removed classic sessions.

But it didn't replace them with:

• real user journeys
• lifecycle states
• funnels that reflect product logic

Teams still reason in:

• pages
• events
• sessions

Products don't work like that.

Users move through:

• onboarding
• activation
• usage
• value moments
• drop-off

GA4 doesn't model this natively.

---

3. Retention Analysis Is Superficial

Retention in GA4 is:

• limited
• coarse
• hard to customize
• disconnected from product meaning

You can see that users returned.

You can't easily see:

• why they returned
• what they used
• what changed

Retention without causality is not actionable.

---

4. Sampling, Thresholds & Privacy Blur Reality

GA4 increasingly:

• thresholds data
• hides segments
• aggregates aggressively
• limits exploration depth

This is understandable for privacy.

But it makes:

• small cohorts invisible
• edge cases disappear
• early signals easy to miss

For product discovery, this is dangerous.

---

The Most Expensive Mistake: Mixing Marketing Analytics With Product Analytics

This is where many teams fail silently.

They use:

• GA4 for traffic
• GA4 for onboarding
• GA4 for feature usage
• GA4 for retention

Everything lives in one place.

This creates:

• misleading correlations
• false attribution
• decisions driven by acquisition noise

Marketing analytics answer:

"How did users arrive?"

Product analytics answer:

"What do users do once they're here?"

These must not be mixed.

---

What Product Decisions Actually Require

To make real product decisions, teams need:

1. A Clear Event Model

Not "button_clicked", but:

• account_created
• onboarding_completed
• feature_X_used
• value_moment_reached

Events must represent business meaning, not UI actions.

---

2. User-Centric, Longitudinal Data

You need to see:

• the same user over time
• behavior changes
• learning curves
• drop-off patterns

GA4 is not built for this depth.

---

3. Funnel Logic That Matches Reality

Product funnels are:

• non-linear
• multi-session
• multi-device
• stateful

Marketing funnels are not.

Using the wrong funnel logic leads to wrong conclusions.

---

4. Ownership of Data

GA4 data lives:

• inside Google
• behind constraints
• with limited modeling flexibility

Serious product teams need:

• raw access
• custom queries
• flexible aggregation

Otherwise, insight velocity is capped.

---

What High-Performing Teams Do Instead

They don't "replace GA4".

They put it in its place.

Typical setup in mature teams:

• GA4 → acquisition & marketing
• Product analytics → behavior & decisions
• Data warehouse → source of truth

This separation:

• reduces confusion
• increases signal quality
• accelerates learning

---

Why This Matters More After PMF (Not Before)

Early-stage startups can "feel" the product.

Post-PMF, intuition breaks.

At that point:

• small changes have big impact
• wrong decisions compound
• teams scale faster than understanding

Using GA4 alone after PMF is like flying a plane with only altitude data.

You need instruments.

---

The H-Studio Perspective: Analytics as Product Infrastructure

At H-Studio, we treat analytics as:

• part of the product architecture
• not a reporting layer
• not a marketing add-on

We design:

• event models aligned with business logic
• privacy-first tracking
• product and marketing analytics separately
• dashboards founders can actually act on

GA4 stays — but it stops pretending to be the brain.

---

Final Thought

GA4 is useful.

But it's not enough.

If your product decisions rely solely on GA4, you're optimizing visibility, not value.

And value is what keeps companies alive.

---

Get an Analytics Architecture Audit

If your product decisions rely on GA4 alone, you're likely missing critical insights about user behavior, retention, and feature adoption. We analyze your event model, tracking gaps, and GDPR risks—and design an analytics setup that actually supports product decisions.

We build data engineering and analytics pipelines that give you ownership over your data and the flexibility to answer product questions. For growth analytics and BI dashboards, we create dashboards founders can actually act on. For privacy-first tracking, we implement server-side analytics that comply with GDPR while preserving insight quality.

Start Your Audit