GA4 Pipeline -Episode 4: Interpret | Why GA4 Doesn’t Explain Behavior

--author Arif Khan | founder zinzu.io

Previous Episode 3: Collect | GA4 Collection & Processing

By the time data reaches this stage, it has already been: 

Defined, Sent, Collected & Processed.

What remains is interpretation.

This is where most users interact with GA4 and where its design tradeoffs become visible.

GA4 interpretation is aggregate-first

GA4 is designed to answer questions like:

• How many users?

• How often?

• How much?

  
  

It does this by working primarily with aggregates.

What gets lost early in interpretation:

• Story across events

• Rich behavioral context

• Long, irregular user journeys

Once data is summarized into aggregates, these details are no longer visible in standard reports.

Filters are shallow and rigid

GA4 filtering is mostly UI-driven, supports limited conditions, and breaks down for behavior-level questions.

Behavior-level questions are difficult to express, such as:

• What behavior results in coupon upselling

• What actions lead to coupon usage

• What sequences result in errors

• How different paths lead to conversions

• How behavior differs by acquisition source (Google vs Facebook vs other sources)

  
  

In essence, extracting stories from data using GA4 UI is not easy.

Conversions flatten context

In GA4, a conversion is simply: an event you label as important

By default, a conversion captures that it happened, not: 

• What led up to it 

• What happened after

• How different users reached it

  
  

Complex behavior is reduced to a count unless the journey is reconstructed elsewhere.

Funnels in GA4 are complex by design

They require you to define exact events, parameters, and order upfront.Even small changes in tracking can break or invalidate them.

Funnels work best for validating known, stable flows not for discovering real-world behavior.

The learning curve is high for meaningful answers

Basic metrics are easy to access.

Meaningful answers are not.

To go beyond surface-level insights, you need:

• Deep GA4-specific knowledge

• Familiarity with undocumented quirks

• Trial-and-error exploration

• Workarounds for missing concepts

GA4 looks simple.

Using it deeply is not.

BigQuery shifts the burden, not the difficulty

BigQuery removes UI limitations, but:

• All logic moves to you

• Behavioral reconstruction is manual

• Session and identity handling is your responsibility

• You must already know what question to ask

  
  

BigQuery gives you data.

It does not give you understanding.

The core limitation

GA4 is optimized for:

• Reporting

• Monitoring

• Standardized metrics

It is not optimized for:

• Behavioral reasoning

• Sequence discovery

• Explaining why users behave the way they do

This is not accidental. It’s a design tradeoff.

Interpreting What the Data Is Telling You

GA4 does a tremendous amount of heavy lifting. It collects events, applies structure, and even provides storage at scale.

But interpretation is a different problem.

Each event a user generates is part of a larger story. When we look only at aggregates, we lose the context that explains why users behave the way they do.

Extracting that story from raw event data is possible, but it’s complex and requires deep technical expertise.

We’re building zinzu as an interpretation layer, a way for both technical and non-technical users to express behavioral questions as naturally as they think, without having to rebuild the plumbing underneath.

What’s Next?

Let’s continue the journey on video

We’ll host a virtual session to walk through these ideas live, including whiteboarding and deeper technical discussion. Details will be shared soon.

To get notified:

• Email your interest: team@zinzu.io

• Follow on LinkedIn: https://www.linkedin.com/in/arifkhan3/

GA4 Pipeline -Episode 3: Collect | What GA4 Does With Events

--author Arif Khan | founder zinzu.io

Previous Episode 2: Send | gtag.js / Mobile SDKs

In the previous episode, we looked at how events are sent.

Now we move to the next step in the pipeline: Collect

This is where GA4 receives events and processes them before you ever see them.

What “collect” means in GA4

When events reach GA4, they are no longer under your control.

GA4 is responsible for:

• Receiving events at scale

• Applying server-side processing

• Preparing data for reporting and export

  
  

At this stage, GA4 is not visualizing data yet. It is processing it.

Events are inputs, not final data

Google Analytics 4 (GA4) doesn’t store or expose data exactly as received.

Events via tags, Measurement Protocol, or uploads are raw inputs to a server-side pipeline that groups, models, and filters them into the insights you see in reports or BigQuery.

What GA4 Receives

• Event name: e.g., page_view.

• Parameters: Up to 25 key-values (e.g., value=49.99).

• Timestamps: Event time and engagement.

• Identifiers: User pseudo-ID, anonymized signals.

Queued for processing: real-time in ~30s, full derivations in 24 hours

Examples of processing during collection

Before data is exposed in reports or BigQuery, GA4 applies processing such as (these are only few of large processing):

• Session grouping: Events are grouped into visits based on timing and signals.

  
  

• Attribution processing: Traffic source and campaign information is evaluated.

  
  

• Privacy enforcement: Consent and privacy rules affect what data is processed and exposed.

  
  

• Thresholding and aggregation rules: Limits may be applied to protect privacy and support scale.

  
  

These steps happen after events are sent, but before you see the data.

Why GA4 feels like a black box

You do not see: 

• The raw processing steps.

• All internal rules

• Every decision made during collection

This is intentional.

GA4 is designed to balance: Scale, Privacy & Simplicity for most users

Understanding this prevents false assumptions about “missing” or “incorrect” data.

What’s Next?

Once events are collected and processed, the only thing left is interpretation.

This is where most people interact with GA4 through reports, explorations, and conversions.

In the next episode, we’ll look at interpretation, and why GA4 often struggles to explain behavior even when the data is technically correct.

→ Next Episode 4: Interpret: GA4 Reports & Explorations