What Explorations Are
Explorations is a separate reporting workspace in GA4 designed for custom analysis.
Standard GA4 reports show predefined views of your data. Explorations let you build your own - combining dimensions, metrics, segments, and filters in flexible ways that standard reports do not support.
Think of standard reports as dashboards and Explorations as a query interface.
Where to Find Explorations
GA4 → Explore (in the left navigation, below Reports)
You will see:
- Your explorations - saved analyses you have built
- Template gallery - pre-built exploration templates
Explorations are saved per user and per property. They are not visible to other users unless you share them.
The Three Most Useful Exploration Types
1. Free Form Exploration
A flexible drag-and-drop table builder. The most versatile exploration type.
Use it to:
- Build custom cross-dimensional tables
- Analyse any combination of dimensions and metrics
- Apply segments to compare audiences
- Export data for further analysis
2. Funnel Exploration
Visualises a multi-step conversion funnel with drop-off rates at each step.
Use it to:
- See where users drop off before converting
- Compare funnel performance by segment (new vs returning, mobile vs desktop)
- Identify the weakest step in your checkout or lead flow
3. Path Exploration
Shows the sequence of pages or events users interact with - forward from an entry point or backward from an exit point.
Use it to:
- Understand what users do before a conversion (backward path)
- Understand what users do after landing on a key page (forward path)
- Spot unexpected drop-off pages or unexpected paths to conversion
How to Build a Free Form Exploration
Step 1: Create a New Exploration
Explore → Blank (or pick a template)
Give it a descriptive name in the top left.
Step 2: Understand the Three-Panel Layout
Variables panel (left): Where you add dimensions and metrics to your exploration’s library. These are not in the report yet - just available to use.
Settings panel (middle): Where you configure the current tab - what technique, dimensions, metrics, rows, columns, filters, and segments to apply.
Canvas (right): The output - your actual report.
Step 3: Add Dimensions and Metrics
In the Variables panel:
- Click the + next to Dimensions → search and add dimensions you need (e.g. Session source / medium, Page path, Device category)
- Click the + next to Metrics → search and add metrics (e.g. Sessions, Key events, Engagement rate, Revenue)
Step 4: Configure the Report
In the Settings panel:
- Rows: Drag a dimension here to define what the rows represent (e.g. Session source / medium)
- Values: Drag metrics here (e.g. Sessions, Key events, Revenue)
- Columns: Optional - drag a dimension here to pivot the table
- Filters: Restrict data to a subset (e.g. only Paid Search traffic)
Step 5: Apply Segments (Optional)
Segments let you compare two groups side by side.
Example: Compare new users vs returning users to see if they convert at different rates.
In the Variables panel → Segments → click + → create or import a segment → drag it into Segment Comparisons in Settings.
Useful Free Form Explorations to Build
Source/Medium Conversion Breakdown
Rows: Session source / medium Values: Sessions, Key events, Session key event rate, Revenue (if ecommerce) Sorted by: Key events (descending)
Shows which traffic sources drive the most conversions and at what rate. Essential for Google Ads performance context.
Landing Page Performance
Rows: Landing page Values: Sessions, Engaged sessions, Engagement rate, Key events Filters: Session medium = cpc (paid traffic only)
Shows which landing pages perform best for Google Ads traffic. Helps identify underperforming pages dragging down campaign results.
Device Category Performance
Rows: Device category Values: Sessions, Engagement rate, Key events, Session key event rate
Quickly shows if mobile users convert at significantly different rates than desktop. Useful for setting mobile bid adjustments in Google Ads.
Product Performance (Ecommerce)
Rows: Item name Values: Item views, Cart additions, Purchases, Item revenue, Purchase to view rate
Shows which products convert best and which have the largest gap between views and purchases.
How to Build a Funnel Exploration
Explore → Funnel Exploration
Define Your Steps
Click Edit Funnel (pencil icon):
- Add Step 1: name it “Landing” → Condition: Event name =
page_view - Add Step 2: name it “Product View” → Condition: Event name =
view_item - Add Step 3: name it “Add to Cart” → Condition: Event name =
add_to_cart - Add Step 4: name it “Begin Checkout” → Condition: Event name =
begin_checkout - Add Step 5: name it “Purchase” → Condition: Event name =
purchase
Click Apply.
The funnel shows the drop-off rate at each step. If 70% of users drop between Add to Cart and Begin Checkout, that step needs investigation.
Use Segment Comparisons in Funnels
Add mobile vs desktop segments to see if the checkout drop-off is device-specific.
How to Build a Path Exploration
Explore → Path Exploration
Forward Path (What Users Do After a Page)
- Set the starting point: Event =
page_view→ with parameterpage_locationcontaining your page URL - The canvas shows what events or pages users go to next
Backward Path (What Users Do Before Converting)
- Click the ending node and select Ending Point
- Set the ending point: Event =
purchase - The canvas shows the steps users took before converting, working backward
Backward paths are particularly useful for understanding the real pre-conversion journey, which often differs from what you expect.
Sharing and Exporting Explorations
Share With Other Users
Explore → click the share icon (top right) → share link
Shared explorations create a copy in the recipient’s account - they can view and edit their copy without affecting yours.
Export Data
Top right of the canvas → Export → Download as Google Sheets, CSV, or PDF
For regular reporting, export to Google Sheets and connect to Looker Studio for automated dashboards.
Limitations of Explorations
- Sampling: For properties with high traffic, GA4 may sample Exploration data at lower date ranges. Switch to shorter date ranges or use unsampled exports via BigQuery for high-volume analysis.
- Data retention: Explorations use the data retention setting of your property (default: 2 months for event-level data, 14 months for user-level aggregates). Extend to 14 months in Admin → Data Settings → Data Retention.
- Sharing: Shared explorations are read-only copies. There is no collaborative editing.
- Not for operational dashboards: For regular monitoring, use Looker Studio connected to GA4, not Explorations. Explorations are for ad-hoc analysis.
A Practical Workflow for Google Ads Advertisers
Weekly:
- Source/Medium Conversion Breakdown - check paid vs organic conversion rate
- Landing Page Performance (paid traffic only) - flag underperforming pages
Monthly:
- Funnel Exploration - where is conversion rate dropping and has it changed?
- Device Category Performance - do bid adjustments need updating?
For campaign reviews:
- Path Exploration (backward from purchase) - what content or pages are in the conversion path?
Final Thoughts
Standard GA4 reports tell you what happened. Explorations help you understand why.
The questions that matter most - why did conversion rate drop, which landing pages are killing paid performance, where are users dropping off the funnel - are not answered in standard reports. They are answered in Explorations.
Build the four core explorations above and check them regularly. That habit will surface more actionable insights than most dashboards ever will.
This is the final article in the GA4 Intro Series. You now have the foundation to use GA4 effectively as a Google Ads advertiser - from understanding the data model to building custom analysis that answers real business questions.
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