Tracking keyword performance is essential for any SEO strategy—but tracking keywords by page type is where the real optimization opportunities live. Product pages behave differently from collection pages. Blogs behave differently from guides. Landing pages behave differently from evergreen content.
Yet Google Search Console and GA4 don’t provide page-type segmentation out of the box.
This is where Looker Studio gives you an unfair advantage.
By building a Keyword Performance by Page Type Dashboard, you can break apart your organic search footprint into the content categories that matter most. Suddenly, you can see exactly which page types drive rankings, which ones are slipping, and where to focus your optimization efforts.
This tutorial walks you through the complete build:
How to classify URLs using a calculated field
How to visualize keyword rankings by page type
How to track trends over time
How to identify high-intent opportunities
How to segment organic keywords using real examples
Let’s dive in.
Key Takeaways
Tracking keyword rankings by page type reveals insights you can’t get from GSC or GA4 alone.
Looker Studio allows you to classify URLs using regex or folder logic automatically.
Segmenting keywords by product pages, collection pages, blogs, guides, and landing pages exposes content-type wins and weaknesses.
Ranking distribution buckets (Top 3, Top 10, Top 20, etc.) shows which page types actually perform.
A page-type keyword dashboard becomes an SEO command center—ideal for SMBs, agencies, and in-house teams.
Why Page-Type Segmentation Matters in SEO
Most SEO teams track total keyword counts or average position—useful but shallow metrics.
But SEO isn’t monolithic.
Search engines rank:
Product pages for transactional intent
Collection pages for category-based intent
Blog posts for informational intent
Buying guides for comparative intent
Landing pages for paid/brand intent
Support articles for helpdesk or FAQ intent
If your blog traffic surges while your product pages collapse, your “overall keyword count” may look fine, but your revenue will suffer.
Segmented keyword tracking solves this.
When you categorize keywords by page type, you uncover patterns like:
Blog posts ranking well but driving low CTR
Category pages with huge impressions but poor ranking
Product pages ranking in the Top 5 but lacking volume
Guide pages rank fast and outperform other page types
This is the level of granularity Google Search Console simply cannot provide on its own.
Looker Studio unlocks it.
The Data Sources You Need
Your dashboard will rely on two components:
1. Google Search Console → Looker Studio Connector
You’ll use the URL Impression Table, which contains:
Query
Landing Page
Impressions
Clicks
Average Position
CTR
Date
2. URL-Based Page Type Classification
The true magic comes from categorizing URLs.
You can classify pages using:
Simple folder structures ("/products/")
Prefix patterns ("/blog/")
Slug naming conventions
Regex for flexible matching
The next section shows you how to build this classification logic correctly.
Step-by-Step Tutorial
Step 1: Connect Google Search Console to Looker Studio
Click Create → Data Source in Looker Studio.
Select Google Search Console.
Choose your property.
Select: Site → URL Impression Table.
Click Connect.
This gives you the core keyword and page-level data you’ll use for segmentation.
Step 2: Create the "Page Type" Calculated Field
This is the heart of the dashboard.
You’ll create a field that automatically assigns each URL to a page type—Product Page, Collection Page, Blog Post, Guide, etc.
Here is the full process broken down into sub-steps.
Step 2A: Open the Data Source Schema
Open your Looker Studio dashboard.
In the right-side panel, find your Search Console data source.
Click the data source name to open the Schema view.
You’ll see a grid listing dimensions and metrics.
Step 2B: Add a New Calculated Field
In the top-right corner, click Add a Field.
Name the field: Page Type.
This opens the formula builder.
Step 2C: Choose Your Classification Logic
You have two options depending on your site structure.
Option A: Clean URL Structure (Most Common)
Perfect for Shopify, WooCommerce, BigCommerce, and well-organized CMS setups.
CASE
WHEN Landing Page CONTAINS "/products/" THEN "Product Page"
WHEN Landing Page CONTAINS "/collections/" THEN "Collection Page"
WHEN Landing Page CONTAINS "/blog/" THEN "Blog Post"
WHEN Landing Page CONTAINS "/pages/" THEN "Page"
WHEN Landing Page CONTAINS "/guides/" THEN "Guide"
ELSE "Other"
END
Option B: Messy or Inconsistent URL Structures (Use Regex)
Regex supports flexible matching when URLs aren’t standardized.
CASE
WHEN REGEXP_MATCH(Landing Page, ".*product.*") THEN "Product Page"
WHEN REGEXP_MATCH(Landing Page, ".*collection.*") THEN "Collection Page"
WHEN REGEXP_MATCH(Landing Page, ".*blog.*") THEN "Blog Post"
WHEN REGEXP_MATCH(Landing Page, ".*guide.*") THEN "Guide"
ELSE "Other"
END
This will correctly classify URLs such as:
/red-product-123
/our-products/specials
/blogging-tips/
/holiday-gift-guide
Step 2D: Validate the Field
Click the Validate button at the top-right.
A green checkmark means the formula is correct. If it fails, Looker Studio highlights the broken line. Common errors include:
Incorrect field name (must match “Landing Page” exactly)
Missing END
Smart quotes instead of straight quotes
Step 2E: Save the Field
Click Save.
Your new dimension Page Type now appears alongside Query, Landing Page, Date, etc.
Step 2F: Use Page Type in Visuals and Filters
You can now incorporate Page Type into your dashboards as:
A breakdown dimension
A table column
A filter control
A segmented time-series chart
A bar chart grouping variable
A drilldown dimension
This field powers most of the visuals you’ll build next.
Step 2G: Test the Page Type Logic with a Diagnostic Table
Before building the full dashboard, make sure your classification works.
Create a simple diagnostic table:
Dimensions
Landing Page
Page Type
Metrics
Impressions
Clicks
Sort by Landing Page.
Scan the results and check:
Product URLs are correctly labeled as Product Page
Blog URLs show Blog Post
Collection URLs show Collection Page
Nothing important is stuck in “Other”
Fix any regex or folder logic issues before continuing.
This testing step saves hours of troubleshooting later.
Step 3: Build the Keyword Chart by Page Type
Your visuals should answer: “Which page types drive keyword performance?”
Recommended charts:
Chart 1: Keyword Count by Page Type (Bar Chart)
Dimension: Page Type
Metric: Count of Query
Sort: Descending
This shows which page categories have the most keyword coverage.
Chart 2: Average Position Over Time by Page Type (Time Series)
Dimension: Date
Breakdown: Page Type
Metric: Average Position
Helps you spot ranking declines or growth by content type.
Chart 3: Keyword-Level Table
Columns to include:
Query
Page Type
Landing Page
Clicks
Impressions
Average Position
CTR
This becomes your main keyword analysis grid.
Chart 4: Distribution of Keywords by Page Type (Pie Chart)
High-level view of content-type representation.
Step 4: Add Filters to Control the Dashboard
You should include:
Keyword Filter
Search for branded vs. non-branded keywords.
Page Type Filter
Switch between products, categories, blog, guide, etc.
Device Filter
Desktop vs. mobile differences can uncover big ranking issues.
Country Filter
Critical for global brands.
Landing Page Filter
Drill into the performance of individual URLs.
Step 5: Organic Keyword Example (Realistic Walkthrough)
Here’s how this looks using an eCommerce brand (dancewear example):
Query | Landing Page | Page Type | Avg Position | Clicks | Impr |
“red leather ballet shoes” | /products/red-ballet-shoes | Product Page | 4.2 | 118 | 4,037 |
“women’s dance shoes” | /collections/womens-dance | Collection Page | 11.7 | 63 | 18,502 |
“best pointe shoes for beginners” | /blog/beginner-pointe-guide | Blog Post | 7.8 | 93 | 9,178 |
“capezio sizing chart” | /pages/size-guide | Guide | 10.3 | 58 | 6,911 |
Insights:
Blog posts outperform category pages.
Product pages rank well but don’t generate high impressions.
Collection pages show strong demand but need content optimization.
This is precisely why page-type segmentation is critical.
Step 6: Add KPI Scorecards by Page Type
For each page type, create scorecards displaying:
Total Clicks
Total Impressions
Average Position
CTR
Keywords Count
Month-over-month ranking change
This provides executive-level clarity instantly.
Step 7: Build Ranking Distribution Buckets
Create a second calculated field:
CASE
WHEN Average Position <= 3 THEN "Top 3"
WHEN Average Position <= 10 THEN "Top 10"
WHEN Average Position <= 20 THEN "Top 20"
ELSE "Top 100"
END
Use this to create visuals like:
Top-3 Keywords by Page Type
Top-10 Keyword Distribution
Page Types Losing Ground
Page Types Driving New Visibility
Step 8: Add an Opportunity Analysis Layer
Create three diagnostic tables per page type:
1. Rising Keywords (Position Improving)
Sort: Position Change ascending (negative = improvement)
2. Falling Keywords
Sort: Position Change descending
3. High-Impression, Low-Ranking Keywords
Filters:
Impressions > X
Average Position > 10
These are high-impact optimizations.
Step 9: Build an Executive SEO Summary Section
Your dashboard should end with:
Page-Type Performance Summary
High-Impact Opportunities
Keyword Trends by Content Type
Landing Page-Level Insights
Recommendations for Optimization
This turns the dashboard into a decision-making tool.
FAQ
How does Looker Studio classify page types?
Using calculated fields with URL patterns or regex. Once built, it works automatically.
Can I use Ahrefs or SEMrush data?
Yes, but you need a paid connector. Blending volume + GSC performance creates powerful insights.
Does this work on Shopify and Wix?
Yes. Both use predictable folder structures ideal for this dashboard.
Can I mix branded and non-branded keywords?
Yes—simply create a branded keyword filter using regex.
What if my URLs don’t have folder structures?
Use REGEXP_MATCH patterns or slug naming rules.