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All lessons in Technical SEO Auditing Lesson 10.2 of 84

Module 1: Introduction to Technical SEO Auditing

  1. 1.1What Is a Technical SEO Audit?
  2. 1.2Technical SEO Audit vs. General SEO Audit
  3. 1.3When Should You Audit a Website?
  4. 1.4Types of Technical SEO Issues
  5. 1.5Defining Audit Scope and Objectives

Module 2: Website Crawling and URL Discovery

  1. 2.1How SEO Crawlers Discover URLs
  2. 2.2Configuring a Website Crawl
  3. 2.3Crawl Scope, Limits, and Exclusions
  4. 2.4Internal Links and Crawl Depth
  5. 2.5Orphan Pages and URL Discovery
  6. 2.6Crawl Data vs. Search Engine Data
  7. 2.7Identifying Crawl Coverage Gaps

Module 3: HTTP Status Codes and Redirect Auditing

  1. 3.1Understanding HTTP Status Codes
  2. 3.2Finding 4xx Errors and Broken Links
  3. 3.3Identifying 5xx Server Errors
  4. 3.4Redirect Chains and Redirect Loops
  5. 3.5Internal Links to Redirected URLs
  6. 3.6301 vs. 302 Redirects
  7. 3.7Auditing 404 Pages and Soft 404s

Module 4: Crawlability and Indexability Auditing

  1. 4.1Crawling vs. Indexing
  2. 4.2Robots.txt Audit
  3. 4.3Meta Robots and X-Robots-Tag
  4. 4.4Noindex and Canonical Conflicts
  5. 4.5XML Sitemap Validation
  6. 4.6Canonical URL Auditing
  7. 4.7Identifying Indexability Conflicts
  8. 4.8Comparing Crawl Data with Google Search Console

Module 5: URL Structure and Duplicate Content Issues

  1. 5.1URL Structure and Normalization
  2. 5.2Duplicate URLs and Duplicate Pages
  3. 5.3HTTP vs. HTTPS and Hostname Variations
  4. 5.4Trailing Slashes and URL Parameters
  5. 5.5Pagination and Faceted Navigation
  6. 5.6Canonicalization Strategies
  7. 5.7Internal Linking to Preferred URLs

Module 6: Technical On-Page Auditing

  1. 6.1Missing and Duplicate Title Tags
  2. 6.2Missing and Duplicate Meta Descriptions
  3. 6.3Missing, Duplicate, and Multiple H1 Headings
  4. 6.4Heading Hierarchy and HTML Structure
  5. 6.5Missing Image Alt Attributes
  6. 6.6Internal Link and Anchor Text Issues
  7. 6.7Empty, Thin, and Near-Duplicate Pages
  8. 6.8Auditing HTML Metadata at Scale

Module 7: Website Performance and Core Web Vitals

  1. 7.1Core Web Vitals: LCP, INP, and CLS
  2. 7.2Lab Data vs. Field Data
  3. 7.3Identifying Slow Pages
  4. 7.4Image and Resource Optimization Opportunities
  5. 7.5JavaScript and CSS Performance
  6. 7.6Mobile Usability and Responsive Rendering
  7. 7.7Prioritizing Performance Fixes

Module 8: JavaScript SEO and Rendering

  1. 8.1How Search Engines Process JavaScript
  2. 8.2HTML Source vs. Rendered HTML
  3. 8.3Client-Side vs. Server-Side Rendering
  4. 8.4JavaScript-Generated Content and Links
  5. 8.5Rendering Failures and Missing Content
  6. 8.6Lazy Loading and Crawl Accessibility
  7. 8.7Diagnosing JavaScript SEO Problems

Module 9: Structured Data and Schema Auditing

  1. 9.1Structured Data and Schema.org Basics
  2. 9.2JSON-LD and Structured Data Formats
  3. 9.3Finding Structured Data Across a Website
  4. 9.4Detecting Invalid or Incomplete Markup
  5. 9.5Schema Validation and Rich Result Eligibility
  6. 9.6Conflicting or Duplicate Structured Data
  7. 9.7Prioritizing Schema Fixes

Module 10: Analyzing Audit Data and Prioritizing Issues

  1. 10.1Reading Crawl Reports and Exports
  2. 10.2Grouping URLs by Issue Type
  3. 10.3Identifying Root Causes vs. Symptoms
  4. 10.4Evaluating Severity, Impact, and Effort
  5. 10.5Separating Sitewide and Isolated Issues
  6. 10.6Identifying False Positives
  7. 10.7Building an Actionable Issue Register

Module 11: Technical SEO Audit Reporting

  1. 11.1Structuring a Technical SEO Audit Report
  2. 11.2Documenting Evidence and Affected URLs
  3. 11.3Writing Developer-Ready Recommendations
  4. 11.4Mapping Issues to Business and Search Impact
  5. 11.5Creating a Prioritized Remediation Roadmap
  6. 11.6Presenting Findings to Stakeholders
  7. 11.7Tracking Fixes and Ownership

Module 12: Fix Verification and Technical QA

  1. 12.1Re-Crawling After Fixes
  2. 12.2Comparing Before-and-After Crawl Results
  3. 12.3Verifying Redirects, Canonicals, and Indexability
  4. 12.4Detecting Regressions and Newly Introduced Issues
  5. 12.5Testing Website Changes Before Deployment
  6. 12.6Confirming Fixes with Search Console and Other Evidence
  7. 12.7Closing Audit Findings and Recording Results

Grouping URLs by Issue Type

In this lesson

  • Why grouping beats URL-by-URL reporting
  • Three ways to group: issue, template, and pattern
  • How to use pivot tables and regex for grouping
  • How to name a finding group clearly

Short answer

Grouping URLs by issue type turns thousands of rows into a few findings. Group first by issue category, then by page template, then by URL pattern using regex or folder. Use pivot tables to count affected URLs per group, and name each finding by what is broken and where, such as 'Product pages missing canonical tags.'

A first-time auditor sent a Batangas resort a 3,200-row spreadsheet titled “Issues.” The developer opened it, scrolled for a minute, and closed it. The same data, grouped, became 18 findings on two pages. Those got fixed.

Three levels of grouping

  1. By issue category (Lesson 1.4): crawlability, indexability, status codes, duplicates, on-page, performance, rendering, schema.
  2. By page template: homepage, category, product, post, location, landing page.
  3. By URL pattern: folders, parameters, subdomains.

How to do it

  • Add a page type column with a formula or regex, for example a URL containing /rooms/ is “Room.”
  • Use segments in your crawler (Screaming Frog Config, Segments) so every tab shows groups automatically.
  • Build pivot tables: rows = page type, columns = issue flags, values = count of URLs.

Naming findings

A good finding name says what is wrong, where, and how many:

  • Weak: “Canonical issues”
  • Strong: “Room pages (48) canonicalize to the HTTP version of the URL”

Practice: well-named finding?

Sort each item into the right group.

  • Blog tag pages (312) indexable with under 50 words

  • Duplicate content

  • All product pages missing Product schema price

  • Various redirect problems

  • Footer links (on 1,200 pages) point to 301 URLs

Common mistakes

  • One finding per URL.
  • Groups too broad, mixing unrelated causes.
  • No counts. Readers cannot judge scale.
1. Why group URLs?
2. Which tool summarizes counts by page type?
3. Which finding name is best?

Your action

Next, you will separate root causes from symptoms.

Key terms

Finding
One reportable problem, describing an issue across a defined group of URLs.
URL pattern
A shared structure, such as /blog/tag/ or ?sort=, used to group URLs.
Pivot table
A spreadsheet tool that summarizes rows into counts by category.
Regex grouping
Using regular expressions to classify URLs into groups by path or parameter.

Questions beginners ask

How many findings should an audit have?

Most good audits have 15 to 40 findings. Hundreds of findings usually means issues were not grouped.

What if one URL has many issues?

That is normal. It can belong to several findings. Grouping happens per issue, not per URL.

How do I group without regex skills?

Use the folder from the URL with a simple formula, such as splitting by /, or use your crawler's segments feature.

Should I include every affected URL in the report?

Summarize in the report and attach the full URL list in an appendix or spreadsheet.

Related lessons

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