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All lessons in Technical SEO Auditing Lesson 9.1 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

Structured Data and Schema.org Basics

Your roadmap for this lesson

  • What structured data is and why search engines use it
  • How Schema.org types, properties, and nesting work
  • The difference between Schema.org and Google's rich result rules
  • What a structured data audit checks

Short answer

Structured data is code that labels page content in a standard vocabulary, usually Schema.org, so search engines understand entities like products, businesses, and articles. Google uses it for rich results such as review stars and prices. A structured data audit checks that markup exists where it should, is valid, matches visible content, and meets Google's requirements.

A human reading “₱1,850 · 4.7 (212 reviews) · In stock” knows it is a product with a price and rating. A machine sees text. Structured data adds labels: this is a Product, this number is its price, this is its aggregateRating.

Types, properties, nesting

Schema.org organizes information into types with properties. Types can nest:

  • LocalBusiness with name, address (a PostalAddress), openingHours, geo
  • Product with name, image, offers (an Offer with price and priceCurrency), aggregateRating
  • Article with headline, author (a Person), datePublished

Two rulebooks

  1. Schema.org defines what is valid vocabulary.
  2. Google’s documentation defines which types earn rich results and which properties are required or recommended.

Markup can be valid Schema.org but still not eligible for any rich result.

What the audit checks

  • Coverage: do the right templates have markup?
  • Validity: is the syntax correct and are types and properties real?
  • Completeness: are Google’s required properties present?
  • Accuracy: does markup match visible content?
  • Conflicts: duplicate or contradictory blocks?
  • Results: do Search Console reports show valid items?

Practice: match the type

Sort each item into the right group.

  • Clinic name, address, opening hours

  • Price, availability, SKU

  • Headline, author, date published

  • Geo coordinates and phone number

  • Aggregate rating from buyers

Common mistakes

  • Expecting rankings from schema alone.
  • Marking up content users cannot see.
  • Treating valid Schema.org as Google-eligible.
1. What does structured data mainly do?
2. Which defines rich result requirements?
3. What is 'offers' in Product markup?

Your action

Next, you will learn JSON-LD and the other structured data formats.

Key terms

Structured data
Machine-readable code describing a page's content and entities.
Schema.org
A shared vocabulary of types and properties supported by Google, Bing, and others.
Type and property
A type is a kind of thing (Product, LocalBusiness). Properties describe it (name, price, address).
Rich result
An enhanced search listing, such as stars, prices, or event dates, enabled by structured data.

Questions beginners ask

Does structured data improve rankings?

Not directly. It helps search engines understand pages and can make listings more visible and clickable through rich results.

Does every Schema.org type get a rich result?

No. Google supports a limited list of rich result types, documented in its Search Gallery, and retires some over time.

Do FAQ rich results still show?

Since 2023, Google shows FAQ rich results mainly for well-known government and health sites. FAQ markup is still valid but rarely displays for others.

Is structured data used by AI search?

Clear entity data helps machines understand your business and content, which supports how search engines and AI systems describe you.

Related lessons

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