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All lessons in Technical SEO Auditing Lesson 9.6 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

Conflicting or Duplicate Structured Data

Covered here

  • Where duplicate structured data comes from
  • Why conflicting values confuse search engines
  • How to detect multiple entities of the same type
  • How to choose one source of truth

Short answer

Duplicate structured data happens when a theme, plugin, tag manager, or app each adds markup for the same entity, often with different values. Search engines may pick the wrong one or ignore both. Detect it by counting entities of each type per page, compare their values, then keep one source and disable the others.

A Bulacan online store on Shopify had three Product blocks per page: one from the theme, one from a reviews app, and one from an SEO app. The theme said price ₱1,299 with no reviews. The review app said 4.8 stars with no price. The SEO app showed an old price of ₱999. Google received three different stories about one product.

Where duplicates come from

  • Theme markup plus an SEO plugin
  • Review apps adding their own Product block
  • Tag Manager scripts added years ago
  • Microdata in the HTML plus JSON-LD from a plugin
  • Multiple Organization or LocalBusiness blocks from footer widgets

How to detect

  1. In your crawl’s Structured Data export, count entities by type per URL. More than one Product, Organization, or BreadcrumbList is a flag.
  2. Extract key values (name, price, ratingValue) from each block and compare.
  3. Check the format column: JSON-LD and Microdata for the same type is a common duplicate.
  4. Look at the rendered page to find each block’s source.

How to fix

  • Choose a source of truth per entity, usually the system with the most accurate data.
  • Disable schema output in the other plugins, apps, or themes.
  • If needed, feed reviews into the main block (aggregateRating inside the main Product).
  • Use @id to connect entities rather than repeating them.
  • Recrawl and recount.

Practice: duplicate problem or fine?

Sort each item into the right group.

  • Two Product blocks with different prices

  • One Organization, one BreadcrumbList, one Product

  • Product in Microdata and again in JSON-LD

  • Three LocalBusiness blocks with different phone numbers

  • Product block linking to Organization by @id

Common mistakes

  • Deleting the wrong block, such as the one with reviews.
  • Fixing one page instead of the app or theme setting.
  • Ignoring old Tag Manager tags.
1. Which is a duplicate problem?
2. How do you detect duplicates at scale?
3. Best fix for theme and app both adding Product markup?

Your action

Next, you will prioritize schema fixes.

Key terms

Duplicate entity
Two or more markup blocks describing the same thing, such as two Product entities for one product.
Conflicting value
Different values for the same property across blocks, such as two prices or two business names.
Source of truth
The single system chosen to output markup for an entity.
Markup source
The theme, plugin, app, or tag that generates a block.

Questions beginners ask

Is having multiple schema blocks always bad?

No. Different entities like Organization, BreadcrumbList, and Product can each have their own block. The problem is the same entity described twice.

How do I know which plugin added a block?

Look for HTML comments, class names, or plugin signatures near the block, or disable plugins one by one on staging.

What about Shopify apps?

Review and SEO apps often add Product markup alongside the theme's. Turn off the duplicate in one of them.

Should I merge everything into one @graph?

It is a clean approach when one system controls all markup, linking entities with @id.

Related lessons

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