Off-Page Authority & Structured Data: Engineering the Knowledge Graph

Link Building

Link Building Fundamentals

Backlink importance

Backlinks are incoming links from external websites that act as "votes of confidence," signaling to search engines that your content is valuable and trustworthy; they remain one of Google's top 3 ranking factors, directly influencing PageRank calculations and domain authority.

┌─────────────┐         ┌─────────────┐
│  External   │─────────▶│  Your Site  │
│   Site A    │ backlink │             │
└─────────────┘         │   (gains    │
┌─────────────┐         │  authority) │
│  External   │─────────▶│             │
│   Site B    │ backlink └─────────────┘
└─────────────┘

Link quality factors

Link quality is determined by the linking site's authority, topical relevance, traffic levels, editorial placement (contextual links beat footer links), the page's own backlink profile, and whether the link appears on a trusted, non-spammy domain.

LINK QUALITY SCORING FACTORS:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Domain Authority    ████████░░  High
Relevance           ████████░░  High  
Traffic             ██████░░░░  Medium
Editorial Placement █████████░  High
Trust Score         ███████░░░  Medium-High
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Overall Quality: STRONG

DoFollow vs NoFollow

DoFollow links (default) pass PageRank and link equity to the destination, helping rankings, while NoFollow links (rel="nofollow") instruct search engines not to pass authority—though Google now treats nofollow as a "hint" rather than directive since 2019.

<!-- DoFollow (default - passes authority) -->
<a href="https://example.com">Quality Resource</a>

<!-- NoFollow (suggests no authority passing) -->
<a href="https://example.com" rel="nofollow">User Comment Link</a>

Anchor text optimization

Anchor text is the clickable text of a hyperlink that provides context to search engines about the linked page's content; optimal anchor text distribution includes branded (40-50%), natural/generic (20-30%), exact-match keywords (5-10%), and partial-match variations (10-20%) to appear natural.

HEALTHY ANCHOR TEXT DISTRIBUTION:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Branded "Nike"           ████████████  45%
Natural "click here"     ██████░░░░░░  25%
Partial "running shoes"  ████░░░░░░░░  15%
Exact "buy nike shoes"   ██░░░░░░░░░░  10%
URL/Naked               █░░░░░░░░░░░░   5%
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Link relevance

Topical relevance between the linking page and destination significantly impacts link value; a link from a fitness blog to a gym website carries substantially more weight than a link from an unrelated cooking site, as search engines evaluate contextual relationship.

HIGH RELEVANCE:
┌─────────────────┐      ┌─────────────────┐
│  Fitness Blog   │──────▶│   Gym Website   │  ✓ Strong Signal
│  (health niche) │      │  (health niche) │
└─────────────────┘      └─────────────────┘

LOW RELEVANCE:
┌─────────────────┐      ┌─────────────────┐
│  Cooking Blog   │──────▶│   Gym Website   │  ✗ Weak Signal
│  (food niche)   │      │  (health niche) │
└─────────────────┘      └─────────────────┘

Domain authority concepts

Domain Authority (DA) is a Moz-developed score (1-100) predicting overall ranking potential of an entire domain, calculated from total backlink quantity/quality, domain age, and various trust signals; while not a Google metric, it correlates with ranking ability.

DOMAIN AUTHORITY SCALE:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
0-20   │████░░░░░░│ New/Low authority sites
21-40  │████████░░│ Growing sites
41-60  │██████████│ Established sites  
61-80  │██████████│ Strong authority
81-100 │██████████│ Top-tier (Google, Wiki)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Page authority concepts

Page Authority (PA) measures the ranking strength of a specific individual page rather than the entire domain, considering that page's unique backlink profile and internal link equity; a high-DA site can have low-PA pages if those specific pages lack links.

┌──────────────────────────────────────┐
│         DOMAIN (DA: 65)              │
│  ┌──────────┐  ┌──────────┐         │
│  │ Homepage │  │ Blog Post│         │
│  │ PA: 58   │  │ PA: 22   │         │
│  │ (linked) │  │ (orphan) │         │
│  └──────────┘  └──────────┘         │
│  ┌──────────┐  ┌──────────┐         │
│  │ Product  │  │ About    │         │
│  │ PA: 45   │  │ PA: 35   │         │
│  └──────────┘  └──────────┘         │
└──────────────────────────────────────┘

Link velocity

Link velocity measures the rate at which a website acquires new backlinks over time; sudden unnatural spikes may trigger algorithmic scrutiny, while consistent gradual growth appears organic—Google's systems can detect and discount artificial link bursts.

NATURAL LINK VELOCITY:
Links│                          ●
  ^  │                      ●
     │                  ●
     │              ●
     │          ●
     │      ●
     │  ●
     └──────────────────────────▶ Time

SUSPICIOUS SPIKE (Avoid):
Links│          ████
  ^  │          ████
     │          ████
     │  ●   ●   ████   ●   ●
     │──●───●───████───●───●──▶ Time
              ↑
         Unnatural spike

Natural link profiles

A natural link profile exhibits diversity in anchor texts, linking domains, link types (dofollow/nofollow mix), and acquisition timing; it avoids patterns like 90% exact-match anchors or links only from one country, which signal manipulation.

NATURAL PROFILE:              UNNATURAL PROFILE:
━━━━━━━━━━━━━━━━━━━━━━       ━━━━━━━━━━━━━━━━━━━━━
✓ Mixed anchor texts         ✗ 90% exact-match
✓ Various domains            ✗ Same 5 sites
✓ DoFollow/NoFollow mix      ✗ 100% dofollow
✓ Gradual acquisition        ✗ 1000 links/day
✓ Multiple countries         ✗ Single PBN network
✓ Different page types       ✗ All from comments
━━━━━━━━━━━━━━━━━━━━━━       ━━━━━━━━━━━━━━━━━━━━━

Link building outreach basics

Link building outreach involves identifying relevant websites, finding contact information, and sending personalized emails requesting backlinks; success depends on offering genuine value (content, data, resources) rather than transactional requests, with typical response rates of 5-15%.

# Basic outreach workflow
outreach_process = {
    "1_prospect": "Find relevant sites (Ahrefs, SEMrush)",
    "2_qualify": "Check DA > 30, traffic, relevance",
    "3_find_contact": "Hunter.io, LinkedIn, site contact",
    "4_personalize": "Reference specific content/author",
    "5_send": "Clear value proposition, single CTA",
    "6_follow_up": "Wait 5-7 days, max 2 follow-ups",
    "7_track": "Log in CRM, measure response rates"
}

# Expected response rates
# Cold outreach: 5-10%
# Warm outreach: 15-25%
# Broken link building: 10-15%

Advanced Link Building

Digital PR strategies

Digital PR combines traditional public relations with SEO by creating newsworthy content (data studies, surveys, interactive tools, expert commentary) to earn high-authority editorial backlinks from news outlets and industry publications—typically yielding DA 70+ links that dramatically impact rankings.

DIGITAL PR CAMPAIGN FLOW:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│ Create Data │───▶│ Press       │───▶│ Journalist  │
│ Study/Story │    │ Release     │    │ Pickup      │
└─────────────┘    └─────────────┘    └─────────────┘
                                            │
┌─────────────┐    ┌─────────────┐          ▼
│ Track &     │◀───│ High-DA     │◀─── News Sites
│ Report ROI  │    │ Backlinks   │     Publish
└─────────────┘    └─────────────┘

HARO and journalist outreach

HARO (Help A Reporter Out) and similar platforms (Qwoted, Terkel, SourceBottle) connect experts with journalists seeking sources; responding with concise, quotable expertise within hours can earn authoritative backlinks from major publications like Forbes, Inc., and industry-specific outlets.

HARO RESPONSE BEST PRACTICES:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
✓ Respond within 2-3 hours
✓ Answer specific question first
✓ Keep under 300 words
✓ Include credentials/bio
✓ Provide quotable soundbites
✓ Offer follow-up availability

HARO SCHEDULE (EST):
Morning:   5:35 AM
Afternoon: 12:35 PM
Evening:   5:35 PM

Broken link building

Broken link building identifies dead links (404s) on relevant websites, then reaches out to webmasters offering your content as a replacement; this provides genuine value by helping sites fix broken user experiences while earning contextually relevant backlinks.

# Finding broken links workflow
# 1. Use Ahrefs/Screaming Frog to find 404s on target sites

# Example: Check for broken outbound links
curl -s https://example.com | grep -oP 'href="\K[^"]+' | \
while read url; do
  status=$(curl -s -o /dev/null -w "%{http_code}" "$url")
  if [ "$status" == "404" ]; then
    echo "BROKEN: $url"
  fi
done

# Outreach template focus:
# "Found broken link → Here's working replacement"

Skyscraper technique

The Skyscraper technique (coined by Brian Dean) involves finding popular linked content in your niche, creating a significantly better version (more comprehensive, updated, better designed), then reaching out to sites linking to the original—offering your superior resource as an upgrade.

SKYSCRAPER PROCESS:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Step 1: Find content with many backlinks
        └─▶ "50 SEO Tips" (200 backlinks)

Step 2: Create 10x better version
        └─▶ "150 SEO Tips + Tools + Examples"
        
Step 3: Outreach to linkers
        └─▶ "Your link to outdated 50-tip 
             article → Here's comprehensive 
             150-tip guide with tools"

Success Rate: 5-15% link conversion
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Resource page link building

Resource pages are curated lists of helpful links on a specific topic; identifying relevant resource pages through search operators (intitle:"resources" + keyword) and pitching your high-quality content for inclusion yields contextually relevant, editorial links.

FINDING RESOURCE PAGES:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Google Search Operators:

"keyword" + intitle:resources
"keyword" + "useful links"
"keyword" + "helpful resources"
"keyword" + inurl:resources
"keyword" + "recommended sites"

Example:
"python programming" intitle:resources
→ Find pages listing Python resources
→ Pitch your Python tutorial/tool
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Guest posting strategies

Guest posting involves writing original content for other websites in exchange for author bio backlinks or contextual links; focus on relevant, high-quality sites (avoid "write for us" farms), provide genuine value, and limit exact-match anchor text to maintain natural patterns.

GUEST POST QUALITY SPECTRUM:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
HIGH VALUE:                  LOW VALUE (Avoid):
✓ Relevant niche site        ✗ Generic "write for us"
✓ Editorial standards        ✗ Accept anything
✓ Real traffic/audience      ✗ No real readers
✓ Limited guest posts        ✗ 90% guest content
✓ DA 40+, real engagement    ✗ PBN-style farms
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

SAFE: 1-2 contextual links, branded/natural anchor

Unlinked brand mentions

Unlinked brand mentions are instances where your brand/product is referenced online without a hyperlink; using tools like Google Alerts, Mention, or Ahrefs Content Explorer to find these and requesting link additions converts existing mentions into backlinks with high success rates (30-50%).

# Finding unlinked mentions
tools = {
    "google_alerts": "Set alert for 'brand name' -site:yourdomain.com",
    "ahrefs": "Content Explorer → 'brand' → filter: no links to domain",
    "mention": "Real-time brand monitoring",
    "buzzsumo": "Content mentioning brand"
}

# Outreach template concept
"""
Subject: Thanks for mentioning [Brand]!

Hi [Name],
Saw you mentioned us in [Article]. Thanks!
Would you consider adding a link for readers 
wanting to learn more? Here's our URL: [link]
"""
# Success rate: 30-50% (they already like you!)

Competitor backlink analysis

Competitor backlink analysis uses tools like Ahrefs, SEMrush, or Moz to examine competitors' link profiles, revealing their link-building strategies, high-value linking domains you can target, and content types that attract links in your niche—essentially reverse-engineering their success.

COMPETITOR GAP ANALYSIS:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
           Links to    Links to    Links to
Domain     Competitor1 Competitor2 YOU
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
forbes.com     ✓           ✓         ✗ ← Target
techcrunch.com ✓           ✗         ✗ ← Target
industry.com   ✓           ✓         ✓   Have
blog.com       ✗           ✓         ✗ ← Target
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Strategy: Target domains linking to 
competitors but not you

Toxic link identification

Toxic links come from spammy, irrelevant, or manipulative sources (PBNs, link farms, hacked sites, foreign spam) that can harm rankings or trigger manual penalties; regular audits using Ahrefs, SEMrush, or Google Search Console help identify these threats before they cause damage.

TOXIC LINK WARNING SIGNS:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🚩 Exact-match anchor from irrelevant site
🚩 Links from sites with DA < 10
🚩 Foreign language spam domains
🚩 Sites with "casino/pharma/adult" in URL
🚩 Sitewide footer/sidebar links
🚩 Sudden 1000+ links from one domain
🚩 Links from known PBN patterns
🚩 Hacked sites with injected links
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Action: Document → Attempt removal → Disavow

Disavow file management

Google's Disavow Tool allows you to upload a text file requesting Google ignore specific toxic backlinks; use sparingly (only after failed removal attempts), format correctly, and maintain/update regularly—incorrect usage can accidentally disavow valuable links.

# disavow.txt format example
# Lines starting with # are comments

# Individual URLs to disavow
https://spamsite.com/bad-page.html
https://another-spam.com/link-page.php

# Entire domains to disavow
domain:spammydomain.com
domain:linkfarm.net
domain:pbn-network.org

# Upload via: 
# Google Search Console → Disavow Tool
# https://search.google.com/search-console/disavow

# WARNING: Use only as last resort!

Link reclamation

Link reclamation recovers lost backlinks caused by site migrations, URL changes, removed content, or linking sites updating/removing pages; monitoring link losses via Ahrefs/SEMrush and promptly reaching out to restore or redirect these links preserves accumulated authority.

LINK RECLAMATION SCENARIOS:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
SCENARIO              │ SOLUTION
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Your URL changed      │ 301 redirect old→new
Content deleted       │ Restore or redirect
Linking page removed  │ Outreach: link elsewhere
Link accidentally     │ Polite reminder email
removed               │
Site migration        │ Map all old→new URLs
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Monitor: Weekly Ahrefs "Lost backlinks" report

Tiered link building risks

Tiered link building creates layers where Tier 1 links point to your site, Tier 2 links point to Tier 1, and so on—attempting to boost lower-tier pages to strengthen your links; this is manipulative, violates Google guidelines, and carries significant penalty risk if detected.

TIERED LINK BUILDING (RISKY):
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
                    ┌──────────┐
         Tier 1     │Your Site │
                    └────▲─────┘
                         │
         ┌───────────────┼───────────────┐
    ┌────┴────┐    ┌────┴────┐    ┌────┴────┐
    │Guest    │    │Resource │    │PR Link  │
    │Post     │    │Page     │    │         │
    └────▲────┘    └────▲────┘    └────▲────┘
         │              │              │
    Tier 2: Low-quality links built TO boost Tier 1
    ┌─┴─┐ ┌─┴─┐    ┌─┴─┐ ┌─┴─┐    ┌─┴─┐ ┌─┴─┐
    │PBN│ │Spam│   │Bot │ │Link│   │...│ │...│
    └───┘ └───┘    └───┘ └───┘    └───┘ └───┘

⚠️ HIGH RISK: Manual penalty, deindexing
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Structured Data & Schema

Structured Data Introduction

What is structured data

Structured data is a standardized format (code) added to web pages that explicitly tells search engines what the content means, not just what it says—enabling rich results, knowledge graph inclusion, and improved understanding for both traditional search and AI systems (AEO/GEO).

WITHOUT STRUCTURED DATA:
─────────────────────────────────────────
Search engines see: "John Smith 555-1234"
Interpretation: Unknown text string

WITH STRUCTURED DATA:
─────────────────────────────────────────
Search engines understand:
{
  "type": "Person",
  "name": "John Smith",
  "telephone": "555-1234"
}

Schema.org overview

Schema.org is a collaborative vocabulary (created by Google, Bing, Yahoo, Yandex in 2011) providing standardized types and properties for structured data; it defines hundreds of entity types (Person, Product, Event, Organization) and their relationships, serving as the universal language for web semantics.

SCHEMA.ORG HIERARCHY:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Thing (root)
├── CreativeWork
│   ├── Article
│   ├── Book
│   └── Recipe
├── Organization
│   ├── Corporation
│   └── LocalBusiness
│       ├── Restaurant
│       └── Store
├── Person
├── Product
├── Event
└── Place
    ├── LocalBusiness
    └── TouristAttraction
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Full reference: https://schema.org/docs/full.html

JSON-LD format

JSON-LD (JavaScript Object Notation for Linked Data) is Google's recommended structured data format; it's placed in a <script> tag, completely separate from HTML content, making it easier to implement, maintain, and dynamically generate without modifying page markup.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "TechCorp Inc",
  "url": "https://techcorp.com",
  "logo": "https://techcorp.com/logo.png",
  "contactPoint": {
    "@type": "ContactPoint",
    "telephone": "+1-555-123-4567",
    "contactType": "customer service"
  }
}
</script>

Microdata format

Microdata embeds structured data directly within HTML using itemscope, itemtype, and itemprop attributes; while valid, it's harder to maintain than JSON-LD because markup is intertwined with content, making updates more error-prone.

<div itemscope itemtype="https://schema.org/Person">
  <h1 itemprop="name">John Smith</h1>
  <p itemprop="jobTitle">Software Engineer</p>
  <p>
    Email: 
    <a itemprop="email" href="mailto:john@example.com">
      john@example.com
    </a>
  </p>
  <p itemprop="telephone">555-123-4567</p>
</div>

<!-- Harder to maintain: schema mixed with HTML -->

RDFa format

RDFa (Resource Description Framework in Attributes) extends HTML5 with attributes like vocab, typeof, and property to embed structured data; it's more flexible than Microdata for complex relationships but less commonly used than JSON-LD for SEO purposes.

<div vocab="https://schema.org/" typeof="Person">
  <h1 property="name">Jane Doe</h1>
  <p property="jobTitle">CEO</p>
  <p>
    Works at: 
    <span property="worksFor" typeof="Organization">
      <span property="name">TechCorp</span>
    </span>
  </p>
</div>

<!-- Format comparison preference: JSON-LD > Microdata > RDFa -->

Common schema types

The most impactful schema types for SEO include Article, Product, LocalBusiness, Organization, FAQPage, HowTo, BreadcrumbList, Review, Event, and VideoObject—each enabling specific rich result types in Google search and improving content understanding for AI systems.

COMMON SCHEMA → RICH RESULT MAPPING:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Schema Type      │ Rich Result
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Article          │ Top Stories, Article
Product          │ Product snippets, price
LocalBusiness    │ Local pack, knowledge panel
FAQPage          │ FAQ accordion dropdowns
HowTo            │ Step-by-step carousel
BreadcrumbList   │ Breadcrumb trail in SERP
Review           │ Star ratings
Event            │ Event listings
VideoObject      │ Video thumbnails
Recipe           │ Recipe cards with images
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Rich results testing

Google's Rich Results Test validates structured data, shows which rich result types are eligible, previews SERP appearance, and identifies errors; always test before deployment to catch issues with required properties, syntax errors, or policy violations.

RICH RESULTS TEST WORKFLOW:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
1. Navigate to:
   https://search.google.com/test/rich-results

2. Enter URL or paste code

3. Review results:
   ┌────────────────────────────────────┐
   │ ✓ FAQPage    - Valid, eligible     │
   │ ✓ Article    - Valid, eligible     │
   │ ⚠ Product    - Missing 'price'     │
   │ ✗ Review     - Invalid format      │
   └────────────────────────────────────┘

4. Click "Preview" to see SERP appearance

5. Fix errors → Retest → Deploy
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Schema validation tools

Beyond Google's Rich Results Test, use Schema.org's validator (schema.org/docs/validator.html) for syntax checking, Google Search Console's Enhancements reports for site-wide monitoring, and tools like Merkle's Schema Generator for creation assistance.

SCHEMA VALIDATION TOOLKIT:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
TOOL                    │ PURPOSE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Google Rich Results     │ Eligibility + preview
Schema.org Validator    │ Syntax validation
Search Console          │ Site-wide monitoring
Merkle Schema Generator │ Code generation
Screaming Frog          │ Bulk schema auditing
Chrome DevTools         │ Live page inspection
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

# Quick CLI validation with jq:
cat schema.json | jq '.' # Validates JSON syntax

Google rich results eligibility

Google grants rich results based on correct schema implementation, adherence to content policies (no spam/misleading content), page quality signals, and mobile-friendliness; having valid schema doesn't guarantee display—Google algorithmically decides based on query relevance and user value.

RICH RESULTS ELIGIBILITY REQUIREMENTS:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
┌─────────────────────────────────────────┐
│  ✓ Valid, complete structured data      │
│  ✓ Follows Google's content policies    │
│  ✓ Content matches schema claims        │
│  ✓ Mobile-friendly page                 │
│  ✓ No manual actions on site            │
│  ✓ Page is indexed                      │
└─────────────────────────────────────────┘
              ↓
    Google's Algorithm Decides
              ↓
    Rich Result Displayed (maybe)

Note: Valid schema ≠ Guaranteed rich result
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Advanced Structured Data

Organization schema

Organization schema defines your brand entity with properties like name, logo, contact info, and social profiles; it's essential for establishing entity identity in knowledge graphs and helps AI systems (AEO/GEO) accurately associate information with your organization.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "TechCorp Solutions",
  "alternateName": "TechCorp",
  "url": "https://techcorp.com",
  "logo": "https://techcorp.com/logo.png",
  "foundingDate": "2010-01-15",
  "founders": [{"@type": "Person", "name": "Jane Doe"}],
  "address": {
    "@type": "PostalAddress",
    "streetAddress": "123 Tech Street",
    "addressLocality": "San Francisco",
    "addressRegion": "CA",
    "postalCode": "94105",
    "addressCountry": "US"
  },
  "sameAs": [
    "https://twitter.com/techcorp",
    "https://linkedin.com/company/techcorp",
    "https://facebook.com/techcorp"
  ]
}
</script>

Local business schema

LocalBusiness schema is critical for local SEO, providing NAP (name, address, phone), hours, geo-coordinates, and service areas; it enables local pack appearances and helps AI assistants answer "near me" queries with accurate business information.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Restaurant",
  "name": "Mario's Italian Kitchen",
  "image": "https://marios.com/photos/restaurant.jpg",
  "address": {
    "@type": "PostalAddress",
    "streetAddress": "456 Main St",
    "addressLocality": "Chicago",
    "addressRegion": "IL",
    "postalCode": "60601"
  },
  "geo": {
    "@type": "GeoCoordinates",
    "latitude": 41.8781,
    "longitude": -87.6298
  },
  "telephone": "+1-312-555-0100",
  "priceRange": "$$",
  "servesCuisine": "Italian",
  "openingHoursSpecification": [
    {
      "@type": "OpeningHoursSpecification",
      "dayOfWeek": ["Monday","Tuesday","Wednesday","Thursday","Friday"],
      "opens": "11:00",
      "closes": "22:00"
    }
  ]
}
</script>

Product schema

Product schema describes items for sale with properties like name, price, availability, reviews, and SKU; it enables product rich results with pricing, availability badges, and ratings—critical for e-commerce SEO and shopping integrations.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "Wireless Noise-Canceling Headphones",
  "image": ["https://shop.com/headphones-1.jpg"],
  "description": "Premium wireless headphones with ANC",
  "sku": "WH-1000XM5",
  "brand": {
    "@type": "Brand",
    "name": "AudioTech"
  },
  "offers": {
    "@type": "Offer",
    "url": "https://shop.com/headphones",
    "priceCurrency": "USD",
    "price": 299.99,
    "priceValidUntil": "2025-12-31",
    "availability": "https://schema.org/InStock",
    "seller": {"@type": "Organization", "name": "TechShop"}
  },
  "aggregateRating": {
    "@type": "AggregateRating",
    "ratingValue": 4.7,
    "reviewCount": 1250
  }
}
</script>

Review schema

Review schema marks up individual reviews or aggregate ratings, displaying star ratings in search results; it requires authentic reviews matching page content and is subject to strict Google policies—misuse can result in manual penalties.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Review",
  "itemReviewed": {
    "@type": "Product",
    "name": "Wireless Headphones XZ500"
  },
  "author": {
    "@type": "Person",
    "name": "Sarah Johnson"
  },
  "datePublished": "2024-11-15",
  "reviewBody": "Excellent sound quality and comfort...",
  "reviewRating": {
    "@type": "Rating",
    "ratingValue": 5,
    "bestRating": 5,
    "worstRating": 1
  }
}
</script>

<!-- Note: Self-serving reviews violate Google policy -->

FAQ schema

FAQPage schema creates expandable question-answer accordions directly in search results, significantly increasing SERP real estate; it's highly effective for AEO as AI systems readily extract and cite FAQ content when answering user queries.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is structured data?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Structured data is code that helps search engines understand page content, enabling rich results in search."
      }
    },
    {
      "@type": "Question", 
      "name": "Does FAQ schema help SEO?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Yes, FAQ schema can increase SERP visibility and click-through rates by displaying expandable Q&A directly in results."
      }
    }
  ]
}
</script>

How-to schema

HowTo schema structures step-by-step instructions, tutorials, and guides; it can trigger rich result carousels showing each step with images, making content highly visible and particularly valuable for AI systems generating instructional responses.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "HowTo",
  "name": "How to Change a Tire",
  "description": "Step-by-step guide to safely change a flat tire",
  "totalTime": "PT30M",
  "estimatedCost": {"@type": "MonetaryAmount", "currency": "USD", "value": "0"},
  "tool": [{"@type": "HowToTool", "name": "Jack"}, {"@type": "HowToTool", "name": "Wrench"}],
  "step": [
    {
      "@type": "HowToStep",
      "name": "Secure the vehicle",
      "text": "Park on flat surface, engage parking brake",
      "image": "https://example.com/step1.jpg"
    },
    {
      "@type": "HowToStep",
      "name": "Loosen lug nuts",
      "text": "Turn lug nuts counter-clockwise before lifting",
      "image": "https://example.com/step2.jpg"
    }
  ]
}
</script>

Article schema

Article schema (including NewsArticle, BlogPosting) provides metadata about written content—author, publication date, publisher, headline; it's essential for Google News eligibility, Top Stories, and establishing content provenance for AI citation systems.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Understanding Schema Markup for SEO",
  "description": "Complete guide to implementing structured data",
  "image": "https://blog.com/schema-guide.jpg",
  "author": {
    "@type": "Person",
    "name": "Jane Smith",
    "url": "https://blog.com/author/jane"
  },
  "publisher": {
    "@type": "Organization",
    "name": "TechBlog",
    "logo": {"@type": "ImageObject", "url": "https://blog.com/logo.png"}
  },
  "datePublished": "2024-11-20",
  "dateModified": "2024-12-01",
  "mainEntityOfPage": "https://blog.com/schema-guide"
}
</script>

Breadcrumb schema

BreadcrumbList schema displays navigation hierarchy directly in search results, replacing the default URL with a clickable breadcrumb trail; this improves user understanding of site structure and click-through rates for deeper pages.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "BreadcrumbList",
  "itemListElement": [
    {
      "@type": "ListItem",
      "position": 1,
      "name": "Home",
      "item": "https://example.com"
    },
    {
      "@type": "ListItem",
      "position": 2,
      "name": "Electronics",
      "item": "https://example.com/electronics"
    },
    {
      "@type": "ListItem",
      "position": 3,
      "name": "Headphones",
      "item": "https://example.com/electronics/headphones"
    }
  ]
}
</script>

<!-- SERP Display: Home › Electronics › Headphones -->

Event schema

Event schema marks up events with dates, locations, ticket availability, and performers; it enables event-specific rich results and is crucial for venues, conferences, and entertainment sites to appear in Google's event search features.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Event",
  "name": "TechConf 2025",
  "description": "Annual technology conference",
  "startDate": "2025-03-15T09:00:00-07:00",
  "endDate": "2025-03-17T18:00:00-07:00",
  "eventStatus": "https://schema.org/EventScheduled",
  "eventAttendanceMode": "https://schema.org/OfflineEventAttendanceMode",
  "location": {
    "@type": "Place",
    "name": "Convention Center",
    "address": {"@type": "PostalAddress", "addressLocality": "San Francisco"}
  },
  "offers": {
    "@type": "Offer",
    "price": 299,
    "priceCurrency": "USD",
    "availability": "https://schema.org/InStock",
    "validFrom": "2024-12-01",
    "url": "https://techconf.com/tickets"
  },
  "performer": {"@type": "Person", "name": "Keynote Speaker Name"}
}
</script>

Video schema

VideoObject schema provides video metadata (title, description, thumbnail, duration, upload date) enabling video rich results with thumbnails directly in SERPs; essential for competing with YouTube in organic results and AI video recommendations.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "VideoObject",
  "name": "How to Implement Schema Markup",
  "description": "Step-by-step tutorial on adding structured data",
  "thumbnailUrl": "https://site.com/video-thumb.jpg",
  "uploadDate": "2024-11-15",
  "duration": "PT12M30S",
  "contentUrl": "https://site.com/videos/schema-tutorial.mp4",
  "embedUrl": "https://site.com/embed/schema-tutorial",
  "interactionStatistic": {
    "@type": "InteractionCounter",
    "interactionType": "https://schema.org/WatchAction",
    "userInteractionCount": 52000
  },
  "author": {"@type": "Person", "name": "John Developer"}
}
</script>

Recipe schema

Recipe schema enables rich cards with images, ratings, cooking time, nutritional info, and ingredients—one of the most visually prominent rich result types; essential for food content to compete in recipe carousels and AI cooking assistants.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Recipe",
  "name": "Classic Chocolate Chip Cookies",
  "image": ["https://food.com/cookies.jpg"],
  "author": {"@type": "Person", "name": "Chef Maria"},
  "datePublished": "2024-10-01",
  "description": "Crispy outside, chewy inside chocolate chip cookies",
  "prepTime": "PT15M",
  "cookTime": "PT12M",
  "totalTime": "PT27M",
  "recipeYield": "24 cookies",
  "recipeCategory": "Dessert",
  "recipeCuisine": "American",
  "recipeIngredient": ["2 cups flour", "1 cup butter", "1 cup chocolate chips"],
  "recipeInstructions": [
    {"@type": "HowToStep", "text": "Preheat oven to 375°F"},
    {"@type": "HowToStep", "text": "Cream butter and sugars"},
    {"@type": "HowToStep", "text": "Add eggs and vanilla"}
  ],
  "nutrition": {"@type": "NutritionInformation", "calories": "150 calories"}
}
</script>

Course schema

Course schema describes educational content with instructor, provider, cost, and format information; it enables course rich results and is increasingly important for AEO as AI systems recommend educational resources based on structured course data.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Course",
  "name": "Complete SEO Masterclass 2025",
  "description": "Learn technical SEO, content optimization, and link building",
  "provider": {
    "@type": "Organization",
    "name": "SEO Academy",
    "sameAs": "https://seoacademy.com"
  },
  "instructor": {
    "@type": "Person",
    "name": "Dr. Sarah Mitchell"
  },
  "offers": {
    "@type": "Offer",
    "price": 199,
    "priceCurrency": "USD"
  },
  "hasCourseInstance": {
    "@type": "CourseInstance",
    "courseMode": "online",
    "duration": "PT40H"
  },
  "aggregateRating": {"@type": "AggregateRating", "ratingValue": 4.8, "ratingCount": 2500}
}
</script>

Job posting schema

JobPosting schema structures employment listings with title, salary, location, and requirements; it enables Google Jobs integration, where listings appear in a dedicated job search experience—essential for recruiters and job boards.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "JobPosting",
  "title": "Senior Software Engineer",
  "description": "We're looking for an experienced engineer...",
  "datePosted": "2024-12-01",
  "validThrough": "2025-01-31",
  "employmentType": "FULL_TIME",
  "hiringOrganization": {
    "@type": "Organization",
    "name": "TechCorp",
    "sameAs": "https://techcorp.com"
  },
  "jobLocation": {
    "@type": "Place",
    "address": {"@type": "PostalAddress", "addressLocality": "San Francisco", "addressRegion": "CA"}
  },
  "baseSalary": {
    "@type": "MonetaryAmount",
    "currency": "USD",
    "value": {"@type": "QuantitativeValue", "minValue": 150000, "maxValue": 200000, "unitText": "YEAR"}
  },
  "applicantLocationRequirements": {"@type": "Country", "name": "USA"},
  "jobLocationType": "TELECOMMUTE"
}
</script>

Nested schema implementation

Nested schema embeds related entities within parent entities, creating rich interconnected data graphs; this provides comprehensive context—like a Product containing Brand, Offers, Reviews, and Organization—helping search engines and AI understand relationships.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "UltraWidget Pro",
  "brand": {
    "@type": "Brand",
    "name": "WidgetCo"
  },
  "manufacturer": {
    "@type": "Organization",
    "name": "WidgetCo Inc",
    "address": {
      "@type": "PostalAddress",
      "addressCountry": "US"
    }
  },
  "offers": {
    "@type": "AggregateOffer",
    "lowPrice": 49.99,
    "highPrice": 79.99,
    "offerCount": 3,
    "offers": [
      {"@type": "Offer", "seller": {"@type": "Organization", "name": "Store A"}, "price": 49.99},
      {"@type": "Offer", "seller": {"@type": "Organization", "name": "Store B"}, "price": 59.99}
    ]
  },
  "review": [
    {"@type": "Review", "author": {"@type": "Person", "name": "User1"}, "reviewRating": {"@type": "Rating", "ratingValue": 5}}
  ]
}
</script>

Advanced Schema Implementation

Schema nesting strategies

Effective schema nesting follows a hierarchical principle: start with the primary entity type, embed directly related entities as nested objects, use @id references for entities appearing multiple times, and limit nesting to 3-4 levels to maintain parseability and avoid redundancy.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "Organization",
      "@id": "https://company.com/#org",
      "name": "TechCorp"
    },
    {
      "@type": "WebPage",
      "@id": "https://company.com/about/#page",
      "isPartOf": {"@id": "https://company.com/#website"},
      "about": {"@id": "https://company.com/#org"}
    },
    {
      "@type": "WebSite",
      "@id": "https://company.com/#website",
      "publisher": {"@id": "https://company.com/#org"}
    }
  ]
}
</script>
<!-- @id enables references without duplication -->

Custom schema development

When standard Schema.org types don't fit, use the closest parent type with additional properties, leverage schema extensions (like health-lifesci.schema.org), or define custom properties prefixed with your namespace—though Google only recognizes official Schema.org vocabulary for rich results.

<script type="application/ld+json">
{
  "@context": {
    "@vocab": "https://schema.org/",
    "custom": "https://yoursite.com/schema/"
  },
  "@type": "SoftwareApplication",
  "name": "DevTool Pro",
  "applicationCategory": "DeveloperApplication",
  "operatingSystem": "Cross-platform",
  
  "custom:deploymentModel": "SaaS",
  "custom:apiVersion": "2.0",
  "custom:sdkLanguages": ["Python", "JavaScript", "Go"]
}
</script>

<!-- Note: Custom properties won't trigger Google rich results -->
<!-- But useful for internal systems and AI understanding -->

Schema for SERP features

Different schema types trigger specific SERP features; strategically implement FAQ for accordion dropdowns, HowTo for step carousels, Review for star ratings, and Product for price/availability badges—combining multiple schemas on one page maximizes SERP real estate.

SCHEMA → SERP FEATURE MAPPING:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Schema Type       │ SERP Feature
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
FAQPage           │ ┌─ Question 1 ─────────── ▼ │
                  │ └─ Question 2 ─────────── ▼ │
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
HowTo             │ [Step 1] [Step 2] [Step 3] →│
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Product + Review  │ ★★★★☆ 4.5 | $99 | In Stock │
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Recipe            │ 🖼️ Image | ⏱️ 30min | ★★★★★  │
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Multiple schemas = More SERP real estate

Merchant Center integration

Product schema syncs with Google Merchant Center for Shopping results; ensure schema pricing, availability, and identifiers (GTIN, MPN) exactly match your product feed—discrepancies cause disapproval and require structured data that meets Merchant Center's stricter requirements.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "Nike Air Max 270",
  "sku": "CW1234-001",
  "gtin13": "0123456789012",
  "mpn": "CW1234-001",
  "brand": {"@type": "Brand", "name": "Nike"},
  "offers": {
    "@type": "Offer",
    "price": 150.00,
    "priceCurrency": "USD",
    "availability": "https://schema.org/InStock",
    "itemCondition": "https://schema.org/NewCondition",
    "shippingDetails": {
      "@type": "OfferShippingDetails",
      "shippingRate": {"@type": "MonetaryAmount", "value": 5.99, "currency": "USD"},
      "deliveryTime": {"@type": "ShippingDeliveryTime", "businessDays": {"@type": "QuantitativeValue", "minValue": 3, "maxValue": 5}}
    }
  }
}
</script>
<!-- Must match Merchant Center feed exactly -->

Product feed optimization

Product feed optimization ensures your structured data, Merchant Center feed, and landing pages maintain consistent, complete data including high-quality images, accurate GTINs, competitive pricing, proper categorization, and attributes like size/color—inconsistencies cause Shopping ad disapprovals.

PRODUCT FEED ALIGNMENT CHECK:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Attribute      │ Schema │ Feed   │ Landing │ Match?
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Title          │ ✓      │ ✓      │ ✓       │  ✓
Price          │ $99    │ $99    │ $99     │  ✓
Availability   │ InStock│ InStock│ InStock │  ✓
GTIN           │ 123... │ 123... │ --      │  ✓
Image URL      │ ✓      │ ✓      │ ✓       │  ✓
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

# Automation: Pull schema from feed dynamically
product_schema['price'] = merchant_feed['price']

Schema for AI systems

For AEO/GEO optimization, implement comprehensive schema that AI systems parse for answers: SpeakableSpecification for voice assistants, detailed FAQPage for conversational queries, ClaimReview for fact-checking, and connected entity graphs that establish your content's authoritative context.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "WebPage",
  "name": "Complete Guide to Schema Markup",
  "speakable": {
    "@type": "SpeakableSpecification",
    "cssSelector": [".article-summary", ".key-takeaways"]
  },
  "mainEntity": {
    "@type": "FAQPage",
    "mainEntity": [
      {
        "@type": "Question",
        "name": "What is schema markup?",
        "acceptedAnswer": {
          "@type": "Answer",
          "text": "Schema markup is structured data code..."
        }
      }
    ]
  }
}
</script>
<!-- SpeakableSpecification helps voice assistants -->
<!-- FAQPage feeds AI answer engines -->

Graph-based structured data

Using @graph creates a comprehensive knowledge graph on your page, connecting multiple entity types (Organization, WebSite, WebPage, Person, Article) with @id references—this semantic web approach provides AI systems complete context about entity relationships.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "Organization",
      "@id": "https://site.com/#org",
      "name": "TechCorp",
      "url": "https://site.com"
    },
    {
      "@type": "WebSite", 
      "@id": "https://site.com/#website",
      "url": "https://site.com",
      "publisher": {"@id": "https://site.com/#org"}
    },
    {
      "@type": "WebPage",
      "@id": "https://site.com/article/#page",
      "isPartOf": {"@id": "https://site.com/#website"},
      "author": {"@id": "https://site.com/#author1"}
    },
    {
      "@type": "Person",
      "@id": "https://site.com/#author1",
      "name": "Jane Smith",
      "worksFor": {"@id": "https://site.com/#org"}
    }
  ]
}
</script>

Schema debugging at scale

Enterprise schema debugging requires automated validation pipelines using Google's Rich Results API, bulk testing with Screaming Frog or custom scripts, Search Console's Enhancement reports for aggregate issues, and alerts for schema errors during deployments.

# Automated schema validation at scale
import requests
from concurrent.futures import ThreadPoolExecutor

def validate_schema(url):
    """Test URL against Google Rich Results API"""
    api_url = "https://searchconsole.googleapis.com/v1/urlTestingTools/mobileFriendlyTest:run"
    # Note: Use actual Rich Results API endpoint with auth
    
    response = requests.post(api_url, json={"url": url})
    return {
        "url": url,
        "valid": response.json().get("testStatus", {}).get("status") == "COMPLETE",
        "errors": response.json().get("richResultsErrors", [])
    }

# Bulk validation
urls = ["https://site.com/page1", "https://site.com/page2", ...]
with ThreadPoolExecutor(max_workers=10) as executor:
    results = list(executor.map(validate_schema, urls))

# Alert on failures
failures = [r for r in results if not r["valid"]]
if failures:
    send_alert(f"Schema errors on {len(failures)} pages")

Dynamic schema generation

Dynamic schema generation programmatically creates structured data from databases, APIs, or CMS content—ensuring accuracy, handling updates automatically, and enabling A/B testing; implement server-side rendering or build-time generation for crawler accessibility.

# Dynamic schema generation example
from flask import Flask, render_template
import json

app = Flask(__name__)

def generate_product_schema(product):
    """Generate Product schema from database object"""
    return {
        "@context": "https://schema.org",
        "@type": "Product",
        "name": product.name,
        "description": product.description,
        "sku": product.sku,
        "image": product.images,
        "brand": {"@type": "Brand", "name": product.brand},
        "offers": {
            "@type": "Offer",
            "price": float(product.price),
            "priceCurrency": "USD",
            "availability": f"https://schema.org/{'InStock' if product.in_stock else 'OutOfStock'}",
            "priceValidUntil": product.price_valid_until.isoformat()
        },
        "aggregateRating": {
            "@type": "AggregateRating",
            "ratingValue": product.avg_rating,
            "reviewCount": product.review_count
        } if product.review_count > 0 else None
    }

@app.route('/product/<id>')
def product_page(id):
    product = db.get_product(id)
    schema = generate_product_schema(product)
    return render_template('product.html', schema=json.dumps(schema))

Schema A/B testing

Schema A/B testing compares different structured data implementations to measure impact on CTR, impressions, and rich result appearance rates; use Google Search Console data, segment by page groups, ensure statistical significance, and test elements like review counts, price display, and FAQ variations.

# Schema A/B testing framework
import random
from datetime import datetime

class SchemaABTest:
    def __init__(self, test_name, variants):
        self.test_name = test_name
        self.variants = variants  # {'control': schema_a, 'variant': schema_b}
        
    def get_variant(self, page_id):
        """Consistent variant assignment per page"""
        random.seed(hash(f"{self.test_name}:{page_id}"))
        return random.choice(list(self.variants.keys()))
    
    def render_schema(self, page_id):
        variant = self.get_variant(page_id)
        return {
            "schema": self.variants[variant],
            "variant": variant,
            "test": self.test_name
        }

# Example: Test FAQ length impact
test = SchemaABTest("faq_length", {
    "control": faq_schema_3_questions,
    "variant": faq_schema_7_questions
})

# Measure in Search Console
# Segment: pages with control vs variant
# Metrics: CTR, impressions, rich result rate

A/B TEST TRACKING:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Metric           │ Control │ Variant │ Δ
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Rich Result Rate │  34%    │  52%    │ +18%
CTR              │  3.2%   │  4.1%   │ +0.9%
Impressions      │  12.4K  │  13.1K  │ +5.6%
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Winner: Variant (7 FAQ questions)