Case Study: How AI Internal Linking Drove 32% More Traffic

Everyone says internal linking helps SEO. But by how much? And what does the process actually look like?

This case study documents how two retro gaming sites, Weplaydos and Weplayretro, used AI internal linking for WordPress to significantly increase organic traffic. Real sites, real numbers, real results.

The Sites

Weplaydos and Weplayretro are content-focused gaming sites covering classic and retro video games. Both feature:

• Large content libraries (hundreds of game pages, guides, and articles)
• Diverse topic coverage (individual games, genres, platforms, gaming history)
• Regular new content publication
• WordPress-based architecture

Combined, these sites now receive over 14,000 sessions per week, approximately 7,000 each.

The Challenge

Large content libraries create a specific internal linking problem: opportunity overwhelm.

With hundreds of pages covering related topics, the potential connections are enormous. A guide about a specific DOS game could link to:

• Other games from the same publisher
• Games in the same genre
• Platform-specific guides
• Historical articles about that gaming era
• Related gameplay guides

Multiply that by hundreds of pages, and you're looking at thousands of potential linking opportunities. Finding them manually? Nearly impossible.

The sites faced three specific challenges:

1. Orphan pages everywhere

Content was being published regularly, but older pages weren't being updated with links to new content. Many pages had zero internal links pointing to them: invisible to both Google and readers.

2. Missed topical connections

Related content existed but wasn't connected. A page about "best DOS RPGs" didn't link to individual DOS RPG game pages, even though those connections would help both readers and search rankings.

3. Manual linking couldn't scale

With new content publishing regularly and a large existing library, manual internal linking would require dedicated hours every week. Time better spent creating content.

The Approach

Instead of manual linking, we implemented AI-powered semantic internal linking. Here's what that means and how it worked.

Semantic Analysis vs. Keyword Matching

Traditional internal linking tools use keyword matching. They scan for matching words and suggest links based on text overlap.

The problem: keywords don't equal meaning. A keyword-matching tool might link "Doom" (the 1993 game) to any page mentioning "doom" in any context. Or link "Commander Keen" to pages about military commanders.

Semantic AI analyzes what content actually means: topics, entities, relationships. It understands that an article about id Software relates to articles about Doom, Quake, and John Carmack, even if those exact words don't appear together.

For gaming sites with hundreds of titles, genres, and cross-references, semantic understanding is essential.

The Implementation Process

Step 1: Full content scan

The AI analyzed every page on both sites. Not just titles, but full content. This built a semantic map of topics and relationships across the entire library.

Step 2: Opportunity identification

The tool identified linking opportunities based on semantic relevance:

• Which pages covered related topics
• Which pages had too few internal links (or none)
• Where anchor text could naturally fit

Step 3: Suggestion review

AI suggestions were reviewed before deployment. Most were accepted. Semantic analysis produces far fewer irrelevant suggestions than keyword matching. But human review ensures quality.

Step 4: Automated deployment

Accepted suggestions were deployed automatically via WordPress REST API. No manual editing of individual posts required.

Step 5: Ongoing maintenance

As new content was published, the system identified linking opportunities both to and from new pages. The initial cleanup became an ongoing process.

The Results

Traffic Impact

Before implementation:

• Combined weekly sessions: ~10,600
• Average pages per session: 1.4
• Significant orphan page problem

After implementation (3 months):

• Combined weekly sessions: ~14,000+
• Average pages per session: 1.9
• Orphan pages reduced by 85%

Total traffic increase: approximately 32%

Other Improvements

Indexing velocity increased. New content got indexed faster because it immediately had internal links pointing to it from relevant existing content.

Rankings improved for mid-tier pages. Pages ranking positions 10-30 saw the most improvement. Internal links from authoritative pages helped push them higher.

User engagement improved. Pages per session increased from 1.4 to 1.9. Readers were finding and exploring related content.

Time saved. What would have required 5-10 hours weekly of manual work happened automatically.

What Worked Best

Analyzing the results, certain tactics drove the most impact:

1. Fixing Orphan Pages First

The biggest quick wins came from pages that had zero internal links. These were essentially invisible. Adding even 2-3 relevant internal links made them discoverable.

If you're starting internal linking optimization, audit for orphan pages first. The ROI is immediate.

2. Linking From Authority Pages

Both sites had a few pages that ranked well and attracted backlinks: comprehensive guides and popular game pages.

Adding internal links from these high-authority pages to newer or underperforming content transferred ranking power. Pages that received links from authority pages showed faster ranking improvements.

3. Topical Clusters

Organizing content into topical clusters (a main guide linking to specific game pages, which link back and to each other) created clear topical structure.

Google rewards comprehensive topic coverage. Clusters demonstrate that coverage through link structure.

4. Anchor Text Variety

Rather than using the same anchor text repeatedly, the AI varied anchors naturally:

• Game titles
• Descriptive phrases ("this classic platformer")
• Partial matches ("another id Software release")

This natural variation avoided over-optimization while still providing contextual signals.

What We Learned

Internal Linking Compounds Over Time

The 32% increase didn't happen overnight. First month showed modest gains. Second month showed acceleration. Third month showed the full impact.

Internal linking benefits compound. As more pages get linked, more authority flows through the site, more pages get indexed, more rankings improve.

Semantic Analysis Matters

Keyword-matching tools would have created irrelevant links, connecting pages that share words but not topics. Semantic understanding prevented those errors and surfaced connections humans would miss.

For sites with diverse content (especially gaming, where titles could match common words), semantic analysis isn't optional.

Automation Enables Consistency

The sites continued publishing new content throughout this period. Without automation, new content would have created new orphan pages. Instead, the system maintained linking health even as content expanded.

Consistency matters more than perfection. Automated systems maintain consistency.

Key Takeaways

If you're considering implementing AI-powered internal linking, here's what this case study suggests:

1. Start with an orphan page audit. Find pages with zero internal links. These are your quick wins.

2. Prioritize linking from authority pages. Identify your strongest pages (most backlinks, best rankings) and add outbound internal links to priority pages.

3. Use semantic analysis, not just keyword matching. Especially for sites with diverse content where keywords might match incorrectly.

4. Automate the ongoing process. Initial cleanup is valuable, but ongoing maintenance is what sustains results.

5. Be patient. Significant results took 8-12 weeks to materialize. Internal linking is a compound investment, not a quick fix.

Replicating These Results

These results came from two specific sites, but the principles apply broadly. Any WordPress site with:

• 50+ pages of content
• Regular content publication
• Related topics that should be connected

...can benefit from systematic internal linking.

The question is whether to do it manually or use tools. Manual works for small sites. For anything larger, AI-powered tools save significant time while finding opportunities you'd miss.

Try It Yourself

WPLink is the tool we used for this case study. It combines:

• Semantic AI analysis that understands content meaning
• Desktop processing that doesn't slow your WordPress site
• Automated deployment via WordPress REST API
• One-time pricing with no recurring subscription

If you're managing a content-heavy WordPress site and want to replicate these results, WPLink can help.

Get WPLink - Lifetime Access →

Results documented from real sites. Individual results vary based on site size, content quality, and competitive landscape.

Last updated: January 2026

Appendix: Metrics Summary