Do Large Language Model (LLM) Referrals Convert Better Than Organic Search? A 2025 Reality Check

The rise of AI-driven search experiences—from Google’s AI Overviews to platforms like ChatGPT, Perplexity, and Claude—has sparked a major question for marketers: does traffic coming from Large Language Models (LLMs) perform better than traditional organic search?

For years, SEO has been the backbone of digital visibility. But with the rise of generative AI tools, businesses are rethinking whether their audience might be shifting away from the SERPs and toward AI answers.

To clear the air, new research examined six months of GA4 data across dozens of websites to compare conversion performance between organic visitors and those arriving via LLM citations or referrals. The findings may surprise you.

LLM Referrals vs Organic Search: What the Numbers Show

The study analyzed 54 websites across both B2B and B2C industries. Here’s the big reveal:

• Visitors arriving through traditional organic search channels converted at roughly 4.6%.

• Meanwhile, users coming from LLM-based referrals converted slightly higher, just under 4.9%.

At first glance, it looks like LLM traffic edges out organic. But here’s the kicker: the difference was so small that researchers concluded it was statistically insignificant. In other words, there’s no proof that LLM-driven visitors are inherently “better quality” than organic ones.

And more importantly? Scale is the real issue.

• Organic search accounted for about one-third of total site sessions.

• LLM referrals made up less than 1% of traffic.

So even if LLM visitors appear marginally stronger on paper, the volume is too tiny to compete with the scale of organic search.

Some analysts have wondered: “Do users really show higher intent when they click through from an LLM to a website?” A detailed study by SALT.agency answers this with data from over 600,000 LLM-generated sessions versus more than 188 million organic sessions. Across the board, traditional organic traffic generally outperforms LLM referrals on engagement and conversion, with a few vertical exceptions like Health, Careers, and Catalog sites where LLM referrals slightly edge out organic traffic in Key Event Conversion Rate (KECVR)

What Does This Mean for Marketers?

1. The Organic Engine Still Rules

Organic search remains the primary driver of traffic and conversions. Until LLMs generate enough volume, they simply can’t replace what SEO delivers today.

2. LLM Traffic = Bonus, Not Baseline

For now, consider any LLM-driven sessions as a supplementary source of visibility—a nice boost when it happens, but not something to bet your pipeline on.

3. Quality Isn’t Guaranteed

The assumption that AI referrals bring “higher intent” visitors doesn’t hold up in practice. Performance varies by industry, by niche, and even by query.

Breaking Down the Data: B2B vs B2C

The study went deeper, splitting results between business-to-business and business-to-consumer websites.

• B2B Sites

  • Organic: ~1.7% conversion

  • LLM Referrals: ~2% conversion
    The difference was minimal, and again, not statistically meaningful.

• B2C Sites

  • Organic: ~8.5% conversion

  • LLM Referrals: ~10.3% conversion
    While B2C LLM traffic looked stronger, the lack of volume made the impact negligible.

This reinforces the conclusion: LLM traffic can perform respectably, but it’s too small to rely on.

Why Marketers Are Getting Excited About LLMs

Despite these modest numbers, the buzz around LLM traffic is real. Why?

• AI adoption is accelerating. Generative engines are already shaping how people ask questions online.

• Citations in AI answers feel authoritative. Getting mentioned by ChatGPT or Perplexity can boost brand credibility.

• The long game matters. While today’s LLM traffic is small, tomorrow’s share could be significant as users shift habits.

But here’s the reality check: today, LLMs don’t replace SEO. They complement it.

How AI Rank Lab Views This Shift

At AI Rank Lab, we track SEO, AEO (Answer Engine Optimization), and GEO (Generative Engine Optimization) together because they form a hybrid strategy.

Here’s how we see the landscape:

1. SEO is still your foundation. You need optimized, crawlable, intent-rich content to rank in search.

2. AEO makes you “AI answer-ready.” Q&A formats, schema markup, and concise explanations help you get cited.

3. GEO future-proofs your visibility. By signaling entities, building knowledge graphs, and feeding LLMs structured data, you position yourself for long-term AI-driven discovery.

In other words: don’t abandon SEO—but don’t ignore AEO/GEO, either.

Why Scale Matters More Than Conversion Rate

Let’s run a simple example:

• Say your site gets 100,000 visits per month.

• Organic search contributes 32,000 sessions at a 4.6% conversion rate → that’s 1,472 conversions.

• LLM referrals provide 240 sessions at a slightly higher 4.9% conversion rate → that’s only 12 conversions.

Even if the conversion rate difference were real, the raw numbers show that organic dominates outcomes.

That’s why SEO remains mission-critical: scale drives impact.

The Myth of “Better Intent” LLM Visitors

Some marketers argue that users who arrive from AI platforms are more serious, since they’re engaging with curated answers instead of scanning search results. But the research shows no universal truth here.

• In healthcare, LLM visitors sometimes stick around longer.

• In e-commerce, organic still delivers stronger sales.

• In career and education sites, LLM referrals sometimes outperform—but again, the volume is too low.

This proves that industry context matters more than platform hype.

What’s Next for AI-Driven Search

AI search is undeniably growing. Recent market reports show that:

• AI engines already handle over 5% of U.S. desktop search traffic.

• Platforms like Perplexity and ChatGPT are building loyal user bases who prefer conversational discovery.

• Google’s AI Overviews are reshaping SERPs, reducing clicks to traditional results.

But we’re still in the early adoption curve. It may take years before LLM traffic even reaches 5–10% of site visits across industries.

That’s why a balanced strategy is essential.

How to Optimize for Both SEO and LLM Referrals

Here’s a practical roadmap to make your content visible in both traditional search and AI answers:

1. Write in a question-answer format. Anticipate how users (and AI) phrase their queries.

2. Use structured data. Schema markup, FAQs, and how-to blocks make your content easier to extract.

3. Build authority. AI models cite sources they consider trustworthy. Publish expert-backed insights and data.

4. Track new metrics. Don’t just monitor keyword rankings—measure AI visibility, citation frequency, and share of AI answers.

5. Experiment with entity optimization. Add llms.txt files, structured entities, and semantic relationships to help LLMs understand your content.

The Balanced Future: SEO + AEO + GEO

The future isn’t about choosing SEO or AI—it’s about blending strategies:

• SEO brings consistent traffic at scale.

• AEO ensures your brand becomes the “answer.”

• GEO sets the stage for emerging generative platforms.

Marketers who align with all three will be best positioned to thrive as search evolves.

Final Thoughts

So, does LLM traffic convert better than organic? The evidence says: not really.

Yes, LLM referrals can sometimes show slightly stronger conversion percentages—but the difference is negligible, and the traffic share is too small to move the needle.

Organic search continues to dominate in both volume and conversions, making it the foundation of your digital strategy.

At the same time, the growth of AI-driven platforms means you can’t ignore Answer Engine Optimization and Generative Engine Optimization. Together, they prepare your content for the future while protecting today’s visibility.

At AI Rank Lab, we believe the smart move is balance:

• Keep SEO strong.

• Layer in AEO and GEO.

• Monitor how AI citations evolve.

• The winners in 2025 and beyond won’t be those who chase hype, but those who build hybrid strategies that work across search engines and AI engines.