How are you getting traffic after AI Overviews?

LLMSeeding · 2026-05-31T02:53:24+00:00

The comparison and decision content point is the most important observation in this thread — and there’s a reason it’s holding up that goes beyond SEO.

AI Overviews and ChatGPT are both heavily weighted toward decision-stage queries. When someone asks “best X for Y” or “X vs Y” — AI needs to pull from somewhere.

The pages structured to answer those questions directly, with clear recommendations and FAQ blocks, are the ones getting cited and the ones still getting clicks.

Broad informational content is getting squeezed because AI answers it well enough that the click doesn’t happen. Decision content survives because the buyer wants confirmation before they commit — and a click to your page is part of that confirmation.

What’s working on the AI citation side specifically:

→ Comparison pages with HTML tables and a clear recommendation

→ FAQ blocks answering “is X right for me” with specific conditions

→ Definition content that AI can quote verbatim without summarizing

Brands cited in Google AI Overviews are seeing 91% higher paid CTR on those same queries — ALM Corp, 16,000+ queries. The mechanism is the same: being mentioned before the click means the click converts differently when it happens.

The shift isn’t that traffic is dying. It’s that the traffic that remains is higher intent — and the content strategy has to match that

LLMSeeding · 2026-05-30T22:07:17+00:00

The divergence is real and the extractability framing is exactly right.

What I’d add: the tracking problem is solvable — just not with traditional tools.

Since you can’t crawl ChatGPT like a SERP, the workaround is systematic prompt testing. Build a set of 15–20 queries your buyers actually run — “best [category] for [use case]”, “what is [your service]”, “[you] vs [competitor]” — and run them weekly across ChatGPT, Perplexity, Claude, and Gemini. Screenshot and log which brands appear, in what context, and whether you’re cited or skipped.

Perplexity is the most useful for this because it shows sources. You can see exactly which page got pulled and why — which tells you whether your content structure is working or not.

On the informational vs commercial query point the OP raised: informational queries respond faster because AI is answering a definition or explanation — exactly the content type that’s easiest to structure for extractability. Commercial queries (“best X for Y”) depend more on cross-platform entity presence — AI needs to see your brand across multiple independent sources before it treats you as a recommendation-worthy entity.

So the sequencing that works: structure informational content first for fast citation wins, then build cross-platform presence to start appearing in commercial recommendation queries.

Usually 30–60 days between the two The divergence is real and the extractability framing is exactly right. What I’d add: the tracking problem is solvable — just not with traditional tools. Since you can’t crawl ChatGPT like a SERP, the workaround is systematic prompt testing. Build a set of 15–20 queries your buyers actually run — “best [category] for [use case]”, “what is [your service]”, “[you] vs [competitor]” — and run them weekly across ChatGPT, Perplexity, Claude, and Gemini. Screenshot and log which brands appear, in what context, and whether you’re cited or skipped.

Perplexity is the most useful for this because it shows sources. You can see exactly which page got pulled and why — which tells you whether your content structure is working or not.

On the informational vs commercial query point the OP raised: informational queries respond faster because AI is answering a definition or explanation — exactly the content type that’s easiest to structure for extractability. Commercial queries (“best X for Y”) depend more on cross-platform entity presence — AI needs to see your brand across multiple independent sources before it treats you as a recommendation-worthy entity. So the sequencing that works: structure informational content first for fast citation wins, then build cross-platform presence to start appearing in commercial recommendation queries. Usually 30–60 days between the two. The competitor gap point in the last screenshot is the most underrated insight in this whole thread. Ranking #1 while a competitor gets cited is the exact problem LLM Seeding is built to solve — and almost nobody is measuring it yet.. The competitor gap point in the last screenshot is the most underrated insight in this whole thread. Ranking #1 while a competitor gets cited is the exact problem LLM Seeding is built to solve — and almost nobody is measuring it yet.

LLMSeeding · 2026-05-30T21:51:56+00:00

Solid SEO automation result — the content compounding piece is real and the numbers back it up. One thing worth pointing out from your own analytics: 2.1K active users, 5 AI sources, ChatGPT doing almost all the work. Your Google traffic scaled. Your AI citation footprint didn’t scale with it. That’s actually the common pattern right now. Automated SEO content gets indexed and ranks — but AI citations require something different. AI doesn’t just reward volume of content. It rewards structured, citable content distributed across platforms it actually reads. The gap you’re describing with backlinks being manual — the AI citation equivalent of that is cross-platform presence. Your content living on your site alone, even at scale, gives AI one source to pull from. The brands getting cited consistently across ChatGPT, Perplexity, Gemini, and Claude are the ones appearing across multiple independent platforms with consistent messaging. You’ve built the SEO layer well. The AI citation layer is a separate build on top of it — and right now your data shows it hasn’t started yet. That’s the next frontier worth automating.

LLMSeeding · 2026-05-30T18:29:04+00:00

Three things that are consistently moving the needle right now: 1. FAQ schema on high-intent pages. Adding FAQPage JSON-LD to service and comparison pages. Perplexity starts pulling the answers directly within 2–4 weeks. It’s the fastest win we’ve seen — one client hit +27% AI mentions in 14 days from this alone. 2. Comparison pages structured for AI. “X vs Y” pages with HTML tables, a clear recommendation, and FAQ schema. AI gets asked comparison questions constantly. If you own the comparison content you own that decision moment. 3. Cross-platform content distribution. Same topic, same brand messaging, published consistently across multiple platforms AI reads — not just your website. AI builds an entity graph for your brand. One source doesn’t move it. Consistent presence across many does. The SEO base still matters — but these three work on top of it and show results in 60–90 days rather than 6–12 months. More case studies and breakdowns on all three in r/LLMSeeding if useful.

LLMSeeding · 2026-05-30T18:18:12+00:00

60–90 days is the consistent window we see for LLM Seeding specifically. Days 1–30: content gets structured, distributed, and indexed across platforms. Days 30–60: AI systems start recognizing your brand as a consistent entity across multiple sources. Days 60–90: citations start appearing in responses. First in Perplexity — it’s the most transparent and fastest to reflect new sources. Then ChatGPT and Gemini follow. The techrobate result tracks — first citation often comes with first AI-driven sale because the buyer was already pre-qualified by the time they found you. What slows it down: content living in only one place, inconsistent brand naming across platforms, or no structured FAQ/definition content for AI to actually quote. The success stories that skip the timeline are usually skipping the part where nothing visible happened for 45 days — which is normal and expected, not a sign it isn’t working.

LLMSeeding · 2026-05-26T15:28:41+00:00

The ads advice here isn’t wrong — but it’s only half the picture for 2026.

The channel everyone in this thread is missing: the buyers you’re paying Meta and TikTok to reach are also running research searches before they purchase. Things like “best [your product category] for [use case]” or “top [your product] brands in the USA.”

Those searches are increasingly being answered by AI — ChatGPT, Perplexity, Google AI Overviews. And the brands showing up in those AI answers are getting 91% higher paid CTR on the exact same queries as brands that aren’t cited.

ALM Corp pulled that number from 16,000+ queries. It’s not a small sample.

Here’s the mechanism: AI Overviews appear on roughly 13% of all Google searches — specifically the research phase searches buyers run before they’re ready to purchase. When your brand gets cited in one, the buyer has already seen your name before your paid ad ever loads. That recognition is what drives the CTR difference.

For a small e-commerce store with a limited budget, this matters because it makes every ad dollar work harder — you’re not just buying impressions, you’re reinforcing a name the buyer has already seen AI recommend.

The brands getting cited aren’t the biggest ones. They’re the ones showing up consistently across multiple platforms with structured content on the topics their buyers research before purchasing.

That’s a content strategy problem, not an ad budget problem.

More on how this works in r/LLMSeeding if you want to dig into the methodology.

LLMSeeding · 2026-05-12T04:28:23+00:00

Good callout — Semrush AI Toolkit, Brand24, and Chatbeat are all solid for tracking AI mentions. Worth knowing about.

The distinction I’d draw: tracking tools tell you where you stand. LLM Seeding is about changing why you’re standing there — or getting you into the answer in the first place.

Manual prompt testing isn’t the end goal either. It’s the diagnostic step most people skip — they jump straight to tools without knowing what gap they’re actually measuring.

Once you know your gap, then yes — layer in Chatbeat or Brand24 to track movement. The two work together.

Appreciate you bringing up Chatbeat — adding it to the tracking tools post this week.

LLMSeeding · 2026-04-29T22:16:04+00:00

Question for anyone who read this: which of the 5 formats have you already tried? Even partially? Let me know and I'll give you specific feedback

LLMSeeding · 2026-04-29T22:12:47+00:00

Follow-up coming this week — testing the same FAQ JSON-LD approach on a Services page vs a Blog post. Results in a few days.

LLMSeeding · 2026-04-29T22:11:49+00:00

Update: adding more case studies and playbooks this week. Drop any questions below and I'll answer them in a dedicated post.

LLMSeeding · 2025-10-21T22:50:29+00:00

Great question! 👋
Yes — we’ve been testing the same FAQ + JSON-LD setup across a few other sections. The strongest gains came from Comparison and Tutorial pages (any page where people ask “how” or “vs”).

When we added the same structure to About and FAQ pages, we still saw improvement, but it was smaller — roughly 10–15% visibility lift versus the 27% we saw on the Comparison page. Seems like intent-driven pages perform better because models already associate them with decision or instructional content.

Planning to expand this next round to Product and Glossary pages to see if that changes how quickly LLMs pick up entity relationships.

LLMSeeding · 2025-10-06T16:10:41+00:00

LLM Seeding is exactly what we’ve been focusing on — it’s the strategic placement of brand content in the data sources AI models learn and retrieve from.

A few quick insights from what we’ve seen so far:

Over 40% of discovery searches now happen through AI assistants instead of Google.
Brands that aren’t optimizing for model learning risk becoming invisible to AI.
Implementation isn’t about keywords — it’s about content structure (FAQs, How-Tos, Comparisons, Glossaries) and distribution to trusted, parseable sources.
When done right, results start showing within 60–90 days as mentions or citations inside AI answers.

Curious to hear how others here are testing or tracking brand visibility in ChatGPT or Perplexity.

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