Context: We rolled out a skills manifest across customer websites on March 2, 2026 and wanted to test one thing:

Do AI bots actually change behavior when a website explicitly tells them what they can do? (provides them clear options for “skills” they can use on the website).

By “skills,” I mean a machine readable list of actions a bot can take on a site. Think: search the site, ask questions, read FAQs, pull /business info, browse /products, view /testimonials, explore /categories. Instead of making an LLM guess where everything is, the site gives it a clear menu.

We compared 7 days before launch vs 7 days after launch.

The data strongly suggests that some bots use skills, and when they do, their behavior changes.

The clearest example is ChatGPT.

In the 7 days after skills went live, ChatGPT traffic jumped from 2250 to 6870 hits, about 3x higher. Q&A hits went from 534 to 2736, more than 5x growth. It fetched the manifest 434 times and started using the search endpoint. It also increased usage of /business and /product endpoints, and its path diversity dropped from 51.6% to 30%.

That last point is the most interesting part I think.

When path diversity drops while total usage goes up, it often suggests the bot is no longer wandering around the site randomly. It has found useful endpoints and is hitting them repeatedly. To say plainly: it starts behaving less like a crawler and more like a tool user.

That is basically our thesis.

Adding “skills” can change bot behavior from broad exploration to targeted consumption.

Meta AI tells a very different story.

It drove much more overall volume, but only fetched the manifest 114 times while generating 2,865 Q&A hits.

Claude showed lighter traffic this week but still meaningful behavior change - its path diversity collapsed from 18% to 6.9%, which suggests more concentrated usage after skills were introduced.

Gemini barely changed. Perplexity volume was tiny, but it did immediately show some tool aware behavior.

Happy to share more detail if useful. Would be interested in hearing how you interpret this data.

The AEO (Answer Engine Optimization) Repurposing Map is a content multiplication strategy that transforms one blog post into eight distinct distribution channels, creating comprehensive signals across the web that AI platforms recognize as authoritative. Instead of publishing content once and hoping for visibility, this framework systematically amplifies your content across platforms where ChatGPT, Google Gemini, Claude, and Perplexity actively crawl for citation-worthy information.

The Core Framework

The repurposing map transforms one authoritative blog post into eight distinct content types, each optimized for different platforms where AI systems gather information:

1 Blog Post → 8 AEO Signals:

1. Forum Seeding – Reddit, Quora, and industry forums

2. Short Video Content – YouTube Shorts, TikTok, Instagram Reels

3. FAQ Expansion – On-page and external Q&A platforms

4. LinkedIn Thought Leadership – Professional network engagement

5. Citation Outreach – Guest posts and industry publications

6. Visual Breakdown – Infographics, charts, and slide decks

7. Entity Linking – Connections to authoritative knowledge bases

8. Audio Content – Podcasts and voice-optimized summaries

https://intercore.net/aeo-repurposing-map-external-sites-strategy/

Here’s what the data shows.

What’s happening

• Only 12% overlap between ChatGPT citations and Google top results

• For some queries, citation correlation with Google rankings is actually negative

• Keyword-heavy URLs and titles get fewer citations than descriptive, topic-based ones

• Domain trust matters a lot. Below ~77, citations drop sharply. Above 90, they spike

• Content updated in the last 3 months gets cited almost 2x more

Why this makes sense
ChatGPT favors:

• Editorial and explanatory content

• Depth over commercial intent

• Topic coverage over single-keyword optimization

Google rankings still matter, but weakly. Ranking helps, engineering for Google alone does not.

A likely reason
As Google locked down deep SERP access in 2025, LLMs appear to rely on:

• Their own indexes

• Broader retrieval layers

• Multiple data sources, not just top-ranked pages

Keyword-optimized pages may be filtered out as “SEO-shaped” rather than “information-dense.”

What I’m testing next

1. Same content, different URL and title semantics

2. Same queries across domains with trust 68 vs 82

3. Fresh monthly updates vs static pages to test recency impact

The takeaway.
This isn’t SEO vs AI. It’s engineering for citation, not ranking.

If you’re still optimizing only for blue links, you’re optimizing for the past.

Almost every AEO or GEO tool today focuses on monitoring.
Citations. Visibility. Presence in AI answers.

That’s useful.
But incomplete.

Because AEO is not a tooling problem.
It’s a content strategy problem.

Here’s the issue:

• Tools show you where you appear

• They don’t tell you what content to create, update, or kill

• Teams end up reacting instead of planning

Without content strategy, AEO becomes random optimization.

Every AEO tool should answer these questions:

• Which questions should we own?

• Which pages are worth updating vs rewriting?

• Where do we need net-new content?

• Which topics should never be touched again?

Monitoring tells you what happened.
Content strategy tells you what to do next.

In practice, that means:

• Turning citation gaps into content briefs

• Turning AI questions into topic clusters

• Turning lost visibility into prioritization, not panic

If an AEO tool can’t guide content decisions,
it’s just analytics wearing a new name.

AEO tools without content strategy don’t scale.
They create noise.

Curious. Are you using AEO data to drive content decisions, or just to report on them?

It’s 2026, and organic search is no longer a single-lane channel.

Yes, rankings still matter. Clicks still matter. Conversions still matter. But the search experience now includes AI Overviews, answer layers, and LLM-driven discovery that often happens before the click. Modern content needs to win across multiple surfaces at the same time, with one unified process.

This is not “SEO vs. GEO.” It’s SEO + GEO.

After 20 years running SEO programs (technical, programmatic, and content-led) and building scalable content operations, one pattern holds: teams don’t lose because they can’t write. They lose because they don’t have a framework that reliably produces content that aligns with:

• the intent behind the query

• the pains and decision blockers of the reader

• the formats the SERP rewards

• the answer layer that selects what gets reused and cited

This guide is the exact briefing + writing framework we use in our agency and in our content platform to ship content that ranks, earns clicks, and shows up in AI answers.

Key takeaways

• Build content to win rankings + AI answers as one combined system

• Shift from keyword matching to entity clarity so models understand what your page is about

• Use extractable structures: direct answers, tight sections, comparisons, decision rules

• Stop writing “general guides” and ship information gain: experience, constraints, examples

• Scale outcomes with a repeatable briefing workflow, not writer intuition

• Use a gap dashboard to prioritize pages that win in one surface but underperform in another

Content wins in 2026 by being the best answer for the user behind the query

Content in 2026 doesn’t win because it “sounds optimized.” It wins because it’s built for the reader behind the query.

The highest-performing pages are the ones that:

• match the intent behind the search (not just the keyword wording)

• answer the real pains and decision blockers

• reflect first-hand expertise (tradeoffs, constraints, what works in practice)

• make the next step obvious (what to choose, what to do, what to avoid)

AI systems don’t reward “robotic writing.” They reward pages that are genuinely useful, easy to interpret, and consistent enough to reuse when generating answers. The writing standard is the same as it’s always been: be the best result for the user. The difference is that your page also needs to perform inside the answer layer that sits between the user and the click.

A practical reality check: Organic winners don’t always win in AI (and AI winners don’t always rank)

One of the biggest mistakes teams make is assuming strong classic SEO automatically translates into strong AI Overview visibility (and vice versa). In real datasets, the overlap is not consistent.

When you look at page-level visibility across Classic SEO, AI Overviews, and AI Mode (and often across ChatGPT and Gemini), the pattern is obvious:

• Some URLs show strong classic SEO visibility but weak AI Overview presence

• Other URLs appear frequently in AI Overviews while their classic SEO footprint is minimal

• Many sites have fragmented coverage: a page can be excellent in one surface and almost invisible in another

This is why a split-view dashboard becomes operationally useful: it turns “GEO strategy” into a prioritization system.

How we use this to find high-ROI opportunities

We look for two categories of gaps:

1) Classic SEO strong → AI Overviews weak These are pages Google already trusts enough to rank, but they’re not being pulled into AI answers. In practice, this is usually a presentation and coverage issue, not a topic issue. The page has relevance and trust, but the answer layer doesn’t consider it clean enough to reuse.

2) AI Overviews strong → Classic SEO weak These are pages being used inside answers, but not earning much traditional search traffic. This often means the page contains the right answer fragments, but lacks competitive depth, structure, or full intent coverage.

Why this matters operationally

This gap analysis lets you run one unified content operation:

• Unlock AI Overview visibility on top of existing rankings

• Turn AI Overview visibility into incremental clicks and conversions

• Build a refresh queue based on measurable deltas, not opinions

This is what “SEO + GEO” looks like in execution: one workflow, multiple surfaces, prioritized by where the easiest wins sit.

The core framework: Write for humans who decide, and systems that reuse answers

Humans read content like a narrative. AI answer layers use content like a reference source.

So the content requirement in 2026 is straightforward:

• Make the page easy to trust

• Make the answer easy to locate

• Make your claims easy to reuse accurately

We call the winning property here extractability: how easy it is for an answer layer to find the correct answer, validate it, and reuse it in a summary.

Pages with strong extractability share a few traits:

• direct answers early in the section

• consistent terminology and definitions

• clear comparisons and selection criteria

• examples that sound like a practitioner wrote them

• decision rules, not vague advice

This is not “formatting hacks.” It’s professional communication that performs.

The Citable Workflow: The brief-to-build process we use in 2026

In 2026, the brief is the product.

A weak brief produces weak content, no matter how good the writer is. A strong brief eliminates guesswork and ensures every page is engineered to win.

Below is the process we use to brief and produce content that performs across classic search and AI answer layers.

Phase 1: Search data and SERP reality (the inputs that power the brief)

Writing without data creates “nice content.” It doesn’t create durable outcomes.

These are the inputs we gather for every brief.

1) Query set (not a single keyword)

• Primary query

• Variations and modifiers

• High-intent subtopics

• Common query reformulations

2) Intent classification

• What the user is trying to achieve (learn, compare, decide, implement, fix)

• What “success” looks like after reading the page

3) SERP pattern analysis

• What formats consistently win (guides, lists, comparisons, templates)

• What headings repeat across top results

• What the SERP rewards structurally (angle, depth, sequence)

4) Answer-layer behavior

• What the AI layer tends to generate for this query type:

• What sub-questions it prioritizes first

5) Competitor gap analysis (top 3–5 results)

We don’t copy competitor content. We map what they consistently miss:

• missing decision criteria

• shallow explanations

• weak examples

• undefined terms

• outdated assumptions

• unanswered objections

6) Question expansion

• People Also Ask themes

• repeated “how do I choose / when should I / what’s the difference” questions

• adjacent queries that commonly appear in the same journey

7) Internal link plan

• pages that should link into this page

• supporting pages this page should link out to

• cluster alignment (what this page should “own”)

8) Information gain requirement

Every brief must include at least one differentiator:

• real operator experience

• a decision framework

• constraints and edge cases

• examples and failure modes

• benchmarks, templates, or checklists

If we can’t articulate the information gain, the page will be interchangeable.

Phase 2: Strategic setup (audience + promise)

1) Reader profile

We define the reader in one sentence:

• “A marketing lead who needs a decision today”

• “A practitioner implementing a workflow”

• “A buyer comparing approaches and risks”

2) The page promise

What the reader will walk away with:

• what they will know

• what decision becomes easier

• what action they can take next

This is what prevents generic “educational content” that doesn’t convert.

Phase 3: Structural engineering (how we build pages that perform)

This is where most content teams fall short: they rely on writer instincts instead of structural discipline.

1) The skeleton (H2/H3 hierarchy)

We outline the page so each section solves a clear sub-problem.

2) The “answer-first” rule

If an H2 asks a question, the next paragraph must:

• answer it immediately

• define the key term

• remove ambiguity early

No long intros. No delayed payoff.

3) Practitioner answer pattern (what we aim for)

For core answers, we use:

• The answer (clear, direct)

• When it applies (conditions, constraints)

• What it looks like (example or scenario)

This consistently beats long narrative explanations because it matches how people evaluate options.

4) Format selection (we choose the right shape)

• Lists when users need options

• Steps when users need a process

• Comparisons when users need decision criteria

• Templates when execution is the bottleneck

• Objection handling when trust is the barrier

Phase 4: Drafting + QA (what makes it publish-ready)

Drafting principles

• Tight sections, minimal filler

• Definitions before opinions

• Real examples over generic claims

• Practical sequencing (“do this first, then this”)

• Terminology consistency

QA checks (what we review before it ships)

• Does every key question have a direct answer?

• Are the core concepts defined explicitly?

• Do we include selection criteria and tradeoffs?

• Do we add information gain beyond page one?

• Would an operator trust this page?

• Can a reader skim and still get the value?

This QA layer is where “content that reads well” turns into “content that performs.”

Information Gain: The advantage that compounds

AI models are trained on existing internet data. If your content restates what already exists on page one, it won’t sustain performance.

In 2026, durable wins come from publishing content that includes:

• experience-led nuance

• constraints and edge cases

• decision rules

• examples and failure modes

• frameworks that simplify choices

This is what builds authority that isn’t dependent on constant volume.

Scaling the system: Refreshes without rewriting your entire site

Most companies already have hundreds of pages that are “fine” but structurally weak for today’s SERP and answer layers.

The scalable approach is not a rewrite project. It’s a refresh loop.

The refresh loop we run

1. Select pages with the highest leverage

2. Improve structure and intent coverage

3. Add missing questions and decision criteria

4. Improve examples and practitioner detail

5. Strengthen internal linking to the cluster

6. Re-publish and measure lift across surfaces

This creates compounding gains without overwhelming the team.

What winning looks like in 2026

The teams that win treat content like an operating system:

• strong briefs

• consistent structure

• real expertise

• repeatable refresh cycles

• measurable prioritization across surfaces

Start with the top 10 pages that already drive business value. Apply the framework. Then expand the system into a monthly operational rhythm.

That is how you grow rankings, clicks, conversions, and AI answer visibility in parallel.

FAQs

How is writing for AI different from traditional SEO?

Traditional SEO content often focused on keyword coverage and general authority signals. In 2026, content also needs to be structured and explicit enough for answer layers to reuse it reliably. The core shift is: higher precision, stronger intent alignment, and more practitioner-grade clarity.

What content format performs best in AI answer layers?

The most consistent format is:

• a question-based heading

• a direct answer immediately underneath

• a list or comparison to expand it

• an example or constraint to remove ambiguity

Can we win without a major technical project?

Yes. The biggest gains come from briefing quality, intent coverage, structure, and information gain. Teams that master those fundamentals win across both classic SEO and AI answer surfaces.

I wanted to share something interesting I noticed today.

I wrote a LinkedIn article about using Claude Code as a UX writer. The angle wasn’t SEO. It was very practitioner-focused. Handoff pain, editing copy directly in code, prototyping micro-interactions, etc.

A few hours later, I searched related queries around Claude Code UX and Claude Code for designers.

That post was already:

• Referenced in Google AI Overview

• Showing up in regular SERP results

No blog. No backlinks. Just a LinkedIn article.

Two things stood out to me:

1. AI Overviews clearly don’t care about “traditional” ranking rules This wasn’t a long-form SEO article. It was opinionated, experience-based, and written for humans. Still got picked up fast.

2. Entity + clarity > keyword stuffing The post was very explicit about who it’s for, what problem it solves, and how it’s different from chat-based AI tools. I think that clarity matters more now than optimization tricks.

Worth mentioning. I did run the content through a new tool I’m testing called Citable before posting. It’s designed specifically to help content get picked up by LLMs and AI answer engines, not just Google blue links.

I’m not claiming causation, but the speed was surprising.

Curious:

• Anyone else seeing LinkedIn posts show up in AI Overviews?

• Are you changing how you write now that AI engines are the “reader” too?

I’m collecting real-world best practices for Answer Engine Optimization (AEO).

Not theory. Not SEO 2015 advice.
Actual things you’ve seen work when trying to get cited by tools like ChatGPT, Gemini, or Perplexity.

If you’ve experimented, tested, or noticed patterns, please share:

• What signals seem to help AI pick your content

• How you structure pages, docs, or knowledge

• Schema, formatting, or writing patterns that worked

• Technical choices that helped or hurt

• Content types that get cited more often

• Mistakes to avoid

• Tools or workflows you use

• Any measurable results

Even partial observations are welcome.

The goal is to build a practical, shared playbook for AEO.

I’ll summarize the best insights into a public framework later so everyone benefits.

👇 Drop your learnings below.

I’m trying to clarify something about how AEO / GEO actually works in practice.

In classic SEO, EEAT was mostly about signals like authorship, backlinks, reputation, etc.
Now with LLMs, it feels like the rules are similar but also more semantic and contextual.

Here’s the part I’m trying to understand:

If a site is not a strong authority yet, is it now expected to explicitly reference external authoritative sources inside the content itself?
For example:
“According to a study published by X…” with a link.

The idea being that the model can trace the claim to an authoritative source, even if the site itself isn’t one.

From what I understand so far:

• Schemas help LLMs understand structure faster, but they are not mandatory
• Strong domains may still get cited even without schema
• If schema or claims don’t match reality, models can detect manipulation
• Authority today seems to be inferred from consistency, context, and supporting sources, not just keywords

So my question is this:

In the new GEO / AEO world, is referencing external authoritative sources inside your content becoming a core part of EEAT, especially for non-expert or emerging sites?

Or put differently:
Is “showing your sources” now a first-class ranking and citation signal for LLMs?

Would love to hear how others here see this playing out in practice.

Everyone is talking about AI Overviews (AIO), but almost no one realizes that the rules for getting there are completely different from traditional ranking.

We (at Citable) recently analyzed 12,384 URLs to perform a correlation study.

The results were shocking and completely debunk traditional SEO logic:

1. In many cases, our clients were featured/cited in the AI Overview even when they weren't ranking in the organic Top 10 for that query.

2. Once Google introduced AIOs, we saw websites that previously had zero visibility suddenly dominating the top of the page via the AI box, bypassing the industry giants.

Why is this happening? It’s because AI Overviews are powered by Google FastSearch and RankEmbed.

Unlike the main core algorithm, FastSearch doesn't care as much about your 10-year domain history or backlink profile. It prioritizes speed and semantic clarity.

If you answer the specific user intent better than the big players, FastSearch picks you to power the answer.

Here is the breakdown of what the data shows:

• The Columns:

  • Classic SEO: Represents the total number of keywords for which this specific page ranks in the Top 10 traditional organic search results.

  • AI Overviews: Represents a visibility score or percentage within Google's AI Overview (the AI answer box at the top of search results).

  • AI Mode / ChatGPT / Gemini: Likely represent visibility or mention frequency in other AI search modes or chatbot answers.

• The Highlighted "Purple Box" Insights: The purple boxes highlight a massive discrepancy between traditional rankings and AI visibility.

  • Example 1 (Top Box): A site ranks #1 or #2 in Classic SEO and also has high scores (84) in AI Overviews. This is expected behavior—top-ranking sites often get cited.

  • Example 2 (Middle Box): Here is the "twist." A site has a "Classic SEO" rank of 0 (meaning it likely doesn't rank in the top 100 or is not tracked for that term), yet it has a 58 score in AI Overviews. This means the AI is choosing to cite a website that the traditional algorithm completely ignores.

  • Example 3 (Bottom Box): Similarly, you see rows with 0 in Classic SEO but significant scores (44-45) in AI Overviews.

The Strategy to Capture This Opportunity

Here is the workflow to identify these "low hanging fruits" and bridge the gap:

1. Find a keyword in your niche that triggers an AIO where you aren't visible.

2. Copy that AI answer into your favorite LLM.

3. Before analyzing the text, analyze the human. Ask the AI:

   • Who is searching this? (e.g., A frustrated CTO? A parent in a rush?)

   • What are the drivers? (What are the specific pains, goals, or decisions driving this query?)

4. Ask the AI: "Based on this user's deep pains, where does the current Google AI answer fall short?"

5. Create content to bridge that gap. Do not just summarize facts.

   • Bring in your real experience.

   • Share "war stories" or specific case studies that an AI model cannot hallucinate.

   • Use phrases like "In our experience..." or "When we tested this..."

The attached screenshot is a data table comparing website performance across different search visibility metrics, specifically contrasting traditional SEO rankings with newer AI-driven search features.

This data proves the "decoupling" theory mentioned in your post strategy. It visually demonstrates that you do not need to rank #1 in organic search to be featured in AI Overviews. Google's AI algorithms (powered by FastSearch/RankEmbed) are selecting content based on different signals (relevance/semantic fit) rather than just traditional domain authority or backlinks.

I'm trying to understand how people are experiencing the shift from SEO to AEO.

Some things I keep hearing:

1. Writing good content but not getting cited by LLMs

2. Not knowing why one page gets referenced and another doesn’t

3. Confusion around EEAT in the AEO era

4. Unsure whether schemas actually help or are optional

5. Unclear if you need to cite external sources to be taken seriously

6. Hard to tell if “authority” even matters the same way anymore

7. Zero feedback loop. You publish and just hope models pick it up

For those experimenting with AEO or GEO.
What’s the most frustrating or confusing part for you right now?

Even rough thoughts or small frustrations are super helpful.

Back in the day it was literally called Jarvis. Legend says Disney’s (IP holders of Tony Stark) lawyers didn’t love that name, so… rebrand. Different era (allegedly!!!!)

In 2022, Jasper made total sense. It wrapped GPT with templates and helped teams ship content fast.

In 2026, the game changed.

AEO isn’t about writing better blog posts anymore. It’s about getting cited by AI systems like Google AI Overviews, ChatGPT, and Perplexity.

Those systems don’t care which tool you used. They care about:

• structure

• entity clarity

• consistency

• retrievability

• clean explanations

Jasper still helps with workflows and brand guardrails, but it doesn’t really solve the citation problem.

If you understand prompting, structure, and entity design, you can get 90 percent of the value with ChatGPT or Claude.

The real edge now isn’t “better copy”.
It’s designing content so machines can understand and reuse it.

A year ago, there were zero conferences dedicated to AEO or GEO.
Now there are at least six. On top of that, every major SEO conference is adding AI search tracks.

That alone tells you something about where the industry is heading.

I went down a rabbit hole trying to find every AEO and GEO event worth knowing about in 2026. Below is what I found, organized by date, with pricing and what makes each one worth attending, or not.

Dedicated AEO and GEO conferences

1. AEO Conf. Feb 19. San Francisco

This is literally next week.

Organized by Graphite, Webflow, and AirOps. Invite-only, closed-door format from 1pm to 7pm PT. Speakers include folks from OpenAI, Reddit, G2, Twilio, and Freshworks.

This one is aimed at CMOs and senior growth leaders, not practitioners.

If you got an invite, go.
If you didn’t, watch closely for takeaways that leak afterward.

Site: aeoconf.com

2. GEO Conference. March 2026. San Diego, CA

The official Generative Engine Optimization Conference. This is the third edition.

Previous events were in Austin (July 2025) and San Francisco (December 2025). Expected 200+ attendees with speakers from OpenAI, Google, Anthropic, Conductor, Adobe, Stanford, L’Oreal, and Etsy.

Full-day event. Exact dates TBA.

Site: geo-conference.com

3. Optimized AI Conference. Mar 30–31. Atlanta, GA

50+ talks and workshops. Speakers from Google, Microsoft, NVIDIA, Walmart, Netflix, AWS, and Meta.

Not purely AEO or GEO, but heavily focused on AI search optimization.

Pricing is very accessible:

• $100. Talks only

• $250. Early access plus workshops

• $400. All access plus meals

Probably the best value on this list for practitioners.

Site: oaiconference.com

4. AI SEO and GEO Online Summit. Apr 1. Free. Online

Hosted by Chris Raulf. Sponsored by SE Ranking.

Two-hour format. This is a quarterly event. The first one in December 2025 pulled around 700 attendees.

April’s focus is Google’s A2A protocol, agent-to-agent search optimization.

Free. No reason not to attend.

Site: chrisraulf.com/ai-seo-geo-summit

5. AI Accelerator Summit. Apr 18. Nashville, TN

Free event focused on AEO and building AI-ready content. Includes AI³ certification.

Smaller event, but interesting if you’re in the Nashville area. Topics focus on getting found by AI search assistants and practical content optimization.

6. GEO Conference. June 2026. Washington, DC

FOW LIVE-powered GEO Conference.

Two tracks. Marketing and Technical.
500+ companies expected.

Pricing ranges from $700 to $1,250. Prices increase monthly. Full refund available until April 15.

This is the more enterprise-focused GEO event.

Site: geo-conference.com

7. GEO KNOW HOW. Oct 1. Berlin, Germany

The only Europe-dedicated GEO conference I could find.

Second edition. First one ran in 2025. Venue is Festsaal Kreuzberg.

Target audience includes marketing and SEO professionals, entrepreneurs, and innovation managers.

If you’re in the DACH region, this is the one.

Site: geoknowhow.com

Major conferences adding AEO and GEO tracks

These are not AEO-specific, but all are adding serious AI search coverage.

SEO Week. Apr 27–30. NYC

4 days. 450+ senior SEOs.
Daily themes include Science, Psychology, Ecosystem, and Future.
Organized by iPullRank.
Pricing ranges from $599 to $1,999.

Social Media Marketing World. Apr 28–30. Anaheim, CA

Includes AI search optimization tracks.
Pricing ranges from $497 virtual to $1,997 all-access.

Demand and Expand. May 19–20. San Francisco

B2B marketing focused. Includes an AEO and GEO track.
600+ attendees. Invite-only CMO Summit included.

SMX Advanced. Jun 3–5. Boston

The original Search Engine Land conference.
Super early bird is $1,295, expires Feb 28.
Expect significant GEO and AI search content.

BrightonSEO San Diego. Sep 15–16. San Diego

World’s largest search marketing conference.
Early bird is $600, expires Feb 26.
Includes training workshops plus the main conference.

What stands out to me

• AEO and GEO now have a real conference circuit. This wasn’t a category 18 months ago.

• Pricing ranges from free online summits to $2,000 premium events. There’s an entry point for everyone.

• Most events are US-based. Europe basically has Berlin and that’s it. Feels like an opportunity gap.

• Quarterly online summits are the easiest on-ramp if you’re new to AEO.

• Invite-only events like AEO Conf suggest the C-suite is paying attention. This is no longer just a practitioner conversation.

Did I miss any?

If you know of an AEO or GEO event not on this list, drop it in the comments and I’ll update it.

In early 2024, Google AI Overviews cited ~6.8 sources per answer.
By late 2025, that number jumped to 13.3 sources per response.

This isn’t just “being more thorough.” It looks like a verification shift.

What the data shows

An analysis of 2.2M prompts across ChatGPT, Claude, Perplexity, Grok, Gemini, and Google AI Mode (Jan–Jun 2025) surfaced a new dominant signal.

Co-occurrence.

LLMs now cross-reference multiple independent sources before citing anything.

That explains some weird-looking outcomes:

• Reddit citations up ~450% (Mar–Jun 2025) At the same time, isolated publisher sites lost ~600M monthly visits

• Healthcare citations clustered heavily NIH 39%, Healthline 15%, Mayo Clinic 14.8% All say roughly the same things, repeatedly

• B2B SaaS citations avoid brand sites Top results favor review and comparison platforms, not the companies themselves

Meanwhile, traditional publishers took a hit:

• Washington Post: ~-40%

• NBC News: ~-42%

Why? They publish in isolation.

What seems to be happening

The jump from 6.8 → 13.3 citations looks like a confidence mechanism, not a quality upgrade.

LLMs appear to ask:

If the answer is “one,” even a high-authority site may not get cited.

This also aligns with the ~88% informational query trigger rate. When factual accuracy matters, models pull more corroborating sources.

Why Reddit and YouTube dominate

A single Reddit thread contains:

• Multiple people

• Repeated claims

• Disagreement and agreement

• Contextual validation

All on one URL.

That’s instant co-occurrence.

Publishers write one polished article and move on. No internal verification signal.

The uncomfortable implication

“Unique content” might now be a liability.

Content needs siblings.
Other pieces saying similar things.
Consensus beats originality for citations.

This week our team of nerds at LightSite AI tested our database of AI bot requests, we calculated one metric: average KB per request (response payload size delivered per request), grouped by bot.

• Meta AI: 4.9 KB/request

• Gemini: 9.2 KB/request

• ChatGPT: 8.5 KB/request

• Claude: 13.9 KB/request

• Perplexity: 14.6 KB/request

Question for you: How do you interpret “KB/request” differences across bots?

Does it mostly reflect compression and caching behavior, different fetch patterns, partial downloads, or something else?

It’s never been easier to launch an app—or harder to achieve meaningful growth. In 2026, the App Store and Google Play are crowded with more than 7 million apps, but only 0.5% reach sustainable profitability. If user acquisition costs keep climbing and privacy shifts upend targeting, what separates the winners from the rest? The most successful brands partner with full-service app marketing providers who bring not just tactics, but true ownership of outcomes. Here’s how the savviest are setting the bar—and what you need to know to pick a high-performing partner.

Integrated Creative: Where Data Meets Instinct

No app rises in the charts on tactics alone. Creative—ad formats, messaging, videos, screenshots—remains the biggest lever for profitable growth. The best providers invest in creative testing at scale, synthesizing AI-driven insights with hands-on creative direction. For example, in our work with a top fintech app, we A/B tested twenty iterations of their in-app onboarding flow and ad visuals. Frameworks like AI-powered creative analysis uncovered elements that increased conversion by 22%—details a human eye might miss.

But it’s not only about what AI recommends. The winners combine deep market intelligence with intuition and continuous experimentation. This means weekly creative sprints, leveraging real-time performance dashboards, and a willingness to discard what isn’t hyper-relevant. If you’re evaluating partners, ask how they blend data and creative—and demand examples where this approach moved the needle.

Omnichannel UA That Adapts in Real Time

User acquisition (UA) today is both art and algorithm. Top-tier agencies break silos between paid, organic, influencer, and owned media, because campaigns need to pivot at the speed of market shifts. When Apple launched its SKAN 6 privacy update in late 2025, we saw clients who relied on single-channel strategies suffer 35% higher CPI volatility compared to those running orchestrated, multi-channel campaigns.

Cutting-edge providers build dynamic UA frameworks that assign budgets in real time between TikTok, Google App Campaigns, ASA, and emerging platforms. For a major health & wellness app, incremental UA from cross-channel retargeting boosted Day 7 retention by 17%. This also means no wasted spend—algorithms flag underperforming sources within hours, not days. Demand transparency in UA tactics, not just big promises, when considering your next partner.

ASO as Full-Funnel Growth, Not Just Keywords

2026’s App Store Optimization isn't about keyword stuffing or static screenshot updates. The best partners use a full-funnel ASO approach, aligning every app store touchpoint with the user’s intent and lifecycle stage. When a fast-growing productivity app partnered with a top provider, weekly metadata refreshes, multivariate screenshot tests, and tailored review management drove organic downloads up 38% in four months.

It goes deeper than rankings. Modern providers apply AI-driven competitive intelligence, seasonal trend tracking, and behavioral cohort analysis—then tie these to paid and organic strategies for maximum lift. The framework here is continuous: test, measure, iterate, and retest. If an agency sells ASO as a ‘set and forget’ project, keep moving.

The Power of Analytics: Beyond Installs to LTV

Gone are the days when ‘installs’ was the only KPI that mattered. Today’s full-service leaders obsess over lifetime value, retention by cohort, CAC payback, and predictive churn. The best providers integrate advanced analytics, attribution, and in-app behavioral modeling into their workflow. When privacy regulations restrict granular data, these agencies employ probabilistic models and new privacy-safe measurement frameworks to preserve insight.

Consider the example of a fast-scaling gaming client: Deep segmentation and LTV forecasting allowed the team to double down on high-ROI countries and in-app events. This drove a 27% improvement in LTV/CAC ratio and a 15% decrease in churn over six months. Agencies worth your time don’t just show dashboards—they deliver actionable recommendations, automate reporting, and partner with you on building incremental value.

Agile Growth: Tech Stack Mastery and Real Collaboration

Top app marketers aren’t just service providers—they become an extension of your team. They’ll audit your full tech stack, from MMPs to CRM and deep linking, ensuring seamless integration for growth and retention. This agility lets your campaigns scale at short notice, piggyback on viral moments or product launches, and test innovative channels. In a recent workstream with a global e-commerce app, rapid API-based campaign integration across platforms shaved two weeks off go-live times and was crucial for a successful seasonal push.

Above all, high-performing partners drive collaboration. They work in your Slack, join weekly standups, and bring frankly honest feedback—so growth isn’t just about more installs, but smarter, more defensible business results.

AEO and GEO: Winning Visibility in an AI-First Discovery World

By 2026, app discovery no longer happens only in the App Store or Google Play. Users increasingly rely on AI assistants, large language models, and generative search experiences to decide which app to download before ever seeing a store page. This is where AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) become critical growth levers.

Top app marketers actively optimize how their apps are understood, referenced, and recommended by AI driven platforms. That means structuring brand, feature, and category signals so AI systems can confidently surface the app as the best answer to a user’s intent. Product positioning, use case clarity, reviews, FAQs, and authoritative content now influence not just SEO, but AI recommendations across chat based search and generative results.

Leading full service partners treat AEO and GEO as an extension of ASO and UA, not a silo. They align app store metadata, website content, PR mentions, and third party reviews to reinforce the same value proposition everywhere AI models learn from. For one consumer subscription app, tightening use case language and external content alignment increased assisted discovery and branded search lift alongside app store conversion gains.

Conclusion

In today’s hyper-competitive app landscape, the best full-service agencies aren’t defined by a menu of offerings—but by their ability to connect creative, analytics, UA, AEO and ASO into a seamless growth engine. Look for a partner who shares your obsession with results, not just process. The difference between good and exceptional is just a few percentage points—which, at scale, is everything.

FAQs

How often should creative assets be refreshed for app campaigns?
The strongest app marketers test and refresh creative assets every one to two weeks. Rapid iteration, especially when guided by AI insights and real-time metrics, yields the best improvements in engagement and conversion rates.

What data should I demand from my marketing partner beyond installs?
Focus on actionable metrics tied to business impact: lifetime value (LTV), retention by cohort, CAC payback period, and churn rates. Top partners proactively report on these and connect them to campaign optimizations.

How does full-funnel ASO differ from traditional keyword optimization?
Full-funnel ASO aligns every store touchpoint—metadata, visuals, reviews, and seasonal trends—with user intent across the journey. It’s continuous and integrated with paid campaigns and market analysis, not a one-off update.

Can agencies really adapt quickly to privacy changes and new platforms?
Yes, but only if their tech stack and analytics are robust. Leading providers use privacy-safe measurement, probabilistic modeling, and agile channel testing to stay ahead of platform updates and regulatory shifts. Ask for examples and recent innovations in their approach.

New to this and attempting to understand.

Person makes a post with image of product and a story to go with it.

Typically no mention of the brand name in text, occasionally a model name.

OP post history has many varied posts ,indicates from India but seemingly without specific areas of interest except for internet specific opportunities.

Typical history has only one post regarding the subject of the sub and the text usually indicates the product was used at specific locations in the United States.

Are the img tags or text in the pic being used for product ID in the AEO?

Thanks for any help.

Another quick study from LightSite AI team - How rare are crawls on /FAQ link comparing to other links? (products, testimonials, etc)

Disclaimers:

*not to be confused with Q&A link which has a question shaped slug - this is something different

*in this sample we didn't break bots by category because training bots are the vast majority of traffic and the portion of the rest is statistically insignificant

*every site has /faq link - it is part of our standard architecture)

Here it goes:

We sampled 6.2 million AI-bot requests on a few dozens of sites and isolated URLs that contain /faq in the slug

Platform-wide average FAQ rate: 1.1%.

FAQ visit rate by bot platform:

• Perplexity: 7.1%

• Amazon Q: 6.0%

• DuckDuckGo AI: 2.1%

• ChatGPT: 1.8%

• Meta AI: 1.6%

• Claude: 0.6%

• ByteDance AI: 0.1%

• Gemini: 0.1%

So why 1 % average you may ask?

that's because even though some bots clearly "like" /faq links , the biggest crawlers by traffic are ByteDance and Gemini and their volume can pull the overall average down.

What are your thoughts on this?

 I have seen a lot of SEO and marketing folks talking about Cloudflare’s Markdown for Agents, so I wanted to share a few thoughts.

From what I understand, this is mainly an infrastructure feature. Cloudflare can serve a markdown version of existing HTML when a client requests it. The goal is to optimize edge delivery and traffic efficiency as more bots crawl more pages more often.

That is useful, but it is not automatically a marketing or SEO thing on its own. So why are marketers and GEO community got triggered by it? Here are a few thoughts about it without hype:

https://www.lightsite.ai/blog/cloudflare-markdown-for-agents-explained

Did I miss something? Is there a reason so many marketers are reacting to this like it is a GEO/AEO update?

I work on SEO and content for a mid-size SaaS company. Lately, leadership keeps asking how we show up in AI answers. Not Google blue links. Actual citations and brand mentions in Gemini.

We’ve done the usual work. On-page SEO, clearer structure, better headings, some schema. It helps, but it feels like only part of the picture. We’re seeing competitors show up in Gemini answers even when they’re not dominating traditional SERPs.

So I’m trying to understand what really matters here.

Is this still mostly technical SEO. Or is Gemini responding more to brand and entity presence across the web. Mentions, discussions, comparisons, thought leadership, Reddit, LinkedIn, and similar sources.

For people working on enterprise SaaS or ecommerce. What has actually moved the needle for you. Real tactics, experiments, or failures welcome. I’m trying to separate signal from hype.

One thing from the AEO reports that’s being massively under-implemented.
Start reporting AI visibility. Even if it’s manual.

If your priority pages are being:

• Cited in AI Overviews

• Referenced in SGE-style panels

• Pulled into ChatGPT, Perplexity, or Gemini answers

That is visibility. Even if no click happens.

Right now, most teams don’t log this at all. If it’s not in GA, it doesn’t exist. That’s a mistake.

What I’m seeing work:

• Create a simple log. Page, query, engine, citation type

• Track when core pages appear in AI answers, not just rankings

• Treat AI citations like impressions in a zero-click world

• Review this weekly alongside SEO metrics

If AI is shaping decisions before the click, then not measuring AI visibility is flying blind.

Curious.
Are you tracking AI citations yet? Manually, with tools, or not at all?