u/Worried-Avocado3568

Hello everyone,

i have managed multiple websites in different niche over the last 4-5 years and i would say over 60-70% websites got good results by the work i have done, but then sometimes my mind get stuck with rhose 30-40% website where i have done everything from building good contextual backlinks to building proper content cluster like everything and every test and error yet it failed to go up, i understand niche wise strategy may change but then foundational and basic things stays almost same

i want to ask SEO community what you typically do in this situation where you have done everything (content cluster, targeting low kd keywords, filling competitor gap, analysing past results and fixing issues if any, doing CRO and making website lead friendly basically everything a SEO mind suggest after deep analysis) but yet failed to achieve desired results, what's after that? what typical workflow you guys follow to figure out SEO (how to reach potential leads) for websites where Nothing much seems to work.

ok so i've done SEO for like 6 years. keywords, links, getting to the top of google. that was the whole job.

but now clients keep asking why they're not showing up in that AI summary thing at the top of the page. and i'm like... i don't fully get how that even works?? sometimes a page that's not even #1 shows up in the AI answer and the actual #1 page doesn't. makes no sense to me.

feels like i used to know how to make google happy and now i don't even know what i'm trying to make happy. an AI? how do you make an AI trust you lol

anyone else just feeling kinda lost trying to explain this stuff to clients without sounding like you don't know what you're doing anymore

I’ve hired 3 different SEO people over the years and none of them have been able to improve my website ranking much.

I’m a remodeling contractor, and now I also have someone running Google Ads. It’s been about 3 weeks with clicks and website visits, but no real leads or calls.

At this point I’m not sure if the problem is my website, SEO, ads, or just the competition.

Anyone in remodeling/home services who gets good results from Google? What actually worked for you?

I was thinking about getting into SEO this year, but I keep seeing people saying that SEO is dead, or that AI is going to replace SEO, and that it's a career with no future.

So I've been really unsure and confused about whether it's worth starting in SEO nowadays

My company is paying an SEO agency five figures a month. For that we get:

- 80 keywords a month and auditing interlinking — the only two things they seem to be doing properly

- Like 5 Reddit comments

- "Auditing and monitoring content" which means every other week, they will look at two articles and give feedback

- Buying zero click backlinks from spam sites

I'm actively pushing to get them kicked out. I'm an SEO and can take all of than on myself, because it's barely any work. I know these guys are scammers, it's fine. I just wanna know, IF they were doing actual work for us, how much would they be charging? Or, how much do SEO agencies generally charge for basic SEO services, keywords, backlinks, throw reddit in there too (not managing or creating content).

I just need a sanity check on whether that's a fair price for an agency, since I'm an in-house guy

Hi there

I'm exploring optimization practices that helps to create a visbility across AI generated platforms.

In that , When came to understand the retrieving signals of AI platforms. Almost all major platforms had similar charcteristics but the citing source is differ from one platform to another based on the keyword intent types.

On the other hand , Business owners are expect their domain visbility across all AI platforms majorly such as ChatGPT , perplexity , deepseek , gemini and more

Here my question is

Even if you cracked the optimization signals, Is it possible to appear across all AI sites If not, Then what approach you would take ? Or reasons you find behind this

Those whoever experienced the same . Please let me know your approach on it

With classic SEO there was always a clean loop: rank moves, traffic moves, you can draw a line to revenue. Google was the one channel, and that correlation made the "so what" obvious.

I've been going back and forth with a few people building/using GEO tools, and it feels like the whole category right now is indexed on measurement — tracking prompt mentions, citations, sentiment across models. That part's maturing fast.

What I haven't seen a good answer to: how do you actually turn that into a repeatable action loop?

• Once you know you dropped out of an answer at some point in a conversation, what's the real playbook — content/schema changes, seeding sources, something else?
• How is anyone tying this back to actual business outcomes, given there's no click/traffic signal like old SEO?
• Is anyone actually re-measuring after a fix to prove it worked, or is it mostly "here's your score" and manual guesswork from there?

Genuinely curious how people here are approaching this. If you've hit a wall trying it, that's just as useful to hear as a win.

It's so unironically lazy and stupid, Jesus Christ. If you want to debate, at least spend some time getting proper talking points backed with proper sources.

I will never not make fun of a person who genuinely uses the AI Overview as evidence.

This one is going to sound dumb. But I need to put it out there because the gap between what I expected and what actually happened was absurd.

Three weeks ago I was doing routine content edits on 12 pages. Not a major overhaul — just tightening language, fixing awkward phrasing, making things read more naturally. The kind of cleanup you do on a Tuesday afternoon when nothing's on fire.

On 7 of those pages, I noticed something weird after republishing. Citations started climbing within days. Not a little. A lot. Those 7 pages collectively saw a 34% increase in citation rate over 3 weeks. Our previous 20 articles — carefully researched, properly structured, published over 2 months — produced a combined 6% lift.

I went back and compared the before/after of each edit to figure out what actually changed. The pattern wasn't what I expected.

On one page, I had written "our platform helps businesses reduce operational costs." I changed it to "companies typically see 15-25% lower operational costs within the first year." That page went from zero citations to being referenced in 4 out of 10 relevant queries.

On another, I replaced "this approach is effective for most organizations" with "this approach works because it mirrors how procurement teams already evaluate vendors." Citations doubled within a week.

The edits that mattered all had one thing in common: they replaced vague, generic statements with specific, almost conversational explanations of WHY something works. Not more data. Not more structure. Just replacing marketing-speak with reasoning that sounds like how a person would actually explain it.

The edits that did nothing? They were the "professional" ones. Swapping "utilize" for "use." Tightening headers. Adding bullet points. Standard SEO polish. Those pages didn't move.

Here's why this is messing with me. I've spent months building systems around content structure, schema, crawlability, entity coverage — the technical layer of GEO. And a 10-minute wording tweak on a Tuesday outperformed all of it. Not because the technical stuff doesn't matter (it does, especially for crawlability). But because AI models extract passages that read like genuine explanations, and most of our "optimized" content reads like a brochure.

I'm not telling you to stop doing technical GEO. I'm saying maybe spend 20 minutes this week reading your top pages out loud. If a sentence sounds like it could appear on any competitor's site without changing a word, that sentence is probably invisible to AI extraction. Replace it with something that sounds like you actually explaining it to a colleague.

That's it. That's the whole insight. Felt too simple to work, but the numbers don't care about complexity.

Something I haven't talked about publicly: I spent the last quarter doing GEO audits for 14 companies across different industries. SaaS, e-commerce, fintech, healthcare, education. Not huge enterprises — mid-market teams with content programs already running.

The single most common pattern wasn't technical. It wasn't structured data issues or crawlability problems or even content quality. It was that 11 of the 14 were optimizing for one AI model and ignoring the rest entirely.

Usually it was ChatGPT. They'd run queries, check if they showed up in responses, maybe track citations over time. A few were obsessed with Perplexity. One was only looking at Gemini because their CEO uses it.

Here's why that's a problem. I ran the same 40 queries across ChatGPT, Perplexity, and Gemini for each client. The overlap in cited sources across all three models averaged 14%. Fourteen. These models are looking at different content, weighting different signals, and producing fundamentally different citations for the same queries.

One client was thrilled because their citation rate in ChatGPT had jumped 40% over two months. Meanwhile their Perplexity citations had flatlined and their Gemini presence had actually dropped. They were winning in one ecosystem and losing in two others, and because they only tracked the one where things looked good, they had no idea.

The differences aren't subtle. ChatGPT seems to weight recency and content structure heavily — clean headers, concise answers near the top. Perplexity leans toward sources with strong domain authority and multiple corroborating references. Gemini in my observation favors content that's been around longer and has accumulated external validation signals. I'm simplifying, but the point is that a content strategy tuned for one model can actively work against you in another.

The teams that were doing it right had one thing in common: they tracked all three. Not obsessively. Not with dashboards and alerts. Just a weekly check — run 10-15 representative queries across each model, note which sources show up, look for patterns. That's it. The teams doing this had adjusted their content to work across models instead of overfitting to one.

One thing I keep coming back to: the GEO conversation in this community treats "AI models" as a monolith. "AI citations" this, "AI visibility" that. But the models disagree with each other constantly, and a strategy that's working great for ChatGPT might be invisible to Perplexity users. If 60% of your potential audience is on a model you're not tracking, you're flying blind.

The fix isn't complicated. Pick 10-20 queries that matter to your business. Run them in ChatGPT, Perplexity, and Gemini once a week. Log what shows up. After a month you'll see which model you've been accidentally optimizing for and which ones you're ignoring.

Not a framework. Not a tool. Just a habit that most teams I worked with hadn't built yet.

I'm starting to think model-specific tracking should be the default in GEO, not the exception. Right now it feels like most teams are optimizing for the model their founder happens to use.

I was doing what everyone else was doing in this area, progressing little by little and learning from YouTube and blogs.

But what I struggled with most was staying up-to-date and in touch with the latest developments and what really mattered in link building. Because sometimes we're told not to do something, and the next day we're told to do it right, and then we're completely lost...

Now I've made it a habit to come here regularly to follow the latest news on SEOclaims, especially regarding link building.

Because it contains all the latest statements from Google on specific topics like link building.

It allows you to adapt immediately and keep track of where you stand ;)

At my company, we work primarily with SEO and geolocation—and you probably know this as well as I do, but the landscape is always very murky; no one really knows exactly how we’re moving forward...

The only way I’ve found to maintain this advantage is by keeping track of every official statement Google makes regarding geolocation or similar topics.

Because, for me, the best information comes straight from Google.

What do you think of my method?

And for those asking about the official statements: I can find them here [seoclaims]

Somewhere around month 4 of our GEO work, I noticed we were spending hours on stuff that *looked* productive but wasn't actually moving citations. So I ran a 60-day experiment: drop 6 common GEO practices and track what happens.

Results genuinely surprised me.

**What I stopped doing:**

**1. Writing dedicated FAQ pages.** Moved Q&A content directly into article bodies instead. Citation rate on those pages went from 8% to 19%.

**2. Obsessing over schema on every single page.** Only added structured data where we had clear entity definitions already. Citations didn't drop — they went up slightly.

**3. Creating separate "AI-optimized" content versions.** We were basically writing everything twice. Killed the dual-track approach. One version, written naturally. Engagement improved.

**4. Chasing citation volume across all models.** Picked two models based on where our audience actually hangs out. Fewer total citations, but the ones we got drove measurable traffic.

**5. Updating content weekly.** Switched to a 21-day refresh cycle. Citations became way more stable — less week-to-week volatility.

**6. Writing "authoritative" intros.** The "In this comprehensive guide..." type openings. Replaced with direct answers. Models started picking up our content faster — average time to first citation dropped from 9 days to 4.

The counterintuitive part? Most of these are things I actively recommended 3 months ago. That's what keeps this field interesting — the playbook isn't settled. What counts as "best practice" right now is still just educated guessing with good data.

Not saying any of these are universally wrong. They worked for us early on. But optimization has a ceiling, and sometimes pulling back is the actual optimization.

Anyone else simplify their GEO stack recently and see better results? Curious what you've cut.

One of the most common things we find when auditing a brand’s AI visibility is that the positioning inconsistency problem runs deeper than most people expect.

It is not just that the website says one thing and the LinkedIn says something slightly different. It is that the About page was written three years ago when the company had a different focus, the founder’s bio on a guest post from 18 months ago describes a slightly different service mix, the Google Business Profile has not been updated since launch, and the most recent press mention describes the company in a way that made sense at the time but no longer matches current positioning.

None of those inconsistencies feel like a big deal in isolation. Taken together, they create a fragmented entity signal that AI systems have a hard time resolving cleanly.

The fix is not complicated but it does require someone actually doing the work of going through every platform and every mention and asking whether the description is accurate, current, and consistent with everything else.

What you are looking for is a situation where if you asked five different AI systems to describe your brand based only on what they could find across the web, they would all give you roughly the same answer. That is what a coherent entity signal looks like.

Most brands are nowhere near that. Not because they have done anything wrong, but because positioning evolves over time and nobody has gone back to make sure the historical record has kept up.

That audit is usually the first thing we do. It is also usually where the most immediate wins are hiding.

Last year, a 31% traffic drop would've triggered panic.

This year?

Not so much.

We were reviewing performance across several sites and noticed something strange.

Organic traffic was falling.

Leads weren't.

In a few cases, revenue was actually increasing.

At first, we assumed attribution issues.

Then we dug deeper.

The common pattern wasn't rankings.

It was visibility inside AI-generated answers.

Users were getting answers from ChatGPT, Google AI Overviews, Gemini, and Perplexity before they ever clicked a website.

The old SEO question was:

>

The new question is:

>

That's a very different game.

Here's what we found.

**Site A**

* Organic traffic: -31% * Qualified leads: +18% * Revenue: +11%

**Site B**

* Organic traffic: -22% * Demo requests: +24%

**Site C**

* Organic traffic: -17% * Sales opportunities: +14%

None of these businesses suddenly became better marketers.

They became easier for AI systems to cite.

A few changes made a disproportionate difference.

**1. Original Data Beats Generic Content**

Most AI systems look for information worth repeating.

Publishing another "10 SEO Tips" article isn't enough.

Publishing a benchmark study, survey, experiment, or dataset is.

Original information creates citation opportunities.

Generic information creates competition.

**2. Entity Clarity Matters More Than Keyword Density**

Many websites still obsess over exact-match keywords.

Meanwhile AI systems are trying to understand:

* Who you are * What you do * What topics you own * Whether other sources reference you

Clear positioning beats keyword stuffing every time.

**3. Topical Depth Is Replacing Page-Level Wins**

A single article rarely dominates anymore.

AI systems often pull information from brands that have covered a topic from multiple angles.

Depth creates trust.

Trust creates citations.

**4. Distribution Is Becoming an SEO Activity**

Reddit discussions.

Industry forums.

Podcasts.

Research reports.

Expert interviews.

The more places your insights appear, the more signals AI systems can associate with your brand.

That's the part many teams still miss.

They're optimizing pages.

The winners are optimizing presence.

I'm not saying SEO is dead.

Far from it.

Search still drives huge amounts of business.

But traffic as the primary KPI feels increasingly outdated.

If an AI answer references your research, your framework, or your expertise and sends fewer but higher-intent visitors, that's probably a win.

The question I'm wrestling with now:

Would you rather have 100,000 visitors who skim your content, or 10,000 visitors who already trust you because an AI engine cited you first?

Hello, I work for a small family owned and operated construction company and was fooling around with seeing how visible we are online. Within the past 5 years we have slowly been getting with the times by utilizing more technology and automation for repetitive tasks. Doing some of those free ranking tests we score low and when i asked my girlfriend to do generic searches on her work computer we come further down the list than I would like to be. we had our website professionally made and was going to try this on my own before going back to them to help add schema for search engines and AI models. Can someone help me get started or point me in a direction to someone who is great at this? It seems like all the places that sell the service are ran with a lot of AI. I would prefer to deal with a real person in America that I can ask the stupid questions to and who actually knows what they are doing. from my limited research it sounds like this is something i could do relatively free or cost effective. FYI I do not have a strong background in coding but like to think I can figure most things out on my own or with googles help.

I've been learning more about SEO recently, and it's interesting to see how much AI is changing the industry.

AI can help with keyword research, content ideas, on-page optimization, technical SEO, competitor analysis, and performance reporting. It definitely saves time and improves efficiency.

At the same time, I don't think AI should replace human expertise. The best results still come from creating original, helpful content that genuinely answers users' questions.

How are you using AI in your SEO workflow? Has it improved your rankings or productivity? I'd love to hear your experiences and recommendations.

Most SEO work is still built around ranking. Get to page one, get the traffic.

But research shows only 17% of sources cited in Google AI Overviews simultaneously rank in the organic top 10 for the same query. Five out of six AI citations come from pages ranking 2 through 10.

The AI retrieval system is evaluating different signals than PageRank. It prioritizes topical comprehensiveness, structured formatting, and entity confidence over domain authority and backlinks.

What actually moves AI citation rates:

FAQ schema and direct answer blocks. Content structured to answer the question in the first 60 words of each section. Tables and extractable lists. llms.txt present and AI crawlers not blocked in robots.txt.

I’ve been auditing sites across these signals and the gap between traditional SEO scores and AI visibility scores is significant. Sites with strong organic rankings often score poorly on AEO and GEO. Sites outside the top 10 sometimes score better for AI citation because their content is better structured.

Pages that do get cited inside AI Overviews receive 120% more clicks than uncited competitors sitting directly beneath the module.

Worth auditing for both layers separately at this point.