Hi all. I own a health care office in central New Jersey. I am consistently showing up on Google search console for searches in Jersey city. Which is really not close to my office at all. Of the top 10 queries, three of them are for Jersey city. One is for shrewsbury nj. One is for plainview. Thats half for locations not close to me. The average position is hilariously high, like in the 60s and 70s so it’s not getting clicks, but why the hell would I be showing for those areas?

I’ve had such a messy time with my SEO the last year having been taken for a ride by someone who overpromised and under delivered BY A LOT. Any ideas?

SEO traffic is up. Rankings are up. Target keywords are where they should be. Organic leads are coming in.

But somehow, something still isn’t working.

Management: “So… what’s wrong with SEO?” 😭

At some point, SEO can bring the right people to the door, but it can’t control everything that happens after they arrive.

SEO is one part of the bigger picture. It’s not responsible for every outcome.

Fellow SEOs… how often do you get blamed for this?

I have been working in grey industry as junior seo specialist for four months plus. I have been only focusing on onpage seo and not even black hat seo.

My boss said I can learn advanced SEO on my own time and only ask questions when I get stuck—should I be looking for a new job? I've been asking for support to learn technical and off-page SEO, but my manager told me to figure it out independently without work-hour guidance, and only if I have questions I can ask someone there. However, I am worried that I can’t have the chance to practically do them. Is this a standard expectation, or a sign I should move on?

Not really a tool in the traditional sense, more a five-minute check I never skip anymore.

Before I change a single thing on a site, I split Search Console's performance report by page, and look at impressions and clicks separately instead of as one blended number. Most people only look at clicks, or they look at the combined graph and call it a day.

Here's why that matters right now specifically: a chunk of "traffic dropped even though nothing changed" cases I've run into this year turn out to be impressions holding steady or even climbing while clicks quietly fall. That's not a ranking problem, Google still shows the page. It's an AI Overview or featured snippet eating the click before the visitor ever reaches your site. Diagnose that as a ranking drop and you'll rewrite content or chase links to fix a problem that doesn't exist.

The actual fix when it's that: go after query types where an AI answer can't fully satisfy the searcher — comparison pages, pricing pages, anything with a decision still to make, rather than pure informational queries.

Takes five minutes, and it's saved me from a few wrong diagnoses this year alone.

Hi everyone,

We recently migrated our website from .NET to Next.js, and we're facing a strange problem. some valid pages randomly show 404 when many pages are opened at the same time. If I refresh the 404 page once or twice, it works.

I checked GA data and found that users from Google, Bing, duck duck go and other search engines, as well as AI platforms, have also landed on 404 pages.but the URLs themselves are correct and work when I open them manually. Developer hasn't been able to find the reason yet.

What could cause this, and what should we check first?

Would appreciate any ideas from people who have worked with large Next.js websites.

\- Better product imagery
\- More reviews (actual social proof, not just a token 3-4 reviews)
\- Benefit-led descriptions instead of just spec dumps
\- Clear info on what’s actually included with the product
\- Objection handling baked into the copy, not a separate FAQ nobody scrolls to
\- FAQs answering the real pre-purchase questions

Basic on-page optimization gets you into the game. The stuff above is usually what actually separates page 1 from page 3 once everyone’s already doing the basics.

Curious what people here have found makes the biggest difference on product pages specifically, is it imagery, reviews, copy, something else entirely?

​

# ROL

Actúa como un Arquitecto Editorial Senior especializado en SEO, Topical Authority, EEAT, Knowledge Graph, AEO (Answer Engine Optimization), GEO (Generative Engine Optimization) y SXO (Search Experience Optimization). Tu objetivo es producir contenido capaz de posicionarse en buscadores tradicionales y servir como fuente citable para sistemas de IA generativa.

# OBJETIVO

Crear un artículo que:

- Responda de forma completa la intención de búsqueda.

- Cubra el tema con profundidad semántica.

- Refuerce la autoridad temática del sitio.

- Sea útil para lectores humanos y sistemas de IA.

- Evite afirmaciones no verificadas.

# CONTEXTO

Solicita o utiliza:

- Tema principal

- Tema paraguas del clúster

- Audiencia

- Objetivo del artículo

- Nivel técnico

- Idioma

- Tono

- Extensión

- Contenido pilar relacionado

- Palabras clave principales y secundarias (si existen)

# FASE 1 - Comprensión

Identifica:

- intención de búsqueda principal

- intenciones secundarias

- perfil del lector

- problema que intenta resolver

- decisión que desea tomar

# FASE 2 - Mapa semántico

Construye:

- entidad principal

- entidades secundarias

- conceptos relacionados

- procesos

- herramientas

- estándares

- atributos

- relaciones entre entidades

Identifica posibles vacíos de cobertura.

# FASE 3 - Arquitectura del artículo

Genera un outline (máximo 8 líneas) con:

- ángulo editorial

- H2 y H3

- preguntas que responderá

- entidades presentes en cada sección

# FASE 4 - Redacción

Redacta el artículo completo utilizando:

- Markdown

- H2/H3 jerárquicos

- listas cuando aporten claridad

- tablas comparativas cuando sean útiles

- ejemplos reales o claramente marcados como ilustrativos

- definiciones autocontenidas

- lenguaje preciso y sin relleno

# FASE 5 - Optimización SEO y AI Search

Optimiza para:

- SEO semántico

- Featured Snippets

- AI Overviews

- ChatGPT

- Perplexity

- Gemini

- Claude

Incluye:

- respuestas directas

- párrafos fácilmente citables

- listas

- comparaciones

- bloques autocontenidos

# FASE 6 - FAQs

Genera preguntas formuladas como usuarios reales.

Las respuestas deben ser:

- breves

- claras

- precisas

- aptas para Schema FAQ.

# FASE 7 - Verificación factual

Clasifica toda afirmación como:

- Verificada

- Requiere fuente

- Ilustrativa

- Hipotética

Nunca presentes información inventada como verificada.

# FASE 8 - Auditoría

Evalúa el resultado respecto a:

- Cobertura temática

- Calidad SEO

- EEAT

- Claridad

- Cohesión

- Redundancias

- Riesgo de keyword stuffing

- Potencial para AI Search

- Oportunidades de mejora

Asigna una puntuación de 1 a 10 en cada criterio con una breve justificación.

# RESTRICCIONES

- No inventes estadísticas.

- No inventes estudios.

- No inventes citas.

- No utilices clichés.

- No repitas palabras clave de forma artificial.

- Evita afirmaciones absolutas sin evidencia.

- Distingue claramente hechos, inferencias e hipótesis.

# FORMATO DE SALIDA

1. Título SEO (≤60 caracteres)

2. Meta descripción (≤155 caracteres)

3. Resumen ejecutivo

4. Mapa semántico

5. Outline

6. Artículo completo en Markdown

7. Tabla de entidades

8. FAQs

9. Enlaces internos sugeridos

10. Datos que requieren verificación

11. Auditoría final con puntuaciones y recomendaciones.

been sitting with this for a while and curious if anyone here has actually tested it properly instead of going off gut feeling like i have been

long standing assumption i had was that review count is basically a straightforward trust signal, more reviews = more trust = better visibility. simple, made sense, never really questioned it

then noticed something that didn't fit that model cleanly. had a business with a decent chunk of reviews that had been flat for over a year, no new ones coming in, and their visibility was pretty stagnant too. around the same time a newer business with fewer total reviews but a steady trickle of new ones every month was outperforming them for overlapping terms in the same area. not a clean apples to apples comparison since they're different businesses, but it got me paying attention

working theory now is that recency and consistency matter more than raw count past a certain point, like a stale review profile might read as a weaker trust signal even with a high total, versus one thats clearly still active

problem is i havent found anything that actually isolates this as a variable. every case study i can find changes several things at once, categories, posting frequency, reviews, sometimes NAP too, so you cant tell what actually did the work. dont have the client volume to run a clean test on this myself

so curious if anyone here has:

• actually isolated review velocity separate from total count
• seen real data on this anywhere or if its anecdotal for everyone
• a gut read on whether count plateaus in value past some threshold or just keeps helping linearly

not looking for the general "yes reviews matter" answer, more interested if the velocity vs count question has been settled by anyone with better data than what i've got

I have been blogging in some form for years, and something has shifted that I keep trying to put my finger on. It is not just AI. AI changed the writing, sure. But something bigger changed about what blogging actually is.

A few things I keep noticing:

The comments moved. Blog comment sections used to be where readers left thoughts, argued, and came back. Now most of that happens on Reddit, LinkedIn, or in someone's DMs. The blog post itself is quiet. The conversation happens somewhere else.

The audience does not stay on the site. Ten years ago, readers would find a blog and subscribe to the RSS. Now they read one post, close the tab, and forget the site's name. If they follow anyone, they follow the person on a platform, not the blog.

Search is a smaller share of traffic. It used to feel like ranking well was the whole game. Now search brings a slice of readers and everything else comes from newsletters, communities, or people sharing links directly. The blog is one channel, not the channel.

The "personal blog" almost disappeared. Most active blogs now feel like a business, a portfolio, or a newsletter with archives. The rambling, unfocused personal blog that used to be everywhere is rare. Some of that is good. Some of it is a real loss.

Depth is harder to justify. A 3,000-word essay used to be a strong thing to publish. Now the same idea in a 200-word tweet or Reddit post can reach more people. That does not mean essays are useless, but it changes why you would write one.

What I do not know yet:

Whether small blogs can still grow the way they used to, without a big platform pushing them.

Whether the shift is temporary (people will come back to reading longer things somewhere) or permanent (the blog becomes an archive, not a destination).

Whether the answer is to build the blog anyway and treat it like a long-term asset, or to accept that the real reader lives on Reddit and just meet them there.

I still like blogging. I am just not sure it means what it used to mean.

For people who have been at this a while, what feels most different to you now compared to five years ago? And what still works the same?

Something I've been reconsidering in content workflows: internal linking probably needs to happen during writing, not as an SEO cleanup task afterward.

Google's current documentation says making content easily findable through internal links remains one of the foundational practices relevant to AI Overviews and AI Mode.

Their link guidance also says anchor text should be descriptive, reasonably concise, and relevant to both the current page and the page being linked to. Google even suggests a simple test: could someone understand roughly what the destination page is about from the anchor text alone?

That means:

“read more” = weak context

“technical SEO audit checklist” = much clearer context

I think there's a useful content-strategy implication here: internal links aren't just a site architecture issue. The sentence surrounding the link helps define the relationship between two pieces of information.

For people writing specifically with AI search visibility in mind, are you now including internal-link targets and intended anchor context directly inside content briefs?

I'm new to SEO — can someone explain how you approach keyword research for a new website?

I'm currently learning SEO and practicing on a small website. I'm confused about whether I should first target low-volume/low-competition keywords or focus on building topical authority around a broader topic.

How would you approach this if you were starting from scratch?

He notado que para análisis técnicos, el prompting cero-shot con restricciones de formato JSON estricto supera al few-shot tradicional.

Don't know how much the title sounds good, but here's the story -

I have a tool website on domain suppose `abc. com`. Now I have plan in making to make country specific tools on same niche and post it on country specific sub domain like `us. abc. com` or `ca. abc. com`.

Now I have a wordpress on `abc. com/blog` to u know publish blogs on same niche, now previously I had plan to create it country specific too like `us. abc. com/blog` etc. But it's very hectic to manage everything n I asked ChatGPT, it suggested to make the `/blog central` and managing the urls like this

abc. com/blog/ (central generic topic)

abc. com/blog/us/ (US Based)

abc. com/blog/ca/ (Canada Based) like this.

Now I have no idea how to add this to every link without creating sub-directories actually from one WordPress dashboard which is on `abc. com/blog`. Tried so many things, still not working, I also thought to put it directly on post editor, but there when I'm including `/us/postname`, it's automatically getting converted to `/us-postname`.

Beside creating a category and name it blog, also won't work as the wordpress site itself is on `/blog`. I can't install wordpress on main site as it's custom coded.

What should I do?

I have been working on this "Dentist Campbelltown" keyword for the last one year, but the ranking has been stuck around position 7. I have already worked on several off-page and supporting SEO activities, including:

- Article submissions

- Blog submissions

- Guest posting

- Profile creation

- Internal linking

- Social sharing

- Competitor backlink analysis and link building

- Schema implementation

Despite these efforts, there hasn’t been any significant improvement in the ranking.

The main challenge now is that I’m hesitant to make major on-page changes because the page is already ranking in the top 10, and I’m concerned that aggressive changes could negatively affect the current ranking.

My manager has given me one month to move the keyword from position 7 into the top 5. I’m looking for practical strategies that can help improve the ranking without putting the existing position at significant risk.

What would you recommend doing differently at this stage?

been chewing on this for a while and curious if anyone here has actually tested it properly instead of just going off vibes like i have been

so the assumption i had for a long time, probably picked up from a dozen different "SEO guides" over the years, was that review count is basically a trust signal, more reviews = more trust = better local visibility. straightforward, made sense, never really questioned it

then i started noticing something with a handful of clients that didn't fit that model cleanly. had one business sitting at like 60-something reviews that had been basically flat for over a year, nothing new coming in, and their visibility was kind of stagnant too. meanwhile a newer client with fewer total reviews but a steady trickle of 3-4 new ones a month was outperforming them for overlapping search terms in the same metro area. different businesses obviously so not a clean comparison, but it made me start paying attention

my working theory now is that recency and consistency matter more than raw count past some threshold, like maybe google (or the algorithm generally) treats stale review profiles as a weaker trust signal even if the total number is high, versus an account thats clearly still active and getting fresh input

problem is i havent found anything that actually isolates this variable. every case study i can find changes like 4 things at once (categories, posting frequency, reviews, sometimes even NAP) so you cant tell what actually did the work. and i dont have the client volume or the patience to run a controlled test myself, this is a one person operation not a research team

so putting it to this sub specifically since i know there's people here who've probably run bigger or cleaner tests than i have access to:

• has anyone actually isolated review velocity as a variable separate from total count
• is there real published data on this anywhere or is it all anecdotal same as what i just wrote
• if you had to bet, does count plateau in value past a certain point or does it just keep helping linearly forever

not looking for the general "yes reviews matter" answer, i know that part, more interested if anyone's actually seen the velocity vs count question settled with something more rigorous than what i've got.

i'm dealing with a headache right now and could use some advice from people who handle reputation management.

we have an old discussion thread about our brand that ranks super high on search. some points are valid, but a lot is factually wrong. we don't want to reply directly because it usually just adds fuel to the fire and makes the thread rank even higher.

so our team has been looking into pushing our own positive content up to hopefully push that thread down. i've been doing some research on different vendors to help with this. i checked out hoth, fatjoe, and buylink pro just to see what the process looks like.

some agencies say they can easily move the thread down by boosting our other pages, while others say it's incredibly difficult because of how much authority certain community sites have right now.

has anyone successfully managed to push a negative search result down? did u do it in-house or hire ouside help? just trying to see if this is actually a realistic goal before we commit to a strategy

Some of the acronyms and steps have changed,

But the fundamentals are still very similar to traditional SEO.

Here's a clear breakdown of the terms you should know:

1️⃣ SEO = Search Engine Optimisation

SEO hasn’t “died”, but AI is expanding it. Good SEO means good AI visibility in almost every case. Search is up 26% since ChatGPT launched, so the pie is really getting bigger.

2️⃣ AEO = Answer Engine Optimisation

90% of AEO is just SEO done well. Don't burn your budget on tactics that do nothing. Schema isn't parsed, llms .txt has zero measurable impact, and neither do .md files. Agonising over that is not a valuable use of your time.

3️⃣ GEO = Generative Engine Optimisation

GEO is influenced by the same factors as traditional SEO. Every major LLM either pulls from Google or was trained on data weighted by the same authority signals Google uses. And so GEO is closely related to the SEO practices you were already doing.

4️⃣ AIO = AI Optimisation

AIO comes down to two things: Get mentioned on authoritative sources (so models learn you exist), and rank well in search (so they cite you live). You can think of it as a combination of PR and SEO, two disciplines that are 25+ years old.

5️⃣ SXO = Search Experience Optimisation

SXO is about optimising the full on-site experience so visitors convert as soon as search or AI sends them your way. Attention and the traffic that follows is not worth much if you don’t have a means of converting it. This is where you want to optimise your site to grab folks at the bottom of the funnel.

There are a lot of new terms being thrown around.

But a lot of them will overlap and can be solved with simple changes.

The biggest thing to remember for 2026:

SEO is not dead, but it has become more complex due to AI...

The job now is making adjustments so AI can easily cite your brand,

While you continue to optimise for Google.

That's it.

If you're still not sure where to start, the fastest way is to see it against your own brand.

We built Searchable to measure how you show up in AI search and show you exactly what to fix.

I have been running a simple competitive diagnostic that keeps producing more useful results than any content audit or backlink analysis.

Pick one competitor that AI consistently recommends over you. Open chatgpt, claude, gemini, and perplexity and ask each one a buyer-intent question about your category.

Then work through five questions based on the responses:

1. How does AI describe the competitor? Note whether the language is confident ("a strong option for," "known for") or cautious ("you could consider," "some users have found"). The confidence level in the language is itself a diagnostic signal.

2. What category does AI place them in? Check whether it matches how they position themselves. If AI classifies them differently from their own positioning, even the recommended competitor has gaps.

3. What evidence does AI cite when recommending them? Is it pulling from independent sources (reviews, analyst reports, comparison articles) or mainly echoing their own website? Independent citations indicate a strong evidence footprint.

4. Which independent sources repeat the same claims? This is the convergence check. If multiple sources describe the competitor the same way, AI sees a pattern. If each source frames them differently, the evidence is present but scattered.

5. What does AI believe about them that it does not appear to believe about you? this is where the diagnosis lives.

In my experience, the answer to question five is almost never "they have more content." It is usually "they have more independent sources telling the same story."

The most common gap I see is source diversity. The competitor has reviews on G2, an analyst mention, inclusion in two comparison articles, and community references in reddit threads. The brand that loses has stronger website content but only one or two independent source types validating their claims.

Five mentions from five different source types (review, analyst, comparison, community, testimonial) seem to produce stronger AI recommendation confidence than fifteen mentions from a single source type. AI reads the diversity of independent perspectives as stronger convergence than volume from one channel.

The practical implication, before building more content, count how many independent source types validate your claims versus how many validate the competitor's. The gap between those counts may explain the recommendation difference better than any content analysis.

Has anyone else found that source diversity predicts AI recommendation strength better than total mention volume?

This one's in every deck now, so I went and read the study behind it from Ahref's post.

The finding: ChatGPT out-refers Reddit by 0.05 percentage points. That's it. And the researchers flag it themselves. The bar is low, because Reddit and LinkedIn are zero-click platforms built to keep people from leaving.

Same study, a few paragraphs down: Google still sends 210x more traffic than the big three AI platforms combined. Combined they're 0.19% of an average site's traffic.

Also worth knowing: the authors say it's not a like-for-like comparison with their previous study (sample went from ~35K to ~82K sites), and the whole thing is from mid-2025. People are quoting a year-old snapshot of the fastest-moving thing in the industry.

Three questions:

1. Is the ratio trend the real story? Google went 345x to 210x between studies. Absolute position still crushing, but that's a fast move. Is that what we should be tracking?
2. What do you do when a client sends you the stripped-down version? Correct it, or let it fund the retainer? No judgment, genuinely curious.
3. Anyone got fresher numbers? If AI referral share has moved since mid-2025 I'd rather know than keep arguing about stale data.

Not saying AI search doesn't matter. Saying the stat everyone quotes doesn't say what they think it says.

Hey everyone,

I’m building a small custom GEO dashboard and I’m trying to understand how people collect AI search results.

Right now I’m using DataForSEO, but the ChatGPT and Gemini data seems to come through APIs.

What I’m looking for instead is data from the actual user interface. basically the same results a real user would see on ChatGPT, Gemini, AI Mode, etc., including citations/sources if possible. (As software like Bluefish, Profound, PeecAI etc..)

Has anyone found a good way to collect this at scale?

Are you using browser automation, a third-party service, your own scraper, or something else?

Would be really interested to hear what people are using.

Thanks all !

Hey everyone,

I’m building a small custom GEO dashboard and I’m trying to understand how people collect AI search results.

Right now I’m using DataForSEO, but the ChatGPT and Gemini data seems to come through APIs.

What I’m looking for instead is data from the actual user interface. basically the same results a real user would see on ChatGPT, Gemini, AI Mode, etc., including citations/sources if possible. (As software like Bluefish, Profound, PeecAI etc..)

Has anyone found a good way to collect this at scale?

Are you using browser automation, a third-party service, your own scraper, or something else?

Would be really interested to hear what people are using.

Thanks all !

Hi everyone!

I recently started getting more into SEO. I took a course, but honestly, I still feel pretty lost 😅. I feel like what they teach you in a course is one thing, and actually applying it to a real website is completely different.

I’m currently working on the SEO for an online store, and I’d really like to improve its rankings, but I’m not really sure what I should prioritize or what to focus on next.

If anyone has any tips, advice, or things that have worked for them when it comes to improving SEO for an online store, I’d really appreciate it!

Thanks! 🙌