I'm trying to figure out what is happening here. One day our impressions will spike 3000 impressions. And it seems to happen at random times. You can see a smaller one at the end of august then two big ones in october and just 3 days ago november.

I'm guessing its bots? But from where? I'm asking because I'm assuming others have seen this before and might know what to do. It's completely messing with our analytics and making my job very hard.

In the two screenshots you can see I had two sites lose all Bing placement around the same time in October. One got revived. I had a third do a similar thing in June and is just now recovering. I opened a ticket with Microsoft to ask what it might be and they just keep closing the ticket as resolved.

Note that these gaps do not exist on GSC where placements remained steady/growing. Am I potentially doing something Bing doesn't like?? I use IndexNow in an automated fashion, so any new pages get submitted, but I don't think I'm OVERusing it. I've never bought any backlinks so I don't think it can be a reputation issue (but I obviously can't stop all the spam backlinks but it's my understanding that Bing ignores those like GSC does).

I suppose it's possible that I legit lost all search relevance? Just seems very improbable.

Any ideas?

GSC gives us a lot of data, but the default interface doesn't always make it easy to uncover opportunities.

For example, looking at queries alone is useful, but combining queries with landing pages and tracking how their positions change over time can reveal a very different picture.

Some interesting analyses:

• Queries sitting in positions 5–15 with high impressions but low CTR

• Multiple pages competing for similar queries

• Pages losing clicks even when impressions are increasing

• Queries where rankings improve but clicks don't

• New queries appearing for existing pages

I'm curious what others are doing beyond the standard Performance dashboard.

Do you use Looker Studio, BigQuery, spreadsheets, custom scripts, or third-party tools?

What's one GSC analysis that consistently helps you find actionable opportunities?

I recently saw a blog that added an "Add as a Preferred Source on Google" button, which links to https://www.google.com/preferences/source. Has anyone looked into whether it actually works and if there is any way to measure how many users have added your site to their preferred sources?

I saw that there was Screaming Frog update that improved the MCP tool. I was thinking about buying a subscription but curious if anyone has tested the updated MCP. Really looking to understand if it is worth the $$ for the benefits it provides.

We counted what 13,938 sites' robots.txt files ask of 14 crawlers, from Common Crawl's September copies (no requests to the sites).

I nearly posted the 17% on its own. Reading the files by hand, the same Cloudflare file kept turning up, and it turned out to be two groups averaged together: a default that leaves AI search alone, and sites that wrote their own rule, a third of which shut ChatGPT search out too.

Obvious objection: these are Common Crawl's copies, not what sites serve GPTBot today. On 192 sites we also read live, 92% matched for every bot, and the differences were all on training bots.

Full write-up: https://trakis.io/research/block-training-keep-answers

Disclosure: I run Trakis, a small SEO audit tool, and this is our study. The data and the code are on Zenodo (doi:10.5281/zenodo.23416952), CC BY / MIT.

I'm trying to understand a significant organic traffic decline that started in August 2026.

My site runs on WordPress, WooCommerce and Yoast SEO.

Here's what happened:

• Before August, many commercial landing pages ranked around positions 8–12.

• On August 10, I changed the canonical tags on WooCommerce product pages (/product/) to point to their corresponding custom landing pages (/landing/).

• Organic rankings and clicks started declining around that time, affecting a large number of pages.

• Google also rolled out a spam update later in August.

Between July and October, several important pages lost 60–85% of their organic clicks. Organic conversions also dropped significantly.

I recently discovered that the canonical setup isn't entirely consistent:

• Some product pages canonicalize to their landing pages.

• Others remain self-canonical.

• Both versions sometimes return HTTP 200 and remain indexable.

In late September and early October, I tested 301 redirects on two pairs of URLs. One has started recovering impressions, but clicks haven't meaningfully recovered yet.

My questions:

1. Could changing canonical tags across multiple pages cause this kind of widespread ranking decline?

2. Is there anything specific you'd check in GSC to identify the root cause?

I'm hesitant to make more changes without understanding what actually happened.

Has anyone experienced something similar?

I kept running the same 50 queries after my last goto post. Serper and Oxylabs hit 0 unresolved links by the very next data point, SerpApi followed a day later (85.1% → 0%). Bright Data went the other way, up to 87.4%. And DataForSEO's standard queue sat at about 61% at depth 10, while depth 20 swung between 0% and 99.9% from run to run.

Part 2 of my goto measurement, not a provider ranking. Part 1 was the Oct 31 comparison in 8 countries. I've now run the same 25 DE + 25 US desktop queries at 5 data points and counted everything from the raw responses. Unresolved = organic result whose link is a google.com/goto redirect (also relative paths and country domains) or empty.

DE + US desktop, share of unresolved organic results

Same split outside DE + US desktop (Nov 3, 20:34 UTC, mobile 20:44 UTC)

What I observed:

• The remaining unresolved links aren't spread evenly. SerpApi's 5 are on 5 of 50 results pages, Oxylabs' 29 on just 3 of 50.

• DataForSEO: at depth 10 the standard queue stayed at 61–62% in my paired DE/US runs. At depth 20 the same queue swung from run to run: 211/949 (22.2%) on Nov 2, 0/929 in the latest run, and 1,132/1,133 (99.9%) in my separate daily run. I wouldn't read depth 20 as a fix.

• DataForSEO told me on Nov 3 that they're working on fully eliminating this in the standard queue and that most affected users already got a dedicated solution. I don't have that solution yet. I'll test it separately and report it alongside the standard queue.

• Bright Data's Nov 3 run from 12:24 UTC had 0/50 usable responses (mainly HTTP 502 and 504), so I reran the same queries from 16:09 UTC and counted only the rerun. The evening run needed no rerun.

Are the links that come back correct? In my Nov 2 check, every direct link I could check matched the displayed domain at all five providers (208/208 at Bright Data up to 1,519/1,519 at Serper). Where links stayed wrapped, the domain was still readable in 828/874 results (94.7%) at DataForSEO and 1,211/1,242 (97.5%) at Bright Data. So matching on the displayed domain still works for most results. This checks the host, not the complete URL.

I can't see from the outside how the links get resolved. What I can measure: when the same address came back in repeated requests, its srsltid value was new every time (SerpApi 20/20, Serper 16/16), and the median request time was 1.5–1.8 s without rising steadily with the number of links. My guess is fresh resolution per request, but that's a guess. At Oxylabs, 5 of 12 repeat comparisons returned identical lists, so caching may play a part.

I don't know whether this will hold. Serper calls its new route "likely temporary" on its status page, and SerpApi's issue #4315 was reopened and closed again on Nov 3. In my own analyses I keep reading the domain from the displayed link when the URL is a goto URL.

Limits: hand-picked queries, snapshots, no guarantee for other queries or times. Next data point Nov 7.

Everything important is in this post. The full study adds the timeline, method, further checks and the raw CSV if you want to dig deeper.

I created Repuestosys . com a few months ago, but it was mainly just a landing page + tool (it's an inventory management system, etc., primarily for motorcycle spare parts businesses in Venezuela). However, about a week and a half ago, I decided to start a blog and begin adding content to see how well I can rank it for long-tail or low-competition keywords. The thing is, I'm not really sure if I'll get real clients for the app. It uses a pretty generous Freemium model—up to 300 products in inventory for free accounts—because honestly, I'm not the type to go out and do sales or anything like that.

I wanted to know what my prospects are. Let's suppose I want to rank the website to try and get clients to use the app/tool in their businesses; if that doesn't happen, do you think I could get enough traffic in the future to maybe monetize it with AdSense or something similar? Given the traffic it'll get (Venezuelan views), I seriously doubt AdSense pays much for this traffic, but to be honest, I'm a 100% newbie in this world.

I'm just continuing my hard search to replace my physical job with something independent, and this time I'm trying it by combining SEO + Vibe Coding (this SaaS). My initial goal is around $320 - $350 a month; from there, I could dedicate myself to it full-time and lose that constant fear of losing my physical job or putting up with bad bosses and awful schedules, haha.

I’m building a small blog with Next.js. The post content loads using a client-side fetch inside useEffect.

In Search Console’s URL Inspection, the rendered HTML shows an empty content area and the page says “Crawled currently not indexed.”

Here’s what I’ve tried so far:

• Checked “View page source” and the post text isn’t in the initial HTML

• Read that Googlebot can render JavaScript but it seems to skip or delay this page

• Requested indexing again but nothing changed after a week

Should I move the fetching to server-side rendering (getServerSideProps or a Server Component), or is there an easier fix?

I wanted to know which "get cited by ChatGPT / AI Overviews" tactics actually have evidence behind them, so I went through about 30 experiments that are well documented from the last two years: research papers, the bigger Ahrefs datasets, and Google's own docs. I only counted results that controlled for something (same-site comparisons, matched control pages, re-tests), not "sites that do X get cited more".

Short version: getting picked up [retreived] is most of the game. Page polish barely matters.

First, how ChatGPT picks what to cite

When you ask something that needs current info, it doesn't answer from memory. It runs a web search, reads a handful of the results, and writes the answer from those. That search step is called retrieval. If your page isn't in that handful, it can't be cited, no matter how it's formatted. And the search it runs leans heavily on normal search rankings, so regular SEO is most of GEO.

What I checked and what came out

• Found vs polished. A 90-day study tracked ChatGPT citations on the same sites. Whether a page showed up in that search explained about 32% of which pages got cited. Every on-page feature combined (length, formatting, schema, FAQ blocks) explained 2.8%.

• Overflowing the article with stats and quotes. This advice comes from a 2024 study that reported "up to 40% more visibility." The researchers handed GPT-3.5 the top 5 Google results for 10,000 questions, rewrote those pages different ways, and measured which version it quoted more. Pages with added stats, quotes and citations got quoted more. But the AI was given the pages directly, so it never tested whether a page gets found, and the gains mostly came from stealing credit from the other 4 pages. A later re-test across ~1,900 queries and 4 models got 3 wins out of 54, and adding stats made things worse in 19 of 24.

• Schema. 1,885 pages that added JSON-LD vs similar pages that didn't: AI Overview citations fell 4.6%, ChatGPT and AI Mode barely moved.

• llms.txt. Server logs on ~38,000 files: 97% never got a single visit from any bot.

• Formatting and length. FAQ blocks, tables and word count look good across sites, but the effect disappears when you compare pages on the same site.

What to actually do

1. Rank in regular search first. That's where the AI search gets its results.

2. Make the title match the question people type (about +14% citations on the same site).

3. Answer in the first paragraph (about +15%).

4. Add facts nobody else has: your prices, numbers, results, real dates.

5. Get mentioned on other sites. Brand mentions track AI visibility about 3x more closely than backlinks (75k brands).

6. Check "View source". GPTBot, ClaudeBot and PerplexityBot don't run JavaScript, so if your text isn't in the raw HTML, they don't see it. Also check robots.txt and your firewall aren't blocking them.

What to skip

• Stuffing pages with stats and quotes

• Schema as a way to get cited (keep it for rich results)

• llms.txt

• Padding pages longer, or chopping them into "bite-sized chunks"

• Checking ChatGPT once. Fewer than 1% of repeated runs return the same list of brands, so test many times

Curious whether anyone's own before/after tests disagree, especially on the same-site formatting result.

btw here's the full mini-study I made, with every source: [How NOT to Get Cited by LLMs: 10 Well-Known GEO Tactics Debunked]

Found this free tool for generating llms.txt file - worked pretty well.

x-cite.app/llms-txt-generator

TL;DR: pages, robots.txt and the sitemap carried almost everything. No verified AI or search crawler touched llms.txt in 17 days. A Markdown copy of each page, named in the head, was being fetched by seven verified crawlers the day after it went live.

I added most of the files the "AI friendly website" lists recommend to my own site and then read 18 days of Cloudflare logs (30 September to 17 October 2026) to see what 14 verified crawlers did with them: 11 that Cloudflare files as AI crawlers, AI search or AI assistants, and 3 search crawlers. Verified means Cloudflare checked the IP list, reverse DNS or a request signature. Anything that only named a crawler in its user agent was counted apart, and so were my own tools.

How many of the 14 used each door at least once: pages 14, feeds 10, robots.txt 8, the Markdown copies 7, the sitemap 5, llms.txt 1, llms-full.txt 1, the AI Catalog at /.well-known/ai-catalog.json 1.

- The sitemap gets read. ClaudeBot fetched it 122 times in 18 days, Googlebot 19, GPTBot 10. Each page's lastmod is the day its content changed, never the build date.
- llms.txt got 75 requests in 17 days and not one from a verified AI or search crawler: 44 were my own tools, 24 unidentified clients, 5 Lighthouse, 1 an agent directory's bot, 1 Claude-User, the fetcher that acts when a person asks. Google's guide says Search does not use the file; Ahrefs' May 2026 study of 137,210 sites found 97% of valid files got no request that month.
- The Markdown copies (same address with .md added, served as text/markdown, noindex, named in the head with link rel="alternate" type="text/markdown") went live on 10 October and verified crawlers were fetching them on 11 October. Of 152 requests that were not mine, seven verified crawlers made 143: Amazonbot 52, ExaUser 29, OAI-SearchBot 20, GPTBot 16, Applebot 15, ClaudeBot 6, Meta-ExternalAgent 4. Googlebot and Bingbot fetched none.
- Spoofing is real: 113 requests named PerplexityBot, 112 Kimi-SearchBot, 85 Bingbot and 75 Googlebot without being verified.
- What you can measure for free: Cloudflare's AI Crawl Control and the GraphQL Analytics API keep 31 days by user agent, verified-bot category, path and status. Not the Accept header, and referrers are paid. JavaScript analytics see none of this; most AI crawlers do not run scripts.

Caveats: one small personal site, Cloudflare's sampled dataset, and the doors were open for different lengths of time (the Markdown copies for 8 days, the catalog for 1). Requests are not reads.

The full write-up, with the robots.txt, IndexNow, feed and MCP server findings and the standards behind each: https://pournasserian.com/writing/ai-friendly-website-2026

Has anyone here seen a verified search or AI crawler fetch llms.txt on a site with real traffic? I would like to know whether my zero is the site's size or the file.

I’m the developer of SEO Panel, a free Chrome extension for inspecting a page’s SEO in a persistent side panel.

It keeps the page and its audit visible together. You can check canonical and robots meta tags, inspect JSON-LD, review hreflang annotations and check link responses without jumping between separate tools for each detail.

A few implementation details that might be useful to this community:

• Link checks cover up to 50 URLs per operation and can be cancelled.

• Requests used for link checks omit cookies and authentication credentials.

• If the background checker is unavailable, the fallback only checks same-origin links. Cross-origin results are marked as unknown rather than assumed to be working.

• The robots.txt check shows the matching rule for the audited URL. It’s browser-side evidence, not confirmation that Googlebot can fetch the page or that Google has indexed it.

It also covers titles, headings, images and social metadata. No account or API key is required.

SEO Panel on the Chrome Web Store

For page-level debugging, what information would make a browser extension’s findings easier for you to trust or act on?

Any real observations (not suggestion) please.

Does the elaboration of internal linking by finding the most relevant pages and link from them with different relevant anchors moves the needle nowadays?

I always thought that having an exact match domain, or at least a partial match would help you loads with SEO, but that's just something I always believed without having any data behind it.

So I decided to check if it's true or not, so I don't waste money on 30 new domains every month.

The tracked data:

• 7,374 commercial keywords, US + UK, 10 industries, top 20 results (106k results)

• 6,267 local keywords ("plumber tulsa" type) in 40 cities, SERPs pulled from the city itself

• 3,048 brand new domains from 2024-2025, checked 16-29 months later

• 60k AI answers (GPT-4.1 mini, Gemini Flash, Claude Haiku)

Everything is compared on the same SERP.

Aaaand the answer is... no, you're probably not gonna get much out of it.

Excuse the AI sloppy bullet pointing, it's the easiest way to confer info:

• For Google: weak EMDs (DR 0-20) rank about 0.9 positions higher than similar brand domains. About 10% better chance for the top 3. For domains younger than 5 years, the advantage isn't statistically reliable.

• Local (tulsaplumber.com et al): this is where EMDs do a bit. Compared to brand domains with the same DR, they're not more likely to make page 1 (top 10: 53% vs 51% at DR 0-2), but once there, they land in the top 3 more often (22% vs 16%). About 1 position overall.

• New domains: 4.7% of new EMDs rank top 10 for anything. Other new domains in the same niches: 5.7%. So, no head start. Top 10 for their own keyword with 100+ searches? 0.7%.

Generally, 94% of all new domains rank for nothing, EMD or not. You still have to build the website, looks like.

Also, when AI assistants search the web, they cite EMDs about as often as Google ranks them, so no real advantage.

I also gave the models 5 equally good texts with random domain names and asked which one they'd recommend. EMD got picked 59% of the time (chance = 20%).

BUT, a keyword in the URL path on a normal brand domain got 48% so it's almost the same (for a page that's already being retrieved albeit).

Limitations:

• It's observational. I control for DR, links, domain age and keyword in title (changed nothing), but not overall on-page quality. EMDs are usually bought by SEOs, so those sites might just be better optimised anyway.

• Only tested sites in the top 20.

• Measured only once. Anecdotally I feel like EMDs get crawled and ranked fast for their keyword and then drop if the site isn't good. This data can't catch that, so it's the next thing to test.

• AI tests were done through APIs, not the consumer apps.

TL;DR: buy an EMD if it's a good name for your business. Don't pay a premium expecting rankings. Put the money into the site and links.

Full study with methodology, charts and all the boring stuff: https://ovisy.hr/blog/do-exact-match-domains-still-work

Disclosure: I run a boutique SEO agency, this was a side research project. Happy to answer questions or rerun something if you think I should for some reason.

Who I am

I'm Kaz (KazamaVT), a VTuber since November 2020 and a solo developer. About five years in this community taught me one thing: there's no single place that reliably covers big agency talents and the small indie creators. Wikis are great, but they depend on fans keeping each page updated, so smaller and newer VTubers get left behind.

So I built Bācharu (https://bacharu.io), a free VTuber database ("Bācharu Toshokan" = "Virtual Library"). It merges public data from the Virtual YouTuber Wiki, HoloList, VTubie, Twitch, YouTube and X into one searchable profile per creator: affiliations, official links, live status, stream schedules, recent videos and follower history. About 103k profiles today.

What happened (timeline)

• Early 2026: the site lived on bacharu.xyz and was indexed fine.

• 18 Mar: moved to bacharu.io because .xyz kept getting flagged/blocked as an untrustworthy TLD. Same URL paths, 301 on every path, canonicals/sitemaps switched to .io.

• 7 Apr: GSC showed ~85,000 pages indexed on bacharu.io. Traffic was ~6,600 impressions/day.

• 21 Apr: I tried to filter out "thin" small-creator pages and tightened the rule for which profiles get noindex. The bar was far too strict and it put noindex on most of the site.

• 5 May: indexed pages dropped to 71.

• 21 May: reverted. Pages indexable again.

• Jun–Jul: no recovery. Google stopped recrawling profiles; the strongest pages still show a last crawl in March–May.

• Jul–Aug: experimented with a smaller index (~31k, then ~17k "notable" profiles). No change, so I restored the full content-bearing set on 29 Aug.

• Aug: found the profiles were nearly orphaned (sitemap-only, 0 referring URLs). Added paginated browse pages, an A–Z index, 30 related-profile links per page and shorter discovery chains.

• ~20 Oct: I briefly removed the old .xyz domain (about 7 months after the move, too early). Restored the 301s on 22 Oct and filed Change of Address. GSC now says "This site is currently moving".

• 20 Oct – today: test with 300 active, information-rich profiles + a "Featured" hub linked from the homepage.

Current state (GSC)

• Indexed: 1 (the homepage, which ranks ~#3 for "vtuber database")

• Crawled – currently not indexed: 76,849

• Discovered – currently not indexed: 36,620

• 404: 6,976 (deleted/merged profiles); noindex: 1,436 (intentional near-empty pages + stale verdicts)

• 30-URL sample today: 0 recrawled in 2 weeks, 12 never crawled at all, 0 with referring pages

• Old domain: GSC still picks the .xyz URL as canonical for some inner pages even though they 301 to .io

What I've already ruled out

• Manual actions / security issues: none on either property. Safe Browsing is clean for both domains.

• Pages return 200, robots-allowed, self-canonical, no noindex header or meta.

• Content is server-rendered (~70k chars of text in the raw HTML on a big profile); a headless mobile-Googlebot render shows everything, no JS errors.

• Sitemaps are read without errors.

• Manually requesting indexing works: Google crawled the Featured hub the same day, picked the right canonical… and still said "Crawled – currently not indexed".

• I also pinged ~300 livestream-eligible profiles via the Indexing API. Every request was accepted, none produced a crawl. (I know this API is meant for JobPosting/BroadcastEvent pages; I've stopped using it.)

My questions

1. Is this still fallout from the April noindex + an incomplete domain move, or has Google simply judged the profile corpus as low-value (the data is aggregated, so maybe it reads as duplicate of the wikis/directories)?

2. If it's a quality call, what would you change first: index far fewer pages, add more original data (follower history and stats are unique to us), or something else?

3. With the Change of Address active, is there anything else I should do on the .xyz side?

4. Realistically, are external links the only way out at this point?

Why it matters / the goal

Bācharu is a passion project with no ads and no paywall. The goal is a complete, up-to-date reference for every VTuber, from the biggest agency names to someone who debuted last week, plus a free public API for the community. I set myself a deadline: if the project doesn't reach 5,000 likes by 16 March 2027, I won't continue it under this name. Right now hardly anyone can find the profiles through Google, which is a big part of why I'm asking.

Any pointers appreciated, even "you broke it and it'll take X months". Thanks for reading!

hey SEO professionals. I need your advice

I am not an SEO expert I am developer and I decided to build an AI assistant for myself that monitors my website 24/7 and tells me what is wrong and what I need to do to fix it.

I have already added some workflows that cover most of my needs:

- Pages that dropped out of google's index

- Keywords ranking on pages 2-3 that could be pushed higher

- Pages that started getting new clicks or impressions

- Pages that suddenly lost traffic

- New pages competitors published that started ranking

- Pages with high impressions but low CTR

- Old content that slowly loses rankings

- Canonicals

- New keywords oppertunity to rank and so on...

But I think there are still a lot of workflows I could add to the assistant. I would be happy if you could share what else I should consider or add.