We classified all 4,004 organic results from 409 buyer queries pulled on Google US desktop on September 1, 2026. Reddit is the largest single domain on page one for GTM and B2B data software: a reddit.com URL appears on 144 of the 200 usable GTM queries and holds 8.0% of every page-one slot. It also gets cited less often than it ranks. On the 74 queries where Google returned a citation list, community pages are 11.5% of the organic results and 6.4% of the citations, an index of 0.56. The only source class Google's AI Overviews reach for meaningfully harder than the blue links do is video, at 3.1% of page one and 7.4% of citations.
Last updated: September 1, 2026. Every SERP in this study was collected on September 1, 2026. This page was revised the same day after an internal fact-check; the corrections are itemised at the end.
Cleanlist ran this study, Cleanlist is in it, and the conclusion is bad for us
We sell B2B data enrichment. We chose the queries, wrote the classifier, and are publishing the result. Our own domain is one of the 560 in the GTM sample.
The uncomfortable finding is ours to own. Our main asset is on-site content, and this study says on-site content is 79.7% of GTM page one already, that it is the most crowded surface in the category, and that the one source class Google's AI Overviews over-reach for is the one we cannot publish on our own domain. cleanlist.ai holds 30 of 1,994 GTM page-one slots (1.50%) and is cited in 14 of 74 captured AI Overviews, while the word "Cleanlist" appears in 2 of them. We are read and not named.
Several vendors that appear in these tables are Cleanlist's own upstream suppliers. They are classified by exactly the same rules as everyone else, and nothing here is a recommendation to move away from them. Queries that frame a vendor with a commercial relationship to Cleanlist as something to move away from were removed from the query set before analysis. Neutral queries about those vendors, such as pricing and accuracy questions, remain and are classified like every other row.
Methodology in one box
Query sets. 409 unique queries. Set A is 206 buyer-intent queries in B2B data, enrichment, prospecting, GTM tooling and list hygiene. Set B is 203 general B2B software buying queries spanning HR, finance, security, devops, analytics, CRM, e-commerce and productivity, used as a contrast set. Set B is what remains of a 305-query file after removing the 100 queries it duplicates from the GTM set and 2 Cleanlist brand-navigational queries. Both full lists are column two of the dataset.
Collection. DataForSEO SERP API, Google Organic Live Advanced, one live pull per query, September 1, 2026. Search engine google.com, location_code 2840 (United States), language English, device desktop, depth 20 with the organic block truncated to the top 10. No personalisation, no signed-in profile.
Base. 6 Set A pulls and 2 Set B pulls returned an empty result set. We treat those as collection failures and exclude them. That leaves 200 usable SERPs in Set A (1,994 organic results) and 201 in Set B (2,010), for 4,004 classified rows.
Classification. Every result is assigned exactly one source type by a deterministic rule stack, printed in full below and stamped into the classification_rule column of the dataset so any row can be traced back to the rule that produced it.
AI Overview citations. An AI Overview element appeared on 197 of the 200 usable Set A SERPs and 193 of the 201 in Set B. Citation reference lists came back populated on 74 of the 197 in Set A and 134 of the 193 in Set B. Every citation percentage on this page uses those narrower bases. The other 123 Set A answers returned a stub with no reference list, which the DataForSEO documentation attributes to asynchronously loaded overviews requiring the load_async_ai_overview parameter that this collection script did not set.
Counting unit. A citation is counted once per domain per answer. Google frequently lists the same domain in several reference slots of a single overview, and counting those slots separately inflates whatever domain repeats most. Both units ship in the dataset: section 2 is one row per reference slot, so the slot figures are recomputable too, and the post reports the slot column alongside the de-duplicated one wherever the two disagree.
Who actually owns page one for GTM software queries?
Vendors do, by a margin that surprised us. Across the 1,994 GTM results, 1,589 (79.7%) sit on a software or services company's own domain. Of those, 868 (43.5% of all results) are that company writing about the category or about named rivals, and 721 (36.2%) are its own product, pricing, docs or educational pages. Community is 235 results (11.8%). Everything else is a rounding error: independent listicles 51 (2.6%), video 43 (2.2%), marketplaces and directories 34 (1.7%), review sites 30 (1.5%), news and research 12 (0.6%). Four out of five links a GTM buyer sees on page one were written by somebody trying to sell them software.
Does Reddit really rank on 72% of GTM buyer queries?
Yes, on the exact-hostname basis this page uses throughout. A reddit.com URL appears in the top 10 on 144 of the 200 usable queries (72.0%). Rolling business.reddit.com in as well brings it to 145 (72.5%), a difference of one query, and we report the exact-hostname number so it matches the domain column of the dataset row for row. Reddit is the number one organic result on 37 queries and inside the top 3 on 97 of them (48.5% of the corpus). It accounts for 160 individual page-one slots, 8.0% of the whole GTM sample, which is 1.9 times the second-placed domain. Community results also rank higher than anything else: mean position 4.8 and 46.4% of them inside the top 3, against 5.3 for vendors' own pages and 5.6 for vendor roundups. On roughly half of these queries a Reddit thread is one of the first three things a buyer sees.
How do you classify a search result by source type?
The rule stack runs strictly in order, first match wins, and each result carries its rule ID in the dataset. R1 video: youtube.com, youtu.be, vimeo.com, tiktok.com, dailymotion.com. R2 community: reddit.com, quora.com, Hacker News, Stack Exchange and Stack Overflow, searchfunder.com, Medium, Substack, dev.to, Facebook, X, plus any host starting community., forum., forums., answers., discuss. or ask., plus linkedin.com outside /products, plus GitHub discussion threads. R3 review site: G2, Capterra, TrustRadius, GetApp, SoftwareAdvice, Trustpilot, PeerSpot, SoftwareReviews, SourceForge and similar, plus gartner.com under /reviews. R4 marketplace or directory: AppExchange, HubSpot ecosystem, Chrome Web Store, Shopify apps, AlternativeTo, SaaSHub, Product Hunt, Datarade, Clutch, GitHub, npm, PyPI, MCP registries, StackShare, Jamstack.org, plus any host starting marketplace., apps., ecosystem. or store..
What separates a vendor's own page from a vendor writing about rivals?
R5 catches news, research, academic and government: a fixed publisher list plus any host ending .edu, .gov, .ac.uk or .europa.eu. R6 catches independent publishers with no product in the category, an explicit list of affiliate and comparison blogs. R7 is the default and treats every remaining domain as a company's own site, then splits it. A result is a vendor roundup if the URL path or the title matches best (excluding "best practice"), top followed by a number, a title starting with "Top", a title starting with a one or two digit number, alternative, vs, versus, competitor, comparison or compared. It is also a vendor roundup if the page names one of 76 competitor brand tokens that does not appear in its own hostname alongside a pricing, cost, review or accuracy marker, which is what catches "Apollo.io Pricing Breakdown 2026" on salesmotion.io. Otherwise it is the vendor's own page.
How accurate is that classifier?
We hand-checked four independent random samples of 60 rows each, 240 in total. The first three were used to find and fix systematic failures: titles like "12 ETL Tools" that carry no keyword, titles starting with "Top" and no number, and the false positive where "best practices" in a URL made an ordinary guide look like a roundup. The fourth sample of 60 was scored against the final frozen ruleset and had 2 clear misclassifications and 3 borderline calls, so roughly 3% to 8% depending on how strictly you score the borderline cases. Every error we found sat inside the vendor own page, vendor roundup and independent publisher triangle. None touched the community, video, review site or directory counts, which are the numbers this study turns on. One judgement call is worth flagging separately: we count LinkedIn posts and Medium as community. Excluding both platforms, GTM community falls from 11.8% to 10.1% and the contrast set from 12.3% to 11.0%, so the headline comparison survives either definition.
Is GTM software page one different from B2B software page one?
Substantially, and the difference is the most useful thing here for anyone planning content. Set B, 203 general B2B software queries collected the same day with the same method, looks like this next to Set A:
| Source type | GTM (1,994 results) | B2B software (2,010 results) |
|---|---|---|
| Vendor roundup or comparison | 43.5% | 49.1% |
| Vendor's own pages | 36.2% | 20.8% |
| Community | 11.8% | 12.3% |
| Review site | 1.5% | 6.6% |
| Video | 2.2% | 5.1% |
| Independent listicle | 2.6% | 4.5% |
| Marketplace or directory | 1.7% | 0.7% |
| News or research | 0.6% | 0.7% |
Community holds almost identically in both. Review sites are 4.4 times larger outside GTM, and video 2.3 times larger. GTM page one is drawn from a much smaller pool too: 560 distinct domains across Set A against 965 across Set B for the same number of slots.
The two sets are not perfectly disjoint. 34 of the 203 Set B queries are sales or GTM adjacent, including "best sdr tools", "hubspot pricing" and "salesloft vs outreach", which if anything pulls the two sets together and makes every gap above a floor. Dropping all 34 and rerunning on the remaining 169 queries barely moves anything: review sites 6.9%, video 5.3%, community 12.8%, and G2 still on 30.2% of them.
Why do G2 and Capterra barely rank for GTM data queries?
We cannot say why from a SERP snapshot, but the size of the gap is not ambiguous. G2 appears on 15 of 200 GTM queries (7.5%) and on 62 of 201 general B2B software queries (30.8%). Gartner is on 8 GTM queries (4.0%) against 48 (23.9%). Capterra is on 4 GTM queries. Across all review sites the share of page one is 1.5% in GTM against 6.6% outside it. This holds when you control for query shape: restricted to "best X" queries only, review sites are 1.4% of GTM page one (64 queries, 640 results) and 10.2% outside it (109 queries, 1,090 results), a 7.3 times gap on like-for-like intent. Of the 15 GTM queries where G2 does rank, 4 are head-to-head comparisons and 3 are "sales intelligence" head terms.
Which specific domains own the most page-one real estate?
Reddit leads with 160 slots on 144 queries. Then pipeline.zoominfo.com with 86 slots on 85 queries, which is ZoomInfo's content hub and is almost entirely category roundups rather than product pages. Then cognism.com 46, youtube.com 43, salesforce.com 32, linkedin.com 32, amplemarket.com 31, zapier.com 30, cleanlist.ai 30, apollo.io 30, demandbase.com 28, clay.com 24, salesgenie.com 24 and salesmotion.io 23. Eleven of the top fourteen are software or services companies ranking on their own domain. The top 10 domains together hold 26.1% of GTM page one. ZoomInfo occupies about 21 times more page-one real estate through its content hub than through zoominfo.com itself, which appears 4 times.
How does page one change between best-of, versus, pricing and how-to queries?
The mix moves hard with query shape. On "alternatives" queries (10 in Set A, 98 results) vendor roundups are 79.6% of page one and vendors' own pages do not appear at all, zero results out of 98. On head-to-head "X vs Y" queries roundups are 67.3% and review sites climb to 12.2%, their best showing anywhere in GTM, though that shape has only 5 queries in Set A so read both figures as indicative. On pricing queries roundups are 58.1% and vendors' own pages 30.0%. On "best X" queries roundups are 51.6%, community 13.1%. On how-to queries the pattern inverts: vendors' own pages are 55.6%, roundups fall to 15.9%, and community rises to its category high of 21.7%. On broad category head terms it is 49.7% own pages against 34.4% roundups. If you are writing a comparison page you are entering the single most crowded format in the category.
What is the gap between what ranks and what gets cited in AI Overviews?
This is the finding worth linking to. On the 74 GTM AI Overviews where Google returned a citation list, we compared the source-type mix of the 737 organic results on those same queries against the citations in those same answers. Counted once per domain per answer, those 74 overviews carry 515 distinct domain-answer citations across 210 hostnames. Counted by reference slot, the same 74 answers carry 576 slots, because Google repeats a domain inside one reference list often enough to matter. Dividing citation share by organic share gives an index where 1.00 means the AI Overview mirrors the blue links.
| Source type | Share of organic | Share of citations (once per domain) | Index | Share of slots | Slot index |
|---|---|---|---|---|---|
| Video | 3.1% | 7.4% | 2.36 | 13.5% | 4.34 |
| Vendor pages, both kinds | 78.4% | 83.1% | 1.06 | 77.3% | 0.99 |
| News or research | 0.5% | 0.6% | 1.07 | 0.5% | 0.96 |
| Review site | 2.2% | 1.6% | 0.72 | 1.4% | 0.64 |
| Community | 11.5% | 6.4% | 0.56 | 6.4% | 0.56 |
| Independent listicle | 2.7% | 1.0% | 0.36 | 0.9% | 0.32 |
| Marketplace or directory | 1.5% | 0.0% | 0.00 | 0.0% | 0.00 |
Video is the only class meaningfully over-weighted on either unit, and the two units disagree about how much: 2.36 on the honest one, 4.34 on the one that counts a repeated YouTube link twice. Community lands at 0.56 on both, which is the most robust number in the table. The news and research row rests on 3 citations, so treat it as noise, and the review-site row on 8.
Is Reddit over-represented inside AI Overviews?
No, and we expected the opposite going in. Reddit is cited in 24 of the 74 captured GTM AI Overviews (32.4%), which sounds large until you set it against how much of page one Reddit already occupies on those same queries. Community pages are 11.5% of the organic slots there and 6.4% of the citations, an index of 0.56. Google reaches for community content roughly half as often as its blue-link presence would predict. The same pattern replicates independently in the 134 captured Set B answers: community 13.4% of organic, 7.9% of citations, index 0.59 on the de-duplicated unit and 0.53 on slots. Two separate query sets, collected the same day, land within 0.03 of each other. Reddit dominates the links and is discounted in the answer.
Why is video cited more than twice as often as it ranks?
YouTube is on 40 of 200 GTM queries organically (20.0%) and cited in 38 of the 74 captured AI Overviews (51.4%). It never ranks in the organic top 3 anywhere in the GTM corpus, and its mean organic position is 8.5, the worst of any source type. When an AI Overview cites video at all it cites a median of 2 videos, up to 5, which is why YouTube's slot count of 78 is more than double its 38 answer-level citations and why the slot-based index overstates the effect. In 25 of those 38 answers, no youtube.com result appeared anywhere on that query's page one. The reference list is the only place a buyer would have seen it. In Set B the same de-duplicated index is 1.66 (6.1% of organic, 10.2% of citations), against 3.61 on slots. One honest caveat: a reference is a link Google attaches to the overview, and an attached video thumbnail is not proof the answer text was drawn from it.
Where do AI Overview citations come from if not page one?
A little over half come from page one and the rest from somewhere else. Of the 515 distinct GTM citations, 281 (54.6%) point at a domain that also ranks in the top 10 for that same query, and the mean per-answer figure is also 54.6%. When a citation does come from page one it is drawn almost evenly from every position: 32 from position 1, 36 from position 3, 24 from position 9, 24 from position 10. Ranking first buys very little citation preference over ranking ninth. The domains most often cited without ranking on that query are youtube.com (25 answers), autobound.ai (9), cleanlist.ai (7), syncgtm.com (7), salesmotion.io (6) and pipeline.zoominfo.com (6). About 45% of the citation surface is invisible to a rank tracker.
How does Cleanlist do in this corpus, and where do we lose?
We rank on page one for 30 of the 200 GTM queries (15.0%), holding 30 slots at a median position of 7, with two number one positions. Twenty-one of those 30 pages are comparison and roundup content, which is exactly the format this study shows is the most crowded in GTM. On the AI Overview side we are cited in 14 of the 74 captured answers (18.9%), fifth among all domains. Then the number that matters: the word "Cleanlist" appears in 2 of those 74 answers (2.7%). Apollo is named in 47 (63.5%), ZoomInfo in 40, Clay in 32, Cognism in 25. Google reads us fifth and names us fifteenth. We have solved ranking and citation in this category. We have not solved being named.
Which subreddits decide GTM software recommendations?
All 160 reddit.com URLs in the GTM set are subreddit threads, spread across 42 distinct subreddits, and the top ten carry 70% of them. r/sales leads with 22 results across 20 queries, then r/CRM with 20 across 20, r/b2bmarketing 19 across 18, r/SaaS 13 across 13, r/LeadGeneration 9, r/Emailmarketing 9, r/MarketingAutomation 6, r/gtmengineering 6, r/GrowthHacking 4 and r/SalesOperations 4. Five threads that held the number one organic position on September 1, 2026, with the query each one outranked everybody for: r/b2bmarketing, "best b2b contact database", r/CRM, "crm data enrichment", r/sales, "what is gtm engineering", r/gtmengineering, "clay alternatives" and r/LeadGeneration, "prospect list building software".
What should a GTM leader actually do with this?
Four things follow from the numbers, and the first one is uncomfortable for anyone whose plan is a content calendar. First, comparison content is table stakes: 46.1% of GTM page one is already a roundup or comparison page, and on alternatives queries it is 79.6%. Second, video is undervalued relative to how Google's answers treat it, at 3.1% of page one against 7.4% of citations, and it is a surface you cannot reach from your own domain. Third, review-site investment pays back far less in GTM than the category's folklore assumes, at 1.5% of page one against 6.6% for general B2B software. Fourth, the citation surface is about 45% invisible to rank tracking, so measuring only positions will miss nearly half of what Google is reading.
How do you participate in these communities without astroturfing?
Undisclosed vendor participation is against the rules of every community in this dataset, Reddit and Quora included, and subreddits enforce it with permanent bans. It also does not work: a thread that reads as a plant gets downvoted out of the ranking position that made it valuable. If you are going to show up, three rules. Disclose the affiliation in the comment itself every single time, not in a bio. Answer the question completely first, including the cases where your product is the wrong answer. Link only when the link is the answer, and prefer a number, a method or a dataset over a landing page. If your honest answer to "what should I use" is a competitor, say the competitor. Anything short of that is manipulation, and it is also the version that gets the account banned and the thread removed.
What did this page get wrong in its first version?
Four corrections, all made on September 1, 2026, a few hours after first publication and before this page was linked anywhere.
The counting unit was wrong. The first version counted every reference slot inside an AI Overview as a separate citation. On this corpus that method returns 576 slots where the honest unit returns 515 distinct domain-answer pairs, and it inflates YouTube from 38 citations to 78. The published video row moved from 13.4% of citations at an index of 4.25 to 7.4% at an index of 2.36, and the frontmatter description, the lede and the "what to do on Monday" section all carried the inflated figure. The companion citation study published the same day flagged the same unit as an error on the same answers, so the two pages disagreed with each other in print. What this does not change: the community index of 0.56, which is identical on both units, the Set B replication, the direction of the video finding, and every organic ranking figure on the page. Both units now ship, and section 2 of the dataset carries the raw reference slots so either can be rebuilt.
Two Reddit ranking figures were in circulation. The first version reported 147 of 208 attempted queries (70.7%) and 147 of 202 usable (72.8%) in the same sentence, and the title said 71%, while the companion study reported 146 of 202 (72.3%). The two are the same statistic on two bases: 147 rolls business.reddit.com into reddit.com, 146 does not. This page now uses the exact-hostname basis only, which on the published corpus is 144 of 200 usable queries, or 72.0%, and reports the roll-up as a stated alternative rather than a second headline.
The page-one overlap statistic appeared twice with two values. 53.5% was the mean per-answer slot overlap and the companion study's 54.4% was the mean per-answer distinct-domain overlap. On the de-duplicated unit this page now publishes one number, 54.6%, which is the same pooled and per-answer.
The corpus changed. Queries that frame a Cleanlist supplier as something to move away from were removed from the query set, along with the reference slots they contributed. Set A fell from 208 attempted to 206, from 202 usable to 200, and from 2,017 to 1,994 classified results. Every figure on this page has been recomputed on the published corpus rather than adjusted. The largest movements are Apollo's naming rate (49 of 76 answers to 47 of 74, 64.5% to 63.5%), Reddit's slot count (163 to 160 on the exact-hostname basis) and the GTM domain pool (561 to 560). Set B was not touched.
What are the limits of this study?
One snapshot on one day, one country, one device, one language, no personalisation. AI Overviews are non-deterministic and personalised, so a rerun will not match these numbers exactly. The Set A query list was chosen by us and is a sample of one market, not a random sample of Google. Classification is a parse of URLs, hostnames and titles, not a human read of every page, with a measured error of roughly 3% to 8% concentrated in the vendor and publisher split. The citation analysis rests on 74 answers, not 197, because the other 123 returned an AI Overview stub with no reference list, which the DataForSEO documentation attributes to asynchronously loaded overviews that require the load_async_ai_overview parameter our collection script did not set. That same gap covers the brand-naming figures: none of the 123 stub answers carries a brand mention, which is what tells us the stubs hold no answer text rather than an answer that named nobody, so both the citation rates and the naming rates on this page divide by 74. Anyone dividing them by 197 instead will understate every share by about 2.7 times.
Where is the dataset and how is it licensed?
The full dataset is at /data/gtm-serp-anatomy-2026-09.csv under CC BY 4.0. Use it, republish it, disagree with it, with attribution to Cleanlist and a link back.
Section 1 is 4,004 rows, one per query and organic position, with twelve columns: corpus (gtm or b2b_software), query, query_type, position, domain, url, page_title, source_type, classification_rule (the rule ID that fired, R1 through R7d), ai_overview_present, ai_overview_citations_captured, and is_cited_in_ai_overview (TRUE, FALSE, or UNKNOWN where no reference list was returned).
Section 2 is 1,682 rows, one per AI Overview reference slot in the order Google returned them, covering the 74 GTM and 134 Set B answers that exposed a reference list. Its columns are corpus, query, reference_slot, reference_domain, reference_source_type and reference_domain_in_query_top10. De-duplicate on query plus reference_domain to rebuild the 515 GTM citations this post reports; count every row to rebuild the 576 slots. reference_source_type reads vendor_unsplit where the R7 default fired on a domain that never ranks anywhere in the corpus, because with no URL and no title there is nothing to split an own page from a roundup on. Every percentage on this page is recomputable from those two sections. If you find an error in the classification, the row-level rule IDs will tell you which rule produced it.
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