80%+ of B2B AI citations come from sources you don't control.
G2, analyst notes, Reddit, podcasts — the corpus the model is actually summarizing lives outside your owned site. The playbook is sequenced: reviews first, community second, earned media third, analyst coverage fourth. Optimize where the model actually reads.
Your Owned Site Is One Input — Not the Loudest One
The companion piece to this one — Attribution After the Click — established that AI answer engines retrieve from a small, top-heavy set of third-party sources when they assemble a B2B shortlist. For software queries, G2 alone is roughly 22% of citations. Add TechRadar, Zapier, Salesforce, HubSpot, GitHub, Gartner, PCMag, Capterra, and TechnologyAdvice and you cross 80% of citations on software queries (G2). Your owned site is in there too — but it is not the loudest input. The model is summarizing the corpus, and most of the corpus lives somewhere else.
This is the gap most B2B marketing teams miss when they start AEO work. They optimize their site harder. They write more capability pages. They polish the homepage. None of it is wrong — and none of it changes the surface they cannot see, because the surface lives on G2, in analyst reports, on Reddit threads, in podcast transcripts, in earned coverage. If the model retrieves more evidence about a rival on those surfaces than about you, the model will name the rival.
The playbook below covers the four off-site surfaces that move B2B AI answers the most, and the concrete moves a marketing team can ship against each.
The Four Off-Site Surfaces That Move B2B AI Answers
- Software review sites — G2, Capterra, TrustRadius. The single largest citation source for B2B software queries. Profile completeness and review velocity are the levers.
- Analyst coverage — Gartner, Forrester, IDC. Slower-moving but high-trust. AI engines weight analyst-grade citations heavily on enterprise queries.
- Community surfaces — Reddit, Quora, Hacker News, vertical Slack and Discord communities. Where buyers ask peers what they actually use. Authentic operator presence is the lever; astroturf is a banned shortcut.
- Earned media and podcasts — industry publications, founder podcasts, original-research pickups. The corpus the model uses for narrative summarization, not just feature comparison.
Each surface compounds differently and requires a different operating motion. Treating them as a single "PR" or "marketing" bucket is the most common reason teams ship motion without moving citations.
1. Review Sites: The Velocity and Completeness Playbook
The model can only retrieve what is there. A G2 profile with 40 reviews, an empty pricing field, and a 100-character description gives a model very little to work with — so when it summarizes your category, it leans on the rival with 400 reviews, a stated pricing band, and a completed comparison grid.
The moves that compound:
- Profile completeness as a one-time fix. Write a profile description above 250 characters that names the buyer, the job, and the positioning. Add detailed pricing — even if the public answer is "starts at X" — because the model retrieves stated pricing as a credibility signal. Fill the comparison grid for every competitor your buyer evaluates you against.
- Review velocity as the ongoing program. Embed a review ask in the customer lifecycle — onboarding completion, quarterly business review, success milestone, NPS-positive responder. Velocity matters more than total: a profile gaining 15 reviews a quarter outranks a profile sitting at 200 reviews from two years ago, because the model weights recency.
- Show up in the category-page question threads. G2 surfaces buyer-asked questions on category pages. Answering them with the brand voice, signed by an operator, gives the model another retrievable piece of evidence tied to your category.
- Link to the G2 profile from your own surfaces. Footer, sales-page social proof, email signatures. Drives review volume and crawl frequency on the page the model is reading.
None of these moves are clever. They are operational. A team that runs them quarterly for two quarters generally shifts a measurable share of category-page real estate, and the answer engines follow.
2. Analyst Coverage: The Briefing Playbook for AI Retrieval
Analyst coverage was a sales-asset surface for two decades — a Magic Quadrant logo went on the deck. It is now a retrieval surface too. Gartner, Forrester, and IDC publish category notes, Hype Cycles, Waves, Cool Vendors, and inquiry-based research that AI engines treat as high-trust corpus on enterprise queries.
The moves that compound:
- Submit briefings on every cycle you qualify for. Most teams submit once, get rejected from a Wave, and stop. The right cadence is quarterly: brief on Hype Cycle inclusion, on Cool Vendor consideration, on emerging category notes, on adjacent inquiry topics. Each briefing is a chance to be named in the resulting note — and analyst notes are retrieved by AI engines long after publication.
- Take inquiry calls seriously. Customer-side analyst inquiries are how analysts form their mental model of your category. Equip the analyst with a one-page positioning brief, three named customers, and one specific differentiator the model could lift cleanly. Analysts who can summarize your brand in a sentence will summarize it that way in the next note.
- Earn quoted positioning, not just inclusion. A logo in a Magic Quadrant is retrievable but generic. An analyst sentence describing your differentiator is what the model will quote when summarizing the category. Brief toward the sentence, not the logo.
- Repurpose analyst sentences onto your own surfaces — with permission. A quoted analyst line on a capability page is a strong retrieval signal, because the model can corroborate it against the source.
Analyst coverage is the slowest of the four surfaces. It is also the highest-trust. Teams selling to enterprise that ignore it leave one of the biggest retrieval signals on the table.
3. Community Surfaces: Presence, Not Astroturf
Reddit, Quora, Hacker News, and vertical Slack and Discord communities are where buyers ask peers what they actually use. AI engines retrieve from these threads heavily, especially for "alternatives to X" and "best Y for Z" queries — the exact queries that decide shortlist composition.
The surface punishes one tactic above all: astroturf. Sockpuppet accounts, sales reps pretending to be customers, drive-by brand mentions in unrelated threads. Mods ban it, communities sniff it, and AI engines increasingly down-weight content from sources with low credibility scores. So the playbook is the opposite of astroturf — it is actual operator presence.
- Founder and operator accounts post under real names. A founder answering a question in r/SaaS or on Hacker News, identifying themselves, is the strongest retrieval signal you can earn on this surface. The model reads the affiliation; the community trusts it; the thread persists.
- Customer success participates where customers ask questions. Not pitching — answering. A CS lead who is genuinely useful in two subreddit threads a month builds more retrievable presence than a year of brand-account posting.
- Monitor competitor-named threads. When a buyer asks "alternatives to [rival]," the answers in that thread become AI retrieval fodder for the next adjacent query. Showing up as a useful comparison — not a sales pitch — gets the brand named in the corpus.
- Original posts beat reactive ones. A short post sharing an actual operating insight — "here is how we measure X" — earns engagement that lifts the thread into retrieval range. Reactive replies do not compound the same way.
This surface rewards patience. A team that lets two or three operators participate consistently for two quarters builds a body of retrievable presence that the model will start naming inside answers. A team that tries to shortcut it with brand-account posting earns nothing.
4. Earned Media and Podcasts: The Narrative Layer
The fourth surface is the corpus the model uses when it summarizes — not "what does this brand do" but "what is this brand about, and why does it matter." Industry publications, founder podcasts, original-research pickups, and bylined commentary feed that narrative summarization.
- Founder podcast tours, sequenced by retrieval weight. Not every podcast is equal. Start with the shows whose transcripts get indexed and quoted in answer engines — the operator-interview shows, the category-defining shows. A founder appearing on three high-retrieval shows beats ten low-retrieval ones.
- Original research that earns the pickup. A piece of original data — a benchmark, a survey, a study — pitched to mid-tier industry publications earns coverage that the model retrieves for years. Citation-bait is not opinion; it is data the writer cannot get elsewhere.
- Bylines in industry publications. A founder or product lead writing a quarterly byline in a category publication earns a retrievable POV the model attaches to the brand. Ghosted thought-leadership rarely earns this; the byline is the lever.
- Track quote pickup, not impressions. A podcast appearance is a success when a sentence from it appears in an AI answer summarizing your category. Measure quote pickup quarterly — the share of earned media that shows up in retrieval — and reinvest where the pickup is real.
How to Sequence the Four Surfaces
A B2B marketing team starting from zero cannot ship all four in a quarter. The right sequence:
| Order | Surface | Why first |
|---|---|---|
| 1 | Review sites (G2, Capterra) | Highest citation share, fastest to operationalize. Profile completeness is a one-week fix; review velocity becomes a recurring program. |
| 2 | Community presence | Free, compounds on operator time, surfaces the queries buyers actually ask. Starting here builds the corpus on the surfaces the model retrieves "alternatives" queries from. |
| 3 | Earned media and podcasts | Narrative layer. Earns the sentences the model quotes when summarizing your category — but requires #1 and #2 to compound on. |
| 4 | Analyst coverage | Highest trust, slowest cycle. Worth the investment for enterprise buyers, but unlikely to move citations in the first two quarters. |
The sequencing is not absolute. A team with strong existing analyst relationships should activate them early. A consumer-leaning brand should weight community presence higher. But for the canonical case — a B2B SaaS team selling to enterprise, starting from low review counts and no analyst coverage — this order maximizes citation movement per quarter.
What Stays Inside Your Site
Investing in the off-site corpus does not mean abandoning the owned site. The model triangulates: a claim made on G2, corroborated on your capability page, and quoted by an analyst is treated very differently than the same claim made only in one place. The owned site remains the surface where you state the positioning cleanly, the capabilities concretely, and the proofs in named form — and where the off-site corpus is corroborating, not contradicting.
The owned site is the spine. The off-site corpus is the body of evidence. The model wants both, retrieves both, and trusts the brand where both agree.
The Standard to Hold
A marketing team running AEO seriously should be able to answer four questions at any moment: where do we stand on the review surface against our top three rivals, who on the team is participating in the communities where our buyers ask, what was the last analyst note we briefed for, and which sentence from earned media has been retrieved by an AI engine in the last quarter. If the answer to any of those is "we don't track that," the off-site corpus is being ceded — by default, to whichever rival is working it.
Your buyer is asking the model. The model is reading the corpus. The corpus mostly lives somewhere other than your site. The teams that win this medium are the ones that stop optimizing only what they own, and start operating where the model actually reads.
