How to Do GEO for a Two-Sided Marketplace

Win AI visibility on both sides at once: build category and comparison content for buyers, dedicated onboarding and earnings content for sellers, back every listing with verifiable trust and liquidity signals like reviews and response rates, mark up listings with schema, and treat AI apps and feeds, not just web pages, as a real distribution channel.

*By Josh Bernstein | August 18, 2026*

ChatGPT now reaches 900 million weekly active users, and three of the best-known two-sided marketplaces on the internet have already noticed: Thumbtack, Angi, and Upwork each built a native app inside ChatGPT in the past year so their supply side shows up the moment a buyer starts typing a question instead of a search. Thumbtack partnered with OpenAI in October 2025 to bring its network of 300,000 service providers into ChatGPT conversations. Angi launched its own ChatGPT app in March 2026, then added a connected app inside Google Gemini five months later. Upwork followed in April 2026, putting its marketplace of more than 18 million professionals across 130 work categories directly into ChatGPT's app ecosystem.

That is not a niche experiment. It is a preview of what generative engine optimization, or GEO, has to solve for any marketplace: two completely different audiences asking two completely different kinds of questions, both increasingly through an AI interface instead of a search box. A single-sided SaaS company or ecommerce brand only has to win one side of an AI conversation. A marketplace has to win the buyer asking "what's the best platform for X" and the seller asking "is it worth listing on X" at the same time, on the same domain, without either content track starving the other.

Why Two-Sided Marketplaces Face a Different GEO Problem

Most GEO advice is written for a single audience: a SaaS company optimizing for "best project management tool" queries, or an ecommerce brand optimizing for product searches. A marketplace runs two of those programs simultaneously under one domain, and the two programs compete for the same internal linking budget, the same content calendar slots, and often the same handful of marketing writers.

The demand side asks discovery and evaluation questions: what's the best marketplace for a given need, is this platform legitimate, how does it compare to a named competitor, and what does it actually cost. The supply side asks a completely different set of questions: how do I start selling or listing here, what are the fees, is it worth my time compared to a competing platform, and how do successful sellers actually make money. An AI engine treats these as separate intents and pulls from different pages, different sources, and often different parts of your own site to answer them.

The business reason this matters is liquidity: a marketplace only works when enough supply meets enough demand in a given category or geography. Historically, liquidity problems got solved with paid acquisition on whichever side was thin. Now that a growing share of both buyer and seller discovery happens inside an AI conversation rather than a list of blue links, a marketplace that only optimizes its demand-side content is quietly starving its own supply, and vice versa. GEO underperformance on either side throttles the whole flywheel, not just one metric.

The Four Query Types Every Marketplace Must Win

Before building anything, it helps to separate the query types an AI engine actually sees for a marketplace, because each one needs a different page, not a shared homepage or generic FAQ.

A fifth type worth naming separately is the pricing and fee question, which both sides ask ("how much does Upwork take" is asked by clients budgeting a project and by freelancers deciding whether to join), so it deserves one clear, current, citable page rather than being buried in a help center article that changes without notice.

Winning Demand-Side Category and Best-Of Queries

The category query is where most marketplace traffic decisions get made before a buyer ever clicks into a specific listing, and it is also the query type most marketplaces answer worst, because their homepage is built for browsing, not for being read and summarized by a model. An AI engine answering "best marketplace for X" wants a page that names specific, current options with enough detail to quote, not a generic value proposition.

Build dedicated category hub pages for your highest-intent categories, each with a direct-answer opening paragraph, a short list of what makes a strong option in that category, and enough real specificity (provider counts, price ranges, typical turnaround) that an AI engine has something concrete to extract. This is the same logic that determines whether a brand gets pulled into an AI best-category listicle at all: vague marketing copy rarely survives the summarization step, while specific, well-organized claims do.

Resist the temptation to make every category page a thin wrapper around search filters. AI engines cannot meaningfully cite a filter interface. They can cite a paragraph that says how many verified providers are active in a category, what the typical price range looks like, and what separates a strong listing from a weak one.

Winning Comparison and Alternatives Queries

Comparison queries are where marketplaces most often undercut themselves, because the instinct is to avoid naming competitors at all. That instinct works against you in GEO. The Princeton and Georgia Tech GEO study from KDD 2024, which analyzed roughly 10,000 queries, found that citing authoritative sources, including competitors, boosted visibility by up to 115% for pages starting from a lower rank position, while adding concrete statistics added 41% and adding direct quotations added 28%. Pages already sitting in the top position benefited least from any of these tactics, which matters here because most marketplaces are not the default answer for every comparison query and have real room to gain.

A genuinely useful comparison and alternatives page names the competitor explicitly, states where that competitor is actually a better fit, and then makes the case for where your marketplace wins. That honesty is exactly what an AI engine is trying to synthesize when it answers a comparison prompt, and a page that already does the synthesis for it is far more citable than one that pretends the competitor doesn't exist.

Keep these pages current. Fee structures, category coverage, and geographic availability change often enough on marketplace platforms that a comparison page written a year ago can quietly become the wrong answer, and an AI engine has no way to know that unless the page itself signals a recent update.

Supply-Side GEO: Getting AI to Recommend You to Sellers and Providers

Supply-side content is the piece most marketplace marketing teams underinvest in, because growth marketing budgets and headcount are usually built around demand generation. But a seller or service provider deciding whether to join your platform is now just as likely to ask an AI engine first, and "is it worth becoming a Thumbtack pro" or "what does Upwork actually take in fees" are exactly the kind of concrete, high-intent questions AI engines answer confidently and directly, with or without your input.

The fix is treating seller acquisition content with the same rigor as buyer acquisition content:

• A dedicated getting-started page per major provider or seller category, not one generic "start selling" page for the whole platform
• Real, current fee breakdowns instead of a link to a support article buried behind a login
• Honest timelines: how long until a typical new seller gets their first booking, sale, or contract
• First-person or named case studies from actual sellers, since AI engines weigh specific, attributable examples over aggregate marketing claims

Thumbtack's 300,000-provider network and Upwork's 18-million-professional marketplace did not get built by demand-side marketing alone, and the same content discipline that made those numbers possible offline needs to exist in AI-citable form now that a growing share of prospective sellers are asking a chatbot before they ever fill out a signup form.

*Demand-side and supply-side queries pull from different content, but neither track can starve the other without weakening marketplace liquidity.*

Trust and Liquidity Signals AI Engines Actually Weigh

Trust is the deciding factor for both a buyer choosing whether to book a stranger through your platform and a seller deciding whether to hand over a percentage of their income for access to your demand. AI engines synthesize an answer to "is this marketplace legit" from multiple sources, not just your own trust page, which means the signal has to be real and consistent everywhere it shows up, not just polished on your domain.

There is no single dominant source AI engines pull trust signals from. Reddit is the single most-cited domain across major AI engines, appearing in roughly 40% of citations overall, including about 24% of Perplexity's citations as of January 2026 and about 12% of ChatGPT's US citations. Wikipedia accounts for roughly 13% of ChatGPT citations, and LinkedIn ranks among the most-cited sources for B2B and brand-reputation queries specifically. For a marketplace, that means the honest answer to "is Angi legit" or "is Upwork worth it" is being assembled just as much from a Reddit thread or a review aggregator as from anything your own site says about itself.

The specific trust signals worth building deliberately for a marketplace include:

• Verified review volume and recency, ideally broken out by category rather than one blended platform-wide score
• Provider or seller verification badges, and a clearly documented process for how verification actually works
• Published dispute resolution and refund policies, since an AI engine answering a legitimacy question is often specifically checking for what happens when something goes wrong
• Response rate and completion rate benchmarks, which function as a marketplace-specific trust equivalent to the expertise and experience signals a single-brand site would build
• Consistency between what your own pages claim and what independent review sites and community threads actually say

None of this is fully controllable through your own domain, which is why turning customer reviews into structured, citable content and monitoring what real users say on Reddit and review sites matters as much as anything published on your marketplace directly.

Structuring Listings and Schema So AI Can Extract and Cite Them

A listing page or category page only helps you in AI search if a model can actually pull a clean, verifiable answer out of it. That means every high-value page needs a direct-answer opening sentence that states the core fact plainly (what the category is, what a typical price range looks like, how many active providers exist), followed by the supporting detail, rather than burying the useful information under three paragraphs of brand voice.

Schema markup remains part of this, even though the visual payoff has changed. Google removed the FAQ rich result from search results entirely on May 7, 2026, following its earlier removal of the HowTo rich result in 2023. Both FAQPage and HowTo remain valid schema.org types, and the markup still helps AI answer engines identify and extract discrete question-and-answer pairs or step sequences when composing a response, even without a visible snippet in Google's own results. For a marketplace, pairing FAQPage schema on trust and onboarding pages with Product or Service schema and AggregateRating on listing and category pages gives AI crawlers a structured, machine-readable version of exactly the specifics they're trying to extract. Full guidance on which schema types actually move the needle for AI citations applies directly here, since marketplace pages are some of the highest-value candidates for it.

Becoming an AI-Native Supply Feed, Not Just a Set of Web Pages

The Thumbtack, Angi, and Upwork examples point to where this is heading: marketplaces that treat their listing and provider data as a structured feed an AI system can query directly, not just a set of web pages a crawler might index. Thumbtack built its ChatGPT app on OpenAI's Apps SDK so the model can proactively recommend a specific plumber or electrician mid-conversation using Thumbtack's own APIs. Angi went further, adding a connected app inside Google Gemini in August 2026 on top of its ChatGPT app, so its provider network is queryable from two major AI ecosystems at once. Upwork's app lets ChatGPT users describe a project and get matched against a live pool of more than 18 million professionals across 10,000 skills, with its AI agent Uma helping scope the work inside the conversation itself.

Not every marketplace has the engineering resources to build a native app today, but the underlying principle applies at any size: clean, structured, current listing and provider data is what makes any of this possible, whether it is exposed through a formal app integration, an API-driven partner relationship, or simply well-marked-up pages a model can parse reliably. The marketplaces investing in this now are building the same kind of durable data advantage that made their earlier SEO investments compound for a decade. Treating your catalog as a feed rather than a website is the version of that bet for the AI-search era.

Balancing Supply and Demand Content Without Cannibalizing Either

The operational risk in all of this is letting one side's content program quietly starve the other. Demand-side content usually has a clearer, more immediate revenue story attached to it, which means it tends to win the internal argument for headcount and editorial calendar space by default. Left unmanaged, that produces a marketplace with a rich comparison and category content library and a thin, outdated seller help center, which eventually shows up as a supply shortage that no amount of demand-side content can fix.

A workable operating model:

1. Run supply-side and demand-side content as two tracks with separate owners, but a shared editorial calendar so neither one gets deprioritized silently
2. Set a minimum content cadence for each side independently, not a blended total, so supply content can't get quietly absorbed into demand work during a busy quarter
3. Cross-link deliberately: a category hub page should link to the relevant seller onboarding guide, and vice versa, since AI engines that crawl one page will often follow that link to build a fuller picture
4. Track citation rate separately for demand-side prompts ("best marketplace for X") and supply-side prompts ("how do I sell on X"), because a healthy blended average can mask one side quietly losing ground
5. Revisit fee, policy, and process pages on a fixed cadence, since these are exactly the pages both sides ask AI engines about most often, and stale numbers erode trust on both sides at once

This is the exact workflow Promptive is built to run: tracking how ChatGPT, Claude, Gemini, and Perplexity answer both your demand-side and supply-side prompts every day, flagging when a competitor gets cited in a comparison you should be winning, and surfacing when your own fee or policy pages are being described inaccurately before more buyers or sellers see the wrong answer. Pair that ongoing tracking with a recurring monthly AI visibility audit that explicitly splits its prompt list between the two sides, so neither one becomes the audit's blind spot.

Frequently Asked Questions

What makes GEO different for a two-sided marketplace compared to a regular SaaS or ecommerce brand?

A marketplace has to win AI visibility for two distinct audiences asking fundamentally different questions on the same domain: buyers researching where to go, and sellers or providers deciding whether to join. Single-sided brands only have to build one content and trust program; marketplaces have to build two, without either one starving the other of resources or internal links.

Which side should a marketplace prioritize first, supply or demand?

Prioritize whichever side is currently the liquidity bottleneck, not whichever side has the bigger existing content library. Most marketplaces default to over-investing in demand-side content because it has a clearer short-term revenue story, which often means the supply side is the more neglected, and therefore more impactful, place to start.

Do comparison pages that name competitors actually help or hurt a marketplace in AI search?

They generally help. The Princeton and Georgia Tech KDD 2024 study found citing authoritative sources, including competitors, boosted visibility by up to 115% for lower-ranked pages, because AI engines favor content that already does the honest synthesis work a model would otherwise have to do itself.

How much does review volume actually matter for AI-generated trust answers?

It matters significantly, but not in isolation. AI engines cross-reference review volume and recency against independent sources like Reddit threads and review aggregators, so a marketplace's own stated review numbers need to be consistent with what shows up on third-party sites, not just impressive on the marketplace's own trust page.

Should a marketplace build a native app inside ChatGPT like Thumbtack, Angi, and Upwork did?

Only if the underlying listing and provider data is already clean and structured enough to support it. The bigger, more accessible lesson from those three launches is treating your catalog as a queryable feed rather than a static set of pages, which pays off in ordinary AI search citations even without a formal app integration.

Does FAQ schema still matter now that Google removed the FAQ rich result?

Yes. The May 7, 2026 removal only affects the visual snippet inside Google's own search results page. FAQPage schema remains a valid schema.org type and still helps AI answer engines identify and extract discrete question-and-answer content when composing a response.

How often should a marketplace update its fee and policy pages for GEO purposes?

At minimum every time a fee structure or policy actually changes, and on a fixed quarterly review cadence even when nothing has changed, since these are among the highest-intent pages both buyers and sellers ask AI engines about, and stale numbers get repeated confidently by a model until someone corrects the source.

Can a small marketplace realistically compete with category giants for AI citations?

Yes, more realistically than in classic SEO, because the Princeton and Georgia Tech study found that pages starting from a lower rank position benefit far more from strong GEO tactics than pages already ranked first. A smaller marketplace with specific, current, well-structured category and trust content has genuine room to be cited over a larger competitor's thinner page.