AEO for a Two-Sided Marketplace: Getting Cited on Both Sides of the Network

Every AEO guide assumes a single audience asking a single family of questions. A two-sided marketplace breaks that assumption on day one. When a buyer asks ChatGPT "where can I hire a freelance illustrator," and a freelancer asks it "what are the best platforms to find illustration work," those are opposite intents pointing at the same brand. Win one and you can still be invisible in the other. A marketplace that treats AEO as one project will pour all its content into whichever side is easier to write for, get cited there, and quietly cede the other side to a competitor. This is how to run AEO as the two-front campaign it actually is.

Why a marketplace is two AEO problems wearing one logo

A marketplace exists to connect two groups who need each other: hosts and guests, drivers and riders, retailers and the brands that stock them, businesses and the shift workers who fill their gaps. Each group arrives at an AI assistant with a different job, a different vocabulary, and a different definition of what makes your platform good.

That split runs deeper than phrasing. The two sides evaluate you on inverted criteria. A guest asking about Airbnb cares about selection, price, and trust as a buyer. A host cares about occupancy, fees, and payout speed as a seller. An AI engine answering each question pulls from different sources, weights different signals, and may quote a review written by the other side entirely. The demand side's glowing reviews do nothing to convince the engine you are good for supply, and can even work against you: a stream of buyer-side content teaches the model to file you as a buyer destination, which is exactly the wrong entity signal when a seller asks their question.

This is why a blended AI share-of-voice number is worse than useless for a marketplace. A platform can hold a healthy overall citation share while being completely absent from one side of its own network. The blend hides the gap that is quietly starving the marketplace of liquidity.

The liquidity asymmetry shows up in your citations too

Marketplace operators know that the two sides are never equally hard to acquire. The durable playbook, documented across marketplace strategy work like NfX's tactics for solving the chicken-or-egg problem, is to constrain and seed the harder side first, usually supply, because supply is the value buyers show up for. Sequoia's writing on two-sided marketplaces and engagement makes the same point about where liquidity is won and lost.

That same asymmetry reappears in your AI citations, and reading it is diagnostic. Run both prompt sets and you will usually find one of two patterns:

• Cited on demand, invisible on supply. The engine names you when buyers ask, but a supplier asking "best platforms to sell on" hears your competitors. Your content, reviews, and press all skew buyer-side, so the model does not recognize you as a supply destination.
• Cited on supply, invisible on demand. Common for newer or vertical marketplaces that recruited supply first. The engine knows you as a place sellers list, but buyers asking for the category still get the incumbent.

The imbalance in your citations tends to track the imbalance in your actual network. That makes the AEO benchmark an early, cheap read on which side of the marketplace is under-supplied with the one thing engines run on: sources that describe that side clearly.

Build two prompt sets, one per side

You cannot measure a two-front problem with a one-front prompt set. Build two, and keep them genuinely separate.

Each set gets scored the same three ways, so you know per side whether the engine cites your domain, mentions your brand, or actively recommends you. Score them independently and never average across sides until you have read each on its own. The discipline of building a prompt set from how people actually ask, weighted by intent, applies twice here, and the underlying method is in how to design a prompt set that reflects how buyers actually ask AI about your category.

The entity problem: teaching the engine you are both

The hardest part of marketplace AEO is not writing more content. It is that AI engines file a brand into a category before they will recommend it, and they do that from co-occurrence, from the company your brand keeps across the sources they read. If almost everything written about you is buyer-side, the engine learns you are a buyer destination and stops surfacing you for seller questions, no matter how good your platform is for sellers. The mechanics of how engines assign a brand to a category, and why co-occurrence beats your own marketing copy, are in how AI engines decide which category your brand belongs to.

The fix is deliberate dual-entity building. You want the engines to associate your brand with both roles, which means the sources they read have to describe both.

• Two content tracks, clearly separated. A supply hub and a demand hub, each with its own answer-first pages, its own vocabulary, and its own FAQ. Do not fold seller answers into buyer pages as an afterthought; a page that reads as buyer-first will be filed buyer-first.
• Side-specific proof. Buyer trust signals (selection, reviews, safety) live on demand pages. Supplier proof (payout speed, fee transparency, earning potential, support) lives on supply pages. Each side's proof only reinforces the entity you want on that side.
• Off-domain balance. If the review sites, Reddit threads, and roundups that engines cite only discuss your buyer experience, seed and earn coverage of the seller experience too. The engine's category verdict comes largely from these third-party sources, not your homepage.

Done well, this makes your brand legible as a dual-role entity, the way strong marketplaces are, so the engine recommends you to whichever side is asking. The broader sequence for making engines recognize and trust a brand as a distinct entity is in how to build a brand entity AI engines recognize and trust.

Getting onto the "best platforms" list, on both sides

Most marketplace demand starts with a list prompt: "best platforms for X." The engine assembles a short roster of three to five names, and getting on it is the whole game. The catch for a marketplace is that there are two such rosters, a buyer roster and a seller roster, and they are populated from different evidence. You have to earn a slot on each. The mechanics of how AI builds those best-tools shortlists, and what gets a brand included, apply to both your rosters and are laid out in how to get your SaaS into AI 'best tools' lists and comparisons.

Practically, that means auditing both list prompts separately, finding which competitors hold each roster, and studying the sources the engine cited to build it. The supply roster is frequently the softer target, because fewer marketplaces invest in seller-side answer content, which leaves an opening a vertical or challenger platform can take faster than it can crack the crowded buyer roster.

Measure per side, and let the gap set the roadmap

Report marketplace AEO as two scorecards, never one. For each side, track cited, mentioned, and recommended share per engine, over time, from clean sessions. The value of splitting it is that the two numbers tell you where to spend. A platform strong on demand and weak on supply should pour its next quarter of content and earned media into supply-side answers, because that is the side starving the network. A platform with the reverse pattern does the reverse. The blended number would have told you to do nothing in particular.

Two hygiene moves keep both sides measurable and current. Give crawlers a clean canonical source of truth that describes both roles with a free llms.txt generator, and keep the facts each side cares about, fees, payouts, selection, current where engines read them with the AI Feed Engine. Running the benchmark, diagnosing the weak side, and closing it as one loop is what how OnlyAEO works is built around, and the arc of a brand going from unnamed to routinely recommended is documented in the FastTrackr AI case study.