AEO for Marketing Agencies

Why AI citations matter for agencies

Agency selection has always been a research-heavy purchase. A prospect assembles a shortlist, checks credentials, compares approaches, and only then books calls. What has changed is where the shortlist gets built. Increasingly the first pass happens inside an assistant — ChatGPT, Gemini, Copilot or Perplexity — where a marketing director types a plain-language question and receives three or four named agencies with a sentence of justification each. If your firm is not in that answer, you are not in the consideration set, and you never see the impression in your analytics.

This matters more for agencies than for most sectors, for two reasons. First, the buying question is inherently comparative: prospects rarely ask "tell me about Agency X", they ask "which agency should I hire for Y". Assistants answer comparative questions by synthesising across multiple sources, so the agencies that get named are the ones whose positioning, pricing and specialisms are stated explicitly somewhere a model can read them. Second, agencies sell expertise. Every model that assembles an answer is looking for demonstrable evidence of that expertise — named practitioners, documented methodology, verifiable outcomes. Vague brand copy gives it nothing to work with.

There is a commercial reason too. AEO is a natural retainer extension for anyone already delivering SEO, content or digital PR, and it is still a pitch differentiator rather than a commodity line item.

The queries worth winning

Start by writing down the questions a prospect actually types. They cluster into five families, and each family needs a different kind of page.

1. Vertical and channel shortlists

"Best B2B SaaS marketing agency", "agency that specialises in healthcare paid media", "who does lifecycle marketing for DTC brands". These are the highest-intent queries and the hardest to win, because assistants prefer sources that make the specialism unambiguous. A generic services page will not surface here; a page titled for the vertical, describing the vertical's specific constraints, will.

2. Agency versus in-house versus freelance

"Should I hire an agency or build an in-house team", "agency vs freelancer for content marketing", "when does it make sense to bring SEO in-house". These are top-of-funnel and often dismissed, but they are where an assistant forms its framing of the whole category. An honest page that sets out the cost, speed and control trade-offs — including cases where in-house genuinely wins — tends to get cited precisely because it reads as balanced.

3. Pricing and engagement models

"How much does a marketing agency cost", "retainer vs project vs performance pricing", "typical agency retainer for a mid-market company". Most agency sites answer this with "contact us", which removes them from the answer entirely. Publishing your model — how you scope, what a minimum engagement looks like, what drives cost up or down — is one of the fastest ways to become the source an assistant quotes.

4. Evaluation and due diligence

"Questions to ask a marketing agency before signing", "how to evaluate an agency proposal", "red flags when hiring an agency", "what should be in an agency contract". Buyers ask these immediately before a decision. Owning them puts your framing inside the prospect's evaluation criteria.

5. Service and deliverable definitions

"What is answer engine optimisation", "what does a fractional CMO actually do", "what is included in a content audit". Definitional queries are the easiest citations to earn and they compound: once a model treats your page as a reference for a term, it tends to reuse it across related answers.

Content that earns the citation

Assistants extract claims, not vibes. The practical test for any page is whether a model could lift a single self-contained sentence from it and attribute that sentence to you. Five formats do this reliably.

Transparent pricing and engagement pages

Publish ranges, minimums and the variables that move them. State what a three-month engagement includes versus a twelve-month one. If you cannot publish numbers, publish the structure — pricing model definitions, how scope is agreed, what triggers a change order. Structure is still extractable; silence is not.

Methodology explainers

Document how you actually work: the audit sequence, the inputs you need from a client, the decision points, the reporting cadence. Name the steps. A model answering "how do agencies approach a technical SEO migration" will reach for the source that describes an ordered process rather than one that asserts excellence.

Vertical case studies with concrete outcomes

Case studies earn citations when they contain specifics a model can quote: the client's situation, the constraint, the intervention, the measured result, the timeframe. Only publish figures you can stand behind and that the client has approved. One verifiable, dated case study outperforms ten anonymised "significant growth" summaries, because unverifiable claims give a model nothing safe to repeat.

Comparison and alternatives pages

Write the comparisons your buyers make — engagement models, channel strategies, build versus buy, and where honest, your firm versus common alternatives. Fairness is a ranking factor in practice: content that concedes where the other option wins gets treated as a more reliable source than content that does not.

Named-expert commentary

Attribute analysis to real people with real bios, roles and credentials. Assistants weight identifiable expertise. A quarterly point of view from your head of strategy, bylined and dated, is more citable than the same words published anonymously. Keep author pages linked from every article that person writes.

Alongside the content, make sure machines can reach it. Publish a clean, crawlable site without content locked behind interaction, and consider a machine-readable summary of your firm using an llms.txt file so assistants can find your positioning, services and key pages without inference.

Schema markup that fits an agency

Structured data will not by itself win a citation, but it removes ambiguity about what your firm is, what it sells and who works there. Use the types that match the page:

• Organization on the homepage — legal name, logo, founding date, social profiles, and sameAs links to authoritative directories and press coverage.
• ProfessionalService for the agency entity itself, including areaServed, address and priceRange where you are willing to state it.
• Service on each service-line page, with serviceType, provider and an offers block describing the engagement model.
• Article on every insight or explainer, with a real author, datePublished and dateModified.
• FAQPage on pricing, process and evaluation pages — question-and-answer pairs map directly onto how assistants retrieve.
• Review and AggregateRating only where genuine, attributable reviews exist; never synthesise ratings.
• Person for strategists and practice leads, with jobTitle, knowsAbout and sameAs pointing to their professional profiles.

A minimal service-page example:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Service",
  "serviceType": "Answer Engine Optimisation",
  "provider": {
    "@type": "ProfessionalService",
    "name": "Your Agency",
    "areaServed": "GB"
  },
  "offers": {
    "@type": "Offer",
    "priceSpecification": {
      "@type": "PriceSpecification",
      "priceCurrency": "GBP"
    }
  }
}
</script>
      

Keep markup consistent with the visible page; contradictions undermine trust in both.

Productising AEO as a retainer line

The same work you do for yourself packages cleanly for clients. A workable structure is a fixed-fee discovery — query mapping, citation baseline, technical crawlability review, schema audit — followed by a monthly retainer covering content production against the mapped queries, schema implementation, entity and profile consistency, and reporting.

What you report matters as much as what you deliver, because AEO lacks the familiar rank-tracking comfort blanket. Useful client-facing metrics include share of tracked queries where the client is cited, which assistants cite them, whether the citation is a link or an unlinked mention, competitor citation share on the same query set, and the accuracy of how the assistant describes the client. Track this over time rather than in snapshots; answers vary between runs, so trend lines are more honest than single readings. An AI citation tracking workflow and an agent experience platform both help turn this into something you can put in a monthly deck.

Price it as a distinct line item rather than folding it into SEO. It is separately scoped, separately measured, and clients understand it as new work.

Quick-start checklist

1. List twenty-five questions a prospect would ask an assistant when choosing an agency like yours, split across the five query families above.
2. Run each one through ChatGPT, Gemini, Copilot and Perplexity and record who gets named and what sources are cited.
3. Identify the recurring source domains — directories, publications, review platforms — and make sure your firm is present and accurate on each.
4. Publish or rewrite a pricing and engagement page that states your model in plain language.
5. Document your delivery methodology as an ordered, named process.
6. Rebuild two case studies per priority vertical with situation, intervention, measured outcome and date.
7. Add author pages for every strategist who publishes, and byline all commentary.
8. Implement Organization, ProfessionalService, Service, Article and FAQPage schema, and validate it.
9. Confirm your site is fully crawlable without JavaScript execution, and publish an llms.txt summary.
10. Re-run the query set monthly, log citation share by assistant, and feed the gaps back into the content plan.

Start with the pricing page and one vertical case study. Those two assets answer the questions prospects ask most often, and they give assistants something concrete to quote. If you want a baseline before you begin, run a free AEO audit or book a demo to see how your firm is currently described.