AEO and GEO get you into the answer. Narrative decides whether the model recommends you once you’re there.
What LLM SEO Actually Is (and Isn’t)
Ask around and you’ll get two definitions of LLM SEO. One says it’s about getting cited: structure your content so ChatGPT, Perplexity, and Google AI Overviews pull from your pages. The other says it’s about getting mentioned: appear in the answer when someone asks about your category.
Both are real. Both are also incomplete. Citation and mention are how you show up. Neither decides whether the model actually points a buyer at you.
Classic SEO ranked documents against a query. An LLM does something different. It reads a question, then answers from a learned story about your category. That story is the thing doing the recommending. If you optimize for appearance without touching the story, you’re polishing your spot in an answer that still sends the buyer somewhere else.
How LLMs Actually Read and Use Your Content
An LLM doesn’t read your page the way a human does. It doesn’t scan your homepage, feel persuaded, and decide to recommend you. Two separate things are happening, and they run on different clocks.
The first is training. The model learned about your category from a huge slice of the web scraped at some point in the past. Your marketing copy is a tiny drop in that pool. What dominates is how everyone else describes your space: the review roundups, the Reddit threads, the comparison blogs, the docs and forum answers where people talk about tools like yours without you in the room. The model compressed all of that into patterns. When someone asks a question, it reproduces those patterns.
The second is retrieval. Tools like Perplexity and Google AI Overviews fetch live sources at query time and summarize them. Here your content can matter directly, but only if it gets pulled into the fetched set, and only if it says something the model can lift cleanly.
So “getting read” splits into two jobs. Show up in the sources that get retrieved. Show up in the corpus the model already absorbed. Most teams obsess over the first and ignore the second, which is why they can win a citation and still lose the recommendation. The model reaches for your page, extracts a line, and drops it into a story it already decided on before it ever saw you.
Try this. Ask Perplexity “what’s the best analytics tool for a Shopify store,” then click through to the sources it cited. You’ll usually find the answer’s framing came from a couple of roundup articles, not from any vendor’s own site. The vendors got quoted. The roundups did the recommending.
Why Content Structure Decides What Gets Cited
Structure won’t fix your narrative. But bad structure will keep you out of answers you’d otherwise win, so it’s worth getting right.
Models and answer engines favor content they can extract without guessing. That means a clear question near a clear answer. A heading that matches how people actually ask (“How much does X cost?”) beats a clever one (“Pricing, reimagined”). A direct sentence that states the claim beats a paragraph that circles it. If a model has to infer what you mean, it’s more likely to grab a competitor’s page that just said it plainly.
A few things that consistently help:
- Answer the question in the first sentence under the heading. Don’t warm up. If the H2 asks “Is X good for small teams,” the next line should say yes or no and why.
- Use headings phrased as real prompts. Buyers and models both start from questions. Structure your page around the questions your category actually gets asked, not around your feature names.
- Keep claims self-contained. A sentence that only makes sense after three paragraphs of setup is hard to lift. A sentence that stands alone gets quoted.
- Make comparisons explicit and fair. Comparison content gets retrieved constantly. If your page lists real tradeoffs instead of a rigged scorecard, it reads as more trustworthy, and trustworthy sources get cited more.
Here’s the catch. All of this decides whether you get pulled in. It does nothing about how you get framed once you’re there. You can be the most extractable page on the internet and still get quoted as “a budget option some teams use,” because the framing lives in the sources around you, not in your schema markup. Structure is table stakes. It earns you a seat. It doesn’t tell the room what to think of you.
Tactics vs. Strategy: AEO/GEO Don’t Change What the Model Believes
AEO and GEO are insertion points. AEO gets your content shaped for answer engines. GEO gets you cited across the sources models draw on. Useful work, and you should do it.
But they operate on the surface. They change how you appear, not what the model believes about who wins in your category. You can nail schema, publish clean comparison pages, and get quoted in the exact articles the model reads. If the consensus story frames a competitor as the default and you as the scrappy alternative, that framing rides along into every answer.
This is the pillar most tools skip. AEO and GEO are tactics. Narrative is strategy. Tactics decide whether you’re in the room. Strategy decides who the room listens to.
How LLMs Learn What to Recommend (The Narrative Layer)
An LLM doesn’t hold opinions about your product. It absorbs the patterns in how the market talks about your category, then reproduces them.
When thousands of articles, forum threads, reviews, and docs describe “the best tool for X” a certain way, the model learns that description as consensus. So when a buyer asks “what should I use for X,” it answers from that consensus. Not from your homepage copy. From the aggregate story the internet already tells.
That’s why recommendation is downstream of narrative. The model learned a frame for your category. Whoever the frame positions as the obvious answer gets recommended, over and over, across phrasings of the same question.
Consensus: The Layer Above the Mechanics
Everything above this point is mechanics. Retrieval, structure, extraction, citation. Consensus is the layer that sits on top of all of it, and it’s the one that decides your fate.
Consensus is the story the market has settled into about your category. Who the default is. Who the specialists are. What the “safe” choice is versus the “risky” one. Which brands go together and which stand apart. No single article writes this. It emerges from thousands of them agreeing, mostly without coordinating. The model treats that agreement as truth, because from its point of view, that’s what truth looks like: the thing most sources say.
This is why two brands with identical citation counts get recommended at wildly different rates. The model isn’t tallying mentions. It’s asking, in effect, “what does the market believe about this space,” and answering from the consensus it learned. Your citations feed that consensus. They don’t override it.
Picture a category where, over three years, the same phrase attaches to one vendor across dozens of high-authority sources: “the enterprise-grade choice.” Meanwhile you get described as “great for startups.” Both descriptions might be genuinely positive. But when a mid-market buyer asks a model for a recommendation, the consensus routes them to the “enterprise-grade” brand, because that’s the frame the market built. You didn’t lose on features or on SEO. You lost on the story that formed above the mechanics.
The practical takeaway is uncomfortable. You can win every technical battle and still lose the war, because the war is fought at the level of what the market agrees is true. That agreement is measurable. It’s the frame the model adopts, the sources that reinforce it, and how your positioning drifts inside it over time. That’s the layer Mavel reads. Not whether you appear, but whose story the answer is built on.
The Narrative Share Problem: You Can Be Cited and Still Lose
Here’s the gap that visibility tools miss. Being cited is not the same as winning the answer. Mavel measures narrative share: whose story the model tells about your category, not just whether your name appears in it.
Picture the difference. You get mentioned in an answer as “another option worth considering.” A competitor gets mentioned as “the standard most teams start with.” Same mention count. Completely different outcome. One frame makes you a footnote. The other makes the sale.
Presence is countable. Framing is what moves the buyer. If your name shows up but the story around it puts someone else at the center, you’re paying for visibility that recommends your rival.
A Real Example: Two Brands, Same Visibility, Different Recommendations
Imagine two project management tools. Both are cited in the same set of articles. Both appear when you ask ChatGPT “what’s the best project tool for a small team.”
Brand A shows up phrased like this: “widely considered the default for teams that want simplicity.” Brand B shows up as: “a feature-rich alternative if you outgrow the basics.”
Identical presence. The model recommends Brand A anyway, because the frame it learned makes A the starting point and B the upgrade you consider later. B could publish twice the content and win every citation battle. Until the story shifts, the model keeps sending new teams to A.
That’s narrative share doing its work, quietly, underneath the metrics most people track.
The AEO Trap: Citation Optimization Without Frame Ownership
The trap is treating citation frequency as the finish line. Teams grind on getting quoted, watch the mention count climb, and wonder why pipeline doesn’t follow.
They optimized how they appear inside a story that positions someone else as the answer. It’s ranking number one for a query nobody trusts. The mechanics work. The outcome doesn’t move.
Citation without frame ownership is motion without direction. You need both, in order. Frame first, then the tactics that get you cited inside it.
What Actually Moves an LLM’s Answer (Narrative + Tactics)
The work is sequenced. First, understand the consensus story the model learned about your category and where your frame is winning or losing inside it. Then use AEO and GEO to reinforce the framing you want across the sources the model reads.
Tactics amplify a frame. They don’t create one. Get the story right and citation work compounds. Get it wrong and you’re louder inside a narrative that still recommends your competitor.
How to Audit Your Narrative Share Before You Optimize for Visibility
Start by reading the frame, not counting mentions. Ask the model the questions your buyers actually ask. “What’s the best tool for [your category]?” “Who should a small team use?” Watch not whether you appear, but how you’re positioned. Default or alternative. Safe choice or edge case. The one they name first or the one they hedge with.
Then trace the sources feeding that framing. The articles and threads the model leans on are where the story lives. Those inputs are where you change the output.
Open ChatGPT right now, ask it to recommend a tool in your category, and read whose frame the answer is built on. If it’s not yours, that’s the work. Mavel does this systematically. Free audit at mavel.ai/analyze.
