An AEO strategy is a measurable plan for winning mentions in AI answers: baseline your share of answers, pick target prompts, restructure pages answer-first, earn third-party mentions, build community presence, publish citeable data, and re-measure weekly. Each step below pairs with the metric that proves it worked.
The pairing matters because AI answers move constantly. SparkToro found the same AI query changes its answer roughly 70% of the time. Our own measurement at Honeyb (our product) confirms it: across 20 buyer prompts run three times each on four engines via API on 13 July 2026, 240 answers in total, the top-ranked brand changed between identical runs 28% to 44% of the time depending on the engine. Any AEO step you cannot verify against repeated measurement is a guess.
The 7-step AEO strategy at a glance
| Step | Action | Metric that proves it worked |
|---|---|---|
| 1 | Baseline measurement | Share of answers naming your brand, per engine |
| 2 | Pick target prompts | Coverage: tracked prompts vs prompts buyers actually ask |
| 3 | Answer-first page structure | Citations of your domain in AI answers |
| 4 | Earn third-party mentions | Mentions on domains the engines already cite |
| 5 | Community and Reddit presence | Target prompts where a community thread citing you appears |
| 6 | Publish citeable first-party data | Citations of your data pages across engines |
| 7 | Re-measure weekly | Trend in share of answers, aggregated across runs |
Step 1: baseline your AEO strategy with share of answers
Before changing anything, measure how often each engine names you today. In our 13 July 2026 test, engines named 4.8 to 5.2 brands per answer on average, so every prompt is a contest for roughly five slots. Because the top pick changed between identical runs up to 44% of the time (Gemini), a single manual check is noise. Run each prompt several times, then record the share of answers that name you. If you are new to how these systems assemble answers, start with our explainer on what an AI answer engine is.
Step 2: pick the prompts worth winning
Track the questions buyers ask at decision time, not vanity queries. A workable starting set is 10 to 50 prompts: comparisons, "best X for Y" questions, and problem descriptions in your category. The metric is coverage. If your tracked set misses the phrasings buyers use, your baseline is measuring the wrong contest. For reference, monitoring tools size their tiers around exactly this: Honeyb (ours) scans 10 prompts daily on its $29/mo Minimum plan and 50 on Multi-Model at $79 (pricing pages, 13 July 2026).
Step 3: restructure pages answer-first
Engines lift passages that answer a question directly. Lead each page with the answer, follow with evidence, and keep one question per section. The metric is citations of your domain in AI answers, and it needs a caveat: Semrush found 62% of AI citations never name the brand being cited. So track citations and brand mentions separately. A cited page that never earns a name-check is a partial win at best. Treat on-page structure as table stakes, because the Ahrefs correlation data in the next step shows it is not the main driver.
Step 4: earn mentions on domains the engines already trust
Ahrefs found AI visibility correlates most strongly with third-party mentions and video, not on-page optimisation. Our citation data shows where those mentions need to live. Across 240 answers, ChatGPT cited 445 distinct domains, Claude 194 and Perplexity 142, and Forbes was the only domain to appear in all four engines' top citation lists. The metric: count your mentions on the specific domains each engine cites for your target prompts, not generic "PR mentions". A mention on a domain the engine never retrieves does nothing for AEO.
Step 5: build a real community and Reddit presence
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Semrush measured Reddit at 40.1% of all AI citations, the single most-cited source. Our own data adds engine-level texture: in the 13 July 2026 test, Reddit accounted for 71 of Perplexity's 498 citations (14%) and YouTube another 40 (8%). Community content is not optional garnish, it is a primary retrieval source.
Share of AI citations
Most-cited domains in AI answers
The metric here is precise: the share of your target prompts where at least one community thread mentioning you appears in the cited sources. Astroturfing gets removed and burns trust, so the tactic is answering real questions in your niche's subreddits and letting genuinely useful answers accumulate citations.
Step 6: publish first-party data worth citing
Engines cite sources that contain specific numbers other pages lack. Original benchmarks, pricing surveys and measurement studies earn citations because no other domain can supply the figure. This entire post runs on that logic: the change rates and citation counts above come from our own 240-answer measurement, which is exactly the kind of asset engines retrieve. The metric is citations of your data pages across engines. Our guide on how to get cited by AI covers the formats that work.
Step 7: re-measure weekly, never once
One-off checks cannot detect whether steps 3 to 6 worked, because baseline volatility swamps any single reading. SparkToro's 70% change figure sets the backdrop, and our per-engine numbers make it concrete.
| Engine | Top-pick change between identical runs | Brand-set overlap between runs |
|---|---|---|
| Gemini | 44% | 54% |
| Perplexity | 43% | 61% |
| ChatGPT | 35% | 42% |
| Claude | 28% | 67% |
The table (Honeyb measurement, 13 July 2026) means even ChatGPT, the steadiest on top picks, kept only 42% of its brand set between identical runs. The metric that survives this noise is a weekly trend in share of answers, aggregated across multiple runs per prompt. Engines also disagree with each other: the same prompt produced the same top brand on only 20% of ChatGPT-Perplexity pairs, rising to 53% for Gemini-Claude, so measure each engine you care about separately.
Where AEO strategy goes from here
The playbook compounds. Baseline, target, structure, earn mentions, join communities, publish data, re-measure. Every cycle sharpens the prompt set and reallocates effort towards the steps moving your share of answers. For the conceptual foundations behind these tactics, see our pillar on answer engine optimization. To get your step-1 baseline without building the measurement pipeline yourself, run a free AI visibility check.
