A DTC coffee brand came to us last month convinced their AEO strategy was working. They'd added FAQ schema to every product page. They'd rewritten their homepage copy with "AI-friendly" H2s. They were tracking mentions in ChatGPT weekly.
They were also invisible. Zero recommendations across 200 buyer-intent queries. Their nearest competitor showed up in 34% of the same prompts.
The gap wasn't strategy. It was tactics. Five specific tactical decisions had quietly nuked their visibility, and they had no idea which ones because everything they read told them they were doing the right things.
We audit a lot of brands. The same five mistakes come up almost every time. Here they are, in order of how much damage they do.
Mistake #1: Treating schema markup as the whole strategy
Schema markup is table stakes, not a moat. Every SEO article from 2023 told you to add FAQPage schema, Product schema, and Organization schema, and you did. Good. So did every competitor. The floor moved up. Your relative position didn't.Here's what actually happens when an LLM answers "what's the best pour-over coffee for beginners?" It doesn't crawl your schema and reward the neatest markup. It pulls from a synthesized understanding of the web — Reddit threads, YouTube reviews, roaster comparison blogs, Serious Eats, Sprudge, r/coffee, a Wirecutter piece from 2022. Schema helps the model parse your page when it does encounter you. It doesn't get you encountered.
We ran a controlled test in October across 40 ecom brands. Half had comprehensive schema on every page. Half had none. Mention rates in ChatGPT and Perplexity across 500 category queries: 12.4% vs 11.1%. Statistically indistinguishable.
The brands that outperformed didn't have better schema. They had more third-party context pointing at them.
Schema helps AI understand you. Third-party content makes AI care about you. What to do instead: Ship the schema and forget about it. Then spend the next 90 days on the stuff that actually moves the needle — getting cited on comparison posts, seeding product review roundups, showing up in Reddit threads where your category gets discussed. If you're auditing your own site and 80% of your AEO work is on-page, you're pulling the wrong lever.Mistake #2: Ignoring third-party content because you can't control it
The single strongest predictor of AI mention rate is third-party citation density. Not domain authority. Not schema. Not word count. The number of independent sources that mention your brand in a category-relevant context.Marketers hate this one because it means the highest-leverage AEO work happens off your own site, where you can't A/B test copy or watch analytics dashboards. So most teams keep polishing product pages while their competitor gets a mention in a G2 comparison article and jumps 400 basis points in Perplexity share.
Look at how the model actually decides. When someone asks Claude for "the best CRM for a 10-person agency," Claude isn't looking at your homepage headline. It's weighing what it's seen on Capterra, G2, TrustRadius, Reddit's r/agency threads, Indie Hackers posts, and the six roundup articles that rank for "best CRM for agencies." Your homepage is maybe 2% of the signal.
The math is brutal but simple. A brand mentioned in 40 category-relevant third-party sources will beat a brand mentioned in 6, even if the brand with 6 has a better product. AI models converge on consensus. Consensus lives off-site.
What to do instead:- Pull the top 25 URLs that ChatGPT cites for your top 10 category queries. (You can do this manually. It takes about two hours.)
- For each URL, check whether you're mentioned. Not linked — mentioned by name.
- Where you're not mentioned, either pitch the author, sponsor a listing where legitimate, or create a competing resource that pulls citations to you.
Mistake #3: Publishing AI-written boilerplate on landing pages
LLMs are unusually good at recognizing their own output — and they discount it. This is the mistake we see most often at scale, and it's the one brands are most defensive about.Here's the mechanism, because it matters. Modern retrieval-augmented systems don't just score content on relevance. They also weight for source quality signals: originality, specificity, first-hand evidence, and something researchers loosely call "epistemic uniqueness." Generic marketing copy that reads like every other page in the category adds no new information to the model's understanding. It gets deprioritized in retrieval and rarely surfaces in citations.
We looked at 12 Shopify brands who'd rewritten their product descriptions with ChatGPT in Q2. Ten of them saw AI mention rate drop 15-40% within 60 days. The two that didn't drop had layered in specific, first-hand product details on top of the generated draft.
The tell for AI-written boilerplate:
- Phrases like "in today's fast-paced world," "elevate your," "crafted with care"
- Symmetrical three-item lists ("quality, durability, and style")
- No proper nouns, no numbers, no specifics
- Every paragraph the same length
Mistake #4: Skipping the review site seeding step
Review platforms are the highest-leverage AEO surface nobody's optimizing. G2, Capterra, TrustRadius, Trustpilot, Yelp, Reddit, Product Hunt, Wirecutter — these are the sources LLMs disproportionately cite for buyer-intent queries. And most brands treat them as afterthoughts.Run this experiment. Ask ChatGPT: "What's the best [your category] for [your ICP]?" Then ask it: "Where did you get that information?" Nine times out of ten, one of the top three sources will be a review aggregator or a Reddit thread. Not a brand's own site.
A B2B SaaS client of ours in the workflow-automation space went from 4 G2 reviews to 47 reviews in six weeks. Their mention rate in "best workflow automation tool for [industry]" queries across ChatGPT, Claude, and Perplexity went from 8% to 31%. Nothing else changed on their site.
The reason this works isn't just review count. It's that review platforms feed the model structured, opinionated, first-hand content with clear pros/cons framing. That's exactly the format LLMs use to construct answers. When Claude generates "the top three options are X, Y, and Z, with X best for teams under 20 and Y best for enterprise," it's often paraphrasing directly from review site synthesis patterns.
What to do instead:| Platform | Priority for | Minimum threshold |
|---|---|---|
| G2 / Capterra | B2B SaaS | 25 reviews, 4.3+ avg |
| Trustpilot | DTC ecom | 100 reviews, 4.2+ avg |
| Reddit (organic) | Any category with active subs | 3-5 authentic mentions/quarter |
| Product Hunt | Early-stage SaaS | Top 5 of the day at minimum |
| Wirecutter / NYT | Consumer hardgoods | Any mention is transformative |
| YouTube reviews | Physical products | 5+ videos with 5k+ views |
Mistake #5: Competing on the wrong query intent
Most brands optimize for queries their customers don't actually ask an AI. They spent a decade learning SEO keyword research, so they bring the same instincts to AEO. But AI query behavior is fundamentally different from Google search behavior, and the difference costs brands most of their visibility budget.On Google, people type "best CRM." On ChatGPT, they type "I run a 12-person marketing agency, we mostly do retainer clients, and we're currently on HubSpot but the price keeps climbing. What CRM should we look at?"
The AI query is longer, more contextual, more specific to the user's situation, and — critically — includes constraints and comparisons that Google users would split across multiple searches. This has three implications most brands miss.
First, your target queries are longer than you think. Optimizing your page for "best CRM for agencies" is fine. Optimizing for "CRM alternatives to HubSpot for small agencies under $200/month" is where mentions actually happen. The tail is where the volume is. Second, intent segmentation matters more than volume. In AI search, a query with 100 monthly instances that's a pure buyer-intent question ("which should I buy") is worth 50x a query with 5,000 instances that's informational ("what is a CRM"). Brands still chase the big informational queries because that's what their keyword tools show them. LLMs are already answering those without citations. Third, comparison queries are underweighted. "X vs Y" queries convert unusually well in AI answers because the model has to name specific brands to answer. If you're not showing up in your top 10 vs-queries, that's the fastest lift available.A skincare brand we work with was optimizing for "best retinol serum" — a query where ChatGPT gives a generic education answer and rarely cites brands. When they shifted focus to "retinol serum for sensitive skin under $40" and "[Competitor] vs [alternatives] for beginners," their mention rate on tracked queries went from 6% to 22% in eight weeks.
What to do instead: Rebuild your target query list from scratch, using actual AI conversation patterns rather than SEO keyword tools. For every generic category query, write 5-10 constrained variants. Include the most common objections, budget ranges, use cases, and competitor comparisons. Then measure separately — because average mention rate across the whole list hides which segments are actually winning.The pattern underneath all five
Every one of these mistakes has the same root cause: applying SEO instincts to a system that doesn't work like SEO.SEO rewarded technical hygiene on your own site. AEO rewards ecosystem presence across the web. SEO rewarded matching keywords. AEO rewards being genuinely useful in ways models can synthesize. SEO rewarded volume. AEO rewards specificity.
If you audit your AEO strategy and 70%+ of the work is happening on your own site, you're playing yesterday's game. The brands winning AI mention share in 2025 are the ones doing 30% on-page work and 70% ecosystem work: seeding reviews, getting cited in third-party comparisons, showing up in Reddit threads, targeting the actual constrained queries their buyers ask.
None of this is complicated. It's just different from what most brands are doing, which is why the ones who fix it first are pulling away.
A quick sanity check
Before you do anything else, run this five-minute audit on yourself:
- Pull your top 10 category queries. Are you mentioned in ChatGPT, Claude, and Perplexity answers?
- If not, who is? Pull the three most-cited sources for each query.
- Are you present on those sources?
- Look at your own top 5 landing pages. Would three words changed make them describe a competitor's product?
- How many reviews do you have on the primary review platform in your category, and what's the average rating?
Foxish measures both sides of this: which queries you show up in across ChatGPT, Claude, Gemini, and Google AI Overviews, and which third-party sources those answers are pulling from. That second part is where most of the actionable work lives, and it's why we built the citation-source view into the free scan. Start a free scan →. First scan is free, no credit card, and you'll see within about five minutes which of these five mistakes is costing you the most.