ChatGPT Ads Are Coming: What This Means for Your Brand's Marketing Strategy

,200/mo enterprise), BrandLight, Otterly.ai (~$99/mo), Peec.ai, Goodie.

Key takeaways

• ChatGPT Search runs on Bing's index; Bing-friendly technical SEO is the highest-leverage move for ChatGPT visibility
• Perplexity surfaces citations as visible cards next to answers, the citation IS the impression and the click
• Claude uses Brave Search by default; a smaller index makes Claude visibility easier to engineer if targeted specifically
• Trustpilot, G2, Capterra, Yelp, Naver Smart Block carry disproportionate weight as LLM-cited authority sources
• Tools for LLM citation tracking: Profound, BrandLight, Otterly.ai, Peec.ai, Goodie

Why most "AI SEO" advice is wrong

Most articles on this topic treat LLMs as a single channel. They are not. ChatGPT Search, Perplexity, Claude, Gemini, Grok, You.com, and (in Korea) Naver Cue each have distinct retrieval backends, distinct synthesis behaviour, and distinct surface formats. A brand visible in Perplexity might be invisible in ChatGPT because Perplexity prefers third-party review sites while ChatGPT (running on Bing) leans on whatever ranks well in Bing.

Treating "AI visibility" as one problem is the most common mistake we see in 2026.

How each LLM actually works

• ChatGPT Search: Microsoft Bing's index + OpenAI's own crawled web data. Honours `robots.txt` plus a separate `OAI-SearchBot` user agent. Ranking signals that lift you in Bing also lift you in ChatGPT.
• Perplexity: own crawler (`PerplexityBot`) plus partner index. Surfaces citations as visible cards. Click-through to source is materially higher than ChatGPT.
• Claude (claude.ai web search): queries Brave Search by default and falls back to a partner API. Brave's index is meaningfully smaller than Google's or Bing's.
• Gemini: Google's index with grounding via the Search Generative Experience pipeline. Whatever ranks in Google ranks in Gemini.
• Grok: real-time X (Twitter) signal heavily weighted. Different optimisation surface entirely.
• Naver Cue: Naver's index and Smart Block content. Foreign brands optimised only for Google are typically invisible here.

The implication: a serious LLM-visibility programme has to address Bing, Brave, Google, X, and Naver, not "AI" as a monolithic surface.

The four-layer Citation Stack

LLMs do not score domains the way Google's PageRank-descended algorithms do. They synthesise from a smaller set of sources per query, weighted toward recency, structural clarity, and citability. Across thousands of LLM answers we have audited, the same four layers consistently surface:

Layer 1: Authority editorial

Trade press, industry reports, and named publications. For marketing-tool queries: MarTech.org, MarketingProfs, Search Engine Land, Lenny's Newsletter, The Drum. For Korean-market queries: Platum, Outstanding, Mobi Inside, ZDNet Korea. A single mention in any of these in the last 12 months disproportionately influences which brands surface.

Layer 2: Review aggregators

G2 and Capterra dominate B2B SaaS visibility. Trustpilot dominates consumer. Yelp still influences local. For Korean shopping queries, Naver Smart Block citations dominate. LLMs cite these because they aggregate signal across many users. Brands with 200 G2 reviews routinely beat brands with 5,000 own-site reviews.

Layer 3: Comparison content

"Brand X vs Brand Y" pages with proper structure surface in LLM-generated comparisons because the format maps directly to the user's prompt. The undervalued move in 2026 is publishing comparison pages against your three closest competitors, even if your sales team is uncomfortable with it.

Layer 4: Structured owned content

Clean About pages, FAQ schema, pricing pages with currency-specific figures, founder-story pages. LLMs use these as raw material for the "what does this brand do / who runs it / what does it cost" parts of an answer. Pages without structured data get omitted; pages with proper FAQ and Organisation schema get cited.

A brand sitting in three of four layers will appear in roughly 30-60% of relevant LLM answers based on our sampling. A brand in zero layers is functionally invisible.

How to audit LLM visibility this month

Concrete process. Allocate two hours.

1. Build a 20-prompt query set covering your category. Mix brand-agnostic ("best Korean Amazon agency"), comparison ("X vs Y"), and long-tail spec ("agency that does Coupang Ads under KRW 10M monthly").

2. Run the prompts in five LLMs: ChatGPT (with Search on), Perplexity, Claude (with web search on), Gemini, and Naver Cue if you serve Korea. Record which brands appear, which cited sources are used, and the order.

3. Categorise the citations by Citation Stack layer. Most B2B brands find they are missing Layer 2 (review aggregators) entirely.

4. Ask one disqualification question in each LLM ("why would I not pick Brand X"). The objections that surface tell you what content gap to close.

5. Track once per quarter. Daily tracking is overkill outside of product launches; quarterly cadence catches drift without burning resources.

Tools that automate this: Profound, BrandLight, Otterly.ai, Peec.ai, Goodie all monitor brand citations across LLMs. Pricing in 2026 ranges from roughly $99/month (Otterly) to