---
title: "ChatGPT Ads Have No Keyword Wall. Here’s What Replaces It."
description: "ChatGPT matches ads to conversations, not a keyword list. Here’s how context changes the match—and what hands-on management can control instead."
url: "https://groas.com/post/there-are-no-keywords-in-chatgpt-how-a-conversation-becomes"
image: "https://pub-87da24ecbbfc4c3bad6875f3aa013712.r2.dev/generated-images/6cc3b9d7-7789-414b-8787-9632d6cde6f4.png"
published: "2026-11-01T06:23:00.992Z"
modified: "2026-11-01T06:23:01.041Z"
---

November 1, 2026 · 10 min read

# ChatGPT Ads Have No Keyword Wall. Here’s What Replaces It.

[Alexander PerelmanHead Of Product @ groas](https://groas.com/author/alexander-perelman)[LinkedIn](https://www.linkedin.com/in/alexander-433793253/)

![A tiny model worker admires his neat wall of keyboard keycaps while a winding stream from a flooded house flows around it, showing keyword walls can't hold conversations.](https://pub-87da24ecbbfc4c3bad6875f3aa013712.r2.dev/generated-images/6cc3b9d7-7789-414b-8787-9632d6cde6f4.png)

In this article

1. [On Google, the typed query gives you a boundary](#on-google-the-typed-query-gives-you-a-boundary)
2. [In ChatGPT, the need emerges across messages](#in-chatgpt-the-need-emerges-across-messages)
3. [The ad has to fit before the bid matters](#the-ad-has-to-fit-before-the-bid-matters)
4. [Four levers survive the loss of keywords](#four-levers-survive-the-loss-of-keywords)
5. [The negative-keyword reflex has nowhere to attach](#the-negative-keyword-reflex-has-nowhere-to-attach)
6. [One water heater, three conversations](#one-water-heater-three-conversations)
7. [Hands-on management should change the inputs, not just admire them](#hands-on-management-should-change-the-inputs-not-just-admire-them)
8. [The next test is a refrigerator, not a keyword list](#the-next-test-is-a-refrigerator-not-a-keyword-list)

A buyer types `emergency water heater replacement chicago` into Google. I can build a keyword wall around that search. In ChatGPT, the same buyer might start with “my basement is flooding” and mention the water heater three messages later. **There is no equivalent wall to build.**

I spent most of the last decade building those walls: exact-match cores, phrase-match nets, negative lists mined after someone changed a landing page without telling me. For a business spending $20k a month on Google, deciding which typed searches could trigger an ad was a large part of the job. But [OpenAI selects ads using conversation context and intent alongside the landing page, title, copy and advertiser hints](https://help.openai.com/en/articles/20001207-ads-in-chatgpt-the-basics). A keyword list cannot enclose a conversation. To understand what management looks like here, start with what the old wall was actually doing.

## On Google, the typed query gives you a boundary

A search query compresses a lot of intent into a few words. `Emergency water heater replacement chicago` tells me the job, the urgency and the location. [Google matches ads to typed search terms through the keywords an advertiser targets](https://blog.hubspot.com/marketing/chatgpt-ads-vs-google-search-ads). I used to tell clients I was buying intent by the word. That was a useful approximation because the words gave me something to work against.

That is why match types and negatives matter. I can target `water heater replacement`, decide how closely a search must relate to it, then exclude terms that bring the wrong people. [Google supports broad, phrase and exact negative keywords; they work literally rather than expanding to close variants](https://support.google.com/google-ads/answer/12437241). Its [running-shoes example](https://support.google.com/google-ads/answer/12437241) makes the distinction plain: a negative broad match for `running shoes` blocks `blue running shoes`, not `blue tennis shoes`.

The mechanism is simple. A typed search gives me text I can inspect. When that text reveals a bad match, I can add a text-based exclusion and stop seeing that kind of search. My 1am negative mining was boring, and it paid. **The keyword wall works because the trigger has words you can fence.**

## In ChatGPT, the need emerges across messages

Now put the water heater buyer in a conversation. They say the basement is flooding. Two turns later, they mention a 12-year-old tank. Then Chicago. Then: can this wait until Monday? [Unlike a short search query, a ChatGPT conversation unfolds across messages](https://blog.hubspot.com/marketing/chatgpt-ads-vs-google-search-ads). No single line contains the whole buying situation.

[OpenAI uses a separate orchestrator LLM to turn parts of a conversation into embedding queries that find candidate ads without requiring the same wording](https://www.searchenginejournal.com/openai-details-chatgpt-llm-ad-stack/593857/). It describes the process as stateful: earlier messages can shape what is relevant later. The practical difference is not that words cease to matter. It is that no one phrase does the work of the Google query. The flooding basement, aging tank, city and timing together make the situation legible.

That also means two people can use the same nouns and need different things. A homeowner whose tank has failed is not the same prospect as someone asking how to drain one before a vacation. Conversely, two people can describe an urgent replacement in different words and still mean much the same thing. [ChatGPT primarily matches ads to conversation context and intent, with advertiser hints contributing rather than guaranteeing visibility](https://blog.hubspot.com/marketing/chatgpt-ads-vs-google-search-ads). **The unit to manage is the situation, not the phrase.** Stop looking for the one query to fence.

## The ad has to fit before the bid matters

The matching and auction process is easier to follow in order:

1. **Find candidates.** The system reads the conversation and identifies ads that could fit its context.
2. **Assess the fit.** It considers [expected relevance and outcomes using the conversation alongside the landing page, title, copy and advertiser hints](https://help.openai.com/en/articles/20001207-ads-in-chatgpt-the-basics).
3. **Run the auction.** OpenAI describes [a relevance-weighted second-price auction, with max CPM bids for views and max CPC bids for clicks](https://help.openai.com/en/articles/20001207-ads-in-chatgpt-the-basics). It [suggests starting CPC bids of $3 to $5 and warns that low bids may limit delivery](https://help.openai.com/en/articles/20001207-ads-in-chatgpt-the-basics).

The sequence matters. Raising a bid does not turn a vague plumbing page into a compelling match for someone describing a failed tank in Chicago. **Bid affects your chance to compete; relevance affects whether the ad fits the conversation.**

There is a limit to how precisely an advertiser can reverse-engineer that decision. [The scoring weights are undisclosed, and winning the auction does not guarantee an impression](https://www.searchenginejournal.com/openai-details-chatgpt-llm-ad-stack/593857/). [When personalization is on, broader ChatGPT signals can also feed the score](https://help.openai.com/en/articles/20001207-ads-in-chatgpt-the-basics). In [SE Ranking’s test, ads appeared on 25.94% of 50,006 commercial prompts, with one sponsored card per answer](https://seranking.com/chatgpt-ads-tracker.html). Limited space and a private scoring formula make a specific-prompt placement promise hard to take seriously. Ask what a manager can change, not what they can guarantee.

## Four levers survive the loss of keywords

The first lever is the **situation you describe to the system**. [Ad-group-level context hints describe conversations, topics or keywords where a product may be relevant](https://help.openai.com/en/articles/20001207-ads-in-chatgpt-the-basics). The word “keywords” is the trap: [OpenAI says hints are not exact-match keywords and do not guarantee delivery in particular conversations](https://help.openai.com/en/articles/20001207-ads-in-chatgpt-the-basics).

For the Chicago water heater business, I would describe the urgent replacement job, the location and the customer who needs an installer. I would also make clear that maintenance questions are not that job. That is more useful than pasting in every possible variation of “water heater repair.” A hint describes a situation; it does not issue a command to show or suppress an ad. Write it like a brief for someone who needs to recognize the right customer, not like a negative-keyword spreadsheet.

![A brick wall with a gap where conversation bubbles pass through](https://pub-87da24ecbbfc4c3bad6875f3aa013712.r2.dev/generated-images/856f72fd-ec20-4cdc-adff-c9808b08384a.png)

The second lever is **the page behind the ad**. [OpenAI considers the landing page, title and copy alongside conversation context](https://help.openai.com/en/articles/20001207-ads-in-chatgpt-the-basics). The page is therefore part of what the system has available when assessing the ad, not merely somewhere to send the click afterward. A page about Chicago water heater installs, same-day tank replacement, permits and price ranges speaks more directly to the flooding-basement conversation than a page headed “Plumbing Services.” I learned to care about page relevance on Google. Here I cannot rely on a tightly chosen keyword to make a vague page look specific. One clear page per job beats one vague page per company.

The third lever is **the outcome you measure**. Expected outcomes are part of OpenAI’s ad decision, but that does not establish that sending it a booked-job event will directly retrain its matching system. I would not sell a client that claim. What an advertiser can do is distinguish form fills from qualified pipeline or booked work in its own assessment, then use that evidence to revise pages, hints and bids. If the water heater business counts every form as a win, a maintenance question and an installation request can look equally valuable in the report. If it tracks booked jobs, a manager has a better basis for deciding which situations merit spend. Measure the job you want before declaring a campaign efficient.

![A control-room wall with three active dials and dark keyword switches](https://pub-87da24ecbbfc4c3bad6875f3aa013712.r2.dev/generated-images/a3dfc6c0-549e-4d12-954a-6d9cec769ef3.png)

The fourth lever is **the amount you are willing to pay to learn**. [The auction uses max CPM bids for views and max CPC bids for clicks](https://help.openai.com/en/articles/20001207-ads-in-chatgpt-the-basics), so bids and budget caps still matter. The [suggested $3 to $5 starting CPC](https://help.openai.com/en/articles/20001207-ads-in-chatgpt-the-basics) is a starting point, not proof that every advertiser should bid there. I would set a cap the business can sustain while it finds out whether the resulting work is worth buying. A low bid that produces little delivery teaches you little; an uncapped learning exercise can teach you an expensive lesson. Decide what you can afford to test before you start interpreting results.

## The negative-keyword reflex has nowhere to attach

On Google, my weekly loop was familiar: read the search term report, find the junk, add negatives and tighten the match that let it through. In ChatGPT, [advertisers do not get search- or prompt-level reporting that shows what users asked](https://www.adweek.com/programmatic/advertisers-want-brand-safety-prompt-matching-incrementality-tracking-chatgpt-ads/). You cannot mine a report you do not receive. More importantly, a negative phrase would not describe the whole situation the system is assessing.

The premium-refrigerator example makes this painfully concrete. On Google, if `cheapest refrigerator` appeared in the search term report, I could add `cheap` as a negative. In ChatGPT, [advertisers have raised precisely this premium-versus-cheapest problem because they lack equivalent query-level control](https://www.adweek.com/programmatic/advertisers-want-brand-safety-prompt-matching-incrementality-tracking-chatgpt-ads/). A buyer asking for the cheapest fridge is a poor fit for a premium seller, but there is no promise that writing “premium” in a hint will exclude that conversation.

So the job changes. Describe the customer you do want, make the page unmistakably about that offer, and check whether the resulting clicks turn into the business you intended. Revise those inputs when they do not. What the deck calls precision, I call Tuesday morning adding 40 misspellings before coffee. I am not nostalgic for it. **Judge ChatGPT management by situations clarified and outcomes measured, not terms blocked.**

## One water heater, three conversations

Return to the Chicago business and hold the product constant. These chats may share words, but they do not deserve the same decision:

- **Chat one:** A homeowner says the basement is wet, the tank is 12 years old and someone needs to come today. This fits the emergency-install situation. I would pair a clear replacement hint with a same-day installation page and consider competing harder for it.
- **Chat two:** A landlord asks whether a tankless upgrade pays back on a rental property. That is a research conversation, not a same-day replacement request. If the business has a useful tank-versus-tankless comparison page, it can test whether this kind of inquiry becomes valuable work. If it only wants urgent installs, I would not treat this as equally promising.
- **Chat three:** A renter asks how to drain a tank before a vacation. The nouns overlap with chat one, but there is no apparent installation job. I would not seek this traffic. I also would not promise that a hint can block it with Google-style certainty.

The distinction follows from the mechanism: conversation context tells the system more than a repeated noun can. **Same water heater, three different buying situations.** A manager should be able to explain which one the business wants, which page supports it and what outcome would show the decision was right. If the explanation starts and ends with a keyword list, it misses the point.

## Hands-on management should change the inputs, not just admire them

If you are shopping for help, ignore the ChatGPT tab in the sales deck. Every deck can have a tab. Ask what changes after launch. Hands-on management means someone revises situation hints when the traffic looks wrong, improves pages that describe too many jobs at once, checks whether apparent conversions become booked work, and adjusts bids and caps as delivery develops. Advice says a page should be specific. Management makes it specific and checks what happens next.

That is why the operating model matters. [groas covers Google, Bing and ChatGPT ads, with bids, keywords and budgets worked around the clock and a landing page for every ad group](https://groas.com/pricing), for a flat monthly fee rather than a cut of spend. The point is not to pretend the keyword work disappears from Google and Bing. It is to avoid carrying that work over as the entire plan for a channel where the match depends on context. If you require prompt-level reports before approving any showing, this channel will frustrate you under its current reporting limits. If you can assess situations and business outcomes, ask a provider to show what it changes, not how many hours it bills.

## The next test is a refrigerator, not a keyword list

Suppose the next advertiser sells premium refrigerators. A prospect chats about replacing a fridge, then says price matters more than anything else and asks for the cheapest option. Start with the model we built: the system considers the conversation, the advertiser’s hints and the ad’s page before an auction determines what can appear. The advertiser cannot reach into a search term report, find `cheapest refrigerator` and block it. It can make the premium offer clear in the hint and on the page, then judge whether the clicks it buys turn into worthwhile sales. That may improve the fit. It does not guarantee the bargain hunter never sees the ad.

Now ask a prospective manager what they would do with that case next week. If they promise match types, negative lists and prompt-level mining, they are selling Google management with a new tab. If they ask which buying situations the business serves, whether the page makes the premium offer plain, what the resulting customers bought and whether the bid still makes sense, they understand the job. **The old manager guarded a wall. The new one has to judge the conversation.**

## Frequently Asked Questions

### How do ChatGPT ads match users differently than Google Search ads?

ChatGPT ads match ads to conversation context and intent rather than matching a typed query to a pre-defined keyword list. The matching system assesses how well the ad fits the broader conversational situation. This allows a buyer to mention a product many messages after the initial query.

### What information does the system use to decide if a ChatGPT ad is relevant to a conversation?

OpenAI uses an LLM orchestrator to turn conversation parts into embedding queries and assesses the ad's fit based on multiple factors. These include the conversation context, the landing page, the title, the copy, and any provided advertiser hints.

### What is the fundamental unit to manage in ChatGPT advertisements?

The unit to manage is the entire situation or context, not a single phrase or keyword. Advertisers should describe the buying situation to the system rather than listing every possible phrase. This ensures the ad fits the overall conversation.

### What are the practical strategies available to manage performance in ChatGPT ads?

Advertisers can manage performance through four levers: describing the situation to the system, optimizing the landing page relevance, measuring desired business outcomes, and adjusting bids and budgets to facilitate learning. These actions refine the ad's effectiveness within the conversation.

### Can I use negative keywords to block unwanted searches in ChatGPT ads?

Unlike Google Search, advertisers do not receive prompt-level reporting that reveals what users asked in the conversation. Because an advertiser cannot mine a report they do not receive, traditional keyword-based negative lists are not available for this channel.

## Paid Ads & SEO, Run For You

Businesses pay one flat monthly fee per service, agencies resell it under their own brand, and groas does the work either way.

[See Pricing](https://groas.com/pricing)

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