--- title: "ChatGPT Ads: An emerging opportunity, but not yet a performance replacement " date: 2026-09-11 author: "Zhenya Brisker" featured_image: "https://www.dacgroup.com/wp-content/uploads/2026/09/ChatGPT-Ads-Blog1-875x438-1.jpg" categories: - name: "Paid media" url: "/insights/blog/paid-media.md" lang: en --- # ChatGPT Ads: An emerging opportunity, but not yet a performance replacement  As consumers increasingly use conversational AI to research problems, evaluate options, and seek advice, ChatGPT is emerging as a new environment for brands to reach people while preferences are still being formed. That makes ChatGPT Ads an important development for advertisers to watch. But being early to a new channel and being ready to scale investment into it are two different things. Based on early advertiser testing, our view is that most brands should not yet shift meaningful conversion-focused investment away from established performance channels and into ChatGPT Ads. Instead, advertisers should consider ChatGPT primarily as an emerging consideration opportunity—one that may warrant controlled testing when the strategic moment, audience, and budget are right. ## The key question is opportunity cost When a new advertising platform emerges, the question is often framed as: “Should we test it?” A better question is: **“Is this the best use of our next media dollar?”** For most advertisers, incremental investment in ChatGPT Ads competes with channels that already have mature optimization systems, established performance benchmarks, sophisticated targeting, and years of historical advertiser data. That creates a high bar. Early evidence from advertiser testing suggests ChatGPT Ads can currently carry significant cost premiums relative to mature digital channels. Results also vary considerably between advertisers and use cases. In one early client account managed by DAC, for example, booked leads generated through ChatGPT Ads were approximately 100 times more expensive than the advertiser’s typical range. In separate DAC client examples, consideration and awareness CPMs were approximately 8.7x and 9.5x more expensive, respectively, than the advertisers’ typical benchmarks. These directional results should not be interpreted as platform-wide benchmarks, but they do illustrate the potential cost and efficiency gaps advertisers may encounter across different stages of the funnel. Independent advertiser results reinforce the same broader point: **performance remains highly variable.** ## Early tests show both potential and volatility Published advertiser experiences have produced a wide range of outcomes. For example, Search Engine Journal reported that [Hostinger invested nearly $70,000 testing ChatGPT Ads and saw CPMs rise above $65 as investment increased](https://www.searchenginejournal.com/6-months-into-chatgpt-ads-advertisers-still-dont-know-what-good-looks-like/587583/). The company also raised concerns about click-through rates, traffic quality, and the difficulty of evaluating return solely through direct conversions. More specific use cases reportedly performed better than broad messaging. The same Search Engine Journal report also cited a B2B advertiser that recorded a $64.34 CPM, $9.29 CPC, and 0.7% CTR. Analysis of the organizations behind its paid traffic found that only a small portion matched the advertiser’s ideal customer profile. These results don’t mean ChatGPT Ads cannot work. They demonstrate something more important for media planners: **conversational relevance does not automatically translate into qualified traffic or efficient business outcomes.** And without mature platform benchmarks, it remains difficult to determine what “good” performance should look like. ## Where ChatGPT Ads may offer more value Performance efficiency is only part of the story. ChatGPT creates a media moment that is fundamentally different from many traditional advertising environments, in which consumers use conversational AI while trying to understand a problem—asking for recommendations, researching potential solutions, and comparing alternatives. In those moments, people may not yet have decided which brand, product, or service they prefer. That gives advertisers a potentially valuable opportunity to participate earlier in the decision journey. For that reason, we currently see a more natural role for ChatGPT Ads in consideration, rather than as a direct substitute for channels already proven to capture and convert demand efficiently. That doesn’t mean every advertiser should immediately launch a consideration campaign. Current CPM premiums mean testing should have a clear strategic rationale. A controlled test may make sense when: - **An important seasonal period** creates an opportunity to influence consumers while preferences are being formed. - **A product or service launch** makes building consideration particularly valuable. - **The audience and use case align naturally with conversational research**, recommendations, or comparison. - **There is sufficient budget flexibility** to prioritize learning without diverting meaningful investment from proven performance channels. For other brands, the better decision may simply be to continue monitoring the platform while its advertising ecosystem develops. **The objective should be learning, not being able to say the brand was an early adopter.** ## Platform transparency is still catching up One of the biggest differences between ChatGPT Ads and mature advertising platforms is not whether advertisers can measure outcomes. Increasingly, they can. The challenge is understanding **why** those outcomes occurred. Advertisers can measure impressions, clicks, spend, CTR, CPC, CPM, and conversions. Conversion measurement can also be supported through tools such as the OpenAI Pixel, Conversions API, dynamic URL parameters, and standard analytics platforms like GA4 and Adobe Analytics. But advertisers currently have less visibility into the competitive and auction dynamics behind performance than they would on mature search and social platforms. There are not yet equivalents to some of the competitive reporting and auction diagnostics advertisers have grown accustomed to elsewhere, and OpenAI has not published broad performance benchmarks across advertisers, industries, or campaign types. Context signals can help influence relevance, but they do not function exactly like traditional keywords or guarantee delivery against specific conversations or audiences. As a result, when performance changes materially, advertisers have fewer signals for determining whether the cause was competition, relevance, available inventory, bidding dynamics, conversational matching, or audience quality. That matters when deciding not only whether a campaign “worked,” but how confidently it can be optimized and scaled. ## Better measurement doesn’t mean greater efficiency The improving measurement ecosystem around ChatGPT Ads is encouraging. Advertisers considering a test should establish measurement that allows results to be evaluated alongside the rest of the media mix. That includes appropriate conversion tracking, consistent UTMs, and analytics-platform measurement aligned to the KPIs associated with the campaign’s funnel stage. But there is an important distinction: **Better measurement helps advertisers determine whether ChatGPT Ads are working more accurately. It does not inherently make the channel more efficient.** That distinction should remain central to investment decisions. ## What would change our view? ChatGPT Ads are still an emerging product, so today’s recommendation should not be treated as permanent. Several developments could materially change the investment case: - **More competitive economics.** As inventory, demand and the auction ecosystem evolve, CPMs and CPCs may move closer to levels that make broader testing economically attractive. - **Stronger performance benchmarks.** Industry and campaign-level benchmarks would give advertisers much-needed context for evaluating results. - **Greater targeting depth.** Expanded geographic and audience capabilities would make the platform relevant to a wider range of advertisers, particularly businesses with tightly defined service areas or markets. - **Improved optimization and transparency.** Better insight into delivery, competition, and performance drivers would give advertisers greater confidence in their ability to optimize and scale campaigns. - **More evidence of incremental business impact.** Ultimately, the strongest argument for increased investment will be evidence that ChatGPT can deliver value that complements or exceeds the incremental returns available elsewhere in the media mix. ## What marketers should do now ChatGPT is becoming an increasingly important part of how consumers discover information, evaluate alternatives, and make decisions. Advertisers should pay attention to that shift. But attention does not require indiscriminate investment. For most brands today, we recommend treating ChatGPT Ads as an **emerging consideration and learning opportunity rather than a replacement for established performance media**. Brands with sufficient budget flexibility, a relevant consumer use case, and a meaningful strategic moment may benefit from a tightly scoped test designed to generate learning. Performance-focused advertisers with limited flexibility may be better served by maintaining investment in proven channels while monitoring how ChatGPT’s advertising capabilities and economics develop. The opportunity is real. So is the need for discipline. The goal should not be to invest in ChatGPT Ads simply because the platform is new. It should be to understand where conversational advertising can create incremental value and to scale investment when the evidence supports it.