{"id":165552,"date":"2026-09-11T10:41:57","date_gmt":"2026-09-11T15:41:57","guid":{"rendered":"https:\/\/www.dacgroup.com\/en-ca\/insights\/blog\/none\/chatgpt-ads-emerging-consideration-opportunity\/"},"modified":"2026-09-11T10:41:57","modified_gmt":"2026-09-11T15:41:57","slug":"chatgpt-ads-emerging-consideration-opportunity","status":"publish","type":"post","link":"https:\/\/www.dacgroup.com\/en-ca\/insights\/blog\/none\/chatgpt-ads-emerging-consideration-opportunity\/","title":{"rendered":"ChatGPT Ads: An\u00a0emerging\u00a0opportunity, but\u00a0not\u00a0yet a\u00a0performance\u00a0replacement\u00a0"},"content":{"rendered":"\n

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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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\u2014one that may warrant controlled testing when the strategic moment, audience, and budget are right.<\/p>\n\n\n\n

The key question is opportunity cost<\/h2>\n\n\n\n

When a new advertising platform emerges, the question is often framed as: \u201cShould we test it?\u201d<\/p>\n\n\n\n

A better question is: \u201cIs this the best use of our next media dollar?\u201d<\/strong><\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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\u2019s typical range. In separate DAC client examples, consideration and awareness CPMs were approximately 8.7x and 9.5x more expensive, respectively, than the advertisers\u2019 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.<\/p>\n\n\n\n

Independent advertiser results reinforce the same broader point: performance remains highly variable.<\/strong><\/p>\n\n\n\n

Early tests show both potential and volatility<\/h2>\n\n\n\n

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<\/a>. 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.<\/p>\n\n\n\n

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\u2019s ideal customer profile.<\/p>\n\n\n\n

These results don\u2019t 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. <\/strong>And without mature platform benchmarks, it remains difficult to determine what \u201cgood\u201d performance should look like.<\/p>\n\n\n\n

Where ChatGPT Ads may offer more value<\/h2>\n\n\n\n

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\u2014asking 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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

That doesn\u2019t 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:<\/p>\n\n\n\n

    \n
  • An important seasonal period<\/strong> creates an opportunity to influence consumers while preferences are being formed.<\/li>\n\n\n\n
  • A product or service launch<\/strong> makes building consideration particularly valuable.<\/li>\n\n\n\n
  • The audience and use case align naturally with conversational research<\/strong>, recommendations, or comparison.<\/li>\n\n\n\n
  • There is sufficient budget flexibility<\/strong> to prioritize learning without diverting meaningful investment from proven performance channels.<\/li>\n<\/ul>\n\n\n\n

    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.<\/strong><\/p>\n\n\n\n

    Platform transparency is still catching up<\/h2>\n\n\n\n

    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<\/strong> those outcomes occurred.<\/p>\n\n\n\n

    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.<\/p>\n\n\n\n

    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.<\/p>\n\n\n\n

    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 \u201cworked,\u201d but how confidently it can be optimized and scaled.<\/p>\n

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