OpenAI Tests Self-serve Ads Manager
ChatGPT ads click-through rate are closer to Social benchmarks than Search

OpenAI is testing an Ads Manager dashboard that would give marketers a self-serve interface to run, monitor, and optimize ChatGPT ad campaigns. An OpenAI spokesperson confirmed the test to Adweek. The tool is still in early development: advertisers in the test group currently receive weekly CSV reports with basic click and impression data rather than real-time analytics.
What the Ads Manager looks like so far
The dashboard is being tested with a small group of partners. In its current form, it offers campaign monitoring but lacks the real-time bidding, granular audience targeting, and deep attribution that Google's Search Ads 360 and Meta Ads Manager provide. The gap between weekly spreadsheet reporting and established platforms' real-time analytics reflects how early ChatGPT's ad infrastructure remains.
The $200,000 minimum spend requirement for early advertisers is still in place, and pricing sits at approximately $60 CPM. Ads appear only on ChatGPT's free and Go tiers in the U.S., with premium paid subscriptions remaining ad-free. About 95% of ChatGPT's roughly 800 million users are on the ad-eligible tiers, which gives OpenAI a large potential inventory base.
ChatGPT's click-through rate challenge
The more significant pressure point for OpenAI may be click-through rates. Independent research from Ahrefs and ppc.land estimates ChatGPT's CTR at approximately 1.3%, compared to Google Search's 29.2%. The structural difference is important: ChatGPT's conversational format keeps users engaged within the app rather than clicking outbound links, which is fundamentally different from search intent where users actively seek external destinations.
That CTR gap creates pressure on OpenAI to demonstrate value beyond clicks. Criteo, the first ad tech partner connecting approximately 17,000 advertisers since March 2, has reported that LLM-referred users convert at roughly 1.5 times the rate of users from other referral channels. That conversion premium could offset lower CTR, but advertisers will need more granular data to evaluate the trade-off, which is precisely what the Ads Manager dashboard is meant to deliver.
Where this fits in OpenAI's ad business strategy
The Ads Manager is the infrastructure layer that shifts ChatGPT advertising from a hand-sold beta to a scalable platform. Once the dashboard is live and functional, advertisers would no longer need direct deals with OpenAI to access inventory.
Combined with reported talks with The Trade Desk and the existing Criteo partnership, the pattern points toward a multi-channel distribution model for ChatGPT ads. OpenAI has projected that advertising could help double consumer ChatGPT revenue to $17 billion in 2026. Whether the platform can close the CTR gap, or convince advertisers that conversion rates matter more than click-through rates, will shape how quickly that projection materializes.
Recap
Does ChatGPT have an Ads Manager?
OpenAI is testing a self-serve Ads Manager dashboard with a small group of partners. The tool lets marketers run, monitor, and optimize ChatGPT ad campaigns, but it is still in early development. An OpenAI spokesperson confirmed the test. No public launch date has been announced.
What is ChatGPT's ad click-through rate?
Independent research from Ahrefs and ppc.land estimates ChatGPT's CTR at approximately 1.3%, compared to Google Search's 29.2%. The gap reflects a structural difference: ChatGPT's conversational format keeps users in-app rather than clicking outbound links. Criteo data shows LLM-referred users convert at 1.5x the rate of other channels, suggesting conversion may matter more than CTR for this format.
How do advertisers track ChatGPT ad performance?
Early ChatGPT advertisers currently receive weekly CSV reports with basic click and impression data. OpenAI's Ads Manager dashboard, once fully launched, is expected to provide real-time campaign monitoring and optimization. The current reporting gap is one of the key areas OpenAI is working to close before scaling the program.

