How to build a ChatGPT Ads campaign

Before account setup

A campaign should begin with a decision worth studying. Define the customer problem, the relevant offer, a primary outcome and the evidence that would change the next budget decision.

For a hypothetical software company, the brief might focus on teams planning a CRM migration. The commercial goal could be a qualified migration consultation. The key uncertainty could be whether migration-specific messaging attracts a more appropriate prospect than a generic product message. These are examples of experiment design, not campaign results.

Check advertiser access and category eligibility before committing launch dates. OpenAI maintains a country availability page and ad policies covering creative and destinations. Approval for one asset does not establish approval for every destination or use case. Sources: availability, ad policies.

Build the campaign structure

OpenAI’s quickstart separates campaigns, ad groups and ads. Campaigns establish high-level setup; ad groups organize themes and context hints; ads carry the creative and destination. Account setup includes billing, verification and team access. Source: campaign quickstart.

Our suggested first-test structure is deliberately interpretable:

• Campaign: one business objective and an agreed test allocation.
• Ad group A: one decision theme that the product genuinely supports.
• Ad group B: a meaningfully different theme only if it needs its own message or destination.
• Creative variants: distinct hypotheses, each recorded in an experiment log.
• Destination: a page that fulfills the specific promise and measures the next action.

Do not divide a small test into so many cells that none produces useful evidence. Conversely, do not combine unrelated use cases merely to make reporting look simpler. The structure should reflect decisions the team can actually make.

Develop creative and destination

OpenAI’s creative guidance emphasizes useful, distinct messages, relevant destinations, valid URLs and ad-page consistency. Its documentation also permits tracking parameters in destination URLs and identifies ad-related crawlers that landing pages should not block. Source: Create ads.

For the migration example, one creative might emphasize implementation clarity while another emphasizes supported integrations. Both need factual support. Changing a few synonyms is not a meaningful strategic test if the claim remains identical.

Write the destination brief at the same time as the ad brief. Identify the first question the page must answer, the evidence a visitor needs, the conversion action and the likely objection. Check the page on a phone, including form labels, error messages and confirmation states.

Verify measurement using known test events. Confirm where records arrive, how duplicates are handled and which system owns the revenue outcome. Keep test activity out of performance reporting where possible.

Review and launch

Create a launch record with the approved offer, source evidence for claims, destination version, tracking setup, budget authorization and review owner. This is the team’s operational record, not an extra platform feature.

Submit the assets through the platform’s review workflow and verify serving status after approval. OpenAI’s quickstart notes that delivery can depend on account review, verification, billing and policy status. It lists conversions among available metrics when conversion measurement is configured. Source: campaign quickstart.

A successfully submitted campaign is not necessarily serving. A serving campaign is not necessarily producing qualified outcomes. Keep those states distinct in internal updates.

Run the learning loop

At each review, describe what changed and what the evidence supports. Start with delivery, then engagement, then destination behavior, then business quality. Investigate the earliest broken step before rewriting the entire campaign.

Keep a change log. Record a revised message as a new hypothesis, not simply “optimization.” If a destination change coincides with a budget increase, acknowledge that the result cannot cleanly isolate either effect.

End the test with a decision: scale a supported hypothesis, revise a defined constraint, or pause because the evidence or economics do not justify continuation. Carry the learning into the next brief.

Use the readiness assessment before launch and the cost framework to set the commercial boundary around the experiment.