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← Articles & field notesField note1 min readRevised

Plan image tests that can produce a decision

Generate variants around a clear hypothesis, then allocate enough comparable exposure to learn.

Generated variants are candidate creative, not a statistical sample. Outcomes depend on rights, factual review, comparable exposure and a defined decision rule.

Image generation makes it easier to prepare alternatives. Learning still requires a clear hypothesis, valid product representation and a comparison with enough compatible exposure.

Change one useful dimension: crop, setting, composition or how a real product detail is shown. Keep the product truthful and check the destination and applicable advertising requirements. More variants do not automatically mean more independent evidence.

The original account anecdote described a tighter crop reporting 2.4× the CTR of an earlier image. Its exposure, dates and assignment method were not established here. It is an observation to investigate, not proof that the crop caused the difference or that a platform will reward every additional variant.

Before deployment, define the primary metric, comparison window, minimum usable evidence and cost limits. Review conversion quality and contribution alongside clicks. Retain the asset version and test record so the team can explain what changed and what it actually learned.

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Plan the creative test

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