Prompt Almanac

Audit intermediate Works with: Any AI

Meta Ads Creative Testing Log and Learning Audit

Table output

Check whether tests are fair and learning is recorded.

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Audit creative testing in Meta ads. Test log or ad data: ad name, concept, format, audience, dates, spend, results and cost per result: [TEST_LOG]. Decisions made after each test: [DECISIONS]. Minimum results for a decision: [MIN_RESULTS].

Build a table with Test, Hypothesis, Variable changed, Budget each, Days run, Results each, Cost per result each, Decision and Confidence. Mark tests where more than one variable changed, budgets differed by more than 10 percent, a version had fewer than the minimum results (inconclusive), or the winner was chosen after peeking early. Cost per result: 120 spent for 15 results is 8.00 and 120 for 11 results is 10.91, and a gap of four results may not be reliable. Ads share audiences and delivery varies, so small differences are often noise.

Summarise what has been learned about angle, format and offer, and what has not, and propose the next five tests as hypotheses with a decision rule written in advance. Record negative and inconclusive results too. Provide a learning log template (hypothesis, change, result, confidence, next step), an example entry such as 'Customer quote openers beat product-first openers on cost per result (8.20 versus 10.90) over 21 results each; confidence medium; next test: quote versus statistic', and a 25-minute weekly review: results by concept (5), decisions to scale, iterate or stop (10), next hypotheses (5), log updates (5). Results from one audience may not transfer to another, and platform rules change [VERIFY].

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