creative-testing-framework

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Design a structured ad creative testing playbook — prioritized variable matrix, isolated test grid, script-computed sample sizes and minimum budgets per variant, holdout control design, iteration cadence, and winner selection criteria. Plans the testing program; it does not launch or edit live ads. Triggers on "/digital-marketing-pro:creative-testing-framework", "design an A/B test for our ads", "our ad creatives fatigue too fast", "build a creative testing roadmap", "how many conversions per variant do we need". Reads the brand profile and guidelines, and pairs with /digital-marketing-pro:c2pa-metadata for AI-generated variants headed to EU placements.

Testing & QA 809 stars 134 forks Updated 4 days ago MIT

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# /digital-marketing-pro:creative-testing-framework ## Purpose Design a systematic creative testing framework that maximizes learning velocity while maintaining statistical rigor across advertising platforms. Produces a complete testing playbook with variable prioritization, sample size requirements, iteration cadence, and documentation standards for continuous creative optimization. ## Input Required The user must provide (or will be prompted for): - **Ad platform(s)**: Where ads are running — Google Ads, Meta Ads, LinkedIn Ads, TikTok Ads, programmatic DSPs, Pinterest, X/Twitter, or multi-platform - **Creative types available**: What formats can be produced — static image, video (short-form/long-form), carousel, text-only, responsive display, HTML5, playable, or collection ads - **Monthly ad budget allocated to testing**: How much budget is available specifically for creative experimentation vs. proven performers - **Current top-performing creative**: Description or reference to the best-performing ads currently running, including their key metrics - **Learning goals**: Which creative elements need optimization — headlines, imagery, CTA copy, video hooks, color palette, offer framing, social proof, format type, or ad copy length - **Audience segments for testing**: The audience groups available for testing — prospecting, retargeting, lookalike, interest-based, demographic, or custom segments - **Campaign objectives**: What the ads are optimized for — awareness (impress...

Details

Author
indranilbanerjee
Repository
indranilbanerjee/digital-marketing-pro
Created
7 months ago
Last Updated
4 days ago
Language
Python
License
MIT

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