Prompt GitHub Copilot for Optimizing Advertisements

GitHub Copilot, the AI-powered coding assistant, is not limited to software development. It can become a formidable ally for optimizing your advertising campaigns by automating data analysis, generating tracking scripts, and creating performance dashboards. Whether you are working on Google Ads, Meta Ads, or programmatic advertising campaigns, Copilot helps you write analysis code faster, detect anomalies in your metrics, and automate repetitive tasks related to ad optimization. By leveraging its intelligent completion and context-aware code generation capabilities, you can build ROAS analysis pipelines, automated A/B testing scripts, and custom reporting tools. This prompt is designed for technical marketers and growth engineers who want to combine the power of code with advertising intelligence to make faster and more accurate data-driven decisions on their media investments.

The prompt

GitHub Copilot

Generate a complete Python script to analyze and optimize my advertising campaigns. The script should: 1) Connect to Google Ads and Meta Ads APIs to retrieve key metrics (CPC, CTR, ROAS, CPA, impressions, conversions) for the last 30 days, 2) Calculate performance by segment (audience, placement, creative, device) and identify underperforming combinations whose CPA exceeds 40% of the average, 3) Generate budget optimization recommendations by reallocating budget from weak segments to strong ones, 4) Create an interactive HTML report with Plotly graphs showing KPI trends and recommendations prioritized by estimated impact on ROAS. Include error handling, logging, and explanatory comments for each step.

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Why it works

This prompt works because it provides Copilot with a complete architecture with numbered steps, specific metrics, and quantified thresholds, which eliminates ambiguity and guides generation toward functional code. The mention of precise technologies (APIs, Plotly, HTML) allows Copilot to rely on well-represented code patterns in its training data. Finally, the request for error handling and comments forces production-quality output rather than a simple prototype.

Expected result

You will get a structured and documented Python script capable of connecting to your ad platforms, extracting performance data, and automatically generating quantified optimization recommendations. The final HTML report will present interactive visualizations of your KPIs with concrete budget reallocation suggestions, ranked by potential impact on your advertising return on investment.

Variants by level

FAQ

Can GitHub Copilot directly connect to my ad accounts to optimize my campaigns?

No, GitHub Copilot does not directly connect to your ad accounts. It generates the code needed to establish these connections via official APIs (Google Ads API, Meta Marketing API, etc.). You will need to configure your API credentials and authentication tokens in your development environment. Copilot excels at writing the connection code, data retrieval, and analysis code, but execution and access configuration remain your responsibility.

Which programming languages are best suited for ad optimization with Copilot?

Python is the most recommended language thanks to its rich ecosystem of data analysis libraries (pandas, numpy), visualization (matplotlib, plotly), and machine learning (scikit-learn). Official SDKs for Google Ads and Meta are also well-maintained in Python. JavaScript/TypeScript is a viable alternative if you work with web dashboards or Google Apps Scripts to automate Google Sheets. Copilot produces particularly relevant results in these two languages for advertising use cases.

How can I ensure that optimization recommendations generated by the code are reliable?

It is essential to integrate statistical validations in your code. Ask Copilot to include significance tests (such as chi-square test or Mann-Whitney test) before concluding that a performance variation is real and not due to chance. Set minimum data thresholds (e.g., at least 100 conversions per segment) before drawing conclusions. Finally, always start by applying recommendations to a small percentage of your budget to validate results before full deployment.

How to use this prompt

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Paste it into ChatGPT, Claude or your favorite AI assistant.
Replace the bracketed variables with your details, then refine the result.

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