ChatGPT Advanced Data Analysis: Data to Content In 10 Mintues (AI Marketing)

Code Interpreter in ChatGPT can turn performance data from channels and websites into concrete content ideas—then generate draft copy—by automatically finding correlations in what already worked. The workflow starts with uploading a ZIP file containing two datasets (last 28 days): website search performance metrics such as top queries, clicks, impressions, CTR, and Google position, alongside page views; and YouTube analytics such as titles, views, watch time, impressions, and CTR. Once the files are uploaded, Code Interpreter unzips them, inspects the data, and treats the datasets as linked signals for “best performing content,” using those results to produce new, targeted ideas for future posts and videos.

The practical output is a list of “out of the box, forward-thinking” concepts derived from the highest-performing items rather than generic brainstorming. For the YouTube side, the process yields multiple title and series directions, including AI ethics, AI for social good initiatives, user case studies, and AI entertainment. The AI entertainment angle is singled out as especially timely because of ongoing Hollywood labor disputes (screenwriter and actor strikes), which makes the topic feel immediately relevant to audience attention. On the website/Google side, the analysis focuses on keyword-level performance—queries tied to impressions, clicks, CTR, and ranking—then uses those patterns to generate new content directions.

After idea generation, the workflow moves from “what to write” to “write it.” A separate step feeds a text transcript (for example, a prior video transcript saved into a file) into Code Interpreter as context. The user then instructs Code Interpreter to extract the most important notes from that context and produce a set of title ideas (the example given is “Mastering video editing,” framed as a step-by-step guide). That extracted summary and title list then become input for a two-agent drafting system inside a Python script.

The script runs two cooperating agents: one agent (labeled “Miss writer”) expands the draft, while the other agent provides the initial structure and context. The assignment is to write an approximately 800-word, in-depth technical blog post, then expand it further for more detail. When executed, the system produces multiple drafts; the example shows the writing process actively generating a long-form post, after which the user copies the result and edits it. The final takeaway is not that AI produces perfect content, but that it accelerates the first draft and helps keep the writing grounded in data-driven insights from prior performance.

Overall, the method links analytics to editorial decisions: upload performance data, let Code Interpreter identify what’s working, generate new content ideas and titles from those patterns, then use agent-based drafting to turn the ideas into usable blog drafts. It’s positioned as a repeatable way to build more data-driven marketing content across YouTube and a website, with the user doing the final human editing and refinement.