Build an AI Data Analysis Agent in Codex
A step-by-step guide to using Codex as an AI data analyst — from raw dataset to root-cause finding to leadership-ready deck in under 30 minutes.
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A Root-Cause Prompt Framework That Actually Works
The exact prompt structure that takes Codex from "here's my data" to "here's why this business metric changed" — with a filled-in example and the one line most marketers forget that prevents generic output.
Prompts to Pressure-Test
Your Findings
Follow-up prompts organized by use case — verifying a finding, isolating a pattern, testing a hypothesis, prioritizing across multiple outputs — so you know what to ask next and what a good answer looks like.
A Repeatable Validation Framework
Three checks — sanity, denominator, and confound — that separate a real finding from an AI-generated pattern that collapses under scrutiny. Run these before you brief anyone.
Analyze Your Datasets with AI
Most marketing teams are data-rich and insight-poor. Moving from a CSV export to a confident finding requires time spent in the weeds of spreadsheets. This guide closes that gap.
Built around a single, repeatable workflow:upload a focused dataset, write a structured root-cause prompt, interpret the output with a critical eye, and validate before you change anything.
Whether you're investigating a drop in MQL-to-SQL conversion, a campaign that underperformed against forecast, or a segment that stopped behaving the way your model predicted, the workflow is the same. Run it once and you'll have a repeatable process you can apply every time a metric moves in a direction you can't immediately explain.
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