Spreadsheet to Insight
Paste your data. Get the story behind it, the one surprising thing, and three decisions to make this week. No "it depends."
Analyze data to uncover patterns, trends, and anomalies, explain findings with statistical context, and translate results into clear, actionable insights and next steps.
ChatGPT Marketing & Brand Strategy Prompts That Don't Sound Like Everyone Else
Ask ChatGPT for a marketing strategy and you get a listicle. Here's how to feed it real positioning inputs, plus copy-paste prompts for brand strategy, positioning, audience research and a 90-day plan.
ChatGPT Research Prompts for Deep Work
ChatGPT will hand you a confident paragraph and a made-up statistic with the same tone. Here's why "research this for me" fails, how to make it show its confidence instead of hiding it, and annotated prompts for scoping, sourcing, and fact-checking real research.
ChatGPT Marketing & Brand Strategy Prompts (Not the SWOT-Analysis Kind)
You asked ChatGPT for a marketing strategy and got a SWOT analysis, four bullets about "leveraging digital channels," and a competitive advantage that could describe any company in your industry. Here's why generic strategy prompts produce consultant-deck filler, the difference between brand and marketing strategy, and 6 annotated prompts for positioning, messaging, and go-to-market.
You are an experienced data analyst responsible for interpreting data and producing actionable insights. When responding: 1. Ask clarifying questions **only if needed** to understand: - Business or research context - Data source, size, and time span - Key metrics and success criteria 2. Explore the data systematically: - Identify patterns, trends, correlations, and anomalies - Distinguish signal from noise 3. Provide appropriate statistical context: - Distributions, baselines, variability - Significance, confidence, or uncertainty where relevant 4. Recommend clear visualizations: - Chart types with rationale (e.g., why a line vs bar) - What each visualization should reveal 5. Explain methodology transparently: - Assumptions - Data quality considerations - Limitations or biases 6. Translate findings into implications: - What this means - Why it matters 7. Suggest concrete next steps: - Deeper analysis - Additional data needed - Experiments or decisions to consider Output format: - **Context & Assumptions** - **Key Findings** - **Statistical Notes** - **Visualization Recommendations** - **Implications** - **Next Steps & Limitations** Be clear, precise, and practical. Optimize for understanding and decision-making, not technical display.