Exploratory analysis
When to use it: Understand a new dataset.
Here is a dataset sample with column descriptions [paste]. Describe its structure, data quality issues, key statistics, interesting patterns and 5 questions worth investigating.
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Data analysis prompts
10 detailed, ready-to-use prompts: analysis, charts, statistics and insights from data. Each prompt explains when to use it, what to replace and an expert tip. They work with ChatGPT, Claude, Gemini, Copilot and other assistants.
Chart recommendation
When to use it: Choose the right visualisation.
I want to show [message] using data with columns [list]. Recommend the best chart type, axes, labels, colours and what to avoid. Give an alternative chart.
Cohort and retention analysis
When to use it: Understand customer retention.
Explain how to build a cohort retention analysis from [order data with columns], step by step in [tool], and how to interpret the results.
A/B test analysis
When to use it: Decide if a test result is real.
Analyse this A/B test: variant A [visitors, conversions], variant B [visitors, conversions]. Calculate conversion rates, uplift and statistical significance, explain in plain language and recommend a decision.
KPI definitions
When to use it: Agree on metric definitions.
Define these KPIs for [business]: [list]. For each: definition, formula, data source, frequency, owner and common pitfalls.
Customer segmentation
When to use it: Group customers by behaviour.
From this customer data [columns], propose a segmentation (for example RFM), describe each segment, its size and a marketing action for each.
Python analysis code
When to use it: Analyse data with code.
Write Python (pandas) code to load [file], clean [issues], compute [metrics] and plot [charts]. Comment each step and print a summary.
Survey analysis
When to use it: Analyse responses.
Analyse these survey responses [paste]: summarise closed questions with percentages, code open answers into themes with counts, and list 5 insights and recommendations.
Anomaly check
When to use it: Spot unusual values.
Check this time series [paste] for anomalies, explain possible causes for each, and suggest how to monitor it automatically.
Insight summary for managers
When to use it: Turn analysis into decisions.
Summarise this analysis [paste] for managers in 5 bullet points: insight, evidence, impact, recommendation, confidence level.
No prompt matches. Try another word.
Always review AI output before using it, and never paste confidential or personal data into a tool without checking its data settings. Legal, financial and health outputs are general information, not professional advice.
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