Topic
Turning raw data or a messy set of facts into a clear conclusion is where most analysis work gets stuck. This page collects prompts built for that exact step: breaking down a dataset or report into key patterns, comparing options against a set of criteria, and structuring findings into a conclusion someone can actually act on. They're useful for analysts, product managers, and anyone who needs to make sense of numbers or unstructured information quickly. Every prompt is free and reusable — plug in your own data or context using the marked {{variable}} fields and get a structured, decision-ready answer instead of a generic summary.
62 prompts · page 1 of 7
Breaks a funnel of four named stages and their visitor counts into per-stage conversion rates and drop-off points, compares them against industry benchmarks, and identifies the single bottleneck holding back the goal metric. Closes with optimization ideas split into quick wins and longer-term fixes, each with an expected-impact estimate, so growth and product teams know exactly which stage to fix first.
Takes a product's current price, cost structure, competitor pricing, and target margin, then works out whether the price is actually defensible. Covers pricing model options, price elasticity, psychological pricing tactics, competitive positioning, and value-based pricing opportunities, closing with a discounting strategy and a recommendation checked against the target margin.
Reads a two-variant test — descriptions, results, sample size, and duration — and delivers a fast verdict: statistical significance, which variant won, confidence level, key learnings, and a rollout recommendation with suggested follow-up tests. A lighter-weight companion to the rigorous version, meant for everyday test readouts rather than high-stakes ship decisions.
A statistics-first review of an A/B test that goes past a single p-value before you ship the result. - Inputs: control and treatment group results, primary metric, test duration, and optional segments - Checks: post-hoc power, statistical significance with confidence interval, absolute and relative effect size - Flags: novelty-effect risk, sample ratio mismatch, peeking problems, and multiple-comparison issues - Output: a ship/kill/keep-testing call with the reasoning behind it
Feeds churn data, customer base size, churn rate, and revenue impact into an analysis of what's actually driving customers to leave. Surfaces churn triggers, the segments most at risk, retention patterns, and lifetime-value impact, then turns those into prevention strategies, win-back campaign ideas, and the success metrics to track — useful for retention teams building a response plan, not just a diagnosis.
Goes past a raw correlation matrix to flag the relationships in a dataset that actually matter before modeling starts. - Inputs: key variables of interest plus dataset columns, types, and row count - Analysis: correlation matrix, non-obvious pairings, multicollinearity risk, categorical-to-numeric associations, and time-lagged correlations - Warns: spurious correlations that look causal but aren't - Output: plain-language findings plus runnable Python code
Scans an industry over a chosen timeframe and data sources to surface major trend shifts, emerging patterns, and disruption signals, then covers technology impacts, consumer-behavior changes, and regulatory shifts before producing evidence-backed predictions for the next two to five years. Useful for strategy and planning teams who need a forward-looking read rather than a snapshot of current competitors.
Turns raw survey responses — respondent count, key questions, and demographic breakdown — into a findings summary right after a survey closes. Produces the top five findings, sentiment analysis, demographic differences, and unexpected insights, plus actionable recommendations, quote-worthy responses to pull into a deck, and suggested chart types for presenting the results.
Applies an existing qualitative coding framework to a new interview transcript instead of building codes from scratch. - Inputs: a set of codes with definitions, plus the transcript text - For each code: locates every relevant passage and quotes it directly - Fits: deductive coding work where the framework is already established - Pairs with a codebook-matching prompt when uncoded passages also need catching