Open Prompts
Submit a prompt
Analysis

Interpret Dashboard Performance Metrics

Paste raw dashboard numbers plus business context, the time period, stated goals, and the prior period's figures to get a narrative read ready for a metrics review meeting. - Performance summary against goals and the previous period - Positive trends and concerning trends called out separately - Progress toward each stated goal and the likely drivers behind changes - Recommended actions and a forecast for the next period

Prompt
Interpret these dashboard metrics:

{metrics}

Context:
- Business: {business}
- Time period: {time_period}
- Goals: {goals}
- Previous period: {previous_period}

Provide:
1. A performance summary
2. Positive trends
3. Concerning trends
4. Progress toward goals
5. Drivers of change
6. Recommended actions
7. A forecast for the next period
Download .md

Variables

Related prompts

Analysis

Explain A Data Anomaly In A Report

Writes up a spike, drop, or unusual pattern the way an analyst should, starting from the anomaly description, time range, affected metrics, magnitude, and supporting data. States what happened in one sentence, quantifies the business impact beyond a percentage change, presents likely causes with evidence, rules out false positives like seasonality or pipeline bugs, and recommends both immediate action and prevention — tailored to a chosen audience and tone.

Analysis

Perform A Cohort Performance Analysis

Paste in user or customer data, define how cohorts should be grouped, pick a metric to track — retention, revenue, or engagement — and set the time period, for a lighter-weight cohort read than a full retention-table build. Covers cohort performance comparison, retention curves, revenue trends by cohort, behavioral patterns, drop-off points, and the factors separating strong cohorts from struggling ones, plus improvement opportunities.

Analysis

Run A Cohort Retention Analysis

Specify the entity type being cohorted, how cohorts are defined (signup month, hire date, etc.), the key metric to track, the time granularity, and the relevant dataset columns, and this prompt builds a full cohort by time period retention or engagement table from raw data. It flags which cohorts meaningfully beat or lag the average, checks whether newer cohorts trend better or worse over time, runs a time-to-event calculation like time to 50% churn, and delivers the Python or SQL code that generates the whole analysis plus a heatmap.