5 Key Findings is a beginner prompt for business insights. This prompt is designed to turn analysis into decisions. It helps the AI extract the most important findings from the data, explain why they matter, and frame actions in business language rather than technical language. Use it when the audience cares more about implications and next steps than methodology. It is best suited for direct execution against a real dataset. The requested output should remain approachable and easy to review, even for someone with limited analytical background.
Analyze this dataset and return exactly 5 key findings, ordered from most to least important.
For each finding:
- A bold one-sentence headline stating the finding
- Two to three supporting sentences with specific numbers from the data
- One sentence on the business implication
Rules:
- No filler or vague statements. Every sentence must contain a specific number or comparison.
- Findings must be distinct — no overlapping insights.
- Use plain language a non-analyst could understand.
End with one sentence: 'The finding that most urgently requires action is [finding N] because [reason].'
Churn Risk Analysis is a advanced prompt for business insights. This prompt is designed to turn analysis into decisions. It helps the AI extract the most important findings from the data, explain why they matter, and frame actions in business language rather than technical language. Use it when the audience cares more about implications and next steps than methodology. It is best suited for direct execution against a real dataset. The requested output should be comprehensive, methodical, and suitable for expert review or production-style work.
Identify customers or users at risk of churning based on behavioral signals in this dataset:
1. Define churn signals from the available columns (e.g. declining purchase frequency, reduced engagement, support ticket spikes, payment failures)
2. Score each customer on a churn risk scale of 1–10 based on the strength of signals present
3. Identify the top 20 highest-risk customers with their risk score and primary churn signal
4. Segment at-risk customers by reason: price sensitivity, product dissatisfaction, competitive alternative, inactivity
5. Recommend one targeted retention action per segment
Return a churn risk table and a 2-sentence executive summary of the overall churn risk level.
Data Storytelling Chain is a advanced chain for business insights. This prompt is designed to turn analysis into decisions. It helps the AI extract the most important findings from the data, explain why they matter, and frame actions in business language rather than technical language. Use it when the audience cares more about implications and next steps than methodology. It is structured as a multi-step chain so the AI can reason through the problem in a deliberate order and produce a more complete result. The requested output should be comprehensive, methodical, and suitable for expert review or production-style work.
Step 1: Identify the single most important insight in this dataset. State it in one sentence, as if telling a non-technical colleague.
Step 2: Find exactly 3 data points that serve as compelling evidence for this insight. For each: state the number, what it means, and why it matters.
Step 3: Find one counterintuitive or surprising finding that adds nuance and prevents oversimplification.
Step 4: Identify the top 2 questions this data cannot answer — what additional data would you need to be fully confident in your recommendation?
Step 5: Write a complete data narrative: opening hook, central insight with evidence, nuance, data gap acknowledgement, and a clear call to action.
Executive Summary is a beginner prompt for business insights. This prompt is designed to turn analysis into decisions. It helps the AI extract the most important findings from the data, explain why they matter, and frame actions in business language rather than technical language. Use it when the audience cares more about implications and next steps than methodology. It is best suited for direct execution against a real dataset. The requested output should remain approachable and easy to review, even for someone with limited analytical background.
Analyze this dataset and write a concise executive summary in exactly 3 paragraphs:
Paragraph 1 — Situation: What does this data describe? What is the main trend over the period shown?
Paragraph 2 — Complication: What is the most significant risk, anomaly, or missed opportunity hidden in the data? Cite at least two specific numbers.
Paragraph 3 — Recommendation: What is the single most important action to take, who should own it, and by when?
Tone: direct, data-driven, no jargon. Max 200 words total. Write as if presenting to a C-suite audience.
KPI Status Report is a intermediate template for business insights. This prompt is designed to turn analysis into decisions. It helps the AI extract the most important findings from the data, explain why they matter, and frame actions in business language rather than technical language. Use it when the audience cares more about implications and next steps than methodology. It is structured as a reusable template, so placeholders can be filled in for a specific table, metric, or business context. The requested output can include more technical detail, prioritization, and interpretation while still staying practical.
Generate a KPI status report for {{reporting_period}} using the data provided.
For each key metric:
- Current value and target (source: {{target_source}})
- Absolute and percentage change vs {{comparison_period}}
- Status label: ✅ On Track / ⚠️ At Risk / 🔴 Off Track
- One-sentence explanation of the primary driver behind the change
- If Off Track: one specific recommended corrective action
Format: a clean table with one KPI per row.
At the bottom, add a 2-sentence overall summary: is the business trending in the right direction, and what is the most urgent issue to address?
IntermediateSingle prompt
06
Opportunity Sizing is a intermediate prompt for business insights. This prompt is designed to turn analysis into decisions. It helps the AI extract the most important findings from the data, explain why they matter, and frame actions in business language rather than technical language. Use it when the audience cares more about implications and next steps than methodology. It is best suited for direct execution against a real dataset. The requested output can include more technical detail, prioritization, and interpretation while still staying practical.
Use this dataset to size the biggest business opportunity available:
1. Identify the metric that has the largest gap between current performance and best-in-class performance (either internal top performer or industry benchmark if known)
2. Calculate the revenue or metric impact of closing 25%, 50%, and 100% of that gap
3. Identify which segment, region, or cohort offers the fastest path to closing the gap
4. Estimate the effort level: is this gap likely due to a process issue (fixable quickly) or a structural issue (requires longer investment)?
5. Write a one-paragraph opportunity statement suitable for an internal business case
IntermediateSingle prompt
07
Pricing Analysis is a intermediate prompt for business insights. This prompt is designed to turn analysis into decisions. It helps the AI extract the most important findings from the data, explain why they matter, and frame actions in business language rather than technical language. Use it when the audience cares more about implications and next steps than methodology. It is best suited for direct execution against a real dataset. The requested output can include more technical detail, prioritization, and interpretation while still staying practical.
Analyze pricing patterns and their relationship to business outcomes in this dataset:
1. Show the distribution of prices across products, tiers, or regions
2. Identify any price clustering (common price points that appear frequently)
3. Calculate the correlation between price and volume/quantity — is there a clear demand elasticity signal?
4. Find the price point with the highest total revenue contribution (price × quantity)
5. Identify any products or segments where price and margin seem misaligned
6. Recommend 2–3 pricing adjustments based on the data, with estimated revenue impact of each
