Data Analysis Prompt Templates
AI prompt templates for data analysis. Extract insights, identify patterns, and interpret results.
Overview
Data analysis is about finding the story in your numbers. These prompts help you move beyond just looking at data to actually understanding what it means for your business. Whether you're exploring a new dataset or trying to answer specific questions, these templates guide you toward actionable insights.
Best Practices
Share the context of your data, not just the numbers. What business does it represent?
Be specific about what decisions you're trying to make with this analysis
Include sample rows of your data so the AI understands the structure
Mention any known data quality issues upfront
Ask for both the findings and the confidence level in those findings
Prompt Templates
1. Exploratory Data Analysis
I have a dataset about [DATASET_DESCRIPTION] with these columns: [COLUMN_LIST]. Please analyze it and tell me: 1. Key summary statistics for each important column 2. Any patterns or trends you notice 3. Potential data quality issues 4. What questions this data could help answer Here's a sample of the data: [SAMPLE_DATA]
I have a dataset about e-commerce orders with these columns: order_id, customer_id, order_date, product_category, quantity, unit_price, discount_applied, shipping_region. Sample: order_id=1001, customer_id=C455, order_date=2024-03-15, product_category=Electronics, quantity=2, unit_price=149.99, discount_applied=0.10, shipping_region=West
Key statistics: Average order value is $127, with Electronics and Home goods making up 65% of orders. I notice a seasonal pattern with higher sales in Q4. Data quality flag: 3% of rows have missing shipping_region values. This data could answer questions about customer purchase patterns, regional preferences, and discount effectiveness.
When you're first looking at a new dataset and want to understand what you're working with
- •Include at least 5-10 sample rows to give enough context
- •Mention if certain columns are more important for your analysis
2. Trend Analysis
Analyze the trend in [METRIC] over [TIME_PERIOD] from this data. I need to understand: 1. Overall direction (growing, declining, stable) 2. Rate of change 3. Any seasonal patterns 4. Notable anomalies or outliers 5. Comparison to [BENCHMARK_OR_PREVIOUS_PERIOD] Data: [DATA_OR_SUMMARY]
Analyze the trend in monthly active users over the past 12 months. I need to compare it to the same period last year. Data: Jan: 45K, Feb: 48K, Mar: 52K, Apr: 49K, May: 55K, Jun: 58K, Jul: 61K, Aug: 59K, Sep: 63K, Oct: 67K, Nov: 72K, Dec: 68K
Overall trend: Strong growth of 51% year-over-year (45K to 68K). Monthly growth rate averages 3.5%. Seasonal pattern: slight dips in April and August, strong performance in Nov-Dec. Anomaly: December showed a rare month-over-month decline (5.5%), worth investigating. You're outpacing typical SaaS benchmarks of 20-30% annual growth.
When you need to track performance over time and identify patterns
- •Always provide comparison data if you have it
- •Mention any external factors that might explain anomalies (holidays, marketing campaigns)
3. Segment Comparison
Compare [METRIC] across these segments: [SEGMENT_LIST]. For each segment, show: 1. The metric value and how it ranks 2. Percentage of total 3. Change from [COMPARISON_PERIOD] 4. Statistical significance of differences Context: [BUSINESS_CONTEXT] Data: [DATA]
Compare average order value across customer segments: New (first purchase), Returning (2-5 orders), Loyal (6+ orders). Context: We're deciding where to focus retention efforts. Data: New=$67 (4,500 customers), Returning=$89 (2,100 customers), Loyal=$142 (850 customers)
Loyal customers have 2.1x higher AOV than new customers ($142 vs $67). While Loyal is only 11% of customers, they generate 23% of revenue. Returning customers show the biggest opportunity: they're 28% of customers but moving them to Loyal status would nearly double their spend. Focus retention efforts on the Returning segment for maximum impact.
When you need to compare performance across customer groups, regions, or product categories
- •Include both the count and the metric for each segment
- •State what decision you're trying to make so the analysis can be tailored
4. Root Cause Analysis
Help me understand why [METRIC] changed by [AMOUNT] between [PERIOD_1] and [PERIOD_2]. Potential factors to investigate: [FACTOR_LIST] Available data: [DATA] What's the most likely explanation and how confident are you?
Help me understand why conversion rate dropped from 3.2% to 2.1% between February and March. Potential factors: new checkout flow launched March 1, mobile traffic increased from 45% to 60%, marketing shifted to new audience segment. Available data: Desktop conversion stayed at 3.8%, mobile conversion dropped from 2.4% to 1.5%
The root cause is the increased mobile traffic combined with a mobile conversion problem. Desktop conversion is stable, but mobile dropped 37.5% (2.4% to 1.5%). Since mobile went from 45% to 60% of traffic, this amplified the impact. The new checkout flow likely has mobile UX issues. I'm 85% confident because the timing aligns perfectly and desktop wasn't affected.
When a metric suddenly changes and you need to figure out why
- •List all the changes that happened around the same time
- •Segment the data different ways to isolate the problem
Common Mistakes to Avoid
Providing raw numbers without business context, making it hard to interpret what 'good' or 'bad' means
Asking for insights without specifying what decisions you need to make, leading to generic observations
Not including comparison data, so there's no baseline to judge performance against
Frequently Asked Questions
Data analysis is about finding the story in your numbers. These prompts help you move beyond just looking at data to actually understanding what it means for your business. Whether you're exploring a new dataset or trying to answer specific questions, these templates guide you toward actionable insights.
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