Data Analysis Report
Generate comprehensive data analysis reports with executive summaries, statistical insights, and actionable recommendations in under 3 minutes
Overview
Turn raw SQL data, Google Analytics exports, or CSV files into executive presentations in 3 minutes. This template generates structured analysis reports with statistical insights, trend identification, and data-driven recommendations.
Built for analysts who need to transform datasets into stakeholder-ready reports without spending hours formatting slides or writing summaries.
Use Cases
- Generate Q4 revenue trend analysis from Salesforce exports for board meetings
- Transform Google Analytics data into conversion optimization reports for marketing teams
- Analyze SaaS subscription churn patterns from SQL databases during weekly product reviews
- Create customer acquisition cost breakdowns from mixed data sources for investor decks
- Build monthly KPI reports from Excel files in minutes instead of hours
Benefits
Time savings you can measure:
- Generate executive summaries in 60 seconds instead of writing them manually
- Skip 2-3 hours of data formatting and visualization planning per report
- Produce consistent reports across teams without style guide enforcement
- Transform ad-hoc analysis requests into structured deliverables in under 5 minutes
Quality improvements:
- Consistent methodology notes across all reports
- Structured statistical analysis that catches edge cases you might miss
- Clear recommendations tied directly to data findings
- Professional formatting ready for stakeholder presentations
Template
Analyze the following dataset and create a comprehensive report:
Dataset description: {{datasetDescription}}
Analysis goal: {{goal}}
Key metrics to analyze: {{metrics}}
Data source: {{dataSource}}
Include:
- Executive summary
- Data overview and quality assessment
- Key findings and insights
- Statistical analysis
- Visualizations needed: {{visualizations}}
- Trends and patterns
- Recommendations
- Methodology notes
Format: {{format}}
Properties
- datasetDescription: Multi-line Text (default:
Describe your dataset (source, size, columns, time range)) - goal: Single Selection (default:
Identify trends)- Options: Identify trends, Compare performance, Find root causes, Forecast future
- metrics: Multiple Selection (default:
Revenue/Sales, Conversion Rate)- Options: Revenue/Sales, Customer Acquisition Cost (CAC), Customer Lifetime Value (LTV), Churn Rate, Conversion Rate, and 5 more
- dataSource: Multiple Selection (default:
SQL Database)- Options: SQL Database, Google Analytics, Salesforce, Excel/CSV files, API endpoints, and 3 more
- visualizations: Multiple Selection (default:
Line charts, Bar charts)- Options: Line charts, Bar charts, Pie charts, Heat maps, Scatter plots, and 3 more
- format: Single Selection (default:
Executive presentation)- Options: Executive presentation, Detailed technical report, Dashboard summary
Example Output
Using the template with SaaS subscription data (15,000 users, Q4 2024, focusing on revenue trends and conversion rates):
Executive Summary Q4 2024 subscription data reveals three critical trends: MRR grew 23% quarter-over-quarter driven by enterprise plan adoption, free-to-paid conversion improved from 12% to 18% following October product updates, and churn concentrated in accounts with low feature usage (under 5 sessions/week). Total MRR reached $847K with 15,000 active subscriptions across starter, professional, and enterprise tiers.
Key Findings
- Revenue growth: Enterprise plans contributed 67% of net new MRR despite representing only 12% of conversions
- Conversion improvement: Free-to-paid conversion jumped 6 percentage points after adding onboarding checklist feature
- Churn pattern: 89% of churned users had fewer than 10 support interactions, suggesting engagement issues rather than service problems
- Feature correlation: Accounts using 3+ core features showed 4.2x lower churn than single-feature users
Recommendations
- Expand enterprise sales focus given disproportionate revenue contribution and 31% higher LTV
- Implement automated engagement triggers for users below 5 sessions/week threshold
- Build feature adoption workflow to move users from single-feature to multi-feature usage
- Investigate October product updates for replicable conversion improvements
Methodology Notes Analysis covers 15,000 user records from October 1 - December 31, 2024. Churn defined as subscription cancellation or payment failure exceeding 30 days. Conversion rate calculated as paid subscriptions divided by total signups within same cohort period. Revenue figures reflect MRR at period end, excluding one-time setup fees.
Common Mistakes When Analyzing Data
Confusing correlation with causation Just because two metrics move together doesn’t mean one causes the other. The template prompts for statistical analysis to help identify spurious correlations, but you still need domain knowledge to validate causal relationships.
Ignoring data quality issues Missing values, duplicate records, or inconsistent date formats can skew results. The data quality assessment section catches obvious problems, but always validate your source data before drawing conclusions.
Cherry-picking time ranges Selecting date ranges that support a predetermined conclusion leads to misleading analysis. Use consistent periods (full quarters, complete months) and compare like-for-like timeframes.
Overlooking statistical significance Small sample sizes or high variance can make trends appear meaningful when they’re actually noise. The statistical analysis section includes confidence considerations, but be skeptical of conclusions based on limited data.
Missing the “so what” factor Describing what happened in the data isn’t the same as explaining why it matters. Strong analysis connects findings to business impact and specific next actions.
Frequently Used With
Teams often combine this template with related analysis workflows:
- Cohort Analysis - Break down user behavior by signup date, acquisition channel, or feature usage patterns
- Funnel Optimization - Identify conversion bottlenecks in multi-step user flows
- KPI Dashboard - Track metrics over time with automated monitoring
- Trend Analysis - Spot patterns in time-series data for forecasting
- User Feedback Analysis - Combine quantitative data with qualitative user insights