Trend Analysis
Generate statistical trend analysis reports with forecasts, correlation matrices, and business implications in 5 minutes for SaaS metrics and e-commerce sales.
Overview
Identify patterns, seasonal fluctuations, and predictive insights in time-series data to make data-driven business decisions. This template generates statistical analysis reports including trend classification, correlation matrices, outlier detection, and 3-month forecasts with confidence intervals.
Use Cases
- Analyze SaaS subscription metrics (MRR, churn, ARPU) for monthly board reports in under 5 minutes
- Identify seasonal patterns in e-commerce sales data to optimize inventory planning
- Detect revenue anomalies in financial reporting dashboards for quarterly reviews
- Generate statistical forecasts for product usage metrics during sprint planning sessions
- Compare marketing campaign performance against industry benchmarks for stakeholder presentations
Key Benefits
Time savings: Generate comprehensive trend analysis reports in 3-5 minutes instead of spending 2+ hours building spreadsheet models and pivot tables.
Statistical rigor: Get correlation matrices, significance tests (ADF, t-tests), and confidence intervals without needing a statistics background.
Actionable forecasting: Receive 3-month predictions with 95% confidence intervals for capacity planning and budget allocation.
Business context: Automatically connect statistical findings to business implications, competitive events, and strategic recommendations.
Template
Perform trend analysis on:
Data description: {{dataDescription}}
Time period: {{timePeriod}}
Variables to analyze: {{variables}}
Analysis type: {{analysisType}}
Context:
{{context}}
Include:
- Trend identification and classification
- Seasonal patterns
- Outliers and anomalies
- Correlation analysis
- Statistical significance tests
- Predictive insights
- Visual representations
- Business implications
Comparison benchmarks: {{benchmarks}}
Properties
- dataDescription: Multi-line Text (default:
Describe the data being analyzed (e.g., user behavior, sales figures, website traffic)) - timePeriod: Single Selection (default:
Last 12 months)- Options: Last 7 days, Last 30 days, Last 90 days, Last 6 months, Last 12 months, and 2 more
- variables: Multi-line Text (default:
List the key variables to analyze (e.g., price, volume, user count, conversion rate)) - analysisType: Single Selection (default:
Time series)- Options: Time series, Regression, Comparative, Predictive
- context (optional): Multi-line Text (default:
Provide business context (e.g., market changes, product launches, seasonality)) - benchmarks (optional): Multiple Selection (default:
Industry averages, Historical performance)- Options: Industry averages, Competitor data, Historical performance, Target goals, Market benchmarks
Example Output
Here’s what this template generates when analyzing SaaS subscription data over 12 months:
Input prompt:
SaaS subscription data including monthly signups, churn rate, and revenue
Time period: Last 12 months
Variables: MRR, customer churn rate, net new customers, ARPU
Analysis type: Time series
Context: Company launched new pricing tier in Q2, competitor entered market in Q3
Generated analysis includes:
- Trend identification: Linear regression with R-squared values, percentage changes, and trend classification (upward/downward/flat)
- Seasonal patterns: Time series decomposition showing seasonal strength and cyclical variations
- Outlier detection: IQR-based anomaly identification across all metrics
- Correlation matrix: Statistical relationships between MRR, churn, new customers, and ARPU
- Significance tests: Augmented Dickey-Fuller tests for stationarity, t-tests for event impacts (pricing tier launch, competitor entry)
- 3-month forecast: Predictions with 95% confidence intervals for capacity planning
- Benchmark comparisons: Performance vs industry averages for MRR growth rate, churn rate, and ARPU
- Business implications: Actionable recommendations based on statistical findings and competitive context
The analysis connects statistical patterns to business events, explaining how the Q2 pricing tier launch and Q3 competitive pressure impacted key metrics.
How to Generate Effective Trend Analysis
Specify exact timeframes: Use precise periods like “Last 90 days” or “Q4 2024” instead of vague references like “recent data” to get accurate seasonal pattern detection.
Include business context: Mentioning events like product launches, pricing changes, or market shifts helps the analysis connect statistical patterns to real causes rather than just reporting numbers.
Choose relevant variables: Focus on 3-6 key metrics that actually relate to each other. Analyzing too many unrelated variables dilutes the correlation analysis and makes findings harder to act on.
Define clear benchmarks: Specify what you’re comparing against (industry standards, your historical performance, competitor data) to get meaningful performance assessments instead of isolated numbers.
Match analysis type to your question: Use time series for tracking changes over time, regression for cause-effect relationships, comparative for A/B scenarios, and predictive when you need forecasts.
Common Mistakes to Avoid
Insufficient data points: Running trend analysis on 5-7 data points produces unreliable statistical tests. Time series analysis needs at least 12-15 observations for meaningful seasonal pattern detection.
Ignoring data quality: Feeding in data with gaps, inconsistent units, or outliers without context leads to misleading correlations. Clean your dataset first or note known anomalies in the context field.
Confusing correlation with causation: High correlation between two metrics doesn’t mean one causes the other. Use the context field to provide actual business relationships.
Overlooking external factors: Seasonal patterns might reflect holidays, school cycles, or industry-specific events. Include this context so the analysis can distinguish true trends from predictable cycles.
Requesting too many variables: Analyzing 15+ metrics simultaneously creates complexity without insight. Start with your top 3-5 KPIs, then run separate analyses for secondary metrics.
Frequently Used With
- Cohort Analysis - Segment your trend data by user acquisition cohorts to understand how different customer groups behave over time
- KPI Dashboard - Turn trend analysis findings into ongoing KPI tracking dashboards for team visibility
- Funnel Optimization - Use trend analysis to identify which funnel stages are degrading over time
- Data Analysis Report - Package trend analysis findings into executive-ready reports with visualizations