Trend Identification & Forecasting Narrative
Identify trends in business data and generate forward-looking forecasting narratives with confidence levels and scenarios.
Your board asks 'what do you expect next quarter?' and you give them last quarter's number plus 10%. That's not a forecast — it's a guess with a spreadsheet. Real trend analysis identifies leading indicators, separates signal from noise, and builds scenario-based projections that actually help you plan.
Who it's for: FP&A analysts building revenue and expense forecasts, business intelligence teams identifying operational trends, strategy teams forecasting market shifts, operations managers predicting demand patterns, CFOs presenting forward-looking projections to the board
Example
"Identify trends and forecast our key metrics for the next 2 quarters" → Trend analysis: 6 key trends identified with supporting data points, leading indicator analysis, 3 forecast scenarios (conservative/base/optimistic) with confidence levels, assumption documentation, risk factors that could alter projections, and a narrative summary for the leadership team
CLAUDE.md Template
New here? 3-minute setup guide → | Already set up? Copy the template below.
npx ccplaybooks add recipe-trend-forecaster# Trend Identification & Forecasting Narrative
## Your Role
You are an expert data analyst and forecasting specialist. Your job is to identify meaningful trends and create actionable forecasting narratives.
## Core Principles
- Separate signal from noise with smoothing techniques
- State assumptions explicitly — projections are assumption-dependent
- Three scenarios: conservative, base, optimistic
- Include confidence levels for each projection
- Set decision triggers tied to metric thresholds
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What This Does
Analyzes historical data to identify meaningful trends, separate signal from noise, and generate forward-looking narratives with scenarios, confidence levels, and decision recommendations.
Quick Start
Step 1: Download the Template
Click Download above to get the CLAUDE.md file.
Step 2: Prepare Historical Data
Gather 6-24 months of the metrics you want to forecast.
Step 3: Start Using It
claude
Say: "Analyze these 12 months of revenue data. Identify trends and forecast the next 3 months with scenarios."
Analysis Sections
| Section | Content |
|---|---|
| Trend Summary | Key directional patterns identified |
| Seasonality | Recurring patterns by period |
| Anomaly Detection | Unusual data points and likely causes |
| Forward Projection | 3 scenarios (conservative, base, optimistic) |
| Confidence Levels | How reliable each projection is |
| Key Assumptions | What must hold true for projections |
| Decision Points | When and what to decide based on trajectory |
Tips
- More data = better trends: 6 months minimum, 12-24 months ideal
- Separate trend from noise: Not every fluctuation is meaningful
- State your assumptions explicitly: Projections are only as good as their assumptions
- Include decision triggers: "If metric drops below X, activate contingency plan Y"
Commands
"Identify trends in this data and forecast 3 months ahead"
"Is the growth trend accelerating, decelerating, or steady?"
"Generate 3 scenarios with probability estimates"
"What are the key assumptions behind this forecast?"
Troubleshooting
Trends are unclear Ask: "Smooth the data (3-month rolling average) to separate trend from noise"
Forecast feels speculative Say: "Mark confidence level for each scenario (high/medium/low) with rationale"
Too many possible scenarios Focus: "Conservative, base, and optimistic — that's all we need for planning"