revenue-analysis

name	Revenue Analysis
description	Use when analyzing revenue, sales, or financial performance. Provides standard approaches for calculating and comparing revenue metrics.
category	analysis
tags	["revenue","financial","sales","metrics"]

Revenue Analysis Skill

This skill provides guidance for performing revenue and financial analysis.

When to Use

Load this skill when the user asks about:

• Revenue trends or comparisons
• Sales performance analysis
• Financial metrics (ARR, MRR, etc.)
• Period-over-period comparisons
• Revenue breakdowns by segment

Standard Metrics

Gross Revenue

SELECT SUM(amount) as gross_revenue
FROM orders
WHERE status = 'completed'
  AND order_date BETWEEN :start_date AND :end_date

Net Revenue (after refunds)

SELECT
  SUM(CASE WHEN type = 'order' THEN amount ELSE -amount END) as net_revenue
FROM transactions
WHERE transaction_date BETWEEN :start_date AND :end_date

Period Comparison Template

WITH current_period AS (
  SELECT SUM(amount) as revenue
  FROM orders
  WHERE order_date BETWEEN :current_start AND :current_end
),
previous_period AS (
  SELECT SUM(amount) as revenue
  FROM orders
  WHERE order_date BETWEEN :previous_start AND :previous_end
)
SELECT
  c.revenue as current_revenue,
  p.revenue as previous_revenue,
  ROUND((c.revenue - p.revenue) / p.revenue * 100, 2) as growth_rate
FROM current_period c, previous_period p

Analysis Workflow

1. Clarify the time period - Always confirm the date range
2. Check for currency/region - Ask if segmentation is needed
3. Identify the revenue type - Gross, net, recognized, etc.
4. Consider filters - Product line, customer segment, channel
5. Calculate baseline - Compute the main metric first
6. Add comparisons - Period-over-period, vs target, vs peers

Best Practices

• Always exclude cancelled/refunded orders unless specifically asked
• Use consistent date boundaries (start of day to end of day)
• Round financial figures appropriately (2 decimal places for currency)
• Consider timezone implications for date filtering
• Group by business-relevant dimensions (not just technical IDs)

Common Pitfalls

• Including pending/draft orders in completed revenue
• Double-counting from joined tables
• Missing NULL handling in aggregations
• Not accounting for currency conversion