Generate Comprehensive Revenue Analysis Reports

Creates comprehensive revenue analysis reports that transform raw financial data into actionable insights via SQL/Python analytics, meaningful visualizations, and executive summaries connecting metrics to business strategy.

Use this skill when building a revenue performance report, calculating MRR/growth-rate/ARPU/CLV metrics, segmenting revenue by product/region/customer type, running cohort revenue analysis, or forecasting revenue trends. Not a fit for non-revenue financial reporting (e.g. balance sheet analysis) or for real-time transactional dashboards rather than periodic strategic reports.

Defines core revenue metrics: total revenue, period-over-period and year-over-year growth rate, revenue per customer (RPC), ARPU, customer lifetime value (CLV), revenue run rate, and recurring-vs-non-recurring revenue breakdown. Segmentation guidance covers slicing revenue by product/service line, geography, customer segment (enterprise/SMB/consumer), sales channel, time period, and acquisition cohort.

Provides SQL queries computing monthly recurring revenue trends with unique customers and revenue-per-customer, and a growth analysis query using LAG() to compute month-over-month growth percentage. Provides a Python analyze_revenue_trends function computing monthly summary statistics, growth rates via pct_change(), and a revenue concentration ratio (share of revenue from the top 20% of customers by spend), plus a create_revenue_dashboard function generating a four-panel matplotlib figure (monthly trend line, segment revenue pie chart, customer value histogram, month-over-month growth bar chart).

Report structure guidance covers an executive summary format (revenue snapshot, growth vs. previous period, performance vs. targets, 3-5 critical insight bullets) and detailed sections for trend analysis (historical performance, seasonality, inflection points), a segmentation table example (segment, current revenue, growth rate, percent of total, key drivers), and performance driver analysis (volume vs. price impact, acquisition vs. expansion revenue, product mix, channel comparison).

Advanced analytics include a Python cohort revenue analysis function pivoting revenue by customer first-order month against calendar month, and revenue forecasting guidance covering trend-based moving-average projections, seasonal decomposition, leading indicator correlation, and conservative/base/optimistic scenario modeling. KPI guidance sets benchmarks: 15-25% annual growth rate for growth companies, gross revenue retention above 90% for subscription businesses, and net revenue retention above 110% for expansion revenue, plus secondary metrics like customer concentration risk (top 10 customers' revenue share).

The recommendations framework structures output into immediate actions (next 30 days), short-term initiatives (next quarter), and strategic investments (6-12 months), each requiring expected revenue impact, required resources, success metrics/timeline, and risk mitigation. Data quality guidance covers verifying completeness across revenue streams, reconciling with accounting/ERP systems, validating segmentation logic, and checking for anomalies.

Uses SQL (window functions like LAG(), date truncation) and Python (pandas, matplotlib, seaborn) for analysis and visualization, intended to reconcile with existing accounting/ERP systems.

Finance and revenue operations analysts building strategic revenue reports who need concrete SQL/Python analysis templates, KPI benchmarks, and a structured recommendations framework rather than a raw data dump.

SELECT month, revenue,
  ((revenue - LAG(revenue) OVER (ORDER BY month)) / LAG(revenue) OVER (ORDER BY month)) * 100 as growth_rate_pct
FROM monthly_revenue;