Finance & Business Operations Topics
Financial management, budgeting, ROI analysis, and business operations. Covers financial forecasting, valuation, and operational metrics.
Business Metrics and Unit Economics
The economics of how a business makes money, computed at the per-customer and per-unit level. Covers customer acquisition cost (blended and by channel, including which spend to include and how to allocate shared costs), customer lifetime value (simple, cohort-based, discounted, and probabilistic or censored-data estimates such as Pareto/NBD and Monte Carlo uncertainty), LTV:CAC ratio and CAC payback period, ARPU and ARPPU, gross, contribution and operating margin, and cost allocation across units. Includes subscription revenue metrics (MRR, ARR, expansion, contraction, customer versus revenue churn and net revenue retention, and how revenue recognition, discounts and refunds affect reported figures), marketplace and delivery economics (take rate, GMV, per-order contribution, supply-side value, break-even), cohort revenue curves, and sensitivity analysis on churn, price and acquisition cost. Reasons about what moves these numbers and whether growth is efficient: diagnosing a falling LTV, a rising CAC or a shrinking margin, deciding whether to scale a channel or segment, and presenting unit economics to executives. Includes SQL and spreadsheet implementations of these calculations. Also covers the growth-metric vocabulary and its arithmetic: AARRR metric mapping, activation-rate definitions, viral coefficient (K-factor) and referral conversion math, and churn and retention rate formulas, defined and computed as inputs to unit economics rather than as growth interventions. Metric governance, dashboard and pipeline engineering, attribution, experimentation, forecasting method and pricing strategy are covered elsewhere.
Scenario and Sensitivity Analysis
Testing how outcomes change under different assumptions through scenario modeling and sensitivity analysis. Covers building base, upside, and downside cases, isolating the variables that most affect results, and communicating the range and key drivers of uncertainty. Emphasizes stress-testing a model rather than producing a single point estimate.