Revenue Operations & Growth Topics
Revenue operations, sales pipeline management, and acquisition-focused growth. Includes sales analytics, pipeline management, revenue forecasting, and customer acquisition strategies. For post-sale customer success and retention, see Customer Success & Experience.
Revenue Forecasting and Modeling
Skills and practices for building, maintaining, and improving revenue and expense forecast models. Covers forecasting approaches such as pipeline based forecasts, historical trending, management guidance, market analysis, and statistical models, as well as scenario analysis for upside base and downside cases. Includes expense modeling, estimating timelines to revenue realization, modeling conversion and adoption assumptions, tracking and reducing forecast variance, measuring and improving forecast accuracy, and scaling forecasting processes across products, sales channels, and geographies. Candidates may be asked to describe model structure, key input drivers, data sources, validation and reconciliation techniques, and how they adapt models for new products or changing business conditions.
Revenue Models and Growth Strategy
Focuses on how companies make money and how to design strategies to grow revenue sustainably. Topics include understanding different monetization models such as subscriptions, freemium, advertising, marketplace fees, transactional pricing, and partner or channel revenue; evaluating tradeoffs between models; pricing and packaging decisions; partnership structures and how they affect revenue recognition and margins; and building revenue growth plans and go to market optimization to scale revenue while balancing unit economics and operational capacity.
Unit Economics and Scaling
Covers measuring and modelling the economics of acquiring and servicing customers and how those economics change as a business grows. Candidates should be able to calculate Customer Lifetime Value for cohorts using retention, spend per period, and margin assumptions; compute payback period and contribution margin per customer; and compare Customer Lifetime Value across acquisition channels and customer segments. Understand the relationship between Customer Lifetime Value and Customer Acquisition Cost and how that ratio informs sustainable growth. Expand analysis to unit economics beyond customers to units of product or transaction level, identifying fixed and variable cost drivers, per unit gross margin, and break even points. Reason about scale effects including economies and diseconomies of scale, what operational components break or become bottlenecks at higher volume, and how unit costs change with automation, capacity constraints, supplier pricing, fraud and support load. Be prepared to build simple spreadsheet models and run sensitivity and scenario analyses, propose operational and pricing levers to improve unit economics, and design experiments and metrics to track improvements over time.
Revenue Metrics and Key Performance Indicators
Comprehensive understanding of revenue oriented and financial metrics used to assess business health, growth efficiency, go to market performance, and operational effectiveness. Includes recurring revenue measures such as Monthly Recurring Revenue and Annual Recurring Revenue, revenue run rate, gross and net revenue retention, churn and retention metrics, Customer Acquisition Cost and Customer Lifetime Value, average deal size and win rate, pipeline coverage, conversion rates by stage, deal velocity, and sales cycle length. Also covers finance and cash metrics such as Days Sales Outstanding, collections, contribution margin, unit economics, revenue growth rates, sales efficiency ratios including the magic number, and other RevOps indicators. Candidates should be able to define each metric, explain why it matters, compute it reliably across time windows and cohorts, handle attribution and edge cases, translate definitions into queries and dashboards, and articulate interdependencies among metrics. Includes building KPI frameworks that align to commercial goals, distinguishing leading versus lagging indicators, prioritizing metrics by company stage and business model such as land and expand versus enterprise sales, using metrics for forecasting and prioritization, and communicating frameworks to leadership and go to market teams while balancing incentives to avoid gaming.