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.
Marketing Operations Case Studies
Covers solving realistic marketing operations and strategy problems using a structured case study approach. Candidates should demonstrate how to define the problem and success criteria, identify and prioritize key metrics and data sources, articulate hypotheses and investigative steps, and propose solutions with trade offs and implementation plans. Expect discussion of process optimization, lead quality and conversion analysis, measurement frameworks, and how to connect proposed changes to business outcomes. Candidates should also show ability to build the business justification for technology or process investments, calculate return on investment and prioritization logic, and describe cross functional impacts on sales, marketing and engineering. For quick or mini case prompts, emphasize clarifying questions, scoping, data requirements, analytical approach, root cause identification, actionable recommendations, and success measurement and iteration.
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 Cycle Fundamentals & 7 Core Steps
Understanding of the basic revenue cycle from lead generation through cash collection. Knowledge of key stages: lead creation, opportunity management, quoting, order management, invoicing, collections, and revenue recognition. Ability to explain how different teams contribute to each stage and what can go wrong at each step.
Pipeline Optimization & Forecasting Challenges
Understanding of pipeline management including: stage progression rates, opportunities stalled in stages, forecast accuracy issues, pipeline coverage ratios. Ability to analyze pipeline health, identify bottlenecks, and propose improvements. Recognition that accurate forecasting requires clean data and healthy pipelines.
Revenue and Commercial Impact
Prepare three to four examples that demonstrate how your work affected revenue, commercial operations, monetization, or business model outcomes. For each example describe the initiative, your role, the levers you pulled such as pricing, sales process improvements, go to market initiatives, partnerships, or product monetization, and the concrete business outcomes such as revenue lift, pipeline increases, reduced sales cycle, margin improvement, or forecast accuracy gains. Include how you measured and validated the commercial impact and any cross functional coordination with sales, marketing, or finance.
Revenue Data Schema & Relationships
Understanding of how revenue-related data is structured: Accounts, Contacts, Leads, Opportunities, Activities, Closed Won Deals, Revenue Records. Knowledge of key fields and relationships between entities. Understanding the difference between transactional data (individual interactions) and aggregate data (summaries).
Data Driven Problem Solving in Revenue Operations
Approaches for using data and analytics to diagnose operational problems, test hypotheses, and recommend changes. Topics include problem scoping, metric definition, exploratory and cohort analysis, root cause analysis, handling incomplete or low quality data, designing experiments or pilots, building a quantitative business case, prioritization frameworks, and communicating findings to influence stakeholders. Candidates should be able to describe practical techniques for instrumentation, validation, and iteration after deployment.
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.