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.
Metrics and Dashboard Design
Knowledge and skills for defining, interpreting, and presenting key business and sales metrics through effective dashboard architecture. Candidates should demonstrate familiarity with common product and sales metrics such as daily active users, monthly active users, churn, retention, lifetime value, customer acquisition cost, and net revenue retention, and explain what those metrics measure and how they interact. They should be able to read and interpret dashboards, spot anomalous trends and red flags, and recommend tracking or metric improvements. On the architecture and design side, candidates should show how to structure data and dashboards to serve different audiences including sales leadership, individual sales representatives, and finance; balance leading indicators such as activity and pipeline metrics with lagging indicators such as revenue and bookings; consider tradeoffs between real time data and data accuracy; and apply dashboard design principles for clarity, actionability, and drill down from summary to detail. Topics include metric definition and calculation, data freshness and governance, audience segmentation and access, visual encoding and layout, alerting and thresholds, and recommendations for instrumentation and measurement improvements.
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.
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.