Evaluate a candidates ability to analyze the financial drivers and per customer economics that determine business sustainability and growth. Core concepts include revenue streams and pricing, gross margin, contribution margin, operating margin, customer acquisition cost, lifetime value per customer, lifetime value to customer acquisition cost ratio, payback period, average revenue per user, churn and retention rates, and metrics for subscription or recurring revenue models such as annual recurring revenue, monthly recurring revenue, expansion revenue, and contraction effects. Candidates should be able to perform back of the envelope calculations and sensitivity analysis, interpret trade offs between growth and profitability, link marketing product and channel activities to financial outcomes, explain how metrics vary by customer segment or acquisition channel, and make strategic recommendations such as pricing adjustments, segmentation strategies, acquisition channel shifts, or investment versus efficiency decisions. Interviewers may request simple calculations, scenario analysis, and prioritized actions grounded in metric changes.
EasyTechnical
0 practiced
Describe how you would design a data pipeline to reliably collect and compute core business metrics (MRR, CAC, LTV). Cover data sources, event vs batch ingestion, idempotence, schema versioning, event deduplication, reconciliation jobs, SLAs for metric refresh, and alerting for metric drift.
MediumSystem Design
0 practiced
Design a metrics dashboard for executives vs product managers to monitor a subscription product. List the top KPIs for each audience, suggested visualizations (e.g., MRR bridge, cohort tables, funnel, ARPU, CAC trends), alert thresholds, and drill-down paths. Explain why certain metrics and visualizations differ by audience and how to enforce consistent definitions.
EasyTechnical
0 practiced
Technical (SQL): Given table subscriptions(user_id, plan_id, start_date DATE, end_date DATE NULL, monthly_price DECIMAL), write an ANSI SQL query or describe approach to compute the monthly churn rate defined as: #customers who canceled in month M / #customers active at the start of month M. Explain how you handle reactivations and mid-month cancellations.
HardSystem Design
0 practiced
Production ML evaluation: You have a churn model in production with AUROC = 0.85. Design an evaluation and monitoring framework to measure the model's business value (e.g., incremental retention, revenue uplift), how to choose and evaluate treatment policies (top-k targeting, thresholding), and what monitoring/guardrails to implement to detect model degradation or harmful behavior.
MediumTechnical
0 practiced
Forecasting: Given 24 months of historical monthly MRR, describe a practical approach to forecast MRR for the next 12 months. Which models would you try (e.g., ETS, ARIMA, Prophet, TBATS), how would you evaluate and select models, how would you incorporate seasonality and known future events (product launches), and how would you present uncertainty to stakeholders?
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