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.
HardTechnical
0 practiced
SaaS unit-economics with expansion/contraction: Derive a discrete-time formula for cohort-level MRR over T months that accounts for monthly churn rate c, monthly contraction rate d (fractional loss from downgrades), and monthly expansion rate e (fractional gain from upsells). Given starting cohort MRR = M0, express MRR_t recursively and compute total undiscounted revenue over 12 months for M0 = $100k, c=0.02, d=0.01, e=0.03.
MediumTechnical
0 practiced
Explain Net Revenue Retention (NRR). Given a cohort with starting MRR = $100,000, contractions = $5,000, churned MRR = $8,000, and expansions = $12,000 over 12 months, compute the NRR and interpret the result. What operational levers would you prioritize if NRR < 100%?
HardTechnical
0 practiced
Explainable segmentation & intervention (hard): Propose an approach to identify and segment high-LTV customers using interpretable models (e.g., decision trees, GAMs). Describe feature selection, how you would evaluate model performance (lift, calibration), how you'd convert model outputs into operational intervention rules, and how you'd assess fairness/bias and regulatory risk when personalizing offers or discounts.
MediumTechnical
0 practiced
Given tables acquisitions(user_id, channel, acquisition_cost, signup_date) and revenue(user_id, date, amount), outline SQL or pandas steps to compute per-channel CAC and 12-month LTV per channel. Include cohorting by acquisition month, handling duplicates (users with multiple acquisition records), attribution windows, and how to report confidence intervals or significance between channels.
MediumTechnical
0 practiced
Growth budget heuristic: As a data scientist advising the growth team, propose an analytical framework and practical heuristics to decide whether to scale marketing spend for a channel. Include metrics to monitor (marginal CAC, marginal LTV, payback period), how to estimate diminishing returns, and decision rules to set budget caps and tests for feasibility.
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