Revenue Models and Growth Strategy Questions
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.
EasyTechnical
59 practiced
You must recommend subscription vs transactional pricing for a new B2C marketplace selling physical goods. List 6 product and business factors you would evaluate to decide the pricing model, and explain how each factor pushes toward subscription or transactional pricing.
HardTechnical
36 practiced
Write a SQL query (standard SQL) to compute the rolling 3-month net revenue retention (NRR) for each cohort (cohort defined by sign-up month) given these tables:users(user_id, signup_date)subscriptions(subscription_id, user_id, mrr_cents, start_date, end_date NULLABLE)Show the approach and explain how you handle expansions and contractions within the period.
MediumTechnical
35 practiced
Design an experiment to increase ARPU in a freemium mobile app. Include hypothesis, target segments, primary and secondary metrics, required sample size logic (high-level), and rollout plan for a successful variant.
MediumTechnical
39 practiced
Describe a repeatable approach to forecast next 12 months of revenue for a subscription product that has monthly cohorts, churn, upgrades, and seasonal marketing spend. What inputs and assumptions do you need, and how would you present uncertainty?
HardSystem Design
31 practiced
You're building a pricing marketplace dashboard for product managers that surfaces price sensitivity signals and recommends price changes. Describe the topology of signals (behavioural, transactional, experimental), the algorithms or heuristics you'd use to surface recommendations, and how you'd validate them.
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