Spotify Business Model & Metrics Questions
Examines Spotify's business model, including revenue streams (subscription plans, advertising, and partnerships), pricing strategy, freemium versus premium dynamics, licensing considerations, and platform economics. Covers key performance indicators such as monthly active users, subscribers, churn, ARPU, customer lifetime value, growth metrics, and competitive positioning, along with strategic decisions around content licensing, podcasts, and monetization diversification.
HardTechnical
0 practiced
Given tables 'subscriptions(user_id, plan, start_date, end_date, monthly_price)' and 'payments(user_id, payment_date, amount)', write SQL to compute monthly recognized subscription revenue per country for calendar months, prorating revenue for mid-month starts and cancellations. Discuss performance strategies and assumptions (e.g., days-in-month proration vs time-weighted).
EasyTechnical
0 practiced
As a BI analyst, explain freemium vs premium dynamics for a streaming service like Spotify. Describe the key metrics you would track (e.g., conversion rate, trial-to-paid, ARPU differences, retention), the typical funnel stages you would instrument, and how these dynamics change reporting and experimentation priorities.
EasyTechnical
0 practiced
Given a subscriptions table 'subscriptions(user_id, start_date DATE, end_date DATE NULLABLE, status VARCHAR)', write an ANSI SQL query to compute monthly churn rate for the last 12 months. Define churn as the percent of users active at the start of a month who are not active at the end of that month. Explain edge cases such as reactivations and partial-month subscriptions.
HardSystem Design
0 practiced
Design the architecture for a C-level KPI portal used by executives to monitor global health. Cover KPIs to surface, drilldown patterns, mobile vs web experience, row-level security per region, data latency SLAs, change-history for each metric, and a simple narrative insights layer. Describe components, APIs, and operational controls needed.
MediumTechnical
0 practiced
Describe how you'd detect fraudulent or bot-driven streaming activity that inflates stream counts and causes overpayment in royalties. List signals (e.g., extremely high streams/day, identical user agents, IP patterns), provide example SQL heuristics or filters, thresholds for flagging, and how you'd integrate flagged records into downstream reporting (quarantine vs adjustment).
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