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.
MediumTechnical
0 practiced
Propose a survival analysis approach to model freemium user lifetime where churn hazard varies by engagement. Specify required data (events, signup, last_active), censoring strategy, how to discretize or bin engagement, which statistical methods to use (Kaplan-Meier, Cox PH), and how BI should present survival curves and hazard insights to stakeholders.
EasyTechnical
0 practiced
Define the DAU/MAU ratio and explain how product teams use it to measure stickiness for streaming services. Discuss scenarios where DAU/MAU may be misleading (e.g., bots, seasonality, high-session frequency among small user subsets) and how BI should complement it with other engagement metrics.
HardTechnical
0 practiced
Spotify is considering heavy investment in exclusive podcasts. Design an ROI framework and BI dashboard to measure content ROI that includes acquisition costs (upfront and ongoing), expected ad/subscription uplift, audience overlap, time-to-positive-ROI, and recommended experiments to validate causality. Describe key visuals and decision thresholds.
EasyTechnical
0 practiced
Write a simple LookML 'view' and 'explore' snippet for a 'user_metrics' dataset that exposes measures monthly_active_users, subscribers_count, and arpu. Indicate primary keys, necessary joins to 'subscriptions' and 'ad_revenue' explores, and describe how you'd handle currency conversion at the model layer.
MediumTechnical
0 practiced
Design a star schema for analytics covering streaming behavior and subscriptions. Include fact_streams and dimension tables dim_users, dim_tracks, dim_subscriptions, dim_countries. For each table describe grain, primary keys, typical measures/attributes, and how you'd implement slowly changing dimensions for user country or plan changes.
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