Lyft Business Metrics Calculation and Understanding Questions
Finance and operations-focused interview topic about calculating and interpreting core business metrics and KPIs for a platform-based business (e.g., ride-hailing). Covers definitions and formulas for metrics such as CAC, LTV, gross margin, contribution margin, revenue per user, driver utilization, and cost per ride; data sources (ride data, marketing spend, driver and rider activity); dashboard design; segmentation and cohort analysis; and using metrics to drive pricing, incentives, growth, and operational decisions.
HardTechnical
0 practiced
You discover reported revenue in San Francisco is inflated by 8% from double-counted refunds over the last 3 months. Outline an end-to-end remediation plan: how to correct historical reports, write dedupe SQL logic, implement monitoring to prevent recurrence, and how to communicate the issue to finance and leadership.
HardTechnical
0 practiced
Using Python/pandas, outline (or implement) an algorithm to compute per-cohort cumulative LTV and 95% confidence intervals using bootstrap sampling from transaction history. Describe inputs, steps to form cohorts, bootstrap procedure, and how to present results in a dashboard.
MediumTechnical
0 practiced
The product team proposes a 5% increase in base fares. As a BI analyst, describe which metrics you would simulate to estimate the impact on demand, revenue, driver supply, and rider satisfaction. Outline an experiment design or modeling approach to validate the change before full rollout.
HardTechnical
0 practiced
Describe how you would model and forecast driver churn and future driver supply in a city. List potential predictive features, modeling approaches (survival analysis, classification), evaluation metrics, and how forecasts would be used operationally to guide incentives.
MediumSystem Design
0 practiced
Design a normalized data model for ride-level analytics that supports fast ad-hoc queries and pre-aggregation. Describe key tables, primary keys, partitioning scheme, recommended indexes, and denormalized summary tables you would maintain for dashboard performance (e.g., daily_city_metrics).
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