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.
MediumTechnical
0 practiced
Implement last-touch attribution within a 7-day lookback window and compute CAC per channel. Use these tables:ad_clicks(click_id, user_id, click_time, channel)installs(user_id, install_time)marketing_spend(channel, date, amount)Explain the SQL approach to assign installs to clicks and compute CAC, and describe limitations of last-touch.
HardTechnical
0 practiced
Given trips(trip_id, city, occurred_at, base_fare, distance_fare, time_fare, surge_multiplier, tip_amount), write SQL to compute hourly GMV and break down GMV into base, surge, and tips for the last 7 days. Show how you derive the surge portion separately from base fare.
MediumTechnical
0 practiced
Design an A/B test to evaluate whether a new driver bonus program increases driver retention without excessively increasing cost-per-ride. Specify primary and guardrail metrics, randomization unit, sample size considerations, expected duration, and analysis plan including how to compute statistical significance.
MediumTechnical
0 practiced
Write SQL to compute driver utilization for a calendar week: percent of logged-in time drivers are actively on trips. Use these tables:driver_sessions(driver_id, session_start, session_end)trips(trip_id, driver_id, start_time, end_time)Account for trips that span multiple sessions and overlapping sessions. Describe assumptions and edge cases.
MediumTechnical
0 practiced
Write SQL to compute month-over-month growth rates for active riders and active drivers. Given rides(user_id, occurred_at) and driver_sessions(driver_id, session_start), produce a table: month, active_riders, rider_mom_growth, active_drivers, driver_mom_growth for the last 12 months.
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