This is where I spent most of my time and still felt like I left stuff on the table.
Start by validating the 10% decline and segmenting it by geography, vehicle type, and time to identify where the drop is concentrated. Then systematically decompose the decline by analyzing each component of the rider funnel—acquisition, activation, retention, and supply—using specific plots and queries to isolate the root cause.
Pro tip: Always quantify the impact of each factor to prioritize where to dig deeper; a 10% overall decline might be driven by a 30% drop in a small but critical segment, so don't assume uniform decline.
Confirm the 10% drop is real and not due to data issues, then segment by city, vehicle type, rider demographics, and time to see if the decline is uniform or concentrated.
Plot new rider sign-ups over time and by channel; compare to previous months to see if acquisition is down. Query acquisition sources and conversion rates to rule out marketing or onboarding issues.
For new riders, plot activation rate (first ride within X days) and for existing riders, plot cohort retention curves. Compare current cohorts to previous ones to identify if activation or retention has worsened.
Plot vehicle availability, idle time, and wait times. Query supply metrics by area and time to see if reduced supply is causing longer waits and thus fewer rides.
Quantify the contribution of each factor to the overall decline using decomposition analysis (e.g., contribution to variance). Identify the largest driver and recommend further investigation or action.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Seasonality was the easy one to address since you just pull year-over-year comps.
Start by structuring your answer around the four root cause categories (seasonality, price/ETA changes, operational outages, competitor entry), explaining how each could drive ride decline. Then, for each cause, specify the minimum data needed to rule it out, emphasizing the importance of data granularity, time series analysis, and control groups.
Pro tip: Demonstrate a hypothesis-driven approach by prioritizing causes based on business context and data availability, and mention how you would validate with A/B tests or causal inference methods where possible.
Break down potential causes into seasonality, price/ETA changes, operational outages, and competitor entry. Briefly explain the mechanism for each.
For each cause, list the specific data required to rule it out, such as historical ride volumes for seasonality, pricing and ETA logs for price/ETA changes, system logs for outages, and competitor launch data.
Describe how you would analyze the data: compare year-over-year trends for seasonality, correlate price/ETA changes with ride decline, check outage timestamps, and assess competitor impact via market share shifts.
Prioritize causes based on likelihood and business impact, and propose validation methods like A/B tests, difference-in-differences, or regression discontinuity to confirm causality.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with a three-segment answer: new users who haven't converted, users showing churn signals in the past 30 days, and high-propensity payers identified by past spend or trip frequency.
Start by clarifying the promotion's goal—likely to drive adoption of a subscription model and increase long-term rider retention. Then, define targeting criteria based on user behavior, value, and likelihood to convert, using a test-and-learn approach to refine over time.
Pro tip: Focus on users with high ride frequency but low current subscription penetration, and consider a holdout group to measure incremental impact—this shows you understand causal inference and avoid cannibalization.
Confirm whether the goal is to acquire new subscribers, increase ride frequency, or test price sensitivity. This shapes targeting rules.
Segment users by ride frequency, spend, tenure, and past promotion responsiveness. Prioritize those with high potential lifetime value and low current subscription usage.
Set concrete criteria (e.g., 3+ rides in last 30 days, no active subscription, not in another trial) to ensure the offer reaches the right users without overlap.
Randomize eligible users into treatment and control. Measure trial start rate, conversion to paid, incremental rides, and retention.
Analyze results, refine targeting based on conversion and LTV, and scale to similar segments if ROI is positive.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The formula itself is pretty clean: incremental LTV times conversion rate minus CAC minus variable costs minus expected fraud loss has to be at least zero, so you solve for conversion as (CAC + variable costs + fraud loss) divided by incremental LTV.
Start by clearly defining the break-even condition: incremental profit equals zero. Then express profit as a function of the conversion rate, incorporating incremental LTV, CAC, variable costs, and fraud loss, and solve for the minimum conversion rate that satisfies the equation.
Pro tip: Explicitly state your assumptions (e.g., fraud loss as a percentage of LTV or as a fixed cost per conversion) and note that in practice, you'd validate these with historical data or sensitivity analysis.
Set incremental profit to zero: Profit = (Conversion Rate × Incremental LTV) - CAC - (Conversion Rate × Variable Costs) - Fraud Loss = 0.
Identify which costs are per-conversion (e.g., variable costs, fraud loss) versus per-user (e.g., CAC). Ensure consistent units.
Solve for conversion rate: Conversion Rate = (CAC + Fraud Loss) / (Incremental LTV - Variable Costs).
Substitute the provided values for incremental LTV, CAC, variable costs, and fraud loss into the formula to compute the minimum conversion rate.
Discuss whether the resulting rate is realistic, and consider sensitivity to assumptions (e.g., fraud loss variability).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
User-level randomization felt obvious given the subscription nature.
Start by clarifying the trial offer's goal and the key metric it aims to move, then systematically address each component: randomization unit, duration, sample size, stratification, guardrails, and success criteria. Emphasize trade-offs and practical constraints, and tie your choices back to Uber's marketplace dynamics and business objectives.
Pro tip: In marketplace experiments like Uber's, always consider interference between users (e.g., drivers and riders) and use cluster randomization or switchback designs when needed; also, pre-register your analysis plan to avoid p-hacking.
Clarify the primary metric (e.g., conversion to paid) and secondary metrics, and state the null and alternative hypotheses. Ensure alignment with business objectives.
Select the unit (e.g., user, session, city) based on interference risk and analysis needs. Stratify by key covariates like city, user tenure, or device type to improve balance and power.
Calculate required sample size using power analysis (α=0.05, power=0.8, MDE). Set duration to cover full business cycles (e.g., 4 weeks) and account for novelty effects and seasonality.
Identify guardrails such as driver acceptance rate, rider wait time, or support tickets. Set up real-time monitoring and stopping rules for harm.
Specify primary success metric, minimum detectable effect, and decision rules (e.g., ship if statistically significant and guardrails not violated). Pre-register the analysis to avoid bias.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.