Started with session conversion rate as the primary, which felt right, then listed cancellation rate and driver utilization as guardrails.
Start by clarifying the goal of lowering ETA (e.g., improving rider experience and marketplace efficiency) and define the primary metric as completed trips or gross bookings. Then structure your answer around a north-star metric and guardrails across the rider funnel, cancellations, marketplace health, and retention, explaining how each metric ties to the ETA change.
Pro tip: Emphasize that ETA reduction should not come at the cost of driver experience or long-term marketplace balance; mention that you'd monitor driver-side metrics like acceptance rate and utilization as key guardrails.
Confirm that the objective is to improve rider experience and overall marketplace efficiency by reducing ETA, and identify the specific intervention (e.g., matching algorithm change, incentives).
Choose a primary metric that directly captures the intended impact, such as completed trips or gross bookings, and justify why it's the best measure of success.
List metrics for each stage: rider funnel (e.g., app opens, search-to-request, request-to-match), cancellations (rider and driver), completed trips, and gross bookings, ensuring no unintended harm.
Add metrics like driver utilization, acceptance rate, ETA reliability, and rider/driver retention to monitor ecosystem balance and sustained impact.
Explain how you would prioritize metrics, set guardrail thresholds (e.g., no more than 1% increase in cancellations), and design an experiment to measure causal impact.
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Two-sided marketplace dynamics, rider conversion, driver earnings, competitive positioning.
Start by framing ETA as a core driver of Uber's marketplace efficiency and customer experience. Then, systematically connect reduced ETA to key business outcomes like increased demand, higher driver utilization, and improved retention. Finally, quantify the impact with metrics and acknowledge trade-offs to show strategic thinking.
Pro tip: Emphasize that ETA reduction is not just about speed but about reducing uncertainty—predictable ETAs build trust and increase conversion. Also, mention that Uber's data science teams often model ETA as a lever for dynamic pricing and matching algorithms.
Explain that ETA is the estimated time for a driver to arrive and is a key factor in a rider's decision to request a ride. It directly impacts conversion rates and overall satisfaction.
Shorter ETAs reduce rider wait anxiety, leading to higher request rates and fewer cancellations. This increases trip volume and market share.
Reducing ETA often means better matching and positioning of drivers, which increases driver utilization and earnings, creating a positive feedback loop.
Use metrics like conversion rate, cancellation rate, driver utilization, and customer lifetime value to show how ETA improvements translate to revenue and growth.
Discuss potential trade-offs such as increased costs or driver incentives, and how Uber balances them to achieve long-term profitability.
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This one tripped me up more than it should have.
Start by acknowledging the correlation but immediately caution against causal interpretation. Then systematically explore potential confounders (e.g., time, location, user type) and aggregation issues (e.g., Simpson's paradox, ecological fallacy) that could explain the relationship. Finally, suggest ways to validate or refute the correlation, such as segmenting the data or running experiments.
Pro tip: Emphasize that correlation does not imply causation and that in Uber's context, ETA is often a proxy for supply-demand imbalance, which itself drives conversion. Mentioning Simpson's paradox shows depth.
State that the observed correlation is interesting but not necessarily causal. Highlight that many factors could drive both ETA and conversion.
Consider variables like time of day, location, user demographics, and supply/demand that might affect both ETA and conversion. For example, high-demand periods may have higher ETAs and also higher intent to convert.
Discuss how aggregating data across different segments (e.g., cities, user types) can create or mask correlations. Mention Simpson's paradox, where a trend appears in groups but disappears or reverses when combined.
Suggest ways to test the relationship, such as stratifying by confounders, using fixed effects models, or running A/B tests where ETA is manipulated.
Summarize that the correlation likely reflects underlying factors, and recommend further analysis before making product decisions.
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This was the meatiest part and I think I did okay but not great.
Start by defining the causal question and the metric (e.g., ETA reduction's impact on conversion or retention), then systematically compare the three designs based on interference, spillover, and feasibility. Emphasize that the choice depends on the level of randomization and the presence of network effects, and conclude with a recommendation for Uber's marketplace.
Pro tip: Always discuss the trade-off between bias and variance: user-level A/B tests are low-bias but high-variance in marketplaces due to interference, while switchback and synthetic control reduce interference but introduce time or geography confounding. Mention that you'd validate with a holdout or pre-period trend analysis.
Define the treatment (lower ETA), the outcome (e.g., conversion, retention), and the unit of analysis. Identify potential interference and spillover effects in a two-sided marketplace.
Discuss when randomizing at the user level works: when interference is minimal, such as in non-marketplace features. Explain that in Uber's case, lowering ETA for some users can affect driver supply and thus ETA for control users, violating SUTVA.
Describe switchback designs that randomize over geography and time to mitigate interference. Explain how alternating treatment across regions and time periods can isolate the effect while controlling for temporal trends.
Discuss synthetic control when randomization is infeasible, e.g., a city-wide ETA change. Use a weighted combination of untreated regions to construct a counterfactual and estimate the impact.
Choose the design based on the setting, and propose validation steps like pre-period trend matching, placebo tests, and sensitivity analysis to ensure robustness.
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Said we can't conclude a positive effect and the interval is consistent with meaningful harm.
Start by explaining what the confidence interval means statistically, then translate it into practical implications for decision-making. Emphasize that the interval includes zero and negative values, so the result is inconclusive and does not support launching the change. Finally, discuss potential next steps such as extending the experiment or segmenting the data.
Pro tip: Mention that the confidence interval is wide and includes both practically significant negative and positive values, so even if the point estimate is positive, the risk of harm is non-trivial. This shows you consider both statistical and practical significance.
Explain that the 95% confidence interval [-5%, +1%] means we are 95% confident the true lift lies between -5% and +1%. Since it includes zero, the result is not statistically significant at the 5% level.
Discuss that the interval includes both negative and positive values, indicating uncertainty about whether the change helps or hurts. The potential downside (-5%) could be costly, so launching is risky.
Conclude that the experiment is inconclusive and does not provide evidence to launch the change. Recommend not launching based on this result alone.
Propose actions such as running the experiment longer to narrow the interval, checking for novelty effects, or analyzing segments to see if certain user groups show a clear positive effect.
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Variance reduction via CUPED, longer runtime, stratified randomization.
Start by clarifying the experiment's goal, metrics, and constraints, then systematically address both precision (reducing variance) and power (increasing sample size or effect size). Discuss trade-offs and practical implementation, emphasizing Uber's scale and infrastructure.
Pro tip: Mention that improving precision often involves using variance reduction techniques like CUPED, which Uber has pioneered, and that statistical power can be enhanced by increasing sample size or using more sensitive metrics, but always consider business impact and feasibility.
Understand the experiment's goal, primary metric, expected effect size, and any business or technical constraints. This sets the foundation for choosing appropriate methods.
Reduce variance in the metric through techniques like stratification, blocking, using covariates (e.g., CUPED), or improving measurement accuracy. This increases the signal-to-noise ratio.
Enhance power by increasing sample size (longer duration, higher traffic allocation), increasing effect size (stronger treatment), or reducing variance. Also consider using more powerful statistical tests.
Use sequential testing, Bayesian methods, or adaptive designs to make more efficient use of data. Ensure proper randomization and avoid common pitfalls like peeking.
Assess trade-offs between precision, power, cost, and time. Recommend a balanced approach and outline implementation steps, considering Uber's scale and infrastructure.
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Classic post-hoc subgroup trap and I named it directly.
Acknowledge the PM's request but explain that segmenting on a post-treatment variable (number of trips) introduces selection bias and multiple testing issues. Propose a pre-specified subgroup analysis with proper statistical corrections, or suggest an alternative like analyzing trip frequency as a continuous outcome or using a causal inference method to estimate heterogeneous treatment effects.
Pro tip: Offer to run the analysis but clearly label it as exploratory and not confirmatory, and set expectations that any findings will need validation in a follow-up experiment. This balances stakeholder needs with statistical rigor.
Ask the PM what decision they want to inform with this analysis. Understand if they are looking for heterogeneous treatment effects or trying to understand behavior of high-frequency users.
Describe how conditioning on a post-treatment variable can induce collider bias, and how multiple comparisons increase false positive risk. Emphasize that any findings may not be causal.
Suggest a pre-registered subgroup analysis based on pre-treatment characteristics (e.g., historical trip frequency) or use a causal forest to estimate conditional average treatment effects.
If the PM insists, agree to run the analysis as exploratory, with clear caveats, and recommend validation in a future experiment. Document the analysis plan to avoid p-hacking.
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