I jumped straight to revenue uplift and forgot to talk about the geo-restriction angle until they nudged me.
Start by framing the campaign's business objective—likely customer acquisition, retention, or market expansion—and tie it to Uber's strategic goals. Then explain the rationale for a city-level restriction using factors like market heterogeneity, operational constraints, and the value of localized experimentation. Emphasize how a limited launch enables controlled testing and data-driven decisions before scaling.
Pro tip: Show that you think like a data scientist by mentioning how you'd measure incrementality and guard against cannibalization, and note that city-level tests often serve as a proving ground for global rollouts.
Determine whether the promotion aims to boost rider acquisition, driver supply, frequency, or enter a new market. This shapes the metrics and success criteria.
Discuss expected benefits such as increasing market share, competing with rivals, testing price elasticity, or driving network effects. Link to Uber's two-sided marketplace dynamics.
Highlight reasons like budget constraints, operational readiness, regulatory differences, or the need for a controlled experiment. Mention that cities vary in demand patterns and competitive intensity.
Explain how a limited launch allows for A/B testing, measuring incremental impact, and iterating before a broader rollout. Emphasize data-driven decision-making.
Mention risks like cannibalization, subsidizing existing users, or driver supply imbalances. Propose metrics to evaluate success, such as lift in trips, retention, and ROI.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the promotion's goal and the specific context (e.g., Uber ride discounts, driver incentives). Then structure your answer around primary metrics (directly measure success), secondary metrics (supporting or diagnostic), and guardrail metrics (ensure no harm). Emphasize that metrics should be tied to the promotion's objective and the company's north star.
Pro tip: Mention that guardrail metrics should include both business and user experience metrics, and that you'd monitor them for statistical significance and practical significance. Also, highlight the importance of segmenting by user cohorts (e.g., new vs. existing, city) to detect heterogeneous effects.
Ask or state the goal of the promotion (e.g., increase rides, retention, or driver supply) to ensure metrics align with business intent.
Choose 1-2 metrics that directly measure the promotion's success, such as incremental rides or conversion rate, and explain how they tie to the objective.
Select supporting metrics that provide context or diagnose why the primary metric moved, like average fare, frequency, or cross-sell rates.
Identify metrics to ensure the promotion didn't cause harm, such as cancellation rate, driver utilization, customer satisfaction, or long-term retention.
Mention analyzing by user segments (new vs. existing, city) and monitoring long-term metrics to avoid short-term gains at the expense of long-term health.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through significance level, power, baseline variance, and effect size.
Start by framing the sample size calculation as a function of the experiment's goals and constraints. Walk through the key inputs—baseline metric, minimum detectable effect, significance level, power, and variance—and explain how they interact. Emphasize that sample size is a trade-off between statistical rigor and practical constraints, and that you'd validate assumptions with historical data.
Pro tip: At Uber, where metrics are often skewed and have high variance, mention that you'd consider using a more sensitive metric (e.g., log-transformed or winsorized) or a variance reduction technique like CUPED to reduce required sample size. Also, always check for network effects or interference, which can inflate required sample size.
Clearly state the primary metric (e.g., conversion rate, ride completion time) and the null and alternative hypotheses. This determines whether you're dealing with a proportion, mean, or ratio metric.
List the required inputs: baseline value of the metric, minimum detectable effect (MDE) you care about, significance level (alpha), power (1-beta), and variance (or standard deviation). For proportions, variance is derived from baseline.
Use the standard formula for means or proportions, or a tool like power analysis in Python (statsmodels) or R (pwr). For more complex designs (e.g., clustered, sequential), use simulation or specialized methods.
Account for factors like multiple testing corrections, unequal allocation, expected attrition, and network effects. Also consider whether you need to detect effects on secondary metrics.
Sanity-check the calculated sample size against historical data and business constraints. If it's too large, consider increasing MDE, reducing variance, or using a more sensitive design.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Smaller MDE means you need way more data to reliably detect it, which I explained fine.
Start by defining MDE as the smallest true effect size the experiment must reliably detect, then explain how it inversely relates to sample size via the power formula. Emphasize that a smaller MDE (e.g., 0.5%) requires a much larger sample, and discuss the practical trade-offs and assumptions involved.
Pro tip: Always clarify that MDE is about statistical detectability, not business significance—and mention that you'd validate the 0.5% threshold against the cost of running a larger experiment and the expected revenue impact.
Explain that MDE is the smallest true effect the experiment is powered to detect with a given significance level and power. It is not the observed effect, but a design parameter.
State that sample size is inversely proportional to the square of the MDE. Halving the MDE quadruples the required sample size, assuming other parameters fixed.
Provide a concrete example: if detecting a 1% MDE requires N users, detecting 0.5% requires roughly 4N users. Mention that this assumes constant variance and power.
Highlight the trade-offs: longer experiment duration, higher cost, and potential novelty effects. Suggest ways to mitigate, such as increasing traffic allocation or using variance reduction techniques.
Note that the calculation depends on baseline conversion rate, variance, significance level (α), and power (1-β). Recommend sensitivity analysis to ensure robustness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I spent the most time and also where I think I did best.
Start by acknowledging the result: a +0.3% lift is positive but not statistically significant, so we cannot confidently conclude the treatment caused it. Then explain the statistical and practical implications, and suggest next steps such as checking power, segmenting, or running a longer test. Finally, communicate to the PM in business terms, focusing on decision-making under uncertainty.
Pro tip: Emphasize that 'not statistically significant' doesn't mean 'no effect'—it means we lack evidence. Frame the conversation around risk and opportunity cost, not just p-values.
State that the observed +0.3% lift is within the range of natural variation and could be due to chance. Confirm the p-value and confidence interval to quantify uncertainty.
Check if the test was adequately powered to detect a lift of this size. Discuss whether a 0.3% lift, even if real, would be practically meaningful for the business.
Consider factors like insufficient sample size, high variance, novelty effects, or segment-specific effects that might be diluted in the overall average.
Propose actions such as extending the test, increasing sample size, running a more targeted experiment, or conducting a deeper dive into segments.
Translate the findings into business impact: the lift is uncertain, so we cannot justify a full rollout based on this test alone. Suggest a decision framework (e.g., cost of continuing vs. potential upside).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.