I went top-down: impressions, view-through rate, click-through rate for the funnel stuff, then flipped to business outcomes like first-trip conversion, rides per user in the following two weeks, and gross bookings.
Start by structuring your answer around the marketing funnel (awareness, consideration, conversion) and then connect those metrics to Uber's core business outcomes like rides, driver sign-ups, and revenue. Emphasize that the choice of metrics depends on the campaign's objective and that you would use incrementality testing to measure true impact.
Pro tip: Mention that you would align metrics with Uber's specific business model (e.g., rider acquisition cost, driver acquisition cost, trip frequency) and use geo-based experiments or holdout groups to isolate the ad's effect from organic growth.
Ask whether the campaign aims to drive rider sign-ups, driver recruitment, or increase trip frequency. This determines which funnel stages and business metrics are most relevant.
Define upper-funnel metrics (impressions, reach, view-through rate, brand lift) and mid-funnel metrics (click-through rate, cost per click, landing page conversion rate) to assess ad performance.
Link funnel metrics to downstream KPIs such as new rider activations, first trip completion, driver sign-ups, cost per acquisition (CPA), and incremental revenue or trips.
Propose using A/B tests, geo experiments, or holdout groups to measure the causal impact of the ad campaign on business metrics, separating it from organic trends.
Calculate return on ad spend (ROAS) and lifetime value (LTV) to CPA ratios, and suggest how to use these metrics to optimize future campaigns.
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This is where the interview got interesting.
Start by framing the challenge as a causal inference problem, distinguishing it from simple correlation. Then propose a randomized experiment (e.g., geo-based or user-level) as the gold standard, and discuss quasi-experimental methods (e.g., synthetic control, instrumental variables) as fallbacks. Finally, emphasize validation and sensitivity analysis to ensure robustness.
Pro tip: Acknowledge practical constraints like ad interference and budget, and suggest a phased approach: start with a pilot experiment to estimate effect size, then scale. This shows you balance rigor with business realities.
Clarify the treatment (YouTube ad exposure), outcome (e.g., conversion, brand lift), and target population. Choose metrics that are sensitive to the ad effect and align with business goals.
Propose a user-level or geo-level randomized controlled trial (RCT) where users/geos are randomly assigned to see ads or not. Discuss randomization unit, sample size, and power analysis.
If RCT is infeasible, suggest quasi-experimental designs like difference-in-differences, synthetic control, or instrumental variables. Explain assumptions and how to test them.
Use appropriate statistical methods (e.g., regression adjustment, CUPED) to estimate the causal effect. Conduct sensitivity analyses and placebo tests to check for confounding.
Translate the estimated effect into business impact, discuss limitations, and recommend next steps (e.g., scale up, optimize targeting).
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Rattled off independence, normality of the sampling distribution (or large enough n for CLT to kick in), and equal or known variance.
Start by listing the core assumptions of the independent two-sample t-test: independence, normality (or large sample size), and equal variances (if using Student's t-test). Then discuss how to check each assumption and what alternatives exist if they are violated, emphasizing practical considerations in A/B testing at scale.
Pro tip: At Uber's scale, with millions of users, the normality assumption is usually satisfied by the Central Limit Theorem, but the independence assumption is often the real challenge due to network effects and interference between users. Mentioning this shows you understand the unique challenges of large-scale experimentation.
List the three main assumptions: independence of observations, normality of the sampling distribution (or approximately normal data), and homogeneity of variances (for Student's t-test).
For each assumption, briefly explain what it means in an A/B testing context: e.g., independence means each user's outcome is unaffected by others; normality means the metric distribution is not heavily skewed or outliers dominate; equal variances means both groups have similar variability.
Mention practical checks: for independence, consider randomization and potential interference; for normality, use histograms, Q-Q plots, or rely on CLT for large samples; for equal variances, use Levene's test or rule of thumb (ratio of variances < 4).
If assumptions are violated, suggest alternatives: Welch's t-test for unequal variances, non-parametric tests (Mann-Whitney U) for non-normal data, or bootstrap methods. Also mention that with large samples, t-test is robust to non-normality but not to dependence.
Emphasize that in large-scale experiments, independence is often the biggest concern due to network effects, and that variance reduction techniques (e.g., CUPED) can help with sensitivity.
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I blanked for a second on the exact formula and just started from first principles: you need the baseline metric, minimum detectable effect, significance level, and desired power.
Start by clarifying the experiment's goal and primary metric, then systematically define the hypothesis, choose the appropriate statistical test, and determine the required sample size using standard power analysis parameters. Emphasize how you'd handle practical constraints like multiple testing, seasonality, and Uber's specific context (e.g., marketplace dynamics).
Pro tip: Always discuss how you'd account for Uber's unique challenges, such as network effects and interference between riders and drivers, which can violate standard independence assumptions in power analysis.
Clearly state the null and alternative hypotheses, and identify the primary metric (e.g., conversion rate, trip completion) that will be used to evaluate the experiment. Ensure the metric is well-defined, sensitive to change, and aligned with business goals.
Select the appropriate statistical test (e.g., t-test, proportion test) based on the metric type and distribution. Determine the minimum detectable effect (MDE) that is practically significant, often derived from business impact or historical data.
Specify the desired statistical power (typically 80% or 90%) and significance level (alpha, usually 0.05). Estimate the variance of the metric from historical data or a pilot study, as it directly affects sample size.
Use the chosen parameters to compute the required sample size per variant using formulas or tools. Translate this into experiment duration by considering daily traffic, allocation ratios, and any expected attrition.
Check assumptions like independence and normality, and adjust for multiple comparisons, sequential testing, or interference effects. Consider running a pilot or sensitivity analysis to ensure robustness.
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Start by explaining why the t-test assumptions are violated with right-skewed data and outliers, then discuss robust alternatives like bootstrapping, permutation tests, or non-parametric methods. Emphasize that the choice depends on the specific metric and business context, and mention the importance of using trimmed means or log transformations when appropriate.
Pro tip: At Uber, where A/B tests are run at massive scale, it's crucial to consider the practical significance and business impact, not just statistical significance. Mention that with large sample sizes, the Central Limit Theorem may make the t-test robust to skewness, but outliers can still distort results, so always visualize the distribution and consider robust methods.
Explain that extreme outliers can inflate variance and bias the mean, making the t-test unreliable. Discuss how skewness violates the normality assumption, especially with small sample sizes.
Acknowledge that with large sample sizes, the sampling distribution of the mean tends to normal due to CLT, but outliers can still dominate the mean and increase variance, leading to unreliable p-values.
Suggest methods like bootstrapping (resampling to estimate the sampling distribution), permutation tests (for hypothesis testing without distributional assumptions), or non-parametric tests like Mann-Whitney U (though it tests different hypotheses).
Mention log transformation to reduce skewness (if metric is positive) or using trimmed means (e.g., 5% trimmed) to mitigate outlier impact, but note that interpretation changes.
Discuss whether the mean is the right metric; sometimes median or other quantiles are more relevant. At Uber, focus on practical significance and ensure the chosen method aligns with business goals.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.