Start by validating the data pipeline and metric definition to rule out instrumentation or ETL issues, then decompose the decline by isolating seasonality and mix shift effects. Use statistical tests and cohort analyses to confirm the decline is real and identify its drivers before jumping to conclusions.
Pro tip: Always check if the decline is uniform across all sub-segments or concentrated in specific ones—a uniform drop often signals a data issue, while a concentrated drop points to a real behavioral or operational change.
Check for data freshness, completeness, and consistency across sources. Verify that the metric 'successful orders per day' is defined and calculated identically in both periods, and look for any recent changes in logging or ETL jobs.
Compare the two-week decline against the same period in previous years or use seasonal decomposition (e.g., STL) to adjust for weekly and yearly patterns. If the decline persists after seasonal adjustment, it's likely not just seasonality.
Break down the metric by key dimensions (e.g., customer segment, restaurant type, order channel, geography) to see if the decline is driven by a change in the mix of orders rather than a true drop in each segment.
Use hypothesis testing (e.g., t-test, Mann-Whitney U) or control charts to determine if the decline is statistically significant and not due to random variation. Consider confidence intervals and effect size.
Compare with other markets, overall company trends, or external industry data to see if the decline is LA-specific. Also check if similar declines appear in other metrics (e.g., sessions, conversion) to infer if it's a real behavioral shift.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the metric and time frame of the drop, then structure your answer around the three sides of the marketplace. For each side, propose one plausible hypothesis that is specific, testable, and grounded in common marketplace dynamics. Briefly explain how you would validate each hypothesis to show analytical rigor.
Pro tip: Acknowledge that a single drop often has interconnected causes across sides, but since the question asks for one per side, pick hypotheses that are distinct and avoid overlap. This demonstrates you can isolate variables while understanding systemic effects.
Ask clarifying questions about the metric (e.g., orders, active users), the magnitude and duration of the drop, and whether it's global or segmented. This ensures your hypotheses are relevant.
Propose a plausible reason for reduced customer activity, such as a new competitor promotion, a recent app update causing friction, or seasonal demand shift. Explain the mechanism.
Propose a plausible reason for reduced restaurant participation or order fulfillment, such as increased commission fees leading to churn, or supply chain issues affecting menu availability.
Propose a plausible reason for reduced dasher supply or productivity, such as a change in pay structure, increased gas prices, or a new competing delivery platform offering better incentives.
Briefly outline how you would test each hypothesis using data (e.g., cohort analysis, A/B tests, external benchmarks) to confirm or rule out causes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the metric definition and the time period, then propose a decomposition framework that isolates each hypothesis's contribution to the decline. Use a combination of causal inference methods (e.g., difference-in-differences, propensity score matching) and sensitivity analysis to quantify the impact of each factor.
Pro tip: Emphasize that quantifying impact requires a counterfactual—what would have happened if the factor were absent—and that you'd validate assumptions with robustness checks and business context.
Clarify what 'successful orders' means (e.g., completed orders, on-time deliveries) and the time frame and segments affected. Ensure alignment on the decline magnitude and data sources.
Translate each hypothesis into a measurable variable (e.g., delivery time, restaurant availability, Dasher supply) and map their relationships in a causal diagram (DAG) to identify confounders and mediators.
Select appropriate methods such as regression with interaction terms, difference-in-differences, or mediation analysis to estimate each factor's marginal contribution while controlling for others.
Run models to estimate the effect of each factor on the decline, then decompose the total drop into attributable components (e.g., using Shapley values or sequential attribution).
Perform sensitivity analyses, check for interactions, and validate with holdout or A/B test data if available. Present findings with confidence intervals and business implications.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This felt like the core of the question and I think I did okay.
Start by clarifying the scenario—what drop, over what time period, and for which user segment—then structure your answer around a standard funnel (e.g., acquisition, activation, engagement, retention, monetization) and specify the key metrics at each stage. Emphasize a systematic diagnostic process: segment the data, compare time periods, and use statistical tests to isolate the largest drop-off and its root cause.
Pro tip: Always tie the funnel to the specific business model (e.g., for DoorDash, focus on order funnel: app open → store view → add to cart → checkout → order placed → delivery completed) and mention that you'd validate findings with A/B tests or holdout groups before recommending action.
Ask clarifying questions to understand the drop: which metric, time frame, platform, and user segment. Define the funnel stages relevant to the product (e.g., for DoorDash: app open, search, store page view, add to cart, checkout, order placed, delivery).
Assign key metrics to each stage: e.g., DAU/MAU for acquisition, conversion rates between stages, average order value, delivery time, and retention rates. Ensure metrics are actionable and aligned with business goals.
Calculate conversion rates at each step and identify the largest drop. Segment by dimensions like device, geography, user cohort, and time to see if the drop is concentrated in a specific group.
Use statistical tests (e.g., t-test, chi-square) to compare periods and segments. Investigate external factors (e.g., seasonality, competitor actions) and internal factors (e.g., app changes, pricing) to isolate the cause.
Propose hypotheses and validate with A/B tests or holdout groups. Recommend targeted fixes and define success metrics to monitor post-intervention.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I suggested a few things: dynamic dasher incentives when supply dips in specific zones, better ETA confidence intervals shown to customers before checkout, and smarter restaurant availability surfacing so closed restaurants don't show up.
Start by briefly recapping the root causes you identified, then propose changes that directly address each cause, prioritizing by impact and feasibility. Structure your answer around a clear framework that connects each change to a measurable outcome and considers trade-offs.
Pro tip: Tie every proposed change to a specific metric it should move (e.g., order completion rate, delivery time) and acknowledge potential second-order effects, showing you think like an owner, not just an analyst.
Briefly restate the key root causes you identified earlier to ground your proposals in evidence. This ensures your changes are clearly linked to the problem.
Rank the root causes based on estimated impact on the key metric and ease of implementation. This helps you focus on the most critical changes first.
Suggest specific product features or modifications that directly address the prioritized causes. For each, explain how it solves the problem and what metric it should improve.
Recommend process or policy adjustments (e.g., dasher incentives, restaurant partnerships) that complement product changes. Highlight how they work together.
Specify how you would measure the success of each change and discuss potential trade-offs or risks. This shows a balanced, data-driven approach.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I picked the dasher incentive idea and immediately ran into the randomization problem.
Choose a concrete improvement from your earlier proposals that is testable and relevant to DoorDash, such as a new dasher incentive or a checkout flow change. Structure your answer by walking through each required element—randomization unit, primary metric, guardrails, duration, and concerns—while tying them back to the specific improvement and DoorDash's marketplace dynamics.
Pro tip: Demonstrate awareness of DoorDash's two-sided marketplace by addressing interference between consumers, dashers, and merchants, and propose mitigation like cluster randomization or switchback tests. Also, mention practical constraints like novelty effects and the need for a pre-experiment power analysis.
Briefly describe the chosen improvement and articulate a clear, testable hypothesis about its impact on a key metric.
Specify the unit of randomization (e.g., user, dasher, store) and justify it. Identify the primary metric (e.g., conversion, order volume) and guardrail metrics (e.g., delivery time, cancellation rate).
Explain how you would calculate the required sample size and duration, considering baseline rates, minimum detectable effect, and power. Mention accounting for weekly seasonality.
Discuss potential interference (e.g., network effects, cannibalization), power limitations, and biases (e.g., novelty, selection). Propose mitigation strategies.
Summarize what results would lead to a launch decision and mention any follow-up analyses or iterations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.