I jumped straight into supply-side causes (vendor inventory lag, picker errors) and almost forgot to consider demand-side spikes like promotions driving unexpected volume.
Start by clarifying the metric definition and scope (e.g., OOS rate = % of items unavailable at time of order or delivery). Then structure your diagnosis around the customer journey: demand forecasting, inventory accuracy, supply chain, and fulfillment operations. Propose solutions prioritized by impact and feasibility, and suggest A/B tests or causal analyses to validate root causes.
Pro tip: Emphasize that OOS is often a symptom of misaligned incentives or data lags, not just supply issues. Show you can quantify the business impact (e.g., lost sales, churn) to prioritize fixes.
Ask how OOS rate is calculated (e.g., per item, per order, per store) and its current baseline. Confirm the time frame and whether it's a recent spike or chronic issue.
Break down OOS by dimensions like store, region, category, SKU, time of day, and customer segment. Identify if it's concentrated in specific areas or widespread.
Consider demand forecasting errors, inventory record inaccuracies, supplier delays, store staffing issues, and platform data lags. Map each hypothesis to available data sources.
Use statistical methods (e.g., regression, causal inference) to test correlations and causality. For example, compare forecasted vs. actual demand, or audit inventory records vs. physical counts.
Suggest interventions like improved forecasting models, real-time inventory sync, dynamic substitution, or supplier collaboration. Prioritize by expected impact on OOS and implementation cost.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the business objective behind the 3-mile cap and the potential trade-offs of extending it. Then outline a structured analysis plan that includes defining success metrics, designing an experiment, and considering operational constraints. Emphasize the need to balance customer experience, Dasher efficiency, and profitability.
Pro tip: Acknowledge that extending the radius may not be uniformly beneficial; propose a segmented approach (e.g., by market density or time of day) to identify where it adds value. This shows strategic thinking beyond a simple yes/no answer.
Ask clarifying questions to understand why the cap exists (e.g., delivery times, Dasher supply, cost) and what problem extending it would solve. Formulate hypotheses about potential impacts on customers, Dashers, and the platform.
Identify key metrics across different stakeholders: customer metrics (order frequency, delivery time, satisfaction), Dasher metrics (utilization, earnings per hour), and business metrics (order volume, delivery cost, profit margin).
Propose an A/B test where treatment groups have an extended radius (e.g., 5 miles) and control groups remain at 3 miles. Ensure randomization at the market or user level, and consider sample size and duration.
Evaluate the trade-offs between increased order volume and potential declines in delivery efficiency or customer satisfaction. Segment results by market density, time of day, and customer type to identify where extension is most beneficial.
Based on the experiment results, recommend whether to extend the radius, possibly in a targeted way. Suggest next steps like dynamic radius based on real-time supply and demand.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Tricky because it's really a pricing and acquisition question disguised as a metrics question.
Start by clarifying the business objective and defining success metrics, then propose an A/B test to measure the causal impact of free delivery on key metrics like orders, retention, and profitability. Consider both short-term and long-term effects, and segment the analysis to understand heterogeneous treatment effects.
Pro tip: Emphasize the importance of measuring incremental impact rather than total impact, and discuss how free delivery might cannibalize existing orders or attract low-value customers. Also, consider the impact on the delivery partner ecosystem and restaurant partners.
Understand why DoorDash is considering this: is it to acquire new customers, increase order frequency, or compete with other platforms? Define the primary goal and how it aligns with company strategy.
Identify key metrics such as order volume, gross bookings, contribution margin, customer acquisition cost, retention rate, and delivery efficiency. Include guardrail metrics like delivery time and customer satisfaction.
Propose an A/B test where treatment group gets free delivery on select orders and control group does not. Randomize at user or market level, ensure sufficient power, and consider duration to capture long-term effects.
Measure the difference in metrics between groups, calculate incremental impact, and assess statistical significance. Segment by user tenure, order value, and restaurant type to understand heterogeneous effects.
Compare the incremental profit from the program to its costs, including subsidized delivery fees and potential cannibalization. Consider scalability and long-term strategic implications before recommending rollout.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.