DoorDash Key Metrics & Dashboard Requirements Questions
Defining and standardizing DoorDash KPIs, identifying data sources, calculating metric definitions, data governance, and designing dashboards and reporting pipelines to monitor product and business performance. Includes data visualization best practices, dashboard design, interactivity, drill-down capabilities, and alignment with business goals across operations, product, and marketplace analytics.
HardTechnical
0 practiced
Design an attribution methodology for DoorDash orders to marketing channels when users have multiple touchpoints across devices and delayed conversions. Compare first-touch, last-touch, time-decay, and data-driven attribution approaches. Propose implementation steps, required data sources (UTM, ad logs, cookies), and validation strategy using incremental experiments.
MediumTechnical
0 practiced
Which visualizations would you choose to show the distribution of order sizes across cuisine categories on a DoorDash dashboard and why? Compare histograms, boxplots, violin plots, and CDFs. Explain interactive behaviors (filter categories, hover tooltips, highlight merchants) that would help product and merchant teams act on the data.
EasyTechnical
0 practiced
Explain the difference between leading and lagging indicators for DoorDash marketplace health. Provide three examples of each (e.g., leading: search-to-cart conversion, offer acceptance rate, estimated delivery ETAs; lagging: monthly revenue, churn, monthly active merchants) and explain how PMs should react to changes in each type.
EasyTechnical
0 practiced
Define 'on-time delivery rate' for DoorDash and provide a robust formula. Explain how to handle edge cases: customer-requested delays, merchant prep delays, partial deliveries, refunded or canceled orders, and scheduled deliveries. List required data fields and potential instrumentation gaps that would affect measurement.
EasyTechnical
0 practiced
Given this table: orders(order_id, merchant_id, created_at, gross_value numeric, merchant_payout numeric, promotions_amount numeric), write a Postgres SQL query to compute take-rate per order defined as: (gross_value - merchant_payout - promotions_amount) / NULLIF(gross_value,0), and then compute daily average take-rate and order_count for the last 30 days.
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