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
75 practiced
Design experiments to measure the impact of reducing estimated delivery times on DoorDash order volume and merchant satisfaction. Define randomization level (user, zip, or city), primary and secondary metrics, sample size calculation (MDE), segmentation, potential confounders and spillovers, safety guardrails, and how to measure merchant satisfaction objectively (NPS vs objective metrics).
HardTechnical
91 practiced
You're planning a rollout of a new definition for 'active user' that will change values across ~50 dashboards. As PM, propose a canary and rollback plan: staging approach, how to run parallel definitions, stakeholders to notify, testing steps, migration timeline, rollback criteria, and methods to measure downstream impact before full rollout.
EasyTechnical
68 practiced
Design a KPI widget for a DoorDash dashboard that displays daily order counts with a 7-day moving average and percent change vs the previous week. Describe the SQL or calculation steps, handling of missing days or partial data, how to display the UI (numeric KPI, sparkline, tooltip), and how to communicate uncertainty.
EasyTechnical
83 practiced
Given these sample tables:orders(order_id, user_id, created_at timestamp, status, total_amount)dasher_activity(dasher_id, event_time timestamp, event_type ['login','accept','pickup','dropoff'])Write ANSI/Postgres SQL to compute: 1) daily active users (DAU) by date, and 2) daily active dashers (unique dashers with at least one activity) by date for the last 30 days. Include how you would handle timezone normalization and late-arriving events.
HardTechnical
76 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.
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