Metric Definition and Implementation Questions

End to end topic covering the precise definition, computation, transformation, implementation, validation, documentation, and monitoring of business metrics. Candidates should demonstrate how to translate business requirements into reproducible metric definitions and formulas, choose aggregation methods and time windows, set filtering and deduplication rules, convert event level data to user level metrics, and compute cohorts, retention, attribution, and incremental impact. The work includes data transformation skills such as normalizing and formatting date and identifier fields, handling null values and edge cases, creating calculated fields and measures, combining and grouping tables at appropriate levels, and choosing between percentages and absolute numbers. Implementation details include writing reliable structured query language code or scripts, selecting instrumentation and data sources, considering aggregation strategy, sampling and margin of error, and ensuring pipelines produce reproducible results. Validation and quality practices include spot checks, comparison to known totals, automated tests, monitoring and alerting, naming conventions and versioning, and clear documentation so all calculations are auditable and maintainable.

MediumTechnical

0 practiced

Given event timestamps in multiple timezones, what are the best practices to normalize dates for daily/weekly/monthly aggregation so metrics are consistent for global users? Include discussion of UTC vs user-local date, reporting needs, and impact on weekly/monthly boundaries.

MediumTechnical

0 practiced

You receive two event ingestion streams for the same action: web_events and mobile_events. Each stream has event_id, user_id, device_id, timestamp, and properties. Define deduplication rules and a strategy to merge these streams into a single, canonical 'purchase' events table for accurate revenue and order-count metrics.

MediumTechnical

0 practiced

Explain how to compute a 28-day rolling retention metric and how it differs from cohort retention. Provide the formula and describe how to compute it efficiently across large datasets in SQL or an analytical engine.

MediumTechnical

0 practiced

A product manager asks for a 'Retention Rate by Cohort' report. Explain how you would define a cohort (first-action date vs signup date), compute N-day retention percentages, and handle users who join mid-window or have multiple first actions. Provide the definition you'd document.

HardTechnical

0 practiced

Create a SQL query for attribution: given the table

sql

events(event_id, user_id, event_name, timestamp, campaign_id nullable)

Compute last-touch campaign attribution for 'purchase' events occurring in the last 90 days: assign revenue to the most recent campaign_id on or before purchase for the same user. Show approach to handle purchases with no prior campaign touch.

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