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First, clarify the definition of 'influenced' by discussing attribution models (e.g., last-touch, first-touch, multi-touch) and the available data fields. Then, write a SQL query that joins purchases with campaign interactions based on user ID and time windows, applying the chosen attribution logic to filter influenced purchases.
Pro tip: Demonstrate awareness of data quality issues like duplicate campaign exposures or missing timestamps, and propose handling them (e.g., deduplication, using COALESCE) to show production-level thinking.
Ask about the definition of 'influenced' (attribution model), the granularity of data (user-level, session-level), and the available columns in purchases and campaign tables.
Select a model (e.g., last-touch, first-touch, linear) based on business context, and explain how it determines which purchases are considered influenced.
Plan how to join purchases with campaign interactions on user ID and time (e.g., campaign exposure before purchase within a lookback window), and apply the attribution rule.
Construct the query using appropriate joins, window functions (e.g., ROW_NUMBER, FIRST_VALUE), and filters to return the influenced purchases.
Mention potential edge cases (e.g., multiple campaigns, time zone issues) and how to handle them, and suggest ways to validate results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.