The dedup constraint is what makes this hard.
Use a window function to find the next visit date for each visitor, then join bookings to visits where the booking timestamp falls between the visit and the earlier of the next visit or 28 days later. To avoid double-counting a booking, assign each booking to the earliest qualifying visit using a row number partitioned by booking.
Pro tip: Explicitly discuss how you handle edge cases like multiple bookings within the same window or bookings that occur after the 28-day cutoff, and mention that you would validate the logic with a small test dataset.
Use LEAD() to get the next visit date for each visitor, then compute the window end as LEAST(next_visit_date, visit_date + INTERVAL '28 days').
Join the visits table to the bookings table on visitor_id where booking_date > visit_date AND booking_date <= window_end.
Use ROW_NUMBER() partitioned by booking_id ordered by visit_date to assign each booking to the earliest qualifying visit, then filter to only the first occurrence.
Group by visit_id and set booked_flag = 1 if any booking was assigned to that visit, else 0.
Check for overlapping windows, bookings exactly at boundaries, and ensure no booking is counted multiple times; consider using a temporary table or CTE for clarity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Collapsing to visitor-level felt more straightforward after the visit-level problem, but the conflicting assign edge case is sneaky.
Start by aggregating the visit-level data to the visitor level, using MIN(visit_timestamp) to identify the first visit and selecting the assignment from that earliest visit. Then left join to the booking data and use a conditional aggregation (e.g., MAX(CASE WHEN booking_date BETWEEN first_visit AND first_visit + INTERVAL '28 days' THEN 1 ELSE 0 END)) to create booked_flag_28d. Ensure the final output has exactly one row per visitor with the correct first visit timestamp and assignment.
Pro tip: When handling conflicting assignments, explicitly state that you're using the assignment from the earliest visit (e.g., via a window function or correlated subquery) and mention that this avoids double-counting visitors and maintains experiment integrity. Also, clarify how you handle ties in visit timestamps (e.g., by using a deterministic tiebreaker like visit_id).
Use a GROUP BY visitor_id with MIN(visit_timestamp) to get the first visit timestamp for each visitor. Alternatively, use a window function like ROW_NUMBER() OVER (PARTITION BY visitor_id ORDER BY visit_timestamp) to rank visits and filter for the first.
Join back to the original visit data on visitor_id and first visit timestamp to retrieve the assignment value from that earliest visit. If multiple visits share the same timestamp, apply a deterministic tiebreaker (e.g., lowest visit_id) to pick one assignment.
Left join the visitor-level first visit data to the booking table on visitor_id. Ensure the join preserves all visitors, even those with no bookings.
For each visitor, check if any booking date falls within the 28-day window starting from the first visit timestamp. Use a conditional aggregation: MAX(CASE WHEN booking_date >= first_visit AND booking_date < first_visit + INTERVAL '28 days' THEN 1 ELSE 0 END) AS booked_flag_28d.
Select visitor_id, first_visit_timestamp, assignment, and booked_flag_28d. Ensure the result has one row per visitor and validate that the flag is correctly computed (e.g., by checking edge cases like bookings exactly on day 28).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The aggregation itself is just GROUP BY assign, country with a SUM and division.
Start by clarifying the data schema and definitions (e.g., what constitutes a visit, a booker, and how duplicates arise). Then outline a SQL/pandas aggregation plan that deduplicates at the appropriate grain (e.g., user-session or user-day) and handles timezone conversion consistently before grouping by assignment group and country. Finally, compute the metrics and discuss validation checks and edge cases.
Pro tip: Explicitly state your timezone assumption (e.g., UTC or user-local) and justify it based on business context; also mention that you would check for duplicate user IDs across assignment groups and decide on a deterministic rule (e.g., first exposure) to avoid double-counting.
Confirm what a 'visit' means (page view, session, unique visitor) and what a 'booker' is (user who completed a booking). Identify the grain of the raw data (e.g., event-level) and potential duplicate sources.
Decide on deduplication logic (e.g., keep first event per user per day) and apply a consistent timezone conversion (e.g., convert all timestamps to UTC or the user's local timezone) before aggregation.
Write an aggregation query (SQL or pandas) that groups by assignment group and country, counting distinct visitors/bookers and computing conversion rate as bookers divided by visitors.
Check for anomalies such as conversion rates >100%, missing countries, or unexpected group sizes. Compare totals against known benchmarks or a quick manual calculation.
Summarize the key assumptions (e.g., timezone, dedup rule) and note any limitations (e.g., users switching groups) that could affect interpretation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.