This took me way longer than it should have.
Start by clarifying the funnel definition and data model, then outline a SQL strategy that deduplicates events to the first occurrence per user-session-product, assigns step order, and uses conditional aggregation or window functions to compute step-to-step conversions. Emphasize that conversions require both steps to occur in timestamp order within the same group, and that results should be grouped by country and event date.
Pro tip: Mention that you would validate the funnel logic with a small sample or sanity checks (e.g., ensuring purchase counts never exceed checkout counts) before scaling to the full dataset, and discuss how you'd handle edge cases like multiple sessions per user or missing steps.
Confirm the funnel steps, table structures, and definitions of user, session, and product. Ask about time zones, date boundaries, and whether events can occur across multiple days.
Use ROW_NUMBER() partitioned by user, session, and product, ordered by timestamp, to keep only the first occurrence of each event type. This ensures each step is counted once per group.
Map event types to step numbers (1-4) and use window functions or self-joins to check that step N+1 occurs after step N within the same group. Only count a conversion if the sequence is valid.
Aggregate by country and event date, counting distinct groups that reached each step. Calculate conversion rates as the ratio of counts between consecutive steps, ensuring the denominator is the previous step's count.
Show the final query or pseudocode, and explain how you would handle edge cases like multiple sessions, missing steps, or out-of-order events. Suggest validation checks and potential optimizations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The 24-hour cross-session window completely blindsided me.
Start by defining the funnel steps and session boundaries, then for each user-product-session, identify the last step reached and check if the next step occurs within the session or within 24 hours. Aggregate drop-off counts per user per month to find the most frequent drop-off step, and compute the median elapsed time from the prior step to drop-off across sessions.
Pro tip: Clarify the definition of 'drop-off' upfront—specifically, whether the 24-hour window is a rolling window from the last step or from session end—and mention how you'd handle edge cases like users with multiple products or incomplete sessions.
Specify the ordered funnel steps and how sessions are defined (e.g., 30-minute inactivity timeout). Determine how to handle multiple sessions per user per month.
For each session, find the last funnel step reached. Check if the next step occurs in the same session or within 24 hours by the same user and product. If not, mark it as a drop-off.
Group drop-off events by user, month, and drop-off step. Count occurrences to determine the most frequent drop-off step for each user in the given month.
For each user's most frequent drop-off step, calculate the time from the prior step to the drop-off event across all their sessions, then compute the median.
Check for ties in drop-off frequency, missing prior steps, or sessions spanning month boundaries. Decide on tie-breaking rules and how to treat incomplete data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Rolling windows with a cross-session purchase lookup are annoying to write cleanly.
Start by clarifying the metric definition and edge cases (e.g., multiple add_to_cart events, timezone handling, country assignment). Then outline a SQL-based solution using window functions or self-joins to match add_to_cart events with subsequent purchases within 7 days, ensuring uniqueness at the user-product level. Finally, discuss how to compute the rolling 7-day conversion rate and validate the results.
Pro tip: Mention the importance of handling time zones consistently and using the add_to_cart timestamp as the anchor for the 7-day window, not the purchase timestamp. Also, consider using a calendar table to ensure all days are included even if no add_to_cart events occur.
Ask clarifying questions about the definition of unique user-product add_to_cart events, how to handle multiple add_to_cart events for the same user-product within the 7-day window, and whether the 7-day window is inclusive. Also confirm time zone and country assignment logic.
Filter events for the relevant month and countries. For each day D, identify unique user-product pairs that had an add_to_cart event on that day. This forms the denominator.
For each add_to_cart event, find if the same user and product had a purchase event within 7 days after the add_to_cart timestamp. Use a self-join or window function to match events, ensuring the purchase occurs after the add_to_cart and within the window.
For each day D and country, count the number of unique user-product pairs that converted (numerator) and divide by the total unique user-product add_to_cart pairs on day D (denominator). This gives the daily conversion rate.
Compute the rolling 7-day average of the daily conversion rates, ensuring that the rolling window is based on calendar days and handles missing days appropriately (e.g., using a calendar table or window functions with date ranges).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.