Straightforward filter-then-aggregate setup but I initially forgot to restrict to position >= 2 and computed it over all rows.
Start by clarifying the data schema and definitions (e.g., what constitutes an ad vs. organic impression, how feed positions are indexed). Then outline a step-by-step SQL or pandas approach: filter to positions >= 2, aggregate per session, compute frequency, and flag sessions exceeding 30%. Finally, discuss edge cases and validation.
Pro tip: Mention that you would validate the 30% threshold by checking the distribution of ad frequency across sessions to ensure it's not an arbitrary cutoff, and consider whether the threshold should be dynamic based on user engagement metrics.
Confirm what counts as an ad impression vs. organic impression, and how feed positions are represented (e.g., 1-indexed). Ensure you understand session identification and any time constraints.
Filter the events table to include only feed positions >= 2. Then group by session_id and count ad impressions and organic impressions separately.
For each session, calculate ad frequency as (ad impressions) / (ad impressions + organic impressions). Handle potential division by zero if a session has no impressions in positions >= 2.
Create a binary flag or filter sessions where ad frequency > 0.30. Consider whether to use > or >= based on business rules.
Check the distribution of ad frequency, investigate outliers, and ensure the flag aligns with business expectations. Discuss potential next steps like A/B testing or user segmentation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The binning part was fine, just a case-when on feed_position.
Start by clarifying the data schema and definitions (impressions, clicks, user_id, position). Then write a SQL query that joins impressions to clicks, bins positions, and computes CTR per bin. Finally, compute user-level mean CTR and subtract it from each bin's CTR to get user-fixed-effect adjusted CTR, ensuring to handle users with no clicks or impressions appropriately.
Pro tip: When computing user-fixed-effect adjusted CTR, be careful with users who have zero impressions or clicks—decide whether to exclude them or impute their mean CTR, and document your choice. Also, consider using a weighted average for the overall adjusted CTR to avoid Simpson's paradox.
Confirm the table structures, what constitutes an impression and a click, and how position is recorded. Ensure you understand the granularity (e.g., per impression or per user-position).
Use a LEFT JOIN from impressions to clicks on impression_id (or equivalent) to retain all impressions, marking clicks as 1 if a click exists, else 0.
Create position bins (1-2, 3-5, 6+) using a CASE statement. Then compute CTR per bin as SUM(clicks)/SUM(impressions).
Calculate each user's overall CTR (total clicks / total impressions) across all positions. Handle users with zero impressions by excluding them or setting mean CTR to 0.
For each user and bin, compute the difference between the bin's CTR and the user's mean CTR. Then average these differences across users per bin to get the adjusted CTR.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one took me the longest to structure.
First, clarify the definitions: D1 retention means a user returns exactly 1 day after their first activity date, and D7 means exactly 7 days after. Then, for each user, determine their first activity date within the last 7 days, assign them to a cohort (ad-exposed vs. zero-ad) based on whether they saw at least one ad on that first day, and compute retention by checking if they had any activity on day 1 and day 7 after their first activity date. Finally, aggregate by cohort date and cohort type to produce the required table.
Pro tip: Be explicit about how you handle users who are not yet eligible for D7 (e.g., those whose first activity was less than 7 days ago) — typically they are excluded from D7 calculation or treated as not retained, and state your choice clearly.
Confirm what 'first activity date' means (e.g., first event of any type), how ad exposure is measured (e.g., at least one ad impression on the first day), and whether retention is exact-day or any activity on or after that day. Also define the 7-day window relative to today.
For each user, find their minimum activity date within the last 7 days. That date is their cohort date. Determine if they saw at least one ad on that date; if yes, they belong to the 'saw ad' cohort, otherwise 'zero ads' cohort.
For each user, check if they had any activity exactly 1 day after their first activity date (D1) and exactly 7 days after (D7). Create binary flags for D1 and D7 retention.
Group by cohort date and cohort type. Calculate D1 retention as the average of the D1 flag (or count of retained users divided by cohort size) and similarly for D7. Ensure to handle cases where D7 is not yet observable (e.g., exclude those cohorts from D7 or mark as NULL).
Return a table with columns: cohort_date, cohort_type, d1_retention, d7_retention. Validate that the numbers make sense (e.g., D1 >= D7 typically) and consider edge cases like small cohort sizes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.