Start by clarifying the table schema and the definition of 'distinct users' (e.g., user_id). Then write a SQL query that groups by channel and counts distinct user IDs, using COUNT(DISTINCT user_id). Finally, discuss potential edge cases like null channels or duplicate records.
Pro tip: Mention that COUNT(DISTINCT user_id) can be expensive on large datasets, and suggest alternatives like using a subquery with GROUP BY user_id, channel first, or leveraging approximate distinct counts if exactness isn't critical.
Ask about the table name, columns (e.g., user_id, channel, date), and whether 'distinct users' means unique user_id per channel. Confirm if there are any filters (e.g., date range) or if all data should be included.
Use SELECT channel, COUNT(DISTINCT user_id) AS user_count FROM acquisitions GROUP BY channel. Ensure the query returns one row per channel with the distinct user count.
Discuss how COUNT(DISTINCT) can be slow on large tables. Suggest optimizations like pre-aggregating in a subquery (SELECT channel, COUNT(*) FROM (SELECT DISTINCT channel, user_id FROM acquisitions) GROUP BY channel) or using approximate functions if acceptable.
Address null channels (e.g., COALESCE or filter out), duplicate records (if user_id can appear multiple times per channel, COUNT(DISTINCT) handles it), and whether to include channels with zero users (use LEFT JOIN if needed).
Mention checking the output for sanity (e.g., total distinct users across channels should equal total distinct users overall if each user is acquired through one channel). Discuss how this metric informs channel performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I started sweating a little.
Start by clarifying the data model: identify the user, channel, and transaction tables, and confirm whether 'acquisition channel' is a user attribute or event-level. Then write a SQL query that aggregates total spend per user per channel, ranks users within each channel using a window function, and filters to the top 3 per channel.
Pro tip: Mention that you would validate the results by checking for ties and deciding on a tie-breaking rule (e.g., earliest acquisition date or highest single transaction) to ensure deterministic output. Also, discuss how you would handle users with multiple acquisition channels, if applicable.
Ask questions to understand the schema: which tables contain user, channel, and transaction data; how acquisition channel is defined; and whether spend is cumulative over all time or a specific period.
Write a subquery or CTE that joins users to transactions and groups by user and channel, summing the transaction amounts to get total spend per user per channel.
Use a window function like ROW_NUMBER() or RANK() partitioned by channel and ordered by total spend descending to assign a rank to each user within their channel.
Wrap the ranked query in an outer query and filter where rank <= 3, then select channel, user, and total spend, ordering by channel and rank.
Check for ties, decide on tie-breaking logic, and consider if any channels have fewer than 3 users. Also, verify that the totals are correct by spot-checking.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining the acquisition month for each user as the month of their first purchase, then for each subsequent month, determine whether the user made at least one purchase. Aggregate these binary indicators by acquisition month and month offset to compute retention rates, ensuring to handle users who have not yet reached a given offset (right-censoring) appropriately.
Pro tip: Clarify whether retention should be measured based on any purchase after acquisition or only in consecutive months, and explicitly state how you handle users with no purchases after acquisition—they should be included in the denominator for all months to avoid inflated retention rates.
For each user, identify their acquisition month as the month of their first purchase. This creates a cohort assignment for every user.
For each user and each month after acquisition, flag whether they made at least one purchase in that month. This yields a binary activity indicator per user per month offset.
Group by acquisition month and month offset (e.g., 1, 2, 3...), and compute the percentage of users in the cohort who were active in that month. The denominator is the total number of users in the cohort.
For months where some users have not yet had the opportunity to be active (e.g., acquisition month is recent), decide whether to exclude those users or mark as not yet observable. Clearly document this choice.
Structure the final table with acquisition months as rows and month offsets as columns, showing retention percentages. Optionally include cohort sizes for context.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
DATE_DIFF between transaction_date and acquire_date, filter to <= 90, SUM per user, then AVG per channel.
Clarify the business context and data definitions first, then outline a SQL-based approach that joins user acquisition data with revenue events, filters to the first 90 days per user, aggregates revenue per user, and computes average revenue per user (ARPU) by channel. Finally, rank channels by ARPU to identify the highest.
Pro tip: Mention that you would validate the 90-day window using user-level cohort analysis and check for data completeness, as missing revenue events can skew ARPU. Also, consider whether to include users with zero revenue, as this impacts the average.
Confirm what 'revenue' includes (e.g., transactions, subscriptions), how to handle refunds, and whether to include users with zero revenue. Define the 90-day window as inclusive of acquisition date or not.
Locate tables for user acquisition (user_id, acquisition_date, channel) and revenue events (user_id, event_date, revenue_amount). Ensure you can join them on user_id.
For each user, select revenue events where event_date is between acquisition_date and acquisition_date + 90 days. Sum revenue per user to get total revenue per user in the window.
Join the per-user revenue back to the acquisition channel, then group by channel and calculate average revenue per user (total revenue / number of users in that channel).
Order the channels by ARPU descending and select the top channel. Optionally, include statistical significance or confidence intervals if the dataset is large.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.