This took me longer than it should have to set up the month truncation logic correctly.
Start by clarifying the schema and definitions: successful revenue likely comes from a transactions table with a status column, and active customers are distinct customers with successful transactions in the period. Use a CTE to filter to the last three full calendar months, aggregate revenue and distinct customers per month and region, then apply a window function to rank regions by revenue within each month.
Pro tip: Explicitly state your assumptions about the data model (e.g., transaction statuses, date column, customer ID) and mention that you'd validate the 'last three full calendar months' logic against the current date to avoid partial months. This shows you think about data quality and edge cases.
Ask or state assumptions about the tables: which table holds transactions, what column indicates success (e.g., status = 'success'), the date column, and how customers are identified. Define 'active customer' as a distinct customer with at least one successful transaction in the month.
Use a date filter to include only complete months. For example, if today is 2024-07-15, the last three full months are April, May, June 2024. This can be done with a WHERE clause on the transaction date.
In a CTE, group by month (formatted as YYYY-MM) and region, summing revenue and counting distinct customer IDs. Ensure you only include successful transactions.
In the outer query, use RANK() or DENSE_RANK() OVER (PARTITION BY month ORDER BY revenue DESC) to assign a rank to each region within its month.
Output month, region, revenue, active_customers, and rank. Order by month and rank for readability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use a window function (ROW_NUMBER) partitioned by customer_id, ordered by amount DESC and transaction_ts ASC, to rank successful transactions. Then filter to ranks 1-3 and select the required columns.
Pro tip: Clarify the definition of 'successful' (e.g., status = 'success') and confirm that ties in amount should be broken by earlier timestamp, which ROW_NUMBER handles deterministically. Also mention that if ties should receive the same rank, use RANK or DENSE_RANK instead.
Identify the table schema, filter for successful transactions, and confirm the tie-breaking rule (amount descending, timestamp ascending).
Use ROW_NUMBER() to assign a unique rank per customer based on the specified ordering, ensuring deterministic tie-breaking.
Construct a query with a window function partitioned by customer_id, ordered by amount DESC and transaction_ts ASC, then filter to ranks <= 3.
Return customer_id, transaction_id, amount, transaction_ts, and the rank, ordering by customer_id and rank for readability.
Consider edge cases like customers with fewer than three successful transactions and verify that ties are handled correctly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, filter transactions to successful ones within the last 180 days and use a CTE with LAG to compute the gap in days between consecutive transactions per customer. Then, average those gaps per customer, and finally join to a segment dimension and average across customers to roll up to segment level, counting distinct customers.
Pro tip: Clarify the definition of 'gap'—whether it's between transaction dates or timestamps—and confirm that customers with only one transaction are excluded from the average gap calculation, as they have no consecutive pair.
Select successful transactions from the past 180 days, ensuring you have customer_id, transaction_date, and segment. Use a CTE to isolate this subset.
In a second CTE, use LAG(transaction_date) OVER (PARTITION BY customer_id ORDER BY transaction_date) to get the previous transaction date, then calculate the date difference in days.
Aggregate the gaps per customer to get the average gap days for each customer, filtering out NULL gaps (first transaction).
Join the per-customer averages to the segment mapping and compute the average gap days per segment, along with the count of distinct customers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.