The rolling window part was fine, lag/lead stuff I can do in my sleep.
First, clarify the schema and business definitions (e.g., gross profit = revenue - cost, refund handling). Then, build a daily profit table by combining order profits and refund profits, bucket product categories into 'Speakers' and 'Other', and finally compute a 7-day rolling sum for each category over the specified date range.
Pro tip: Mention that you would validate the rolling window by checking edge cases (e.g., first 6 days) and ensure refunds are correctly attributed to the refund date, not the order date, to avoid double-counting or misalignment.
Confirm table structures, join keys, and business definitions for gross profit, refunds, and category bucketing. Ensure you know how to classify products as 'Speakers' vs 'Other'.
Join orders, order items, and products to calculate profit per order item (revenue - cost). Aggregate by order date and category bucket to get daily order profit.
Join refunds with order items and products to calculate refund amounts (as negative profit). Aggregate by refund date and category bucket to get daily refund profit.
Union order and refund daily profits, then generate a complete date series for the specified range. Left join to ensure all dates and categories are present, filling missing values with 0.
For each category bucket, compute a 7-day rolling sum of daily gross profit over the date range, using a window function with ROWS BETWEEN 6 PRECEDING AND CURRENT ROW.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
My first instinct was to just group by product and sum revenue per week then divide.
First, clarify the exact 7-day window and the definition of 'non-test, non-cancelled orders' and how refunds are handled. Then, write a SQL query that computes net revenue per product for the current and prior 7-day periods, calculates week-over-week growth, and returns the top 2 products by growth. Finally, validate the results and consider edge cases like products with no prior revenue.
Pro tip: Always confirm the date boundaries and whether the comparison is inclusive or exclusive; also, handle division by zero when prior revenue is zero by using NULLIF or a similar function.
Confirm the specific 7-day window (e.g., last 7 days vs. a given date range), what constitutes a 'non-test' order (e.g., exclude orders with test flag), and how refunds are recorded (negative revenue on refund date).
Write a query that aggregates revenue (including refunds as negative) per product for the current 7-day window and the prior 7-day window, ensuring only non-test, non-cancelled orders are included.
Compute the growth rate as (current_revenue - prior_revenue) / prior_revenue, handling cases where prior revenue is zero or negative appropriately.
Order the products by growth rate descending and limit to the top 2, ensuring that only products with sufficient data are considered.
Check for anomalies, such as products with very small prior revenue leading to huge growth percentages, and consider whether to filter out such cases or present them with caveats.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the 6-day window and D1 definition, then outline a SQL-based approach: identify each user's first event date, filter to those within the window, and flag D1 retention by checking for any event exactly one day later. Finally, aggregate by country to compute the retention rate as the percentage of retained users.
Pro tip: Mention that you would validate the D1 definition with stakeholders and check for edge cases like time zones or users with multiple events on the same day, as these can significantly impact retention calculations.
Confirm the exact 6-day window, how to handle users with multiple first events, and time zone considerations. Ensure D1 is defined as any event exactly one calendar day after the first event date.
Use a subquery or window function to find the minimum event date for each user, ensuring you capture the true first event date.
Select only users whose first event date falls within the specified 6-day window (e.g., between start_date and start_date + 5 days).
For each user, check if they have any event on the calendar day immediately following their first event date. Flag them as retained if so.
Group by country, calculate the number of retained users divided by total users in the cohort, and express as a percentage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.