This one is mostly mechanical once you realize you need two layers: first aggregate per (month, customer) to find who clears the 30-order threshold, then join that back to get their share of total orders.
First, aggregate orders by customer and calendar month to compute each customer's monthly order count, then flag those with more than 30 orders as high-frequency. Finally, for each month, calculate the percentage as the sum of orders from high-frequency customers divided by the total orders in that month, multiplied by 100.
Pro tip: Clarify the definition of 'high-frequency' upfront—specifically, whether it's based on orders placed in that month or a rolling window—and confirm that the denominator includes all orders, not just those from high-frequency customers.
Confirm that high-frequency customers are those with more than 30 orders in a given calendar month, and decide whether to include or exclude incomplete months.
Group the orders data by customer ID and calendar month, then count the number of orders per customer per month.
For each month, flag customers whose order count exceeds 30 as high-frequency.
For each month, compute the total number of orders and the total number of orders placed by high-frequency customers.
Divide the high-frequency order total by the overall order total for each month and multiply by 100 to get the percentage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The tie-handling is what makes this annoying.
Clarify the definitions of 'high-frequency customers' and 'total order value', then outline a SQL-based approach using CTEs to filter, aggregate, rank, and handle ties. Walk through the logic step-by-step, emphasizing window functions and tie-breaking.
Pro tip: Always confirm the threshold for 'high-frequency' and whether 'total order value' includes discounts, refunds, or delivery fees—these details can significantly impact the analysis and show you think like a business analyst.
Ask clarifying questions to define 'high-frequency customers' (e.g., order count > X) and 'total order value' (e.g., sum of order subtotals). Confirm if ties should be broken by any secondary criteria or if all tied customers are returned.
Using a subquery or CTE, identify customers whose order frequency exceeds the threshold and exclude them from the analysis.
Group orders by month and customer, summing the order value to get each customer's total spend per month.
Use a window function like RANK() or DENSE_RANK() partitioned by month and ordered by total spend descending to identify the top spender(s).
Filter to rows where rank = 1, ensuring all tied customers are included. Present the final result with month, customer, and total spend.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
LAG() over a partition by restaurant ordered by month, pretty standard.
Start by clarifying the data model and defining the metric (total sales) and time grain (month). Then write a query that computes month-over-month change for a single restaurant using window functions, and finally generalize it by partitioning by restaurant ID to get the change for every restaurant.
Pro tip: Mention that you would validate the results by checking edge cases like the first month (where there is no previous month) and ensuring that the window function is correctly partitioned and ordered. Also, discuss how you would handle missing months or incomplete data.
Ask questions to confirm the definition of 'total sales' (e.g., sum of order amounts), the time period, and the table structure. Ensure you understand how restaurants are identified and how sales are recorded.
Write a SQL query that filters for a specific restaurant, aggregates sales by month, and uses the LAG window function to get the previous month's sales. Then calculate the difference or percentage change.
Modify the query to remove the filter and add PARTITION BY restaurant_id in the window function. This computes the MoM change for each restaurant independently.
Address scenarios like the first month (where previous sales is NULL), missing months, and restaurants with no sales in a month. Validate the query with sample data or by checking known values.
Consider performance implications (e.g., indexing, partitioning) and how to present the results clearly, such as a table with restaurant_id, month, sales, and MoM change.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, walk through the query line by line, explaining each clause and its purpose in computing the overall percentage. Then, restructure the query to group by month, ensuring the bottom-30% classification is recalculated per month or kept static as appropriate, and clearly state any assumptions.
Pro tip: Clarify whether the bottom-30% restaurants should be determined globally or per month—this choice significantly impacts the metric and shows you understand business context. Also, mention how you'd handle edge cases like months with insufficient data.
Restate the objective: compute the percentage of customers who ordered from restaurants in the bottom 30% by sales. Identify the key tables and metrics involved.
Break down each part: subquery to rank restaurants by sales, filter for bottom 30%, join with orders, count distinct customers, and compute the percentage.
Decide whether to recompute the bottom 30% per month or use a fixed set. Then, add date truncation to month and group by month, adjusting the percentage calculation accordingly.
Construct the SQL with a CTE for monthly restaurant sales, another for bottom 30% per month, and a final aggregation joining orders and customers, grouped by month.
Check for correctness, mention potential pitfalls (e.g., ties, incomplete months), and explain how the metric might be interpreted by stakeholders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.