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Start by clarifying the schema and definitions: identify the orders table, the order date column, and the customer's preferred delivery date column. Then write a query that counts same-day orders (where the two dates match) and divides by the total number of orders, multiplying by 100 and rounding to two decimal places. Use conditional aggregation or a subquery to compute the percentage in a single pass.
Pro tip: Mention that you would confirm whether 'same-day' means the dates match exactly or if time components should be ignored, and whether to include only completed orders or all orders. This shows attention to data quality and business context.
Ask about the table structure, column names, and any filters (e.g., order status, date range). Confirm the definition of 'same-day' and whether to consider time zones or timestamps.
Locate the order date column (e.g., order_date) and the preferred delivery date column (e.g., preferred_delivery_date). Ensure both are in comparable formats (e.g., DATE type).
Count the number of orders where order_date = preferred_delivery_date (numerator) and the total number of orders (denominator). Use conditional aggregation (SUM(CASE WHEN ... THEN 1 ELSE 0 END)) or subqueries.
Divide the numerator by the denominator, multiply by 100.0 to avoid integer division, and round to two decimal places using ROUND(..., 2).
Assemble the final SQL query, ensuring proper handling of NULLs and edge cases (e.g., zero orders). Optionally, test with sample data or explain how you would verify the result.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use a window function like ROW_NUMBER() partitioned by customer_id and ordered by order_date to identify each customer's first order, then filter to those rows and compute the percentage where the delivery was same-day. Alternatively, use a subquery with MIN(order_date) per customer and join back to the orders table, handling ties if multiple orders share the same date.
Pro tip: Clarify how to handle ties for the first order (e.g., multiple orders on the same day) and whether 'same-day delivery' is a boolean flag or derived from timestamps; this shows attention to data quality and business logic.
Use a window function (e.g., ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date)) or a subquery with MIN(order_date) to select the earliest order for each customer.
Check if the first order qualifies as same-day delivery, either via a boolean column or by comparing order_date and delivery_date.
Calculate the ratio of customers whose first order was same-day to the total number of customers with at least one order, using COUNT and division.
Decide how to treat ties (multiple first orders on the same date) and customers with no orders, and mention any assumptions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.