Straightforward once you break it into two steps: count orders per customer per month, then aggregate at the month level.
Clarify the metric definition and data model, then write a SQL query that aggregates orders per customer per month, filters for >30 orders, and computes the percentage of distinct customers meeting that threshold relative to all distinct customers that month. Validate with edge cases and consider performance optimizations for large datasets.
Pro tip: Explicitly state your assumptions about what constitutes an 'order' (e.g., completed vs. all orders) and whether the denominator includes all customers or only those with at least one order that month; this shows attention to detail and prevents misinterpretation.
Define 'order' (e.g., completed orders), 'distinct customer' (unique user ID), and the time window (calendar month). Confirm whether the denominator is all customers or only those with orders that month.
Locate the orders table with fields like order_id, customer_id, order_date, and status. Ensure you have a way to filter valid orders (e.g., status = 'completed').
Write a subquery or CTE that groups by customer_id and month, counting distinct orders (or order rows) to get order_count per customer per month.
For each month, count distinct customers with order_count > 30, and divide by the total distinct customers that month (either all customers or those with orders). Multiply by 100 for percentage.
Check results for edge cases (e.g., months with no customers, customers with exactly 30 orders). Consider indexing or partitioning strategies for large-scale data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the schema and definitions (order count per customer per month, handling ties, and whether '30 or fewer' is inclusive). Then outline a SQL solution using window functions: first filter customers with monthly order count ≤ 30, rank them per month by order count descending, and select the top rank(s). For the follow-up, remove the filter and find the customer with the maximum total orders across all months.
Pro tip: Explicitly discuss tie-handling for the monthly top customers (e.g., using RANK() to return all ties) and mention that the follow-up requires a simple aggregation without the 30-order limit, showing you can adapt the query logic.
Ask about the table structure (e.g., orders table with customer_id, order_date, order_id) and confirm definitions: monthly order count per customer, '30 or fewer' inclusive, and how to handle ties for the highest count.
Write a subquery or CTE that groups orders by customer and month (using DATE_TRUNC or EXTRACT) and counts orders, producing columns like customer_id, month, order_count.
Filter the monthly counts to order_count ≤ 30, then use a window function (e.g., RANK() OVER (PARTITION BY month ORDER BY order_count DESC)) to identify the top customer(s) per month, returning all ties if needed.
Remove the ≤30 filter and aggregate total orders per customer across all months (SUM(order_count) or COUNT(*)), then select the customer with the maximum total orders, handling ties if necessary.
Mention potential edge cases: months with no qualifying customers, customers with zero orders, ties for top spot, and performance considerations for large datasets (e.g., indexing, partitioning).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the schema and assumptions (e.g., sales table with restaurant_id, date, amount). Then write a SQL query that aggregates monthly sales for 2021, uses LAG to get previous month's sales, computes the difference, and filters out months without a prior month. For the follow-up, generalize by partitioning by restaurant_id and using the same logic.
Pro tip: Mention that you would handle missing months carefully—if a month has no sales, it might be absent from the data, so you may need to generate a complete date spine to correctly compute month-over-month changes. Also, clarify whether 'month-over-month change' means absolute difference or percentage change.
Ask about the table structure (e.g., sales transactions with restaurant_id, date, amount) and confirm that 'monthly total sales' means sum of sales per month. Clarify if the change is absolute or percentage, and whether to include only months with a prior month in the same year.
Write a subquery or CTE that groups by month (using DATE_TRUNC or EXTRACT) and sums sales for the given restaurant, filtering for year 2021.
Use LAG(sales) OVER (ORDER BY month) to get previous month's sales, then calculate the difference (or percentage change). Ensure the window is ordered correctly.
Exclude rows where the previous month's sales is NULL (i.e., the first month). This can be done with a WHERE clause on the LAG result.
Add restaurant_id to the GROUP BY and PARTITION BY clauses. The window function becomes LAG(sales) OVER (PARTITION BY restaurant_id ORDER BY month). Then filter similarly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one took me a minute to structure mentally.
First, clarify the business context and metric definitions, especially how to handle ties and edge cases. Then, outline a step-by-step SQL or Python approach: aggregate monthly sales per restaurant, rank restaurants into quartiles, identify customers who ordered from bottom-quartile restaurants, and compute the percentage of distinct customers. Finally, discuss potential pitfalls and validation checks.
Pro tip: Mention the importance of defining 'bottom quartile' precisely (e.g., using NTILE(4) or percentile thresholds) and how tie-breaking rules can affect results. Also, highlight that this metric could be used to monitor restaurant partner health and customer segmentation.
Confirm what 'total sales' means (e.g., sum of order amounts), the time period (monthly), and how to handle ties in ranking. Ask about the granularity of data and whether 'distinct customers' are identified by user ID.
Write a query to sum sales for each restaurant for each month. Ensure you include all restaurants with sales in that month.
Use a window function like NTILE(4) to assign each restaurant to a quartile based on monthly sales. The bottom quartile is quartile 4 (if ordered ascending) or quartile 1 (if ordered descending).
Join the quartile assignments back to the orders table to find all orders placed at bottom-quartile restaurants in that month. Then, extract distinct customer IDs.
Calculate the percentage as (number of distinct customers who ordered from bottom-quartile restaurants) / (total distinct customers who ordered in that month) * 100. Present results per month.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.