The core join wasn't bad but I tripped on the conversion rate piece.
Start by clarifying the table schemas and definitions (e.g., what counts as a visitor, purchaser, and conversion rate). Then write a SQL query that aggregates page views and purchases separately by month, joins them on month, and computes the metrics. Finally, extend the query to include average order value by summing purchase amounts and dividing by distinct purchasers.
Pro tip: Always clarify ambiguous terms like 'visitor' (unique user vs. session) and 'conversion rate' (purchasers/visitors vs. purchases/visitors) before writing SQL—this shows you think like a data scientist, not just a coder.
Ask clarifying questions about table schemas, what constitutes a visitor (e.g., distinct user_id), a purchaser (e.g., distinct user_id with a purchase), and how conversion rate is defined. State your assumptions explicitly.
Write a subquery or CTE that groups page_views by month and counts distinct visitors (e.g., COUNT(DISTINCT user_id)).
Write a subquery or CTE that groups purchases by month and calculates distinct purchasers (COUNT(DISTINCT user_id)) and total revenue (SUM(amount)).
Join the two aggregated tables on month, compute conversion rate as distinct purchasers divided by distinct visitors, and average order value as total revenue divided by distinct purchasers. Handle division by zero.
Show the complete SQL query, explain each part, and discuss any edge cases (e.g., months with no purchases, timezone considerations).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.