Pretty surface-level as asked, but I second-guessed myself on edge cases like nulls and zero-value transactions.
Start by clarifying the definition of 'average purchase value' and the context (e.g., per user, per transaction, or per session). Then outline a step-by-step method to compute it, including data sources, necessary metrics, and potential pitfalls. Finally, discuss how you would validate and interpret the results.
Pro tip: Mention that you would segment the average by user cohorts or product categories to uncover actionable insights, and highlight the importance of excluding refunds or fraudulent transactions to avoid skewing the metric.
Ask whether 'average purchase value' means average revenue per transaction, per user, or per session, and confirm the time frame and population (e.g., all users vs. new users).
Determine which tables or logs contain purchase events, user IDs, transaction amounts, and timestamps. Ensure you have access to clean, complete data.
Decide between simple average (total revenue / number of purchases) or weighted average (e.g., if aggregating across segments). Consider using SQL or Python for computation.
Filter out refunds, cancelled orders, test transactions, and outliers. Address missing values and ensure currency consistency.
Sanity-check the number against historical trends or benchmarks. Segment by dimensions like user demographics or product type to provide deeper insights.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.