The mechanics are straightforward: filter by event_date, count impressions and clicks separately using conditional aggregation, then divide.
Start by clarifying the schema and definitions: what tables contain impressions and clicks, how timestamps are stored, and what 'last calendar week' means (e.g., Monday-Sunday). Then write a SQL query that filters events to the last complete calendar week, aggregates clicks and impressions separately, and computes the ratio. Use a single query with conditional aggregation or subqueries to ensure accurate totals.
Pro tip: Always clarify the definition of 'last calendar week' and whether to include partial weeks. Also, consider edge cases like ads with zero impressions and whether to use SUM(clicks)/SUM(impressions) or AVG(click-through rate per ad) — the former is correct for overall CTR.
Ask about table structures, column names, and how clicks and impressions are recorded. Confirm the definition of 'last calendar week' (e.g., previous Monday to Sunday) and whether to include only complete weeks.
Use date functions to restrict the data to the last calendar week. For example, in PostgreSQL: WHERE event_date >= date_trunc('week', current_date) - interval '1 week' AND event_date < date_trunc('week', current_date).
Sum clicks and impressions separately across all ads. If clicks and impressions are in the same table with an event type, use conditional aggregation: SUM(CASE WHEN event_type = 'click' THEN 1 ELSE 0 END) AS clicks, SUM(CASE WHEN event_type = 'impression' THEN 1 ELSE 0 END) AS impressions.
Divide total clicks by total impressions, ensuring to handle division by zero. Use CAST to float to avoid integer division: CAST(clicks AS FLOAT) / NULLIF(impressions, 0) AS ctr.
Show the final SQL query and explain the result. Mention potential pitfalls like timezone differences, data completeness, and whether to include ads with zero impressions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty much the same query with a JOIN and a GROUP BY tacked on.
First, clarify the metric definition and time period, then write a SQL query that joins the click events table to the campaigns table on campaign_id, filters for the same time period, and groups by campaign_type to compute CTR (clicks/impressions) per type. Finally, validate the results and discuss any data quality or interpretation considerations.
Pro tip: Always confirm whether CTR should be computed as total clicks divided by total impressions (weighted) or as an average of per-campaign CTRs (unweighted), as this choice can significantly change the results and reflects analytical rigor.
Confirm the definition of CTR (clicks/impressions) and the exact time period. Ensure you understand which tables contain clicks, impressions, and campaign metadata.
Determine that the events table (with clicks and impressions) must be joined to the campaigns table on campaign_id. Verify that campaign_type is available in the campaigns table.
Construct a query that filters events to the same time period, joins to campaigns, groups by campaign_type, and calculates CTR as SUM(clicks)/SUM(impressions). Use appropriate aggregation functions.
Check for nulls, duplicate joins, or missing campaign types. Discuss whether the CTR differences are statistically significant and what they imply for campaign performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.