Start by clarifying the schema and definitions (e.g., what constitutes a viewer, liker, and the exact time window). Then outline a SQL approach using CTEs to compute daily distinct viewers and likers per platform and app version, calculate the like-through-rate, and compare the last day's rate to the prior 14-day average using window functions. Finally, flag segments where the drop is >= 5 percentage points.
Pro tip: Mention the importance of handling edge cases like missing data, timezone consistency, and ensuring distinct counts are accurate (e.g., using COUNT(DISTINCT user_id)). Also, discuss how to interpret the flag in a business context, such as potential bugs or user experience issues.
Ask clarifying questions about the table structure, definitions of 'viewer' and 'liker', and the exact date range (e.g., last 14 days including or excluding the last day). Confirm that 'daily' means per calendar day and that platform and app version are dimensions.
Write a query to aggregate the data by date, platform, and app version, counting distinct users who viewed and distinct users who liked. Ensure proper filtering for the two-week window.
Compute the like-through-rate as distinct likers divided by distinct viewers for each segment and day. Handle division by zero (e.g., using NULLIF or CASE).
Use window functions to calculate the average like-through-rate over the prior 14 days (excluding the last day) for each segment. Then compute the difference between the last day's rate and this average.
Identify segments where the difference is <= -0.05 (i.e., a drop of 5 percentage points or more). Output the flagged segments with relevant metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the funnel stages and define each metric precisely, then write a SQL query that aggregates daily counts for each stage and computes conversion rates between consecutive stages. Use a date filter for two consecutive dates and ensure the output includes both dates for comparison.
Pro tip: Mention the importance of defining a 'like impression' as a distinct event from viewing, and consider whether users can appear in multiple stages (e.g., a user who views and likes) – this affects whether you use distinct user counts or event counts.
Confirm what each stage means: feed viewers (users who loaded the feed), users who received a like impression (users who saw a post with a like button), and users who liked a post (users who clicked like). Discuss whether these are unique users per day and how to handle multiple events per user.
Determine the source tables (e.g., feed_views, impressions, likes) and the date column. Filter for the two consecutive dates, ensuring you use the correct timezone and date boundaries.
For each date, compute the count of distinct users at each stage. Use conditional aggregation or separate subqueries to get the three counts per date.
Calculate stage-to-stage conversion rates: (users who received like impression / feed viewers) and (users who liked / users who received like impression). Present as percentages or decimals.
Structure the final output with columns: date, feed_viewers, like_impression_users, likers, conv_rate_1, conv_rate_2. Validate that counts are non-increasing across stages and check for anomalies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The outages join was the part I actually liked.
Start by defining the metric (likes per session) and the dimensions (platform, app version) to monitor. Then design a data quality check that computes daily (or hourly) likes per session for each platform-version combination and flags deviations from expected baselines. Finally, incorporate an outages table to exclude periods of known outages from the anomaly detection, ensuring that only unexpected drops trigger alerts.
Pro tip: Use a robust baseline like a rolling median with a threshold based on historical variability (e.g., 3 MAD) to reduce false positives from natural fluctuations. Also, consider segmenting by platform and version to isolate issues quickly.
Clearly specify the metric: likes per session, computed as total likes divided by total sessions. Identify the dimensions to monitor: platform (iOS, Android, Web) and app version.
For each day (or hour), calculate likes per session for each platform-version combination. This creates a time series for each segment.
Use historical data to compute a robust baseline (e.g., rolling median over past 7 days) and flag segments where the metric drops significantly (e.g., below 3 MAD or a percentage threshold).
Join with the outages table to identify periods of known outages. Exclude those periods from anomaly detection or adjust thresholds accordingly to avoid false alarms.
Trigger alerts for anomalies not explained by outages. Provide context (e.g., affected platform-version, magnitude of drop) to facilitate root cause analysis.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.