The timezone conversion part is what gets you.
Start by clarifying the schema and metric definitions, then build a date spine of the last 7 days to ensure zero-fill. Compute daily distinct session users and daily distinct completed-order users per city, platform, and user-local date, then join and divide, handling division by zero.
Pro tip: Explicitly state that you are converting timestamps to user-local dates using the users.timezone before aggregating, and that you will use a date spine to emit zeroes for days with no data—this shows you understand the nuance of timezone-aware daily metrics and complete reporting.
Confirm table structures, join keys, and definitions: conversion rate = distinct users with ≥1 completed order that day / distinct users with ≥1 session that day, using user-local dates.
Generate a series of the last 7 user-local dates (or a global date range) to left join against, ensuring days with no data produce zeroes.
Join sessions to users, convert session timestamps to user-local dates using users.timezone, filter to last 7 days, and count distinct users per city, platform, and date.
Join completed orders to users, convert order timestamps to user-local dates, filter to last 7 days, and count distinct users with at least one completed order per city, platform, and date.
Left join the session and conversion aggregates to the date spine, compute conversion rate as converted_users / session_users with safe division (e.g., NULLIF or CASE), and replace NULLs with 0.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the metric definition and data model, then outline a SQL-based approach that uses user-local dates and deduplicates users across platforms. Finally, discuss how to validate results and handle edge cases like low-volume city-platform pairs.
Pro tip: Always check for data completeness and time zone consistency before flagging drops; a 30% drop could be due to missing data or a logging issue rather than a real trend. Also, consider using a Bayesian or confidence interval approach to avoid false positives from small sample sizes.
Confirm the definition of conversion rate, the 7-day windows (e.g., rolling or fixed), and how to handle users active on multiple platforms. Ask about data availability and time zone handling.
Write a query that assigns each user to a single platform (e.g., by first activity or primary platform) to avoid double-counting, and converts timestamps to user-local dates using a time zone offset table.
Calculate the 7-day average conversion rate for each city-platform pair for the current and preceding windows, ensuring the windows are non-overlapping and aligned with user-local dates.
Identify pairs where the drop exceeds 30%, then validate by checking sample sizes, data completeness, and potential confounders (e.g., seasonality, platform changes).
Present the flagged pairs with context, suggest follow-up analyses (e.g., root cause, statistical significance), and discuss potential actions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the definition of churn: users with at least one completed order in a historical window (e.g., 90-180 days ago) but zero completed orders in a recent window (e.g., last 90 days). Then, write a SQL query that joins user orders with a timezone conversion, aggregates order counts per user per window, and filters for churned users, finally calculating last order local date and days since last order.
Pro tip: Always confirm the exact windows and timezone handling with the interviewer, as different definitions can drastically change the churn list. Also, consider using a calendar table or date spine to ensure all dates are covered, especially for users with no orders in the recent window.
Confirm the historical and recent windows (e.g., 90-180 days ago vs. last 90 days) and the definition of 'completed order'. Ensure you understand how to handle users' local timezones.
Use the users' timezone information to convert order timestamps to local dates. This is crucial for accurate window filtering and for reporting last order local date.
For each user, count completed orders in the historical window and in the recent window. Use conditional aggregation or separate subqueries.
Select users who have at least one order in the historical window and zero orders in the recent window.
For each churned user, find the maximum order local date (which will be in the historical window) and calculate days since last order as the difference between today's date and that date.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The Wilson CI formula in SQL is genuinely painful to write from memory.
First, clarify the definitions of 'local days', 'city', and 'cancelled orders', and confirm the time windows. Then, write a SQL query that aggregates orders into two 14-day periods per city, computes cancellation rates and Wilson confidence intervals, and calculates the absolute increase. Finally, rank cities by the increase and return the top 3 with their intervals.
Pro tip: When comparing periods, ensure you're using the same set of cities in both periods to avoid bias from cities with no prior data; consider filtering to cities with sufficient order volume in both periods to make the comparison meaningful.
Ask clarifying questions about 'local days' (e.g., timezone handling), 'city' (e.g., shipping city vs. billing city), and 'cancelled orders' (e.g., status definitions). Confirm the exact date ranges for the current and prior 14-day periods.
Write a SQL query to count total orders and cancelled orders per city for each 14-day window. Use conditional aggregation or separate subqueries, ensuring proper date filtering and grouping.
Calculate the cancellation rate for each city and period. Implement the Wilson score interval formula (or use a built-in function) to compute 95% confidence intervals for each rate.
Compute the absolute difference in cancellation rates between the current and prior periods for each city. Rank cities by this difference in descending order and select the top 3.
Return the top 3 cities along with their cancellation rates and Wilson confidence intervals for both periods, and the absolute increase. Optionally, include a brief interpretation of the intervals.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.