Start by filtering users to exclude test accounts and invites to the 7-day window, then deduplicate invites to the earliest per user per survey. Join to responses with a strict answered_at cutoff, and aggregate to compute response rate (any response) and completion rate (completed responses) per survey.
Pro tip: Always clarify the definitions of 'response' and 'completion' and the exact cutoff logic before writing SQL; in product analytics, ambiguous metrics can lead to drastically different results.
Select invites sent in the 7-day window, exclude test users, and use ROW_NUMBER() to keep only the earliest invite per user per survey.
Left join the deduplicated invites to survey_responses on invite_id or user_id and survey_id, ensuring answered_at is before the cutoff (e.g., end of window or a fixed date).
Group by survey_id and compute the number of invites, number of responses (any response), and number of completions (e.g., where completed = true or status = 'complete').
Compute response rate as responses / invites and completion rate as completions / invites (or completions / responses, depending on definition), using safe division to avoid divide-by-zero.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I had not done reweighting in SQL before, only in Python.
First, clarify the data structure and confirm that invites and completes are available at the country level. Then compute the weight for each country as (share of invites) / (share of completes), apply these weights to the country-level average ratings, and compute the weighted average. Finally, present the weighted average along with a detailed breakdown showing each country's invites, completes, share of invites, share of completes, weight, and average rating.
Pro tip: Always check for countries with zero completes or zero invites; these can cause division by zero or infinite weights, so discuss how to handle them (e.g., exclude or smooth). Also, mention that the weighted average should be computed as sum(weight * rating) / sum(weight) to ensure proper normalization.
Confirm that you have country-level data for invites, completes, and average ratings. Clarify that the goal is to correct for non-response bias by reweighting countries based on their invite share relative to complete share.
Calculate each country's share of total invites and share of total completes. Then compute the weight for each country as (share of invites) / (share of completes).
Multiply each country's average rating by its weight. Sum these weighted ratings and divide by the sum of the weights to get the country-reweighted average rating.
Check for countries with zero completes or zero invites, which would cause division by zero. Decide on a strategy (e.g., exclude, merge, or use a small epsilon) and document it.
Show a table with each country's invites, completes, share of invites, share of completes, weight, and average rating. Then present the final weighted average and interpret what it means for correcting non-response bias.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.