Product Analytics SQL Queries

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Translates product questions into battle-tested SQL for funnels, user retention cohorts, DAU/MAU stickiness, and event analytics.

The Product Analytics SQL Queries skill transforms business and product questions into precise, production-grade SQL across major dialects including Postgres, BigQuery, Snowflake, and DuckDB. Rooted in Lean Analytics methodologies, it specializes in the four foundational product data patterns: conversion funnels, cohort retention curves, active-user stickiness ratios, and event volume aggregations. By enforcing structured Common Table Expressions (CTEs), strict denominator anchoring, safe handling of incomplete cohorts, and automated sanity-check protocols, this skill prevents common analytical bugs like inflated user counts and recency bias, making it indispensable for product managers and analysts auditing metrics or building trustworthy dashboards.

주요 기능

01Utilizes readable, modular Common Table Expressions (CTEs) instead of nested subqueries
026 GitHub stars
03Automatically excludes recency-truncated, immature cohorts to avoid false metric drops
04Prevents common analytical errors by enforcing half-open intervals and date-truncated joins
05Generates clean, dialect-aware SQL for funnels, N-day retention, and DAU/MAU stickiness
06Includes a pre-execution sanity-check checklist to verify denominators and monotonicity

사용 사례

01Measuring product stickiness via trailing DAU/MAU ratios across customizable time horizons
02Building ordered multi-step conversion funnels to identify drop-off stages from signup to purchase
03Calculating Day-N and weekly cohort retention without understating incomplete cohorts