BigQuery Queries for E-commerce Conversion Funnel and Attribution Analysis

Write BigQuery queries to analyze the conversion funnel for [COMPANY NAME] e-commerce, using exported GA4 data.\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\nMain table: \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\`[PROJECT].analytics_[ID].events_*\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Query 1 — Conversion Funnel by Stage:**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\nAnalyze stages: session_start -> view_item -> add_to_cart -> begin_checkout -> purchase\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Volume and conversion rate between each stage\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Segmented by: device_category, source/medium, country\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Comparison: current period [START_DATE to END_DATE] vs prior period\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Use UNNEST(event_params) to extract parameters\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Query 2 — Drop-off Points by Page:**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Pages with highest exit rate after add_to_cart\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Average time between add_to_cart and begin_checkout\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- % of users who return to cart in another session\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Segmented by product category\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Query 3 — Multi-Touch Revenue Attribution:**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Linear model: distributes revenue equally across all touchpoints\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Time-decay model: higher weight for recent interactions\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- First-click vs last-click vs linear comparison\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- ROAS per channel with each model\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Use window functions and session stitching by user_pseudo_id\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Query 4 — Buyer Cohort Analysis:**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- First purchase as cohort month\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Repurchase month-over-month (month 1 to month 12)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Cumulative revenue per user by cohort\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- [MONTH] cohort vs historical average\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Query 5 — Automated Alerts:**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\nQuery to identify daily anomalies:\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Drop > [X]% in conversion rate vs 7-day average\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Increase > [Y]% in bounce rate on key pages\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Products with zero stock receiving traffic\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\nUse BigQuery Standard SQL syntax with DATE_SUB, PARSE_DATE, SAFE_DIVIDE functions. Comment each CTE.