Funnel Analysis with dbt + BigQuery

This project demonstrates how to build an end-to-end analytics workflow for user funnel analysis, from raw events to business insights.

Project Overview

We analyze a typical product growth funnel with 4 steps:

1. Signup
2. Profile Completed
3. KYC Passed
4. First Payment

The objective is to identify where users drop off, and to analyze differences by acquisition channel, cohort, and geography.

Tech Stack

• Python → generate synthetic seed data (events.csv)
• dbt → transform raw events into clean, business-ready models
• BigQuery (Google Cloud) → data warehouse
• Dashboard (Looker) (ongoing)

Repository Structure

• seeds/
• events.csv # Synthetic event data
• models/
• staging/ # Raw events cleaned (views)
• intermediate/ # Funnel path logic (views)
• marts/ # Business-ready facts (tables)
• schema.yml # Tests (nulls, accepted values)

Dashboard (ongoing)

The dashboard walks through:

• Funnel Overview → conversion rates at each step
• By Acquisition Source → Ads vs Organic vs Partner
• Cohort Analysis → conversions over time
• Geography → country-level drop-offs

Reproducibility

To reproduce locally:

# Install dependencies
pip install dbt-bigquery

# Run seeds (load events into BigQuery)
dbt seed

# Build models
dbt run

# Run tests
dbt test