AI-Automated Reporting & Data Warehouse
Built an end-to-end AI-powered marketing data warehouse using BigQuery, Looker Studio, and Gemini Agent Builder that surfaces campaign patterns and answers business questions on demand.
Tools Used
The Problem
Leadership required daily, weekly, and monthly reports across paid channels, e-commerce, and offline sales. Three separate manual processes were consuming 15+ hours per week and introducing human error in reporting.
The Goal
Build a single source of truth data warehouse that automates all reporting, surfaces AI-generated insights, and enables leadership to query marketing performance conversationally.
Results
- ✓ Eliminated 15+ hours/week of manual reporting work
- ✓ Daily, weekly, and monthly dashboards auto-refresh without human input
- ✓ Gemini AI can answer ad-hoc questions about campaign performance
- ✓ Pattern detection identifies anomalies within 24 hours of occurrence
- ✓ Leadership now has a single dashboard replacing 6 separate reports
Strategy
Data Architecture
Built a medallion architecture in BigQuery:
- Bronze layer: Raw API pulls from Meta, Google, Shopify, and offline POS
- Silver layer: Cleaned, deduped, and normalised data
- Gold layer: Business-ready metrics (ROAS, Revenue, CAC, LTV)
AI Layer
Integrated Gemini Agent Builder to create a conversational interface over the data warehouse. Business users can ask questions like “Which campaign drove the most store walk-ins last week?” and get instant answers.
Looker Studio Dashboards
Built role-specific dashboards:
- Executive: P&L view, ROAS trend, Revenue by channel
- Marketing: Campaign performance, A/B test results, Audience insights
- Operations: Order velocity, Inventory-demand correlation
Execution
The implementation took 8 weeks — 2 weeks for data pipeline setup, 3 weeks for dashboard development, and 3 weeks for AI integration and training with business stakeholders.
The result was a system that paid for itself in the first month through time savings alone.