🚀 E-Commerce Business Intelligence Platform

End-to-End Analytics Solution | dbt + BigQuery + Looker Studio

Transforming raw transaction data into strategic business intelligence through modern data engineering practices

🎯 Business Problem & Solution

Problem: E-commerce companies struggle with fragmented data across multiple systems, making it impossible for executives to get real-time insights into business performance, customer behavior, and revenue trends.

Solution: Comprehensive business intelligence platform that transforms raw transactional data into executive-ready dashboards, enabling data-driven decision making for strategic growth.

👥 Target Audience

Primary Users:

E-commerce Executives - Strategic decision makers needing KPI visibility
Marketing Teams - Customer segmentation and campaign performance analysis
Operations Managers - Sales performance and inventory insights
Data Teams - Scalable analytics infrastructure and automated reporting

📊 Dashboard Story Framework

Page 1: Business Foundation

"Where we stand and what drives our business"

Business Scale Context - £8.66M revenue, 4,293 customers, 19 countries
Revenue Concentration Analysis - Pareto chart revealing 20/60 rule risk
Geographic Market Intelligence - UK volume vs EU value opportunity
Strategic Risk Assessment - Customer dependency and diversification needs

Page 2: Customer Intelligence

"Who our valuable customers are and how they behave"

RFM Segmentation Dashboard - Champions, Loyal, At-Risk customer analysis
Customer Lifecycle Journey - Retention conversion points and LTV patterns
Geographic Behavioral Analysis - UK frequency vs EU value strategies
Actionable Segmentation - Targeted retention and acquisition opportunities

Page 3: Operational Excellence

"What sells, when, and where - optimization opportunities"

Product Portfolio Intelligence - Revenue concentration and inventory optimization
Operational Timing Analysis - Peak patterns and resource allocation insights
Geographic Product Performance - Market-specific product strategies
Efficiency Opportunities - Data-driven operational improvements

📊 Live Dashboard

🔗 View Interactive Dashboard

📊 View Presentation →
Complete business case and technical implementation walkthrough

Experience the complete analytics story across three strategic domains:

Business Foundation - Revenue patterns, customer concentration, geographic opportunities
Customer Intelligence - RFM segmentation, lifecycle analysis, behavioral insights
Operational Excellence - Product portfolio optimization, timing intelligence, geographic performance

🔍 Key Business Insights Generated

Customer Intelligence

Revenue Concentration Risk: Heavy dependence on top customers creates vulnerability - diversification strategy needed to reduce business risk
Customer Lifecycle Challenge: Large "Lost" customer segment indicates retention issues - opportunity for re-engagement campaigns
Value Segmentation Opportunity: Clear high-value customer identification enables targeted premium service and loyalty programs

Geographic Strategy

EU Premium Market Potential: European customers demonstrate willingness to pay premium prices - opportunity for premium product positioning
Untapped Revenue Growth: EU market is significantly underutilized despite proven high-value behavior - expansion strategy required
Market-Specific Strategies: Different purchasing behaviors between regions suggest need for tailored marketing and product approaches

Operational Intelligence

Weekend Underutilization: Lower weekend performance represents missed revenue opportunity - weekend-specific promotions needed
Operational Efficiency Gap: Extreme weekday concentration may create capacity constraints and service quality issues
EU Market Leadership: German market success provides blueprint for expanding into similar European markets

📈 Business Impact & Key Achievements

£8.66M in revenue analyzed across 373 days of operations
4,293 customers segmented across 19 countries with 4,000+ products
100% test coverage with 103/103 passing tests for enterprise-grade data quality
Three-layer data architecture with comprehensive documentation and lineage

Strategic Insights Delivered

🚨 Customer Concentration Risk: Top 10% customers drive 61% of revenue
🌍 Geographic Expansion Opportunity: EU customers demonstrate 79% higher AOV
⏰ Operational Intelligence: 91% revenue concentration on weekdays
📈 Product Portfolio Efficiency: 80% revenue from 30% of products

🏗️ Data Architecture & Technical Implementation

📋 Data Models Architecture

graph TD
    A[Raw Data<br/>BigQuery Dataset] --> B[Staging Layer<br/>Data Cleaning & Standardization]
    B --> C[Intermediate Layer<br/>Business Logic & Aggregations]
    C --> D[Marts Layer<br/>Analytics-Ready Models]
    D --> E[Looker Studio<br/>Interactive Dashboard]
    
    F[dbt Tests<br/>Data Quality Validation] --> B
    F --> C
    F --> D
    
    G[dbt Documentation<br/>Data Lineage & Context] --> B
    G --> C
    G --> D
    
    H[Git Version Control<br/>Code Management] --> I[CI/CD Pipeline<br/>Automated Testing]
    I --> J[Production Deployment<br/>BigQuery Data Warehouse]
    
    style A fill:#ff6b6b
    style E fill:#4ecdc4
    style F fill:#45b7d1
    style G fill:#f9ca24
    style H fill:#6c5ce7

Staging Layer (`models/staging/`)

stg_invoices - Cleaned and standardized transaction data with data quality filters

Intermediate Layer (`models/intermediate/`)

int_customer_metrics - Customer-level aggregations and behavioral analysis
int_product_metrics - Product performance metrics and portfolio analysis
int_order_items - Enriched transaction line items with business classifications
int_rfm_analysis - RFM scoring and customer segmentation logic

Marts Layer (`models/marts/`)

Core Models

fct_sales - Primary fact table with daily incremental refresh
dim_customers - Customer dimension with RFM segmentation

Analytics Models

customer_insights - Customer concentration analysis and revenue distribution
product_intelligence - Product portfolio optimization and geographic performance
business_overview - Executive-level KPIs and business metrics

Technology Stack

Layer	Technology	Purpose
Data Warehouse	Google BigQuery	Scalable cloud data storage and compute
Transformation	dbt (Data Build Tool)	SQL-based ELT transformations with testing
Visualization	Looker Studio	Interactive dashboards and self-service BI
Version Control	Git/GitHub	Code versioning and collaboration
Documentation	dbt Docs	Automated data lineage and model documentation
Testing	dbt Tests + Custom SQL	Comprehensive data quality validation

🧪 Data Quality & Testing Framework

Enterprise-Grade Quality Assurance: 103/103 Tests Passing (100%)

Test Coverage Strategy

Source Tests - Raw data validation and freshness monitoring
Model Tests - Business logic validation and data integrity
Cross-Model Tests - Revenue reconciliation and customer consistency
Custom Business Tests - RFM logic validation and data relationship checks

Key Quality Metrics

✅ Data Integrity: All primary keys unique, foreign keys validated
✅ Business Logic: RFM segmentation mathematically sound
✅ Cross-Model Consistency: Revenue totals reconciled across models
✅ Data Freshness: Automated monitoring for data pipeline health

-- Example Custom Business Test: Revenue Reconciliation
SELECT 
  'fct_sales' as source,
  SUM(line_total) as total_revenue
FROM {{ ref('fct_sales') }}
UNION ALL
SELECT 
  'business_overview' as source,
  total_revenue
FROM {{ ref('business_overview') }}
-- Test fails if revenues don't match across models

🛠️ Advanced Technical Implementation

Advanced dbt Patterns

Incremental Models: Efficient daily refresh patterns for large datasets
Surrogate Keys: Complex key generation handling edge cases and data quality
Macro Usage: Reusable SQL components for consistent transformations
Custom Tests: Business-specific validation beyond standard dbt tests

Data Engineering Best Practices

Layered Architecture: Clear separation of staging, intermediate, and marts
Comprehensive Testing: 100% test coverage with multiple test types
Documentation: Rich model and column documentation with business context
Version Control: Structured commit history and branching strategy

Performance Optimisation

Partitioning: Date-based partitioning for query performance
Clustering: Strategic clustering by customer_id and product_id
Incremental Processing: Efficient handling of daily data updates
Query Optimisation: Optimized SQL for BigQuery's distributed architecture

🚀 Getting Started

Prerequisites

Google Cloud Platform account with BigQuery access
dbt Cloud account or local dbt installation
Looker Studio access for dashboard creation

Setup Instructions

Clone the repository

git clone https://github.com/yourusername/ecommerce-analytics
cd ecommerce-analytics

Install dependencies
```
pip install dbt-bigquery
dbt deps
```
Configure BigQuery connection Update profiles.yml with your GCP credentials

Run the data pipeline

# Execute full pipeline
dbt run

# Validate data quality  
dbt test

# Generate documentation
dbt docs generate && dbt docs serve

Access outputs
- Data Models: Available in BigQuery datasets
- Documentation: Local dbt docs server
- Dashboard: Live Looker Studio dashboard

🎯 Skills Demonstrated

Analytics Engineering

✅ Data Modeling: Dimensional modeling with fact and dimension tables
✅ SQL Development: Advanced SQL with window functions, CTEs, and aggregations
✅ Data Quality: Comprehensive testing strategy and validation frameworks
✅ Pipeline Development: End-to-end ELT pipeline with error handling

Business Intelligence

✅ Dashboard Design: User-focused design with clear narrative structure
✅ Data Visualization: Effective chart selection and visual hierarchy
✅ Business Analysis: Insight generation and strategic recommendations
✅ Stakeholder Communication: Clear presentation of complex analytical findings

Technical Skills

✅ Cloud Platforms: Google Cloud Platform and BigQuery optimization
✅ Modern Data Stack: dbt, Looker Studio, Git integration
✅ Version Control: Git workflow with meaningful commit history
✅ Documentation: Technical writing and knowledge sharing

Business Acumen

✅ E-Commerce Analytics: Deep understanding of retail business metrics
✅ Customer Segmentation: RFM analysis and lifecycle management
✅ Financial Analysis: Revenue analysis and profitability insights
✅ Strategic Thinking: Actionable recommendations for business growth

📚 Project Documentation

Data Lineage & Dependencies

Automated Documentation: dbt generates comprehensive data lineage
Model Descriptions: Business context and technical implementation details
Column Definitions: Clear descriptions of all metrics and dimensions
Test Documentation: Explanation of data quality validation rules

Code Documentation

SQL Comments: Detailed explanation of complex business logic
README Files: Setup instructions and architectural decisions
Change Log: Git commit history documenting all improvements
Technical Decisions: Documentation of modeling choices and trade-offs

🔗 Links & Resources

📊 Live Dashboard - Interactive business intelligence dashboard
📖 dbt Documentation - Run dbt docs generate && dbt docs serve for complete data lineage
🔧 GitHub Repository - Full source code with commit history
💼 LinkedIn Profile - Professional background

Built with ❤️ for data-driven decision making

Part of a comprehensive Business Intelligence Portfolio demonstrating end-to-end analytics solutions for data-driven organizations.

Name		Name	Last commit message	Last commit date
Latest commit History 20 Commits
analyses		analyses
images		images
macros		macros
models		models
seeds		seeds
snapshots		snapshots
tests		tests
.gitignore		.gitignore
README.md		README.md
dbt_project.yml		dbt_project.yml
packages.yml		packages.yml

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