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🚀 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

Business Foundation

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

Customer Intelligence

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

Operational Insights

📊 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
Loading

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

  1. Clone the repository

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

  2. Install dependencies

    pip install dbt-bigquery
dbt deps

  3. Configure BigQuery connection Update profiles.yml with your GCP credentials

  4. Run the data pipeline

    # Execute full pipeline
dbt run

# Validate data quality  
dbt test

# Generate documentation
dbt docs generate && dbt docs serve

  5. Access outputs

🎯 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

🏦 Fintech Relevance

These analytics patterns transfer directly to financial services:

E-commerce Metric Banking Equivalent
Customer segmentation Account tier classification
Revenue per customer Fee income / Interest margin
Purchase frequency Transaction frequency
Cohort retention Deposit retention by vintage
Product affinity Cross-sell propensity
RFM scoring Customer value scoring

Built with ❤️ for data-driven decision making

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