https://github.com/narasimhakasu/thelook-ecommerce-analysis

End-to-end data analytics project using Google BigQuery and Looker Studio on TheLook E-commerce dataset. Includes SQL transformations, staging and summary tables, documentation, and a 4-page interactive dashboard with insights on revenue, products, customers, and distribution operations.
https://github.com/narasimhakasu/thelook-ecommerce-analysis

bigquery dashboard data-analytics ecommerce looker-studio sql

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End-to-end data analytics project using Google BigQuery and Looker Studio on TheLook E-commerce dataset. Includes SQL transformations, staging and summary tables, documentation, and a 4-page interactive dashboard with insights on revenue, products, customers, and distribution operations.

• Host: GitHub
• URL: https://github.com/narasimhakasu/thelook-ecommerce-analysis
• Owner: narasimhakasu
• Created: 2025-09-13T10:30:24.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-09-13T10:58:15.000Z (10 months ago)
• Last Synced: 2025-10-16T05:09:41.605Z (9 months ago)
• Topics: bigquery, dashboard, data-analytics, ecommerce, looker-studio, sql
• Homepage:
• Size: 2.1 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# TheLook E-Commerce Analysis

## 📖 Table of Contents

- [Project Overview](#-project-overview)

- [Business Objectives](#-business-objectives)

- [Tools & Technologies](#-tools--technologies)

- [Project Structure](#-project-structure)

- [Dashboard Pages](#-dashboard-pages)

- [Key Insights](#-key-insights)

- [How to Reproduce](#-how-to-reproduce)

- [Recommendations](#-recommendations)

- [References](#-references)

- [Author](#-author)

---

## 📌 Project Overview

This project analyzes **TheLook E-Commerce** public dataset (available in Google BigQuery) to uncover business insights across revenue, customer behavior, product performance, and distribution operations.  

The goal was to design a complete **data pipeline + interactive dashboard in Looker Studio**, following an end-to-end data analytics process.

The project is structured to demonstrate skills aligned with professional data analyst roles.

---

## 🎯 Business Objectives

1. How is the overall business performing in terms of **revenue, profit, and orders**?  

2. What are the key **monthly trends** in customer acquisition and revenue?  

3. Which **product categories** drive the most revenue and profitability?  

4. How do **customer demographics (age, gender, repeat status)** impact sales?  

5. What are the insights on **distribution centers and operations efficiency**?  

---

## 🛠️ Tools & Technologies

- **BigQuery SQL** → Data extraction, transformation, and summary tables  

- **Google Looker Studio** → Dashboard design & visualization  

- **GitHub** → Project documentation & version control  

- **Google Sheets/Docs** → For supporting documentation  

---

## 📂 Project Structure

- **sql_queries/** (All SQL scripts)  

  - [orders_lifecycle_summary.sql](sql_queries/orders_lifecycle_summary.sql)  

  - [revenue_summary.sql](sql_queries/revenue_summary.sql)  

  - [monthly_revenue.sql](sql_queries/monthly_revenue.sql)  

  - [products_summary.sql](sql_queries/products_summary.sql)  

  - [customer_summary.sql](sql_queries/customer_summary.sql)  

  - [distribution_operations_summary.sql](sql_queries/distribution_operations_summary.sql)  

  - [order_stage_summary.sql](sql_queries/order_stage_summary.sql)  

- **docs/** (Documentation)  

  - [business_questions.md](docs/business_questions.md)  

  - [data_pipeline.md](docs/data_pipeline.md)  

  - [methodology.md](docs/methodology.md)  

  - [recommendations.md](docs/recommendations.md)  

- **Dashboard/** (Dashboard screenshots)  

  - [page1_overview.png](dashboard/page1_overview.png)  

  - [page2_products.png](dashboard/page2_products.png)  

  - [page3_customers.png](dashboard/page3_customers.png)  

  - [page4_operations.png](dashboard/page4_operations.png)  

  - [dashboard_overview.pdf](dashboard/dashboard_overview.pdf)  

- **README.md** → Project overview (this file)  

---

## 📊 Dashboard Pages

Explore the full interactive dashboard here:  

👉 [TheLook E-Commerce Dashboard](https://lookerstudio.google.com/s/m2vkZuDORB4)

The dashboard is divided into 4 pages:

1. **Business Overview** → Revenue, profit, orders, order stages  

2. **Product Insights** → Revenue by category, top products, monthly product revenue  

3. **Customer Insights** → Demographics, repeat customers, age buckets  

4. **Distribution & Operations** → Revenue & orders by distribution centers, operational metrics  

---

## 🔑 Key Insights

- Revenue is strongly driven by **Men’s category**, followed by **Women’s**.  

- **Repeat customers** form a significant share of revenue growth.  

- Older age groups **(55+) dominate** purchases.  

- Distribution centers vary in performance, highlighting opportunities for **logistics optimization**.  

---

## 🚀 How to Reproduce

1. Connect to **BigQuery public dataset**: `bigquery-public-data.thelook_ecommerce`  

2. Run the queries from [`sql_queries/`](sql_queries/) to create staging and summary tables.  

3. Import summary tables into **Looker Studio**.  

4. Rebuild dashboard pages using charts, tables, and filters.  

5. Compare insights with documentation in [`docs/`](docs/).  

---

## 📈 Recommendations

- Improve **repeat customer retention strategies** (loyalty programs).  

- Optimize **distribution center load balancing**.  

- Focus on **high-revenue categories** while reducing underperforming products.  

- Leverage **age bucket segmentation** for targeted marketing.  

---

## 📑 References

- Data Source: [TheLook E-Commerce Public Dataset](https://console.cloud.google.com/marketplace/details/bigquery-public-data/thelook-ecommerce)  

- Google BigQuery Documentation  

- Looker Studio Documentation  

---

## 👤 Author

**Narasimha Kasu**  

📧 *narasimha.kasu9@gmail.com*