https://github.com/erickkhosasi/thelook-data_analysis

Final project for my SQL mini bootcamp. This project explores an e-commerce dataset to uncover key business insights. Data insights were queried in Google BigQuery and visualized with Google Sheets.
https://github.com/erickkhosasi/thelook-data_analysis

bigquery data-analysis e-commerce sql

Last synced: 6 months ago
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Final project for my SQL mini bootcamp. This project explores an e-commerce dataset to uncover key business insights. Data insights were queried in Google BigQuery and visualized with Google Sheets.

• Host: GitHub
• URL: https://github.com/erickkhosasi/thelook-data_analysis
• Owner: erickkhosasi
• Created: 2025-09-18T16:51:42.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2025-09-18T17:11:25.000Z (7 months ago)
• Last Synced: 2025-09-18T19:56:37.561Z (7 months ago)
• Topics: bigquery, data-analysis, e-commerce, sql
• Homepage:
• Size: 2.04 MB
• Stars: 1
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# The Look Sales & Retention Analysis with SQL (BigQuery)

## Project Overview

This project was completed as the final assignment for my **SQL with BigQuery Mini Bootcamp**. The goal is to analyze an e-commerce dataset to uncover key insights on **sales performance, customer behavior, return rates, and retention**. The analysis highlights the business challenges of high product return rates, dependency on first-time orders, and sales concentration in top countries.

The project concludes with **data-driven recommendations** to improve customer lifetime value (CLV), optimize retention strategies, an strengthen market-focused campaigns.

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## Dataset

- **Source:** Google BigQuery public dataset  

- **Dataset ID:** `bigquery-public-data.thelook_ecommerce`  

- **Database:** `thelook_ecommerce`  

- **Tables Used:**  

  - `order_items`  

  - `orders`  

  - `users`  

  - `products`  

- **Periods Covered:** 2019–2025  

⚠️ **Note:** The dataset is updated regularly. As a result, query outputs may differ slightly depending on when you run them.

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## Exploratory Data Analysis (EDA)

- **Customer Behavior / Activeness**  

  Segmented customers based on last active date to understand churn and retention patterns.  

- **Customer Geographic & Demographic Segmentation**  

  Analyzed users by **country, gender, and age group** to identify top markets and demographic-driven sales.  

- **Revenue Distribution**  

  Explored revenue contribution across geographies, demographics, product categories, and order types (first vs. repeat purchases).  

- **Return Orders**  

  Investigated product return trends, quantifying **return rate and revenue loss** from returned orders.  

- **Average Order Value (AOV)**  

  Evaluated AOV across different dimensions to identify whether revenue is driven by order frequency or spending per order.  

- **Logistics Performance**

  Evaluated shipping performance for completed orders across all countries.

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## Tools

- **Google BigQuery** – Core tool for querying and analyzing data at scale.  

- **Google Sheets** – Used to visualize aggregated results and present insights in a clear format.  

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