In this project, I have utilized my technical skills in SQL, Big Query, and Tableau that I learned through the Revou Mini Course to analyze the data. The dataset used for this project is from Google Data Cloud.

E-commerce is a vast and ever-changing field, and analyzing big data can help businesses to identify trends and patterns to improve their performance, campaigns, and decision-making processes.

In this project, I have used SQL to retrieve specific data from the dataset, such as category, name, department, product_id, order_id, and created_at. I have joined the bigquery-public-data.thelook_ecommerce.products and bigquery-public-data.thelook_ecommerce.order_items tables to get a better understanding of the sales patterns.

By sharing this project, I hope to provide insights into the e-commerce industry and inspire others to leverage their technical skills to analyze big data. Feel free to explore the code and data visualization on Tableau.

This project analyzes e-commerce data from Google Data Cloud using SQL, Big Query, and Tableau to identify trends and patterns for business improvement.

• Top selling product category Intimates and Jeans Category dominates the market with Average Sales is 9502. They contributed 36.66% on total sales
• Best selling costumer segment by category product 100% of Intimates buyer is Women and 50:50 on Comparison between male and female buyers
• Month to month sales rate Decreasing on Feb 2020 about 7.6% and Feb 2021 about 2.5%, then we can closed sales at better 24% than before in July 2022.

• Campaign focused on the top 5 categories especially in the Intimates and Jeans category because they contribute 36.66% of total sales.
• Women's interest is the key to selling products in the intimates category
• Find out what happened in Feb that is important as a decision to come, or can make new innovations during Feb for trying to maintain the upward sales trend.