In the fast-paced world of online sports retail, staying competitive requires more than just a quality product selection. Our project embarks on a mission to empower an online sports retail company with data-driven insights, leveraging the power of SQL to optimize revenue generation.
The primary goal of this project is to harness the potential of the company's extensive product data to make informed decisions that maximize revenue and enhance customer experience. By delving into product performance, sales patterns, customer preferences, and market trends, we aim to uncover actionable insights that drive strategic growth.
Data description of 5 files can be found here - sql_notebook.ipynb
The data and tasks were taken from DataCamp.
We employed our SQL skills to analyze product data for an online sports retail company. Our task involved working with numeric, string, and timestamp data, which encompassed pricing and revenue figures, ratings, reviews, descriptions, and website traffic statistics.
Throughout the analysis process, we applied various techniques, including aggregation, data cleansing, labeling, working with Common Table Expressions, and establishing correlations. Our ultimate goal was to generate actionable recommendations that could empower the company to optimize its revenue potential.
All our consistent steps:
- Counting missing values
- Nike vs Adidas pricing
- Labeling price ranges
- Average discount by brand
- Correlation between revenue and reviews
- Ratings and reviews by product description length
- Reviews by month and brand
- Footwear product performance
- Clothing product performance
If you wish to contribute, kindly adhere to the following instructions:
- Begin by forking the repository.
- Generate a new branch dedicated to your modifications.
- Implement the changes and commit them to your branch.
- Finally, submit a pull request to the development branch.
The license is used: "MIT License".