Superstore Customer Preferences

Project 2: Superstore Preferences

This project explored a dataset of transactions. The purpose of this project is to figure out ways to bring more people into the store and increase profits. I analyzed the supermarket dataset to explore different customer preferences regarding payment methods, visiting times, and membership in the loyalty programs based on gender and location. This presentation answers the following questions:

1. Which branch has the best results in the loyalty program?

2. Does the membership depend on customer rating?

3. Does gross income depend on the proportion of customers in the loyalty program? On payment method?

4. Are there any differences in indicators between men and women?

5. Which product category generates the highest income?

Github SQL Code: https://github.com/campermatt589/SQL_Analysis_Projects/tree/main/Superstore-Project 

Data Source:  

• Source: https://www.kaggle.com/datasets/aungpyaeap/supermarket-sales 

Tools and Methods:

• Exploratory Data Analysis

• Google Sheets

• Google BigQuery / SQL           

In this project, I used Google Sheets to clean the data. SQL was used to analyze the data. Once the queries were executed, I downloaded the results into Google Sheets. In Google Sheets, I created visuals that answered the above questions. 

The SQL code used in this project is available on my github in the SQL_Analysis_Projects repository. 

Customer Rating By Branch and By Customer Type

Gross Income by Customer Type and Payment Method

What are some of the different indicators between men and women?

Insights Learned:

• Branch C is more successful in the loyalty program and has a higher rating among all customer types, 

• Customers tend to use cash more than credit card or e-wallet.

• Members bring in slightly more income than regular shoppers.

• Men prefer cash and e-wallet more than women but women prefer credit card more. 

• With exception to fashion and accessories, women buy more products in each line overall. 

• The "Food and Beverages" and "Sports and Travel" categories rank first and second among highest grossing product lines,  respectively.

Recommendations:

• Improve customer service at branches A and B. 

• Market the loyalty program to customers to help them save money on their next trip. 

• Install ATMs at Superstore locations.

• Account for the amount of purchases for each product line. Use the data from each product line and increase supply of the products in demand. 

• Begin marketing select products toward the male demographic. 

Limitations

• The dataset does not include specific products that are purchased. More data is needed on what specific products are purchased and how to market products to based on their demographics. 

• Dataset has a 50-50 split in gender demographics (500 males and 500 females). This dataset could be a sample of a larger dataset.