Supermart Sales Analysis

Table of Contents

• Project Overview
• Data Sources
• Recommendations

Project Overview

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In the age of big data, supermarkets collect a massive amount of sales data daily. This project leverages the power of data analysis and visualization to gain valuable insights from a fictional supermarket's sales dataset. Using tools like Excel for data cleaning and transformation, and Power BI for analysis and visualization, we aim to uncover patterns, make data-driven decisions, and enhance business strategies.

Data Sources

The primary dataset used is 'supermarket_sales.xlsx' file containing detailed information for each sales made in different months, on various product lines and in different localities by the supermart.

Tools

• Power query- Data cleaning,
• Pivot table-Data analysis
• Excel- Visualizaton
• Power BI- visualization & creating reports

Data Cleaning & Preparation

In the initial data preparation phase,

1. Data Loading & inspection.
2. Handling missing values.
3. Data cleaning & Formatting Using Power query.

Exploratory Data Analysis (EDA)

EDA involves exploring sales data, to answer key questions. such as:

• Which locality has the highest sales.

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• Which Product line has the highest sales.

• What is the peak period with the most sales.

• The most used payement type for purchase of products.

Results/Fiindings

1. Electronic accessories sold the highest number of products which is 971 products.
2. Foods & beverages is the product line with the highest sales with $53,471.
3. Yangon city sold the most products with a quantity of 1,859.
4. Naypyitaw city has the highest sales of $105,303.
5. Cash has the highest payment type with $112,206.
6. Credit card has the lowest payment type with $100,767.
7. Yangon city has the highest e-wallet payment with $39,324.
8. Mandalay city has the lowest e-wallet payment with $33,513
9. Mandalay city has the highest credit card payment of $37,344.
10. Naypyitaw city has the lowest credit card payment of $30,327.
11. Naypyitaw city has the highest cash payment of $43,085.
12. Yangon city has the lowest cash payment of $33,781.

Recommendations

Based on the analysis, I recommend the following actions 1.Capitalize on the popularity of electronic accessories by ensuring a diverse and up-to-date product range, effective display, and well-trained staff in this section. 2. Continue promoting and expanding the variety of food and beverage products, offering attractive deals, and enhancing in-store displays to boost sales even further. 3. Consider increasing marketing efforts and promotions in Yangon city to further boost sales, possibly by offering location-specific discounts or events. 4. Leverage Naypyitaw's strong sales performance by focusing on customer engagement, loyalty programs, and special events to solidify the market presence. 5. While cash remains popular, consider incentivizing digital payment methods to reduce cash handling, streamline transactions, and improve security. 6. Promote the convenience and security of credit card payments through awareness campaigns and collaboration with credit card companies. 7. Foster the trend of e-wallet payments by offering exclusive e-wallet promotions and ensuring seamless integration of e-wallet services. 8. Implement targeted marketing campaigns to promote e-wallets in Mandalay city, showcasing the convenience and rewards associated with this payment method. 9. Capitalize on Mandalay's preference for credit card payments by offering credit card-specific discounts, loyalty rewards, and streamlined checkout processes. 10. Promote credit card usage in Naypyitaw through targeted marketing efforts, highlighting the advantages of using credit cards for convenience and rewards. 11. Encourage digital payments while still accommodating cash transactions by offering discounts or incentives for choosing digital payment methods. 12. Analyze monthly payment trends to tailor promotions and discounts based on payment preferences during each month.

Limitations

I had to remove all zero from my total tax & cost of goods columns which could have affected my accuracy of my calculations, and also subtract 5% tax from the total sales to get my total sales inclucluding tax. with all these i can say there is a correlation with the various data points in both the excel & Power BI dashboard.

Refrences

• Data cleaning by Alex The Analyst
• Power BI Analysis by Soumen Roy