Introduction
The dataset for this project originates from a Burmese retail store's sales records for the year 2019. The analysis aimed to answer key questions that reflect important key performance indicators (KPIs) and to gain a thorough understanding of the metrics and trends within the data. This project offers an in-depth analysis of the sales records and provides insights to support the growth of the retail store.
Data structure
The dataset contains sales records for the year 2019. The data includes the following details: Invoice ID, Branch, City, Product line, Unit Price, Quantity, Tax, Total, Date, Time, Payment Method, Cost of Goods Sold, Gross Margin, Gross Income, Rating, Customer Type, and Gender.
Data Cleaning and Preparation
The dataset was loaded into Power BI's Power Query Editor for cleaning in preparation for analysis. Initially, the dataset contained an empty row and column, which were removed. Additionally, misspellings, duplicates, and outliers were corrected or removed to ensure that only unique and accurate values were used for the analysis.
Here is a picture of the dataset after cleaning:
Analysis and insights
The analysis revealed that the total cost of goods sold by the retail store is 30,759K, with a total gross income of 1,538K
The city with the highest amount of goods sold
The retail store sold goods to three cities which include: Naypitaw, Yangon and Mandalay
The retail store generated the most sales from the city of Naypyitaw, while Yangon and Mandalay recorded the least and similar amounts of sales.
Total Gross Income By City
The retail store also generated the highest gross income from sales to the city of Naypyitaw, while it earned the least gross income from sales to both Yangon and Mandalay.
Goods Sold By Product Line
This analysis reveals that food and beverages were the best-selling product line, while health and beauty products were the least sold by the retail store.
Total Gross Income By Product Line
The Food and Beverages line generated the most profit for the retail store, while the Health and Beauty line was the least profitable.
The Payment method with the most unit of goods sold
The retail store made sales through payment methods like Cash, E-wallet and Credit Cards channels.
Analysis of the dataset showed that the retail store sold more units of goods through cash transactions compared to the e-wallet and credit card channels.
What Payment Method Brought in the most Gross Income
In reference to the data above, more gross income was generated through cash payments compared to e-wallet and credit card payments.
Strategic Recommendations
1)Enhance Payment Channel Incentives:
Cash transactions generated more sales and profits compared to e-wallet and credit card channels. To boost sales through e-wallet and credit card transactions, consider offering discounts on purchases made via these channels. This could encourage increased usage and enhance profitability.
2)Boost Sales in Low-Performing Cities:
Cities like Yangon and Mandalay experienced lower sales. To increase awareness and sales in these areas, consider implementing targeted publicity campaigns. Utilize promotional emails, flyers, and other marketing materials to highlight the retail store's offerings. Additionally, opening new branches in these cities could significantly boost sales.
3)Stimulate Interest in Least Sold Product Lines:
Product lines such as Home and Lifestyle, and Health and Beauty products experienced lower sales. To stimulate consumer interest, consider creating combo or special packages featuring these products. This approach could attract more buyers and boost sales for these categories.
Here is an overview of the analysis:
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