This project analyzes a sales dataset to extract critical insights, identify underlying trends, evaluate customer purchasing patterns, and deliver data-driven recommendations to enhance strategic decision-making and business performance.

The primary goal of this project is to analyze the company's sales performance and generate strategic insights by:

• Understanding total sales volume and revenue generation over time.
• Identifying the top-performing product categories and bestselling products.
• Analyzing regional sales patterns to pinpoint areas of strength and opportunity.
• Evaluating payment method preferences among customers.
• Discovering monthly seasonality and sales peaks.
• Providing data-driven recommendations to boost sales and improve marketing strategies.

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The datset for this analysis consit of 240 rows and 10 columns some of which are;

• Regions: North America, Europe, Asia
• Product Categories: Electronics, Home Appliances, Clothing, Books, Beauty Products, and others
• Product name
• Metrics Captured: Units Sold, Unit Price, Total Revenue, Payment Methods, Regional Sales

Check out the dataset here Sales Perfromance dataset

• Power BI
• Power Query

The data came in a structured Csv format and hence didn't require deep cleaning, missing values were handled, columns changed inot proper data format and a couple of neccessary measures were created.

Which product categories contribute most to total revenue?

Which region generates the highest revenue and sales volume?

How do monthly sales vary? Are there any noticeable seasonal trends?

What payment methods are most popular among customers?

Which products are the top sellers?

Power BI dax functions was used to create and insert relevant measures to enhance the useability of the dataset. Here are the queries deployed in this analyis:

Average order value = [Gross revenue]/[Total transactions]

Gross revenue = SUM(Sheet1[Total revenue])

Total transactions = COUNT(Sheet1[Transaction ID])

Total units sold = SUM(Sheet1[Units Sold])

• January being the highest grossing month suggests strong performance early in the year, likely tied to seasonal demand (e.g. post-holiday shopping or New Year promotions).
• Electronics dominate revenue, followed by home appliances and sporting goods. Beauty and books contribute marginally.
• North America is the leading region, contributing almost half of total revenue.
• Credit cards are by far the preferred payment method - used in over 60% of sales made.
• High-ticket electronics such as Canon EOS R5 Camera and LG OLED TV are leading revenue drivers, despite low unit sales.

Recommendation

• Expand product offerings in Electronics category.
• Explore Regional Growth in Asia and Europe by targeting marketing or localized promotions.
• Offer Promotions in Off-Peak Months through discounts or marketing campaigns. This will Boost sales in months like July and August.
• Diversify Payment Options by integrating other payment methods to reduce the dominance of credit cards.