داش بورد

I started a sales data analysis journey with the goal of extracting clear insights to support decision-making.

The first step was reading and understanding the data, where we identified several text inconsistencies in the fields, such as:

•Accessories was written with 3 S’s → corrected to 2 S’s.

•Door to Door was written with 3 O’s → restored to the correct form.

•Standardized terms like Key Account, Retail, Consumer, and Upper that appeared in multiple formats.

After cleaning, we added new columns:

•Final Sales = Sales after deducting discounts.

•Sales Category to classify customers into: Low, Medium, High.

•Additional columns for Day Name and Month Name for analysis purposes.

All cleaning and transformation were done using Power Query.

Then we designed an interactive Dashboard showing:

•Total Sales: 9,059,343

•Total Quantity: 275,077

•Average Sales Per Unit: 21.82%

•Total Discount: 415,176

Key Insights:

•Top sales months: January, March, December.

•Top sales day: Tuesday.

•Customer distribution: Low (40%), Medium (31%), High (29%).

•Top selling locations: Alexandria and Sinai.

Category-Level Analysis:

1.Accessories: Strong sales in March, July, September – Saturdays lead – Low category dominant – Top city: Canal.

2.Bikes: Peak sales in January – Tuesdays lead – Medium category on top – Alexandria & Sinai lead in sales.

3.Clothes: Peaks in March, July, September – Saturdays lead – Low category dominant – Giza top in sales.

4.Components: High in March, July, September – Fridays lead – Low category dominant – Sinai & Canal lead in sales.

Outcome: A clear vision that enables precise marketing decisions and optimizes sales performance in the most active periods and locations.