Sales analysis dashboard

As a data enthusiast, I dove into a rich dataset to uncover key insights into our sales performance. The results? Total revenue soared to $18M, with 53K orders and an average transaction price (ATP) of $341. Qtr4 2021 stood out with peak revenue, while supervisors like Diego Araujo ($6,315,114) and Diogo Carvalho ($6,098,516) led the team.

Breaking it down by product group:

Wheat: $4.5M

Yeast: $2.2M

Flour: $1.2M

Liquor: $1M

Candy: $0.9M

And more, down to Energy at $0M

On the channel side, Retail dominated with $9M, followed by Distributor at $6M and Online at $3M. Category-wise, Food hit $47K and Drink $5K. These visuals track trends over 2019-2021, showing growth patterns ideal for strategic decisions!

How I Built It (Simplified):

Data Prep in Power Query: Started with raw data import, then handled preprocessing and cleaning. A key step was normalizing the data to ensure consistency (e.g., standardizing formats for dates and categories) and denormalizing for reporting efficiency.

Data Modeling: In Power BI, I created relationships between tables (e.g., linking sales to product and supervisor tables via IDs) for seamless analysis.

Dashboard Creation: Used Power BI’s interface to build interactive visuals, including bar charts for yearly trends and pie charts for breakdowns.

Key Measures and KPIs Explained:

Revenue: Sum of all sales amounts to track total income.

Orders: Total count of transactions for volume insights.

ATP: Average revenue per order, calculated as Revenue / Orders.

Other KPIs like top supervisors use DAX to highlight leaders.