Automobile Sales Dashboard

Question 1: Sales Performance Analysis:

How have sales varied over time and across different product lines and regions?

Identify the top-performing products and regions in terms of sales.

Question 2: Customer Segmentation and Order Behavior:

Segment customers based on their purchasing patterns and order size.

Analyze the frequency of orders and average spend per order for each customer segment. Question3: Sales Forecasting:

Predict future sales based on historical data.

Identify factors that significantly impact sales and use them to forecast future trends.

Question 4: Product Profitability Analysis:

What are the most Profitable Products with Profit Margin?

Question 5: Geographic Sales Analysis:

Analyze sales performance by geographic location, focusing on identifying high-growth markets and underperforming regions.

Question 6: Inventory Analysis:

Analyze the inventory turnover ratio to understand how quickly inventory is sold and replaced over a specific period.

Step-by-step activities:

Python:

• Python was utilized initially to clean and preprocess the dataset. Using libraries like pandas, the data was read from its source format in CSV, and any inconsistencies or missing values were addressed.
• Further manipulation included normalizing the data, creating calculated fields, and ensuring the data types were consistent for analysis.

SQL:

• Data Aggregation: SQL queries were employed to aggregate sales data from the cleaned dataset. This involved grouping data by relevant categories such as country, product line, and year. Summations and averages of sales figures were calculated to provide a basis for comparison and trend analysis.
• More complex SQL queries were used to calculate metrics like sales growth rate, inventory turnover ratio, and total units sold. These queries fed into the visualizations in Power BI and allowed for dynamic interaction with the data.

Power BI:

• Data Modeling: In Power BI, the imported and aggregated data was modeled to establish relationships, particularly creating a date dimension table that enabled the year slicer to filter across all visuals consistently.
• Dashboard Creation: The dashboard was designed with various visuals:
  • A bar chart comparing current and previous year sales by country,
  • A card visual to display the sales growth rate, total orders, and total sales,
  • A detailed table for inventory management metrics,
  • A line chart showing total sales over years to depict trends,
  • A tooltip showing average sales by country and product line.