Scatter plots in Power BI - Real Business Scenario

Scenario Mode

👤 Your Role: You are a sales analyst at a retail company.

📋 Request: Your manager wants to understand the relationship between advertising spend and sales revenue across different stores to identify if higher advertising leads to higher sales.

📊 Data: You have monthly data for 10 stores including Store ID, Advertising Spend (in $), Sales Revenue (in $), and Store Size (in square feet).

🎯 Deliverable: Create a scatter plot in Power BI showing Advertising Spend on the X-axis and Sales Revenue on the Y-axis. Use Store Size to adjust the size of the dots to add more insight.

Sample Data

Store ID	Advertising Spend ($)	Sales Revenue ($)	Store Size (sq ft)
Store 1	5000	45000	1500
Store 2	7000	60000	1800
Store 3	3000	28000	1200
Store 4	8000	75000	2000
Store 5	2000	22000	1100
Store 6	6000	58000	1700
Store 7	4000	39000	1400
Store 8	9000	80000	2100
Store 9	3500	32000	1300
Store 10	10000	90000	2200

1

Step 1: Load the sample data into Power BI by importing the table with columns Store ID, Advertising Spend, Sales Revenue, and Store Size.

Use 'Get Data' > 'Excel' or 'CSV' depending on your file format, then load the data into Power BI.

Expected Result

Data table with 10 rows and 4 columns loaded into Power BI.

2

Step 2: Create a scatter plot visual on the report canvas.

From the Visualizations pane, select the Scatter chart icon.

Expected Result

An empty scatter plot visual appears on the report canvas.

3

Step 3: Assign data fields to the scatter plot: set 'Advertising Spend' to X-axis, 'Sales Revenue' to Y-axis, and 'Store Size' to Size. Use 'Store ID' as Details to identify each point.

Drag 'Advertising Spend' to X Axis, 'Sales Revenue' to Y Axis, 'Store Size' to Size, and 'Store ID' to Details in the scatter plot fields.

Expected Result

Scatter plot shows 10 dots representing stores, positioned by advertising spend and sales revenue, with dot sizes reflecting store size.

4

Step 4: Format the scatter plot for clarity: add data labels showing Store ID, set axis titles, and adjust colors for good contrast.

In Format pane, turn on Data labels, set X-axis title to 'Advertising Spend ($)', Y-axis title to 'Sales Revenue ($)', and choose a color palette with high contrast.

Expected Result

Scatter plot clearly shows labeled points with readable axis titles and distinct colors.

5

Step 5: Analyze the scatter plot to identify the relationship between advertising spend and sales revenue.

Observe the trend of dots from left to right and bottom to top.

Expected Result

You see that stores with higher advertising spend generally have higher sales revenue, and larger stores tend to have bigger dots.

Final Result

Scatter Plot: Advertising Spend vs Sales Revenue

Y-axis (Sales Revenue $)
|
|          * Store 10 (biggest dot)
|        * Store 8
|       * Store 4
|      * Store 2
|     * Store 6
|    * Store 1
|   * Store 7
|  * Store 9
| * Store 3
|* Store 5
+------------------------------------> X-axis (Advertising Spend $)

✓There is a positive relationship between advertising spend and sales revenue.

✓Stores with higher advertising spend tend to generate more sales.

✓Store size influences the size of the dots, showing bigger stores generally have higher sales.

✓This visualization helps identify which stores might benefit from increased advertising.

Bonus Challenge

Add a trend line to the scatter plot to quantify the relationship between advertising spend and sales revenue.

Show Hint

Use the Analytics pane in Power BI scatter plot visual to add a trend line and observe the slope.