This Power BI dashboard provides a Shop Customer Data Analysis to help understand customer spending behavior based on various demographic and professional factors. Below is a breakdown of the key insights:

Dataset is available here : https://www.kaggle.com/datasets/datascientistanna/customers-dataset

Key Metrics (Top Section)

• Count of Customers: 161 total customers.
• Average Annual Income: $111.57K.
• Average Family Size: 3.67 members.
• Average Work Experience: 4.30 years.
• Average Spending Score: 51.90 (on a scale of 1-100).

Visualizations & Insights

1. Spending Score by Gender (Donut Chart)
   • Males and females have nearly equal average spending scores (~51.66 vs. 52.19).
2. Spending Score by Family Size (Pie Chart)
   • Customers with a family size of 3 have the highest average spending score (60.88).
   • Family sizes 5 and 6 also show relatively high spending behavior.
3. Distribution of Age (Bar & Line Chart)
   • The highest count of customers falls around age 22, with an average spending score of 58.
   • Spending scores vary across age groups, with peaks around ages 22 and 50.
4. Spending Score by Profession (Bar Chart)
   • Doctors have an average spending score of 51.90.
5. Spending Score by Experience Level (Treemap)
   • Expert Level professionals have the highest spending score (85.00).
   • Senior Level follows with a score of 55.40.
   • Other levels (Mid, Entry, Junior) have scores in the 49-52 range.

Filters (Right Side)

• Dropdown filters for Profession, Family Size, Experience Level, and Gender allow dynamic analysis based on different categories.

Conclusion

This dashboard provides valuable insights into how different demographics (age, gender, profession, experience level, and family size) influence spending behavior. Businesses can use this data to target specific customer segments more effectively.