The Customer Behavior Analysis Dashboard is an end-to-end Data Analytics project that transforms raw customer transaction data into actionable business insights.
The project follows a complete analytics workflow:
- Data Collection from Excel
- Data Cleaning & Preprocessing using Python
- Exploratory Data Analysis (EDA)
- Interactive Dashboard Development in Power BI
The dashboard helps analyze customer demographics, purchasing behavior, subscription trends, product performance, and revenue generation to support data-driven business decisions.
The objective of this project is to:
- Analyze customer purchasing behavior.
- Identify high-performing product categories.
- Understand customer demographics and spending patterns.
- Examine subscription adoption among customers.
- Generate actionable insights through interactive visualizations.
The raw dataset was collected in Excel format and contains customer shopping and transaction details.
| Column Name |
|---|
| Customer ID |
| Age |
| Gender |
| Item Purchased |
| Category |
| Purchase Amount (USD) |
| Location |
| Size |
| Color |
| Season |
| Review Rating |
| Subscription Status |
| Shipping Type |
| Discount Applied |
| Promo Code Used |
| Previous Purchases |
| Payment Method |
| Frequency of Purchases |
- Imported raw customer data from Excel.
- Inspected dataset structure and data quality.
Tools Used:
- Python
- Pandas
- NumPy
Cleaning activities performed:
- Removed duplicate records
- Handled missing values
- Corrected inconsistent entries
- Standardized categorical values
- Verified data types
- Checked data integrity
- Prepared data for analysis
Analyzed:
- Customer demographics
- Product category performance
- Revenue distribution
- Subscription behavior
- Customer satisfaction trends
- Purchase frequency patterns
- Imported cleaned data into Power BI
- Created KPI measures using DAX
- Designed interactive visualizations
- Added slicers and filters
- Built a business-friendly dashboard
🔗 Live Dashboard:
https://app.powerbi.com/links/DlW8pyrbMM?ctid=0e061a39-ceed-4e09-ae89-509fa6698399&pbi_source=linkShare
| KPI | Value |
|---|---|
| Total Customers | 3.9K |
| Average Purchase Amount | ₹59.76 |
| Average Review Rating | 3.75 |
Observation
- 73% of customers are non-subscribers.
- 27% of customers are subscribers.
Insight
The low subscription rate indicates opportunities to improve customer retention through loyalty programs and personalized marketing campaigns.
Observation
- Clothing generates the highest revenue.
- Accessories contribute the second-highest revenue.
- Footwear and Outerwear contribute comparatively less.
Insight
The Clothing category is the strongest revenue driver and should remain a key focus area for inventory planning and promotional strategies.
Observation
- Clothing has the highest sales volume.
- Accessories rank second.
- Footwear and Outerwear have lower sales counts.
Insight
Customer demand is concentrated in Clothing and Accessories, highlighting their importance in business growth.
Observation
| Age Group | Performance |
|---|---|
| Young Adult | Highest |
| Middle-aged | High |
| Adult | Moderate |
| Senior | Lowest |
Insight
Young adults are the most valuable customer segment and contribute the largest share of revenue.
Observation
- Young adults purchase more frequently.
- Middle-aged customers also show strong engagement.
- Senior customers have lower purchasing activity.
Insight
Targeted campaigns toward younger demographics can significantly improve customer engagement and revenue.
The dashboard includes interactive slicers for:
- Yes
- No
- Male
- Female
- Clothing
- Accessories
- Footwear
- Outerwear
- Standard
- Express
- Free Shipping
- Store Pickup
- Next Day Air
- 2-Day Shipping
These filters allow users to explore customer behavior across multiple dimensions.
- 👥 Approximately 3,900 customers are included in the dataset.
- 💰 Average purchase amount is ₹59.76.
- ⭐ Average customer review rating is 3.75/5.
- 👕 Clothing is the highest-performing category in terms of revenue and sales.
- 👨💼 Young adults contribute the largest share of revenue.
- 📦 Subscription adoption is relatively low, indicating growth opportunities.
- 🚀 Customer retention strategies could increase long-term business value.
- Python
- Pandas
- NumPy
- Power BI
- Microsoft Excel
- Customer Segmentation using Machine Learning
- Customer Churn Prediction
- Customer Lifetime Value Analysis
- Sales Forecasting
- Recommendation System
Dosapati Sai Santhosh
B.Tech – Artificial Intelligence & Data Science
- GitHub: https://github.com/your-github
- LinkedIn: www.linkedin.com/in/sai-santhosh-905998293
⭐ If you found this project useful, consider giving it a star on GitHub.