Customer Shopping Behavior Analysis
Project Overview
Understanding Customer Behavior
This project analyzes 3,900 customer purchases across product categories. We uncover spending patterns and customer segments.
Our goal: guide strategic business decisions through data-driven insights.
18 Customer behavior pattern
3,900
Total Purchases
18
Data Points
Dataset
Dataset Summary
Data Quality
Clean, comprehensive dataset enables robust analysis.
3,900
Total Rows
Customer purchases analyzed
18
Data Columns
Detailed attributes per record
37
Missing Values
Only in Review Rating column
Python Analysis
Exploratory Data Analysis
We prepared and cleaned data using Python for comprehensive analysis.
01
Data Loading
Imported dataset using pandas library.
02
Initial Exploration
Used df.info() and .describe() for statistics.
03
Missing Data
Imputed Review Rating using median values.
04
Standardization
Renamed columns to snake case format.
05
Feature Engineering
Created age_group and purchase_frequency_days columns.
06
Database Integration
Connected to PostgreSQL for SQL analysis.
Key Insight: Dropped promo_code_used column after verifying redundancy with discount_applied field.
SQL Analysis
Business Transaction Insights
We performed structured PostgreSQL analysis to answer critical business questions.
1
Revenue by Gender
Male customers: $157,890. Female customers: $75,191.
2
High-Spending Discount Users
839 customers used discounts but spent above average.
3
Top Products by Rating
Gloves (3.86), Sandals (3.84), Boots (3.82) lead ratings.
4
Shipping Comparison
Express: $60.48 average. Standard: $58.46 average.
Subscriber Analysis
1,053 subscribers averaging $59.49 spend.
2,847 non-subscribers averaging $59.87 spend.
Customer Segments
Loyal: 3,116 customers.
Returning: 701 customers.
New: 83 customers.
Repeat Buyers
958 subscribers with 5+ purchases.
2,518 non-subscribers with 5+ purchases.
50%
Hat Discount Rate
49.7%
Sneakers Discount Rate
49.1%
Coat Discount Rate
Visualization
Power BI Dashboard
Interactive dashboard presents insights visually for stakeholder engagement.
Visual Analytics
Dashboard displays revenue trends, customer segments, and product performance.
Real-time filtering enables deep-dive analysis.
Key Metrics
- Revenue by demographics
- Purchase patterns by season
- Subscription impact analysis
- Product category performance
Customer Behavior Dashboard Power Bi
Recommendations
Business Recommendations
Boost Subscriptions
Promote exclusive benefits for subscribers to increase conversion.
Loyalty Programs
Reward repeat buyers to move them into loyal segment.
Review Discounts
Balance sales boosts with margin control strategies.
Product Positioning
Highlight top-rated and best-selling products in campaigns.
Targeted Marketing
Focus on high-revenue age groups and express-shipping users.
Next Steps: Implement these recommendations to drive revenue growth and customer retention.