This project explores customer purchasing behavior using an ecommerce dataset (2020–2023) sourced from Kaggle.
The primary objective was to analyze behavioral patterns, customer segmentation, churn dynamics, return impact, and revenue forecasting to derive business-relevant insights.
Although the project was completed as part of a group assignment, the full analysis, dashboard design, and interpretation were executed independently.
The work focuses on analytical reasoning and business interpretation rather than visualization aesthetics.
- Analyze temporal sales trends (monthly & yearly)
- Identify core customer demographics driving revenue
- Evaluate product category performance
- Examine churn behavior and retention patterns
- Assess return ratios across product segments
- Study payment behavior and spending trends
- Forecast potential future revenue trajectory
- Source: Kaggle – Dataset Link
- Time Period Covered: January 1, 2020 – September 15, 2023
- Data Type: Transactional ecommerce data (customer demographics, product categories, payment methods, churn status, returns, purchase amounts)
All analysis and transformations were conducted directly within Tableau.
- Core Segment: Customers aged 25–45 represent the most active purchasing demographic.
- Revenue Drivers: Home and Electronics categories contribute the highest revenue.
- Payment Behavior: Credit Card and PayPal dominate transaction usage.
- Churn Observation: Active and churned customers display similar spending levels, indicating retention may be influenced more by engagement frequency than transaction size.
- Seasonality Pattern: Sales decline between August–November, followed by a moderate rise in December.
- Forecast Risk Indicator: A projected slight revenue decline by 2025 suggests the need for stronger customer retention and marketing strategies.
- Tableau Desktop
- Customer Segmentation Analysis
- Churn & Retention Analysis
- Return Ratio Evaluation
- Time-Series Trend Analysis
- Revenue Forecast Modeling
- KPI Design & Dashboard Development
- Monthly & Yearly Sales Trends
- Customer Age & Gender Distribution
- Sales & Quantity by Product Category
- Payment Method Distribution & Avg Spend
- Churned vs Active Customer Analysis
- Return Analysis by Category
- Top 10 Customer Revenue Contribution
- Category Preference by Gender
- Revenue Forecasting
Customer_Behavior_Tableau.twbx– Tableau Packaged WorkbookDashboard_screenshots.pdf– Dashboard Report Exportecommerce_customer_data_custom_ratios.csv– DatasetREADME.md– Project Documentation
This project demonstrates the ability to transform raw transactional data into structured business insights using data exploration, segmentation logic, and predictive reasoning.
The emphasis was placed on analytical interpretation and identifying potential business risks/opportunities rather than purely aesthetic dashboard design.
Monika
- LinkedIn: Monikanahadiya
- Email: monikanahadiya@gmail.com