This project focuses on analyzing e-commerce logistics performance and customer experience using real-world commerce data.
The goal is to identify delivery efficiency, SLA adherence, regional bottlenecks, and the impact of delivery delays on customer satisfaction.
The project follows an industry-style end-to-end analytics pipeline: Python → SQL Server → Power BI.
- Python (Pandas, NumPy)
- SQL Server (KPI analysis & validation)
- Power BI (Dashboard & visualization)
- GitHub (Version control & documentation)
- Total Orders
- Average Delivery Time
- Late Delivery Percentage (SLA breach)
- Average Customer Review Score
- City-level delivery performance
- Payment method performance
- Late deliveries have a significant negative impact on customer review scores.
- Certain cities consistently show higher average delivery times, indicating last-mile logistics challenges.
- Over 90% of orders are delivered on time, but delayed deliveries disproportionately affect customer satisfaction.
- Payment type analysis helps understand revenue contribution and order behavior.
The following features were engineered in Python:
- Delivery Time (days)
- Delivery Delay (days)
- SLA flag (
is_late) - Cleaned and validated timestamps
- Delivered-order filtering for business relevance
- Executive KPI cards for quick decision-making
- On-Time vs Late delivery analysis
- Top cities by average delivery time
- Impact of delivery delays on customer reviews
- Interactive slicers for city, delivery status, and payment type
- Data Cleaning & Feature Engineering (Python)
- Exploratory Data Analysis (Python)
- KPI Validation & Business Analysis (SQL Server)
- Interactive Dashboard Creation (Power BI)
Next-Gen-Commerce-Logistics │ ├── Data/ │ └── cleaned_olist_commerce_data.zip │ ├── Python/ │ ├── data_cleaning.py │ ├── exploratory_data_analysis.py │ └── python_to_sql_connector.py │ ├── Sql/ │ ├── table_creation.sql │ └── kpi_analysis.sql │ ├── PowerBi/ │ ├── executive_dashboard.pbix │ └── dashboard_preview.png │ └── README.md
- Review Python scripts for data preparation and feature engineering
- Run SQL scripts for KPI analysis
- Open the Power BI file to explore the interactive dashboard
If you’d like to discuss this project or provide feedback, feel free to connect with me on LinkedIn.