Timely delivery is a critical factor in customer satisfaction for e-commerce businesses. Delays in delivery can lead to negative customer experiences, increased support costs, and potential revenue loss.
This project analyzes e-commerce delivery performance data to identify patterns in delivery delays, evaluate logistics efficiency, and highlight operational factors impacting on-time delivery performance.
Using SQL-based analysis and exploratory visualizations, the project investigates delivery timelines, delay distribution, and operational bottlenecks that could affect fulfillment efficiency.
E-commerce companies must ensure reliable delivery timelines to maintain customer trust. However, operational constraints such as logistics inefficiencies, shipment processing delays, and regional delivery challenges can cause late deliveries.
The goal of this analysis is to answer:
- What percentage of deliveries are delayed?
- Which factors contribute most to delivery delays?
- Are certain regions, carriers, or time periods more prone to delays?
- What operational improvements could reduce delivery delays?
The analysis follows a structured workflow similar to real-world analytics projects.
- Loaded delivery dataset
- Examined data structure and key variables
- Checked for missing values and inconsistencies
- Reviewed shipment and delivery timelines
Calculated:
- Delivery duration
- On-time vs delayed shipments
- Average delivery time distribution
Analyzed delivery performance by:
- Region
- Shipping carrier
- Order processing time
- Seasonal order trends
Used charts and aggregation queries to identify patterns in delivery delays and operational bottlenecks.
The notebook includes multiple analytical charts such as:
- Delivery time distribution
- Delayed vs on-time deliveries
- Regional delivery performance
- Shipping carrier comparison
These visualizations help identify trends and operational inefficiencies.
- SQL – Data extraction and aggregation
- Python – Data analysis
- Pandas – Data manipulation
- Matplotlib / Seaborn – Visualization
- Jupyter Notebook – Analysis environment
- Git & GitHub – Version control
Some key findings from the analysis include:
- A significant portion of deliveries experienced delays beyond the expected delivery window.
- Certain regions showed higher delay rates, suggesting logistics or infrastructure challenges.
- Peak order periods were associated with increased delivery delays.
- Differences between shipping providers highlighted potential efficiency gaps.
Full insights are documented in the insights.md file.
This analysis demonstrates how delivery performance data can support operational decision-making.
Potential business actions include:
- Identifying high-delay regions requiring logistics improvements
- Evaluating shipping partner performance
- Adjusting operational capacity during peak demand periods
- Improving delivery forecasting and fulfillment planning
These insights help businesses reduce delivery delays and improve customer satisfaction.
👉 Click to View the Dashboard (PDF)
For a detailed explanation and insights about this project, check out my blog post here:
How I Used Python & SQL to Improve Delivery Times on Shopify-Style Orders
Possible extensions of this analysis include:
- Building a delivery delay prediction model
- Creating an operational performance dashboard in Power BI
- Integrating logistics cost analysis
- Developing carrier performance monitoring dashboards
ecommerce-logistics-delivery-performance-analysis
│
├── dashboard
│ └── e-commerce.pdf
│
├── datasets
│ └── dataset
│
├── notebook
│ └── E_Commerce_Order_&_Supply_Chain.ipynb
│
├── Insights.md
└── README.md
I’m a data science learner building hands-on projects using real-world data and BI tools.
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