ecommerce-data-analyzer

What This Skill Does

The ecommerce-data-analyzer is a powerful and specialized tool designed for e-commerce sellers to transform raw CSV data into actionable business intelligence. It automates the extraction and visualization of key performance indicators, helping users track sales trends, monitor inventory, and assess profitability. By processing order history, it generates comprehensive reports including sales trajectories, product performance rankings, channel-specific revenue breakdowns, and predictive inventory alerts to prevent stockouts. The tool simplifies complex financial analysis and includes a professional PDF export feature, making it easy to share business performance reports with stakeholders.

Installation

To integrate this tool into your OpenClaw environment, execute the following command in your terminal: clawhub install openclaw/skills/skills/baolige2023/ecommerce-data-analyzer Ensure you have the prerequisite environment installed: Python 3.8+ and the necessary libraries (pandas, matplotlib, and reportlab). You can install these via pip install pandas matplotlib reportlab flask before running the python scripts/app.py start script.

Use Cases

• Inventory Management: Use the tool to forecast when specific products will go out of stock based on current sales velocity, allowing for timely restock planning.
• Profitability Audit: Calculate net profit margins by overlaying cost data against gross sales to identify high-margin vs. low-margin SKUs.
• Channel Strategy: Compare revenue performance across platforms like Amazon, Shopify, and standalone sites to optimize advertising spend.
• Reporting: Quickly generate professional PDF summaries for weekly or monthly business reviews without manual data entry or chart creation.

Example Prompts

1. "Analyze my latest sales_data.csv file and generate a report showing the top 5 performing products by revenue for this month."
2. "Compare the sales growth trends between Amazon and my Shopify store over the last quarter, and output the analysis to a PDF."
3. "Predict when my inventory for 'Model-X' will hit zero based on the current sales speed in the provided data, and alert me if I need to reorder this week."

Tips & Limitations

• Data Formatting: Ensure your CSV headers exactly match the required fields (Order Date, Product Name, Quantity, Amount, Channel, Inventory) to avoid parsing errors.
• Cost Accuracy: Profitability analysis is only as accurate as the cost data you provide; ensure your cost records are up-to-date.
• Payment Note: Note that a small fee of 0.001 USDT is charged via SkillPay per report generation. Ensure your API credentials are configured correctly to avoid processing delays.