4.1 KiB
🧠 Automated E-Commerce Performance & Sentiment Analysis Dashboard
🎯 Project Overview
This project automates the process of analyzing sales performance and customer sentiment by combining:
- Python – for data cleaning, aggregation, and sentiment analysis.
- n8n – for workflow automation, integration, and notifications.
The goal is to deliver real-time, actionable insights to business stakeholders.
💼 Business Goal
To automatically track:
- Which products are performing well.
- What customers feel about them.
- When there’s a sudden drop in sales or rise in negative reviews.
This enables teams to respond quickly to business or customer issues.
🧰 Tools Used
| Tool | Purpose |
|---|---|
| n8n | Automates data fetching, scheduling, and notifications. |
| Python | Handles data processing and sentiment analysis using NLP. |
| Google Sheets / Database | Stores analyzed data for reporting or dashboards. |
| Slack / Email | Delivers automated summaries and alerts. |
🔄 Workflow Overview
[Start: Cron Node] ↓ [Fetch Sales Data: HTTP/DB Node] ↓ [Fetch Reviews: HTTP Node] ↓ [Execute Python Script] ↓ [Store Data: Google Sheets/DB Node] ↓ [Send Report: Slack/Email Node] ↳ [If Node: Alert on Issues]
⚙️ Step-by-Step Process
1. Data Ingestion
- Cron Node: Triggers workflow daily (e.g., every morning at 3 AM).
- HTTP/DB Node: Pulls sales data (e.g., order ID, product, quantity, price).
- HTTP Node: Fetches customer reviews (e.g., rating, review text).
- Merge Node: Combines both datasets.
Example:
Sales data → 500 daily transactions
Reviews data → 120 new reviews fetched via API.
2. Data Processing & Sentiment Analysis
- Python Node cleans, aggregates, and analyzes the merged data:
- Cleans missing or duplicate records.
- Calculates total revenue, units sold, and average sentiment per product.
- Uses VADER sentiment analysis to categorize reviews as Positive, Neutral, or Negative.
Example Output:
| Product | Revenue | Units Sold | Avg Sentiment | Negative Reviews |
|---|---|---|---|---|
| SmartWatch X2 | $15,000 | 120 | 0.78 | 2 |
| Wireless Charger Mini | $8,500 | 70 | -0.32 | 8 |
3. Data Storage & Reporting
- Google Sheets / Database Node: Saves processed data for dashboarding (e.g., in Looker Studio or Tableau).
- Slack / Email Node: Sends a daily report.
Example Slack Message:
📊 Daily E-commerce Report (Oct 6, 2025)
Top Product: SmartWatch X2 – $15,000 in revenue
Most Negative Reviews: Wireless Charger Mini (8 negative reviews)
4. Automated Alerts
- If Node checks conditions:
- Sentiment score < -0.5
-
10 negative reviews
-
50% drop in revenue compared to yesterday
If triggered, a Slack/Twilio alert is sent.
Example Alert:
⚠️ Alert: Product Wireless Charger Mini sentiment dropped to -0.65 with 12 new negative reviews.
📂 Data Sources
| Data Type | Example Source | Connection Method |
|---|---|---|
| Sales Data | Shopify / WooCommerce | API or SQL |
| Product Reviews | Store API / Trustpilot | HTTP Request |
| Storage | Google Sheets / PostgreSQL | API or DB connection |
| Notifications | Slack / Email | n8n integration |
✅ Summary
| Role | Tool | Description |
|---|---|---|
| Workflow Automation | n8n | Handles scheduling, API calls, and alerts. |
| Data Analysis | Python | Cleans and processes data; performs sentiment analysis. |
| Storage & Dashboards | Google Sheets / DB | Maintains daily summary records. |
| Reporting | Slack / Email | Shares reports and alerts with stakeholders. |
🚀 Outcome
A fully automated system that:
- Fetches and analyzes e-commerce data daily.
- Tracks both sales trends and customer sentiment.
- Sends visualized reports and alerts automatically — saving time and improving response to performance issues.