# 🧠 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.

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## 💼 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.

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## 🧰 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. |

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## 🔄 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]



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## ⚙️ 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.

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### **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 |

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### **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)

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### **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.

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## 📂 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 |

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## ✅ 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. |

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## 🚀 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.