E-Commerce Competitor Strategy Dashboard

This project provides a real-time dashboard that helps e-commerce businesses analyze competitor data, customer sentiment, and forecast pricing strategies. By using various machine learning models and APIs, the dashboard generates actionable strategic recommendations to optimize pricing, promotions, and customer satisfaction.

Features

• Load and analyze competitor data (prices, discounts, etc.).
• Perform sentiment analysis on product reviews.
• Forecast future discounts using ARIMA.
• Generate strategic recommendations using a large language model (LLM).
• Display competitor data and sentiment analysis results on an interactive dashboard.

Prerequisites

Before running the project, make sure you have the following installed:

• Python 3.x
• pip (Python package installer)

Setup

1. Clone the repository

git clone https://github.com/sahilmate/e-commerce-competitor-analysis.git
cd e-commerce-competitor-analysis

2. Install dependencies

Create a virtual environment (optional but recommended):

python -m venv venv

Activate the virtual environment:

• On Windows:

  venv\Scripts\activate

• On macOS/Linux:

  source venv/bin/activate

Install the required dependencies from requirements.txt:

pip install -r requirements.txt

3. Create .env file

Create a .env file in the root directory of the project and add your sensitive API keys and webhook URL.

GROQ_API_KEY="your_groq_api_key"
SLACK_WEBHOOK_URL="your_slack_webhook_url"

4. Running the project

After setting up the environment, you can run the project by using the following command:

streamlit run competitor_strategy_dashboard.py

This will launch the Streamlit app, and you can open the dashboard in your browser.

Project Structure

e-commerce-competitor-strategy-dashboard/
├── competitor_strategy_dashboard.py # Main Streamlit application file
├── .env                             # File to store your API keys and webhook URLs
├── requirements.txt                 # Python dependencies
├── price_data.csv                   # CSV file with competitor price data
├── review_data.csv                  # CSV file with customer review data
├── webscraping.ipynb                # Jupyter notebook file for web scraping 

Dependencies

Here are the Python libraries required for the project:

• json
• pandas
• requests
• numpy
• plotly
• streamlit
• openai
• sklearn
• statsmodels
• transformers
• dotenv
• selenium
• webdriver_manager

You can install all the dependencies using:

pip install -r requirements.txt

Requirements.txt

pandas==1.5.3
numpy==1.23.4
requests==2.28.1
plotly==5.10.0
streamlit==1.15.2
openai==0.27.0
sklearn==1.1.2
statsmodels==0.13.5
transformers==4.28.0
python-dotenv==0.21.1
selenium==4.8.1
webdriver_manager==3.8.5

API Keys and Webhooks

The project requires two key components to work:

1. GROQ API Key: Used to generate strategic recommendations based on competitor data, sentiment, and forecasting. You can obtain your API key from Groq.

2. Slack Webhook URL: Used to send the strategic recommendations directly to a Slack channel. You can create a Slack Webhook URL here. First create a Slack workspace, then search for "Incoming Webhooks" and add the channel name to integrate webhooks on the channel. Copy the Slack Webhook URL as well.

Make sure to store these values securely in the .env file, as shown below:

GROQ_API_KEY="your_groq_api_key"
SLACK_WEBHOOK_URL="your_slack_webhook_url"

Slack Webhook Image:

Streamlit Dashboard Images:

License

This project is licensed under the MIT License - see the LICENSE file for details.