🌟 AI-Enabled E-Commerce Revenue Intelligence & Pricing Optimization System

AI-Powered Business Analytics Platform • Revenue Forecasting • Pricing Intelligence • Decision Support

Revenue Intelligence Platform for Pricing Optimization, Forecasting, Elasticity Analysis & Business Decision Support

🖼️ Project Overview

An AI-powered business analytics platform designed to forecast revenue trends, optimize pricing strategies, estimate price elasticity, and simulate business outcomes through interactive dashboards and decision intelligence workflows.

This project focuses on:

Revenue forecasting
Pricing optimization
Elasticity analysis
Business KPI monitoring
Revenue simulation
Scenario analysis
Explainable analytics workflows
Interactive business dashboards

Built using:

Streamlit
Scikit-learn
SHAP
Plotly
Pandas & NumPy
Random Forest Regressor

🚀 Key Business Capabilities

Revenue forecasting & trend analysis
Dynamic pricing optimization
Price elasticity estimation
Revenue impact simulation
Scenario & stress testing
Explainable AI insights using SHAP
Drift detection & model monitoring
Interactive business dashboards
Executive-ready analytics workflows

💼 Business Problem

E-commerce businesses often struggle to:

forecast future revenue accurately
optimize pricing strategies dynamically
estimate pricing sensitivity
identify revenue-driving factors
simulate business impact before pricing decisions

Poor pricing decisions can lead to:

revenue loss
reduced profitability
inefficient promotions
inaccurate demand planning
reduced operational efficiency

This project demonstrates how AI-driven analytics can support:

smarter pricing decisions
revenue optimization
pricing intelligence
business forecasting workflows

📈 Business Impact

This system helps simulate:

pricing strategy optimization
revenue growth forecasting
profitability analysis
pricing sensitivity monitoring
business KPI intelligence
scenario-based decision support

The platform demonstrates how analytics workflows can improve:

pricing effectiveness
revenue planning
strategic decision-making
business forecasting accuracy

🧠 Platform Features

Module	Function
📈 Revenue Forecasting	Predict future revenue trends
💰 Pricing Optimization	Optimize pricing strategies
📊 Elasticity Analysis	Estimate pricing sensitivity
🧠 SHAP Explainability	Explain revenue-driving factors
⚠️ Drift Detection	Monitor model/data drift
🧪 Stress Testing	Simulate business scenarios
📉 KPI Monitoring	Track business performance
🌐 Streamlit Dashboard	Interactive analytics workflows

📸 Platform Screenshots

⭐ Dashboard Overview

⭐ Data Quality Validation

⭐ Model Evaluation Dashboard

⭐ Historical Analytics

View Additional Dashboard Screens

⭐ Forecast Simulator

⭐ Pricing Optimizer

⭐ SHAP Explainability

⭐ Model Drift Detection

⭐ Train Revenue Model

⭐ Estimate Price Elasticity

📊 Key Business Insights

The analysis revealed several important pricing and revenue patterns:

🔹 Pricing sensitivity directly impacts revenue

Small pricing adjustments significantly affected revenue trends across multiple scenarios.

🔹 Certain product categories showed higher elasticity

Some product segments were considerably more price-sensitive than others.

🔹 Revenue forecasting improved business planning

Forecasting workflows helped simulate future revenue fluctuations and operational planning.

🔹 SHAP explainability improved interpretability

SHAP analysis highlighted the strongest drivers affecting revenue behavior.

🔹 Drift monitoring supported model reliability

Data drift detection helped identify changing business patterns over time.

These insights supported:

pricing optimization workflows
revenue intelligence strategies
business decision support systems

🧬 Revenue Intelligence Workflow

Business & Pricing Data
          ↓
Automated Data Cleaning Pipeline
          ↓
Feature Engineering & Analytics
          ↓
Revenue Forecasting Models
          ↓
Pricing Optimization Engine
          ↓
Elasticity Analysis & SHAP Insights
          ↓
Scenario Simulation & Drift Detection
          ↓
Interactive Streamlit Dashboard
          ↓
Business Decision Intelligence

🧩 End-to-End Workflow

Generate synthetic ecommerce transaction data
Clean and validate raw business data
Engineer pricing & revenue features
Train forecasting and optimization models
Estimate price elasticity patterns
Generate SHAP explainability insights
Detect model/data drift patterns
Simulate pricing and revenue scenarios
Explore interactive business dashboards

🧠 Tech Stack

Category	Technologies
Language	Python 3.10+
Data Analysis	Pandas, NumPy
Machine Learning	Scikit-learn, Random Forest
Explainability	SHAP
Visualization	Matplotlib
Dashboard/UI	Streamlit
Testing	PyTest
Utilities	Joblib

📁 Project Structure

Ecommerce-Revenue-Pricing-Optimizer/
│
├── app/
│   ├── cleaning.py               # Data cleaning pipeline
│   ├── data_loader.py            # Loads raw + processed data
│   ├── forecasting.py            # Model training, evaluation, plots
│   ├── pricing.py                # Elasticity + dynamic pricing
│   ├── insights.py               # Business insights & data quality
│   ├── drift_utils.py            # PSI-based drift detection
│   └── streamlit_app.py          # Full Streamlit dashboard
│
├── data/
│   ├── processed
│   │   └── modeling_data.csv
│   │   
│   ├── raw
│   │   └── transactions.csv
│   │   
│   └── synthetic_generator.py
│
├── models/
│   ├── revenue_model.pkl
│   └── elasticity.json
│
├── reports/
│   ├── csv
│   │   └── evaluation_report.txt
│   │   
│   └── visuals
│       ├── actual_vs_predicted.png
│       ├── residual_distribution.png
│       ├── feature_importance.png
│       └── error_over_time.png
│
│
├── screenshots/
│   ├── dashboard_overview.png
│   ├── data_quality.png
│   ├── estimate_price_elasticity.png
│   ├── forecast_simulator.png
│   ├── historical_analytics.png
│   ├── model_drift.png
│   ├── model_evaluation.png
│   ├── pricing_optimizer.png
│   ├── shap_explainability.png
│   └── train_model.png
│
├── tests/
│   ├── test_data_loader.py
│   ├── test_forecasting.py
│   ├── test_pricing.py
│   ├── test_app_model_file.py
│   ├── test_shap_explainability.py
│   ├── test_drift_detection.py
│   ├── test_visualizations.py
│   ├── test_forecast_stress.py
│   ├── test_pricing_stress.py
│   └── test_data_integrity.py
│
├── README.md
└── requirements.txt

⚙️ Installation

1️⃣ Clone Repository

git clone https://github.com/girishshenoy16/Ecommerce-Revenue-Pricing-Optimizer.git
cd Ecommerce-Revenue-Pricing-Optimizer

2️⃣ Create Virtual Environment

python -m venv venv
venv\Scripts\activate

3️⃣ Install Dependencies

python.exe -m pip install --upgrade pip
pip install -r requirements.txt

▶️ Running the Project

1️⃣ Generate Synthetic Data

python data/synthetic_generator.py

2️⃣ Launch Streamlit Dashboard

streamlit run app/streamlit_app.py

🧰 Important First-Run Actions (Inside Streamlit)

After launching the dashboard:

✔ Train Revenue Model

Use the sidebar option:

➡️ Train Revenue Model

This generates:

revenue_model.pkl
evaluation reports
model performance visualizations

✔ Estimate Price Elasticity

Use the sidebar option:

➡️ Estimate Price Elasticity

This generates:

elasticity.json
pricing intelligence insights
elasticity-driven optimization workflows

After completing these steps, all dashboard modules become fully functional.

🧪 Testing the Project

Run all tests:

pytest 
pytest -q

Run verbose tests:

pytest -v

The testing suite includes:

forecasting tests
pricing optimization tests
SHAP explainability tests
drift detection tests
visualization tests
stress tests
data integrity validation

🔮 Future Scope

Prophet/SARIMA forecasting enhancements
Multi-product elasticity estimation
Real-time pricing intelligence systems
Automated retraining pipelines & workflows
Inventory-aware price optimization engines
Advanced business KPI monitoring
Cloud deployment & API integration

🤝 Contribution

Contributions, suggestions, and improvements are welcome.

If you found this project valuable, consider starring the repository.

Name		Name	Last commit message	Last commit date
app		app
data		data
reports		reports
screenshots		screenshots
tests		tests
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
requirements.txt		requirements.txt

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