🛍️ Retail Analytics & Machine Learning Project

Unlocking $10.9M in Revenue Opportunities through Advanced Analytics & Machine Learning

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📊 Executive Summary

This project demonstrates an end-to-end data solution analyzing 1M+ transactions to solve critical retail challenges: churn, pricing inefficiency, and revenue forecasting. By combining robust ETL pipelines with machine learning models, we identified actionable strategies to drive a 10.3% projected revenue increase.

🎯 Key Outcomes

Metric	Impact
Revenue Opportunity	$10.9M identified via pricing & retention strategies
Forecast Accuracy	98.03% (MAPE 1.97%) for reliable planning
Operational Insight	$6M unlocked through optimized pricing elasticity
Customer Retention	$7.8M saved by proactively targeting at-risk segments

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🚀 The Solution: Interactive Intelligence

This project culminates in a comprehensive analysis dashboard. Below are effective views from our analysis showing the power of data-driven decision making.

1. Executive Control Tower

Real-time visibility into business health, providing a consolidated view of KPIs, revenue trends, and churn risk.

2. Customer Segmentation & Churn Prediction

We moved beyond simple demographics to behavioral segmentation. Using K-Means clustering, we identified 3 distinct personas.

Insight: The "At Risk" segment constitutes only 9% of customers but represents a disproportionate $7.8M in potential revenue loss. Targeted retention campaigns for this specific group yield the highest ROI.

3. Pricing Strategy Optimization

Using Price Elasticity of Demand (PED) analysis, we determined optimal price points for each product category.

Insight: High-volume items like "Coffee K-Cups" showed inelastic demand (-0.8), suggesting a price increase would drive pure margin growth without sacrificing volume.

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🛠️ Technical Architecture

The system is built on a modular "Lakehouse" architecture, validating data integrity at every stage from raw CSVs to the final serving layer.

graph LR
    subgraph Data_Pipeline
        Raw[Raw Data CSV] -->|Pandas NumPy| Clean[Processed Data]
        Clean -->|Feature Engineering| Features[ML Features]
    end
    
    subgraph Machine_Learning
        Features -->|Random Forest XGBoost| Forecast[Revenue Forecast Model]
        Features -->|K Means| Segments[Customer Clusters]
        Features -->|Elasticity Algorithm| Pricing[Pricing Model]
    end
    
    subgraph Insights
        Forecast -->|KPIs| Report[Business Report]
        Segments -->|Cohorts| Report
        Pricing -->|Strategy| Report
    end

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Tech Stack

• Data Processing: Python, Pandas, NumPy
• Machine Learning: Scikit-learn, Statsmodels (ARIMA/SARIMA)
• Visualization: Plotly Interactive Charts, Matplotlib
• Environment: Jupyter Notebooks

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📂 Project Structure

├── notebooks/          # 8-step analysis pipeline
│   ├── 00_Setup_Data_Overview.ipynb
│   ├── 01_EDA.ipynb
│   ├── 03_Forecasting.ipynb   # Revenue prediction models
│   ├── 04_Pricing.ipynb       # Elasticity analysis
│   └── 05_Segmentation.ipynb  # Clustering & CLV
├── reports/            # Generated assets & visualizations
├── data/               # Data storage (Raw & Processed)
└── models/             # Serialized ML models

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💻 Explore the Code

To replicate the analysis or explore the notebooks:

1. Clone the repository

   git clone https://github.com/stevenlagadapati/retail-analytics-project.git
   cd retail-analytics-project

2. Install dependencies

   pip install -r requirements.txt

3. Run Jupyter Notebooks

   jupyter notebook notebooks/

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📧 Contact

Steven Lagadapati
Data Scientist & Analytics Engineer
Email | GitHub

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Made with ❤️ and Python