🚀 Marketing Analytics Capstone Project

By Aniket Singh Parmar

Aman Pandey

Karan Sharma

Khushi Sandhya

Priyaraj Singh

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🔍 Project Overview

This end-to-end Marketing Analytics project delivers data-driven insights to optimize sales performance, category strategy, and regional expansion. Leveraging Python, MySQL, Excel, and Power BI, I conducted Exploratory Data Analysis (EDA), built an interactive dashboard, and derived actionable business recommendations to maximize revenue.

🎯 Key Objectives:
✔ Analyze sales trends & seasonality
✔ Identify top-performing categories & regions
✔ Develop a Power BI dashboard for real-time decision-making
✔ Provide data-backed business strategies

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🛠️ Tools & Technologies Used

Category	Tools
Data Analysis	Python (Pandas, NumPy, Matplotlib, Seaborn), Jupyter Notebook
Database	MySQL (Data Storage & Querying)
ETL & Cleaning	Python (Data Cleaning Scripts), Excel (Data Validation)
Visualization	Power BI (Interactive Dashboards), Excel (Pivot Tables)
Development	VS Code (Scripting), Git (Version Control)

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📂 Data Pipeline & ETL Process

📊 Data Sources

• MySQL Database: Extracted from marketing_analytics.clean_sales_data
• Excel Dataset: data/cleaned_data.csv (Pre-processed for analysis)

⚙️ ETL Workflow

1. Extract: Pulled data from MySQL & Excel
2. Transform:
   • Cleaned missing values & outliers using Python (data_cleaning.py)
   • Normalized pricing data for consistency
   • Aggregated sales by region, category, and time period
3. Load: Stored refined data back into MySQL and fed into Power BI

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📈 Exploratory Data Analysis (EDA) - Key Insights

📌 Sales Performance Analysis

🔹 Revenue Distribution: Right-skewed, indicating a few high-value sales drive most revenue
🔹 Top Category: Electronics dominates with 35% of total sales
🔹 Regional Leader: South region outperforms others by 22% in sales volume

📌 Pricing & Demand Trends

💰 Mid-range products have the highest sales volume
📉 Premium products show lower volume but higher margins

📌 Seasonality & Time-Based Trends

🎄 Festive seasons (Q4) see a 30% spike in sales
📅 Weekend sales are 15% higher than weekdays

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📊 Power BI Dashboard - Interactive Analytics

📌 Dashboard Highlights

✅ Sales Distribution (Histogram) – Visualizes revenue concentration
✅ Category Performance (Stacked Bar Chart) – Compares Electronics, Furniture, Appliances
✅ Regional Heatmap – Identifies high-growth markets
✅ Trend Analysis (Line Chart) – Tracks monthly sales fluctuations

📂 Dashboard File: Available in the /dashboard folder of this repository.

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💡 Business Recommendations

Area	Recommendation
Category Strategy	Focus on Electronics & Furniture (High revenue drivers)
Regional Expansion	Invest more in South region (Highest sales potential)
Pricing Strategy	Optimize mid-range pricing & introduce bundled offers for premium products
Seasonal Marketing	Launch targeted campaigns during festive peaks (Q4)

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🗂 Project Structure

📂 Marketing_Analytics_Capstone/
├── 📂 data/                   # Raw & Cleaned Datasets
│   ├── raw_sales_data.csv
│   └── cleaned_data.csv
├── 📂 scripts/                # Python ETL & Analysis
│   ├── data_cleaning.py
│   └── eda_analysis.py
├── 📂 notebooks/              # Jupyter Notebooks
│   └── sales_trend_analysis.ipynb
├── 📂 dashboard/              # Power BI Files
│   └── marketing_dashboard.pbix
├── 📂 reports/                # Insights & Summaries
│   ├── eda_report.pdf
│   └── business_recommendations.pptx
└── 📜 README.md               # Project Documentation

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🎯 Conclusion & Impact

This project transforms raw sales data into strategic insights, enabling businesses to:
✔ Increase revenue by focusing on high-performing categories
✔ Optimize pricing based on demand trends
✔ Enhance marketing ROI with data-driven seasonal campaigns

🚀 Final Deliverables:
📌 Power BI Dashboard (Downloadable .pbix file)
📌 EDA Report (Detailed Analysis & Visualizations)
📌 Business Recommendations (Actionable Strategies)

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⭐ Feel free to explore the repo and provide feedback!

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