https://github.com/shriansh8619/eda_customer_behavior

This project analyzes Nielsen transaction data using Python to uncover sales trends, customer preferences, and purchasing patterns. It provides insights to optimize inventory, create targeted promotions, and improve store performance. The goal is to help supermarkets enhance strategies and boost profitability based on data-driven insights
https://github.com/shriansh8619/eda_customer_behavior

matplotlib numpy pandas python seaborn

Last synced: 4 days ago
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This project analyzes Nielsen transaction data using Python to uncover sales trends, customer preferences, and purchasing patterns. It provides insights to optimize inventory, create targeted promotions, and improve store performance. The goal is to help supermarkets enhance strategies and boost profitability based on data-driven insights

• Host: GitHub
• URL: https://github.com/shriansh8619/eda_customer_behavior
• Owner: shriansh8619
• Created: 2024-12-19T12:34:09.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-30T18:07:10.000Z (about 1 year ago)
• Last Synced: 2025-02-20T06:19:05.908Z (12 months ago)
• Topics: matplotlib, numpy, pandas, python, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 2.61 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Customer Behavior Analysis Project

Understanding customer behavior is key to improving business strategies. This project analyzes transaction data to identify preferences, buying patterns, and decision-making trends. Using **Exploratory Data Analysis** (EDA) techniques like data visualization and summary statistics, we uncover valuable insights to enhance customer satisfaction, retention, and profitability.

### The Nielsen Advantage

Nielsen's transaction data from supermarkets and grocery stores provides a clear picture of consumer habits, offering businesses a powerful tool to understand their customers.

### Project Goal

The aim is to identify key customer trends and buying patterns. Using Python's data analysis tools,it help businesses refine strategies and improve customer retention and profits.

### Customer Behavior Analysis Insights

This analysis used a rich dataset with details like monthly sales, store codes, bill IDs, product quantities, and brand information. Leveraging Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn, we achieved the following:

- **Sales Trends:** Identified peak and low sales months to optimize inventory and promotions

  ![image](https://github.com/user-attachments/assets/48d1a3f7-e476-47ad-9327-48d18e2c45be)

- **Top Performers:** Analyzed sales by store to highlight high and low performers.

  ![image](https://github.com/user-attachments/assets/45ffdb69-9e50-4cd4-ab34-3b758683c8fd)

- **Customer Preferences:** Pinpointed popular products, brands, and categories for targeted marketing.

![image](https://github.com/user-attachments/assets/c513d691-86e6-4b1e-848b-5f5533f88bbc)

- **Daily & Weekly Patterns:** Understand customer purchasing habits across days and weeks, informing staffing decisions and inventory control.

![image](https://github.com/user-attachments/assets/005cd582-6c7b-4934-926b-7b3f4ce6e68e)

### Key Business Insights

The analysis provided valuable insights for supermarkets:

- **Store-Specific Strategies:** High-performing stores like N8 set benchmarks, while low performers like N7 need targeted improvements.

- **Data-Driven Promotions:** Promotions at the start of the month, when customers spend more, can boost sales.

- **Inventory Optimization:** Understanding sales patterns helps manage stock levels efficiently, avoiding both shortages and surpluses.

### Next Steps for Exploration

This analysis sets the stage for further investigation:

- **Validate Findings:** Use statistical methods to confirm insights.

- **Explore Additional Factors:** Examine how demographics, store locations, and promotions affect customer behavior.

### Conclusion: Data-Driven Success

By analyzing Nielsen transaction data with Python, this project highlights the power of data-driven insights in the retail industry. Supermarkets can use these findings to optimize strategies, improve customer experiences, and drive sustainable growth.